R/miRspongeR.R
In zhangjunpeng411/miRspongeR: Identification and analysis of miRNA sponge regulation
Defines functions
moduleSurvival
moduleFEA
moduleDEA
netModule
spongeValidate
sample_cor_network
sponge_sample_specific
spongeMethod
integrateMethod
sponge_parallel
cernia_parallel
cernia
muTaME_parallel
muTaME
hermes_parallel
hermes
ppc_parallel
ppc
sppc_parallel
sppc
pc_parallel
pc
miRHomology_parallel
miRHomology
mcode
mcode.post.process
mcode.fluff.complex
mcode.find.complexex
mcode.find.complex
mcode.vertex.weighting
cluster.save
cluster
dtHybrid
recommendation
graphWeights
parMM
predCor
combpvalue
calCMI
querymiRTargetbinding
Documented in
integrateMethod
moduleDEA
moduleFEA
moduleSurvival
netModule
querymiRTargetbinding
sample_cor_network
spongeMethod
sponge_sample_specific
spongeValidate
## Query miRNA-target interactions by combining expression data and putative miRNA-target
## interactions
querymiRTargetbinding <- function(ExpData, miRTarget, type = c("all", "miRNA", "target")) {
colnames(ExpData) <- gsub("\\.", "-", colnames(ExpData))
ExpDataNames <- colnames(ExpData)
miRTarget <- as.matrix(miRTarget)
if (type == "all") {
miRTargetCandidate <- miRTarget[intersect(which(miRTarget[, 1] %in% ExpDataNames),
which(miRTarget[, 2] %in% ExpDataNames)), ]
} else if (type == "miRNA") {
miRTargetCandidate <- miRTarget[which(miRTarget[, 1] %in% ExpDataNames), ]
} else if (type == "target") {
miRTargetCandidate <- miRTarget[which(miRTarget[, 2] %in% ExpDataNames), ]
}
return(miRTargetCandidate)
}
## Internal functions (calCMI, combpvalue, predCor) of hermes method are from the website:
## http://califano.c2b2.columbia.edu/hermes Copyright Columbia University in the City of New
## York. You may not use this file except in compliance with the License. You may obtain a copy
## of the License at http://califano.c2b2.columbia.edu/hermes-license
## calCMI: Calculate conditional mutual information
calCMI <- function(dmat) {
# rank data
N <- nrow(dmat)
cdim <- ncol(dmat)
idat <- apply(dmat, 2, order)
ydat <- idat
sapply(seq_len(cdim), function(i) ydat[idat[, i], i] <- seq_len(N))
ddim <- 2^cdim
dim2 <- 2 * cdim
# init
rm(dmat, idat)
poc <- vector()
kon <- vector()
cmi <- 0
npar <- 1
poc[1] <- 1
kon[1] <- N
poradi <- seq_len(N)
NN <- matrix(0, nrow = 1, ncol = ddim)
marg <- matrix(0, nrow = 8 * ddim, ncol = dim2)
marg[1, ] <- c(rep(1, cdim), rep(N, cdim))
Imm <- matrix(c(0, 1), ncol = 1) # Binary matrix for all combinations
for (d in seq(2, cdim)) {
Imm <- rbind(cbind(matrix(0, nrow = nrow(Imm), ncol = 1), Imm),
cbind(matrix(1, nrow = nrow(Imm), ncol = 1), Imm))
}
chi2 <- c(0, 7.81, 13.9, 25, 42)
run <- 0
# partition
while (npar > 0) {
run <- run + 1
apoc <- poc[npar]
akon <- kon[npar]
apor <- poradi[seq(apoc, akon)]
Nex <- length(apor)
margave <- floor((marg[npar, seq_len(cdim)] + marg[npar, seq(cdim + 1, dim2)])/2)
J <- (ydat[apor, ] <= (matrix(1, nrow = Nex, ncol = 1) %*% margave)) * 1
cI <- matrix(0, nrow = Nex, ncol = ddim)
amarg <- matrix(1, nrow = ddim, ncol = 1) %*%
subset(marg, subset = seq_len(nrow(marg)) %in% npar)
for (d in seq_len(ddim)) {
cI[, d] <- matrix(1, nrow = Nex, ncol = 1)
for (k in seq_len(cdim)) {
if (Imm[d, k] == 1) {
cI[, d] <- 1 * (cI[, d] & (!J[, k]))
amarg[d, k] <- margave[k] + 1
} else {
cI[, d] <- 1 * (cI[, d] & J[, k])
amarg[d, k + cdim] <- margave[k]
}
}
}
NN <- colSums(cI)
tst <- ddim * sum((NN - Nex/ddim * matrix(1, nrow = 1, ncol = ddim))^2)/Nex
if ((tst > chi2[cdim]) | (run == 1)) {
# decide partition or not
npar <- npar - 1
for (ind in seq_len(ddim)) {
if (NN[ind] > ddim) {
npar <- npar + 1
akon <- apoc + NN[ind] - 1
poc[npar] <- apoc
kon[npar] <- akon
marg[npar, ] <- amarg[ind, ]
poradi[seq(apoc, akon)] <- apor[which(cI[, ind] != 0, arr.ind = TRUE)]
apoc <- akon + 1
} else {
if (NN[ind] > 0) {
Nxx <- apply(amarg[ind, seq(cdim + 1, dim2)] - amarg[ind, seq_len(cdim)] + matrix(1,
nrow = 1, ncol = cdim), 2, prod)
Nz <- amarg[ind, 6] - amarg[ind, 3] + 1
Jx <- 1 * ((ydat[, 1] >= amarg[ind, 1]) & (ydat[, 1] <= amarg[ind, 4]))
Jy <- 1 * ((ydat[, 2] >= amarg[ind, 2]) & (ydat[, 2] <= amarg[ind, 5]))
Jz <- 1 * ((ydat[, 3] >= amarg[ind, 3]) & (ydat[, 3] <= amarg[ind, 6]))
Nxz <- sum(1 * (Jx & Jz))
Nyz <- sum(1 * (Jy & Jz))
cond <- (NN[ind] * Nz)/(Nxz * Nyz)
if (is.infinite(cond))
cond <- 1
if (cond == 0)
cond <- 1
cmi <- cmi + NN[ind] * log(cond)
}
}
}
} else {
Nxx <- apply(marg[npar, seq(cdim + 1, dim2)] - marg[npar, seq_len(cdim)] + matrix(1,
nrow = 1, ncol = cdim), 2, prod)
Nz <- marg[npar, 6] - marg[npar, 3] + 1
Jx <- 1 * ((ydat[, 1] >= marg[npar, 1]) & (ydat[, 1] <= marg[npar, 4]))
Jy <- 1 * ((ydat[, 2] >= marg[npar, 2]) & (ydat[, 2] <= marg[npar, 5]))
Jz <- 1 * ((ydat[, 3] >= marg[npar, 3]) & (ydat[, 3] <= marg[npar, 6]))
Nxz <- sum(1 * (Jx & Jz))
Nyz <- sum(1 * (Jy & Jz))
cond <- (Nex * Nz)/(Nxz * Nyz)
if (is.infinite(cond))
cond <- 1
if (cond == 0)
cond <- 1
cmi <- cmi + Nex * log(cond)
npar <- npar - 1
}
}
# normalize
cmi <- cmi/N
return(cmi)
}
## combpvalue: Combine p-values using Fisher's method
combpvalue <- function(p_values) {
# calculate chi-square statistic and combined p-value
Q <- -2 * sum(log(p_values))
Degree <- length(p_values)
Combp <- pchisq(Q, 2 * Degree, lower.tail = FALSE)
return(Combp)
}
## predCor: Predict competing endogenous RNAs via evidence for competition for miRNA regulation
## based on conditional mutual information (CMI) or partial pearson correlation (PPC)
predCor <- function(expr, num_perm, method = c("CMI", "PPC")) {
exprRownames <- rownames(expr)
expr <- as.matrix(expr)
# generate random sequence
num_sample <- ncol(expr)
perm_seq <- t(sapply(seq_len(num_perm), function(i) sample(num_sample)))
# identify mediator candidate
num_cand <- nrow(expr) - 2
# evaluate significance of triplet
tri_id <- matrix(0, nrow = 2 * num_cand, ncol = 3)
tri_cor <- matrix(0, nrow = 2 * num_cand, ncol = 1)
tri_pval <- rep(1, 2 * num_cand)
for (i in seq_len(2)) {
for (j in seq_len(num_cand)) {
idx_tar <- i # target index
idx_miR <- j + 2 # miRNA index
idx_mod <- 2/i # modulator index
idx_tri <- (i - 1) * num_cand + j # triplet index
tri_id[idx_tri, ] <- c(exprRownames[idx_tar], exprRownames[idx_miR], exprRownames[idx_mod])
# calculate Cor( target ; miRNA | modulator )
data <- t(expr[c(idx_tar, idx_miR, idx_mod), ])
# construct null distribution
rand_exp <- lapply(seq_len(num_perm), function(i) expr[idx_mod, ][perm_seq[i, ]])
nulldata <- lapply(seq_len(num_perm), function(i) cbind(t(expr[c(idx_tar, idx_miR),
]), rand_exp[[i]]))
if (method == "CMI") {
tri_cor[idx_tri, ] <- calCMI(data)
null <- unlist(lapply(seq_len(num_perm), function(i) calCMI(nulldata[[i]])))
} else if (method == "PPC") {
tri_cor[idx_tri, ] <- corpcor::pcor.shrink(data, verbose = FALSE)[1, 2]
null <- unlist(lapply(seq_len(num_perm), function(i) corpcor::pcor.shrink(nulldata[[i]],
verbose = FALSE)[1, 2]))
}
# calculate p-value
tri_pval[idx_tri] <- max(1, sum(tri_cor[idx_tri, ] <= null))/num_perm
}
}
# evaluate significance of interaction
tri_idx <- order(tri_pval)
tri_pval <- tri_pval[tri_idx]
tri_id <- tri_id[tri_idx, ]
tri_cor <- tri_cor[tri_idx]
pcomb <- as.vector(sapply(seq_along(tri_pval), function(i) combpvalue(tri_pval[seq_len(i)])[1]))
# identify final mediators
min_pval <- min(pcomb)
return(min_pval)
}
## Internal functions (parMM, graphWeights, recommendation, dtHybrid) of cernia method are from
## the website: https://github.com/dsardina/cernia Copyright 2016 Rosalba Giugno Licensed under
## the Apache License, Version 2.0 (the 'License'); you may not use this file except in
## compliance with the License. You may obtain a copy of the License at
## http://www.apache.org/licenses/LICENSE-2.0
## Unless required by applicable law or agreed to in writing, software distributed under the
## License is distributed on an 'AS IS' BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,
## either express or implied. See the License for the specific language governing permissions and
## limitations under the License.
## Matrix multiplication in parallel
parMM <- function(cl, A, B) {
if (!all(is.na(cl)) && is.object(cl)) {
nA <- nrow(A)
ncl <- length(cl)
# Split an indice equally
i <- seq_len(nA)
if (ncl == 1) {
splitIndices <- i
} else {
fuzz <- min((nA - 1)/1000, 0.4 * nA/ncl)
breaks <- seq(1 - fuzz, nA + fuzz, length = ncl + 1)
splitIndices <- structure(split(i, cut(i, breaks)), names = NULL)
}
# Split a matrix equally according to the rows of the matrix
splitRows <- lapply(splitIndices, function(i) A[i, , drop = FALSE])
fun <- get(as.character("rbind"), envir = .GlobalEnv, mode = "function")
R <- do.call("fun", lapply(clusterApply(cl = cl, x = splitRows, get("%*%"), B), enquote))
} else {
R <- A %*% B
}
return(R)
}
## The first step of DT-Hybrid recommendation algorithm: generating the weights for each pair of
## target nodes
graphWeights <- function(n, m, A, lambda = 0.5, alpha = 0.5, S = NA, S1 = NA, cl = NA) {
if (nrow(A) != n || ncol(A) != m) {
stop("The matrix A should be an n by m matrix.")
}
has.similarity <- (!all(is.na(S)) && is.matrix(S) && !all(is.na(S1)) && is.matrix(S1))
if (has.similarity) {
if (nrow(S1) != m || ncol(S1) != m) {
stop("The matrix S1 should be an m by m matrix.")
}
if (nrow(S) != n || ncol(S) != n) {
stop("The matrix S should be an n by n matrix.")
}
}
Ky <- diag(1/colSums(A))
Ky[is.infinite(Ky) | is.na(Ky)] <- 0 #BugFix: 1/0=Infinite replaced with 0
kx <- rowSums(A)
Nx <- 1/(matrix(kx, nrow = n, ncol = n, byrow = TRUE)^(lambda) * matrix(kx, nrow = n, ncol = n,
byrow = FALSE)^(1 - lambda))
Nx[is.infinite(Nx) | is.na(Nx)] <- 0 #BugFix: 1/0=Infinite replaced with 0
kx[is.infinite(kx) | is.na(kx)] <- 0 #BugFix: 1/0=Infinite replaced with 0
W <- t(parMM(cl, A, Ky))
W <- parMM(cl, A, W)
W <- Nx * W
rownames(W) <- rownames(A)
colnames(W) <- rownames(A)
if (has.similarity) {
X5 <- parMM(cl, A, S1)
X6 <- parMM(cl, X5, t(A))
X7 <- parMM(cl, A, matrix(1, nrow = m, ncol = m))
X8 <- parMM(cl, X7, t(A))
S2 <- X6/X8
W <- W * (1 + (alpha * S) + ((1 - alpha) * S2))
}
W[is.nan(W)] <- 0 #This should never happen
return(W)
}
## The second step of DT-Hybrid recommendation algorithm: generating ecommendation scores of each
## RNA-RNA pair
recommendation <- function(A, lambda = 0.5, alpha = 0.5, S = NA, S1 = NA, cl = NA) {
n <- nrow(A)
m <- ncol(A)
W <- graphWeights(n = n, m = m, A = A, lambda = lambda, alpha = alpha, S = S, S1 = S1, cl = cl)
R <- parMM(cl, W, A)
return(R)
}
## Make projection from bipartite network using DT-hybrid sources
dtHybrid <- function(miRTarget) {
# Extract miRs and their targets
mir <- unique(miRTarget[, 1])
tar <- unique(miRTarget[, 2])
# Create the matrix of the miRTarget
A <- matrix(nrow = length(tar), ncol = length(mir), data = 0)
colnames(A) <- mir
rownames(A) <- tar
for (i in seq_len(nrow(miRTarget))) {
A[which(tar %in% as.character(miRTarget[i, 2])),
which(mir %in% as.character(miRTarget[i, 1]))] <- 1
}
# Make projection from bipartite network using DT-hybrid sources
cl <- makeCluster(detectCores() - 2)
M <- recommendation(A, cl = cl)
W <- graphWeights(nrow(M), ncol(M), M, cl = cl)
stopCluster(cl)
return(W)
}
## Internal function cluster from ProNet package
## (https://github.com/cran/ProNet) with GPL-2 license.
cluster <- function(graph, method="MCL", expansion = 2, inflation = 2,
hcmethod = "average", directed = FALSE, outfile = NULL, ...)
{
#method<-match.arg(method)
if(method=="FN"){
graph <- simplify(graph)
fc <- fastgreedy.community(graph, merges = TRUE, modularity = TRUE)
membership <- membership(fc)
if(!is.null(V(graph)$name)){
names(membership) <- V(graph)$name
}
if(!is.null(outfile)){
cluster.save(cbind(names(membership),membership),outfile=outfile)
}else{
return(membership)
}
}else if(method=="LINKCOMM"){
edgelist <- get.edgelist(graph)
if(!is.null(E(graph)$weight)){
edgelist <- cbind(edgelist,E(graph)$weight)
}
lc <- getLinkCommunities(edgelist,plot=FALSE,directed=directed,hcmethod=hcmethod)
if(!is.null(outfile)){
cluster.save(lc$nodeclusters,outfile=outfile)
}else{
return(lc$nodeclusters)
}
}else if(method=="MCL"){
adj <- matrix(rep(0,length(V(graph))^2),nrow=length(V(graph)),ncol=length(V(graph)))
for(i in seq_along(V(graph))){
neighbors <- neighbors(graph,v=V(graph)$name[i],mode="all")
j <- match(neighbors$name,V(graph)$name,nomatch=0)
adj[i,j] = 1
}
lc <- mcl(adj,addLoops=TRUE,expansion=expansion,inflation=inflation,allow1=TRUE,max.iter=100,ESM=FALSE)
lc$name <- V(graph)$name
lc$Cluster <- lc$Cluster
if(!is.null(outfile)){
cluster.save(cbind(lc$name,lc$Cluster),outfile=outfile)
}else{
result <- lc$Cluster
names(result) <- V(graph)$name
return(result)
}
}else if(method=="MCODE"){
compx <- mcode(graph,vwp=0.9,haircut=T,fluff=T,fdt=0.1)
index <- which(!is.na(compx$score))
membership <- rep(0,vcount(graph))
for(i in seq_along(index)){
membership[compx$COMPLEX[[index[i]]]]<-i
}
if(!is.null(V(graph)$name)) names(membership)<-V(graph)$name
if(!is.null(outfile)){
cluster.save(cbind(names(membership),membership),outfile=outfile)
invisible(NULL)
}else{
return(membership)
}
}
}
## Internal function cluster.save from ProNet package
## (https://github.com/cran/ProNet) with GPL-2 license.
cluster.save <- function(membership, outfile){
wd <- dirname(outfile)
wd <- ifelse(wd==".",paste(wd,"/",sep=""),wd)
filename <- basename(outfile)
if((filename=="")||(grepl(":",filename))){
filename <- "membership.txt"
}else if(grepl("\\.",filename)){
filename <- sub("\\.(?:.*)",".txt", filename)
}
write.table(membership,file=paste(wd,filename,sep="/"),
row.names=FALSE,col.names=c("node","cluster"),quote =FALSE)
}
## Internal function mcode.vertex.weighting from ProNet package
## (https://github.com/cran/ProNet) with GPL-2 license.
mcode.vertex.weighting<-function(graph, neighbors){
stopifnot(is.igraph(graph))
weight <- lapply(seq_len(vcount(graph)),
function(i){
subg<-induced.subgraph(graph,neighbors[[i]])
core<-graph.coreness(subg)
k<-max(core)
### k-coreness
kcore<-induced.subgraph(subg,which(core==k))
if(vcount(kcore)>1){
if(any(is.loop(kcore))){
k*ecount(kcore)/choose(vcount(kcore)+1,2)
}else{
k*ecount(kcore)/choose(vcount(kcore),2)
}
}else{
0
}
}
)
return(unlist(weight))
}
## Internal function mcode.find.complex from ProNet package
## (https://github.com/cran/ProNet) with GPL-2 license.
mcode.find.complex <- function(neighbors, neighbors.indx, vertex.weight,
vwp, seed.vertex, seen)
{
res<-.C("complex",as.integer(neighbors),as.integer(neighbors.indx),
as.single(vertex.weight),as.single(vwp),as.integer(seed.vertex),
seen=as.integer(seen),COMPLEX=as.integer(rep(0,length(seen))), PACKAGE = "miRspongeR"
)
return(list(seen=res$seen,COMPLEX=which(res$COMPLEX!=0)))
}
## Internal function mcode.find.complexex from ProNet package
## (https://github.com/cran/ProNet) with GPL-2 license.
mcode.find.complexex <- function(graph, neighbors, vertex.weight, vwp)
{
seen<-rep(0,vcount(graph))
neighbors<-lapply(neighbors,function(item){item[-1]})
neighbors.indx<-cumsum(unlist(lapply(neighbors,length)))
neighbors.indx<-c(0,neighbors.indx)
neighbors<-unlist(neighbors)-1
COMPLEX<-list()
n<-1
w.order<-order(vertex.weight,decreasing=TRUE)
for(i in w.order){
if(!(seen[i])){
res<-mcode.find.complex(neighbors,neighbors.indx,vertex.weight,vwp,i-1,seen)
if(length(res$COMPLEX)>1){
COMPLEX[[n]]<-res$COMPLEX
seen<-res$seen
n<-n+1
}
}
}
rm(neighbors)
return(list(COMPLEX=COMPLEX,seen=seen))
}
## Internal function mcode.fluff.complex from ProNet package
## (https://github.com/cran/ProNet) with GPL-2 license.
mcode.fluff.complex <- function(graph, vertex.weight, fdt=0.8, complex.g, seen)
{
seq_complex.g<-seq_along(complex.g)
for(i in seq_complex.g){
node.neighbor<-unlist(neighborhood(graph,1,complex.g[i]))
if(length(node.neighbor)>1){
subg<-induced.subgraph(graph,node.neighbor)
if(graph.density(subg, loops=FALSE)>fdt){
complex.g<-c(complex.g,node.neighbor)
}
}
}
return(unique(complex.g))
}
## Internal function mcode.post.process from ProNet package
## (https://github.com/cran/ProNet) with GPL-2 license.
mcode.post.process<-function(graph, vertex.weight, haircut, fluff, fdt=0.8,
set.complex.g, seen)
{
indx<-unlist(lapply(set.complex.g,
function(complex.g){
if(length(complex.g)<=2)
0
else
1
}
))
set.complex.g<-set.complex.g[indx!=0]
set.complex.g<-lapply(set.complex.g,
function(complex.g){
coreness<-graph.coreness(induced.subgraph(graph,complex.g))
if(fluff){
complex.g<-mcode.fluff.complex(graph,vertex.weight,fdt,complex.g,seen)
if(haircut){
## coreness needs to be recalculated
coreness<-graph.coreness(induced.subgraph(graph,complex.g))
complex.g<-complex.g[coreness>1]
}
}else if(haircut){
complex.g<-complex.g[coreness>1]
}
return(complex.g)
})
set.complex.g<-set.complex.g[lapply(set.complex.g,length)>2]
return(set.complex.g)
}
## Internal function mcode from ProNet package
## (https://github.com/cran/ProNet) with GPL-2 license.
mcode <- function(graph, vwp=0.5, haircut=FALSE, fluff=FALSE, fdt=0.8, loops=TRUE)
{
stopifnot(is.igraph(graph))
if(vwp>1 | vwp <0){
stop("vwp must be between 0 and 1")
}
if(!loops){
graph<-simplify(graph,remove.multiple=FALSE,remove.loops=TRUE)
}
neighbors<-neighborhood(graph,1)
W<-mcode.vertex.weighting(graph,neighbors)
res<-mcode.find.complexex(graph,neighbors=neighbors,vertex.weight=W,vwp=vwp)
COMPLEX<-mcode.post.process(graph,vertex.weight=W,haircut=haircut,fluff=fluff,
fdt=fdt,res$COMPLEX,res$seen)
score<-unlist(lapply(COMPLEX,
function(complex.g){
complex.g<-induced.subgraph(graph,complex.g)
if(any(is.loop(complex.g)))
score<-ecount(complex.g)/choose(vcount(complex.g)+1,2)*vcount(complex.g)
else
score<-ecount(complex.g)/choose(vcount(complex.g),2)*vcount(complex.g)
return(score)
}
))
order_score<-order(score,decreasing=TRUE)
return(list(COMPLEX=COMPLEX[order_score],score=score[order_score]))
}
## Utility methods for identifying miRNA sponge interactions For input expression data, the
## columns are genes and the rows are samples. For input miRTarget, the miRNA-target
## interactions could be miRNA-mRNA, miRNA-lncRNA, miRNA-circRNA, miRNA-pseudogene, etc. For
## input mres, each row contains five elements: Mirna, Target, energy, gap_l, gap_r.
## 1. miRHomology
# Original version
miRHomology <- function(miRTarget, minSharedmiR = 3, padjustvaluecutoff = 0.01, padjustmethod = "BH") {
miRTarget <- as.matrix(miRTarget)
m1 <- nrow(miRTarget)
n1 <- ncol(miRTarget)
miR <- miRTarget[, 1]
tar <- miRTarget[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
# Initialize variables
ceRInt <- matrix(NA, m2 * (m2 - 1)/2, 2)
C <- matrix(NA, m2 * (m2 - 1)/2, 2)
for (i in seq_len(m2 - 1)) {
for (j in seq(i + 1, m2)) {
Interin1 <- miRTarget[which(miRTarget[, 2] %in% targetSym[i]), 1]
Interin2 <- miRTarget[which(miRTarget[, 2] %in% targetSym[j]), 1]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff) {
ceRInt[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- targetSym[i]
ceRInt[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- targetSym[j]
C[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- M3
C[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- M5
}
}
}
# Extract RNA-RNA pair based on the homology of sharing miRNAs
C[, 2] <- p.adjust(C[, 2], method = padjustmethod)
index <- which(C[, 2] < padjustvaluecutoff)
ceRInt <- ceRInt[index, ]
C <- C[index, ]
if (is.vector(C)) {
miRHomologyceRInt <- c(ceRInt, C)
names(miRHomologyceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs")
} else {
miRHomologyceRInt <- cbind(ceRInt, C)
colnames(miRHomologyceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs")
}
return(miRHomologyceRInt)
}
# Parallel version
miRHomology_parallel <- function(miRTarget, minSharedmiR = 3, padjustvaluecutoff = 0.01, padjustmethod = "BH") {
miRTarget <- as.matrix(miRTarget)
m1 <- nrow(miRTarget)
n1 <- ncol(miRTarget)
miR <- miRTarget[, 1]
tar <- miRTarget[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
index <- t(combn(m2, 2))
Res <- foreach(i = seq_len(nrow(index))) %dopar% {
Interin1 <- miRTarget[which(miRTarget[, 2] %in% targetSym[index[i, 1]]), 1]
Interin2 <- miRTarget[which(miRTarget[, 2] %in% targetSym[index[i, 2]]), 1]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff) {
C1 <- targetSym[index[i, 1]]
C2 <- targetSym[index[i, 2]]
C3 <- M3
C4 <- M5
} else {
C1 <- NA; C2 <- NA; C3 <- NA; C4 <- NA
}
tmp <- c(C1, C2, C3, C4)
return(tmp)
}
Res <- do.call(rbind, Res)
# Extract RNA-RNA pair based on the homology of sharing miRNAs
Res[, 4] <- p.adjust(as.numeric(Res[, 4]), method = padjustmethod)
miRHomologyceRInt <- Res[which(as.numeric(Res[, 4]) < padjustvaluecutoff), ]
if (is.vector(miRHomologyceRInt)) {
names(miRHomologyceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs")
} else {
colnames(miRHomologyceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs")
}
return(miRHomologyceRInt)
}
## 2. Positive Correlation (PC) method
# Original version
pc <- function(miRTarget, ExpData, consider.miR.expr = "TRUE", minSharedmiR = 3, poscorcutoff = 0, padjustvaluecutoff = 0.01,
padjustmethod = "BH") {
if (consider.miR.expr == "TRUE") {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "all")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
} else {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "target")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
}
m1 <- nrow(miRTargetCandidate)
n1 <- ncol(miRTargetCandidate)
colnames(ExpData) <- gsub("\\.", "-", colnames(ExpData))
ExpDataNames <- colnames(ExpData)
miR <- miRTargetCandidate[, 1]
tar <- miRTargetCandidate[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
# Initialize variables
ceRInt <- matrix(NA, m2 * (m2 - 1)/2, 2)
C <- matrix(NA, m2 * (m2 - 1)/2, 4)
for (i in seq_len(m2 - 1)) {
for (j in seq(i + 1, m2)) {
Interin1 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[i]), 1]
Interin2 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[j]), 1]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff) {
ceRInt[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- targetSym[i]
ceRInt[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- targetSym[j]
tarExpIdx1 <- which(ExpDataNames %in% targetSym[i])
tarExpIdx2 <- which(ExpDataNames %in% targetSym[j])
# Calculate Pearson correlation of each RNA-RNA pair
M6 <- cor.test(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2])$estimate
M7 <- cor.test(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2])$p.value
C[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- M3
C[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- M5
C[(i - 1) * m2 + j - sum(seq_len(i)), 3] <- M6
C[(i - 1) * m2 + j - sum(seq_len(i)), 4] <- M7
}
}
}
# Extract positive correlated RNA-RNA pairs.
C[, 2] <- p.adjust(C[, 2], method = padjustmethod)
C[, 4] <- p.adjust(C[, 4], method = padjustmethod)
index <- which(C[, 2] < padjustvaluecutoff & C[, 3] > poscorcutoff & C[, 4] < padjustvaluecutoff)
ceRInt <- ceRInt[index, ]
C <- C[index, ]
if (is.vector(C)) {
PCceRInt <- c(ceRInt, C)
names(PCceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"correlation", "p.adjusted_value of positive correlation")
} else {
PCceRInt <- cbind(ceRInt, C)
colnames(PCceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"correlation", "p.adjusted_value of positive correlation")
}
return(PCceRInt)
}
# Parallel version
pc_parallel <- function(miRTarget, ExpData, consider.miR.expr = "TRUE", minSharedmiR = 3, poscorcutoff = 0, padjustvaluecutoff = 0.01,
padjustmethod = "BH") {
if (consider.miR.expr == "TRUE") {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "all")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
} else {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "target")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
}
m1 <- nrow(miRTargetCandidate)
n1 <- ncol(miRTargetCandidate)
colnames(ExpData) <- gsub("\\.", "-", colnames(ExpData))
ExpDataNames <- colnames(ExpData)
miR <- miRTargetCandidate[, 1]
tar <- miRTargetCandidate[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
index <- t(combn(m2, 2))
Res <- foreach(i = seq_len(nrow(index))) %dopar% {
Interin1 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[index[i, 1]]), 1]
Interin2 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[index[i, 2]]), 1]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff) {
C1 <- targetSym[index[i, 1]]
C2 <- targetSym[index[i, 2]]
tarExpIdx1 <- which(ExpDataNames %in% targetSym[index[i, 1]])
tarExpIdx2 <- which(ExpDataNames %in% targetSym[index[i, 2]])
# Calculate Pearson correlation of each RNA-RNA pair
M6 <- cor.test(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2])$estimate
M7 <- cor.test(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2])$p.value
C3 <- M3
C4 <- M5
C5 <- M6
C6 <- M7
} else {
C1 <- NA; C2 <- NA; C3 <- NA; C4 <- NA; C5 <- NA; C6 <- NA
}
tmp <- c(C1, C2, C3, C4, C5, C6)
return(tmp)
}
Res <- do.call(rbind, Res)
# Extract positive correlated RNA-RNA pairs.
Res[, 4] <- p.adjust(as.numeric(Res[, 4]), method = padjustmethod)
Res[, 6] <- p.adjust(as.numeric(Res[, 6]), method = padjustmethod)
PCceRInt <- Res[which(as.numeric(Res[, 4]) < padjustvaluecutoff & as.numeric(Res[, 5]) > poscorcutoff & as.numeric(Res[, 6]) < padjustvaluecutoff), ]
if (is.vector(PCceRInt)) {
names(PCceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"correlation", "p.adjusted_value of positive correlation")
} else {
colnames(PCceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"correlation", "p.adjusted_value of positive correlation")
}
return(PCceRInt)
}
## 3. Sensitivity Partial Pearson Correlation (SPPC) method
# Original version
sppc <- function(miRTarget, ExpData, minSharedmiR = 3, poscorcutoff = 0, padjustvaluecutoff = 0.01,
padjustmethod = "BH", senscorcutoff = 0.3) {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "all")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
m1 <- nrow(miRTargetCandidate)
n1 <- ncol(miRTargetCandidate)
colnames(ExpData) <- gsub("\\.", "-", colnames(ExpData))
ExpDataNames <- colnames(ExpData)
miR <- miRTargetCandidate[, 1]
tar <- miRTargetCandidate[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
# Initialize variables
ceRInt <- matrix(NA, m2 * (m2 - 1)/2, 2)
C <- matrix(NA, m2 * (m2 - 1)/2, 5)
for (i in seq_len(m2 - 1)) {
for (j in seq(i + 1, m2)) {
Interin1 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[i]), 1]
Interin2 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[j]), 1]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff) {
ceRInt[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- targetSym[i]
ceRInt[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- targetSym[j]
tarExpIdx1 <- which(ExpDataNames %in% targetSym[i])
tarExpIdx2 <- which(ExpDataNames %in% targetSym[j])
miRExpIdx <- which(ExpDataNames %in% intersect(Interin1, Interin2))
# Calculate sensitivity correlation of each RNA-RNA pair
M6 <- cor.test(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2])$estimate
M7 <- cor.test(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2])$p.value
M8 <- M6 - corpcor::pcor.shrink(cbind(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2],
ExpData[, miRExpIdx]), verbose = FALSE)[1, 2]
C[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- M3
C[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- M5
C[(i - 1) * m2 + j - sum(seq_len(i)), 3] <- M6
C[(i - 1) * m2 + j - sum(seq_len(i)), 4] <- M7
C[(i - 1) * m2 + j - sum(seq_len(i)), 5] <- M8
}
}
}
# Extract RNA-RNA pairs with sensitivity correlation more than senscorcutoff.
C[, 2] <- p.adjust(C[, 2], method = padjustmethod)
C[, 4] <- p.adjust(C[, 4], method = padjustmethod)
index <- which(C[, 2] < padjustvaluecutoff & C[, 3] > poscorcutoff & C[, 4] < padjustvaluecutoff &
C[, 5] > senscorcutoff)
ceRInt <- ceRInt[index, ]
C <- C[index, ]
if (is.vector(C)) {
SPPCceRInt <- c(ceRInt, C)
names(SPPCceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"correlation", "p.adjusted_value of positive correlation", "sensitivity correlation")
} else {
SPPCceRInt <- cbind(ceRInt, C)
colnames(SPPCceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"correlation", "p.adjusted_value of positive correlation", "sensitivity correlation")
}
return(SPPCceRInt)
}
# Parallel version
sppc_parallel <- function(miRTarget, ExpData, minSharedmiR = 3, poscorcutoff = 0, padjustvaluecutoff = 0.01,
padjustmethod = "BH", senscorcutoff = 0.3) {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "all")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
m1 <- nrow(miRTargetCandidate)
n1 <- ncol(miRTargetCandidate)
colnames(ExpData) <- gsub("\\.", "-", colnames(ExpData))
ExpDataNames <- colnames(ExpData)
miR <- miRTargetCandidate[, 1]
tar <- miRTargetCandidate[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
index <- t(combn(m2, 2))
Res <- foreach(i = seq_len(nrow(index))) %dopar% {
Interin1 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[index[i, 1]]), 1]
Interin2 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[index[i, 2]]), 1]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff) {
C1 <- targetSym[index[i, 1]]
C2 <- targetSym[index[i, 2]]
tarExpIdx1 <- which(ExpDataNames %in% targetSym[index[i, 1]])
tarExpIdx2 <- which(ExpDataNames %in% targetSym[index[i, 2]])
miRExpIdx <- which(ExpDataNames %in% intersect(Interin1, Interin2))
# Calculate sensitivity correlation of each RNA-RNA pair
M6 <- cor.test(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2])$estimate
M7 <- cor.test(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2])$p.value
M8 <- M6 - corpcor::pcor.shrink(cbind(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2],
ExpData[, miRExpIdx]), verbose = FALSE)[1, 2]
C3 <- M3
C4 <- M5
C5 <- M6
C6 <- M7
C7 <- M8
} else {
C1 <- NA; C2 <- NA; C3 <- NA; C4 <- NA; C5 <- NA; C6 <- NA; C7 <- NA
}
tmp <- c(C1, C2, C3, C4, C5, C6, C7)
return(tmp)
}
Res <- do.call(rbind, Res)
# Extract RNA-RNA pairs with sensitivity correlation more than senscorcutoff.
Res[, 4] <- p.adjust(as.numeric(Res[, 4]), method = padjustmethod)
Res[, 6] <- p.adjust(as.numeric(Res[, 6]), method = padjustmethod)
SPPCceRInt <- Res[which(as.numeric(Res[, 4]) < padjustvaluecutoff & as.numeric(Res[, 5]) > poscorcutoff &
as.numeric(Res[, 6]) < padjustvaluecutoff & as.numeric(Res[, 7]) > senscorcutoff), ]
if (is.vector(SPPCceRInt)) {
names(SPPCceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"correlation", "p.adjusted_value of positive correlation", "sensitivity correlation")
} else {
colnames(SPPCceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"correlation", "p.adjusted_value of positive correlation", "sensitivity correlation")
}
return(SPPCceRInt)
}
## 4. Partial Pearson Correlation (PPC) method
# Original version
ppc <- function(miRTarget, ExpData, minSharedmiR = 3, num_perm = 100, padjustvaluecutoff = 0.01,
padjustmethod = "BH") {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "all")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
m1 <- nrow(miRTargetCandidate)
n1 <- ncol(miRTargetCandidate)
colnames(ExpData) <- gsub("\\.", "-", colnames(ExpData))
ExpDataNames <- colnames(ExpData)
miR <- miRTargetCandidate[, 1]
tar <- miRTargetCandidate[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
# Initialize variables
ceRInt <- matrix(NA, m2 * (m2 - 1)/2, 2)
C <- matrix(NA, m2 * (m2 - 1)/2, 3)
for (i in seq_len(m2 - 1)) {
for (j in seq(i + 1, m2)) {
Interin1 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[i]), 1]
Interin2 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[j]), 1]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff) {
ceRInt[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- targetSym[i]
ceRInt[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- targetSym[j]
tarExpIdx1 <- which(ExpDataNames %in% targetSym[i])
tarExpIdx2 <- which(ExpDataNames %in% targetSym[j])
miRExpIdx <- which(ExpDataNames %in% intersect(Interin1, Interin2))
inputdata <- t(cbind(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2], ExpData[, miRExpIdx]))
C[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- M3
C[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- M5
C[(i - 1) * m2 + j - sum(seq_len(i)), 3] <- predCor(inputdata, num_perm, method = "PPC")
}
}
}
# Extract significant RNA-RNA pairs.
C[, 2] <- p.adjust(C[, 2], method = padjustmethod)
C[, 3] <- p.adjust(C[, 3], method = padjustmethod)
index <- which(C[, 2] < padjustvaluecutoff & C[, 3] < padjustvaluecutoff)
ceRInt <- ceRInt[index, ]
C <- C[index, ]
if (is.vector(C)) {
PPCceRInt <- c(ceRInt, C)
names(PPCceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"p.adjusted_value of RNA competition")
} else {
PPCceRInt <- cbind(ceRInt, C)
colnames(PPCceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"p.adjusted_value of RNA competition")
}
return(PPCceRInt)
}
# Parallel version
ppc_parallel <- function(miRTarget, ExpData, minSharedmiR = 3, num_perm = 100, padjustvaluecutoff = 0.01,
padjustmethod = "BH") {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "all")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
m1 <- nrow(miRTargetCandidate)
n1 <- ncol(miRTargetCandidate)
colnames(ExpData) <- gsub("\\.", "-", colnames(ExpData))
ExpDataNames <- colnames(ExpData)
miR <- miRTargetCandidate[, 1]
tar <- miRTargetCandidate[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
index <- t(combn(m2, 2))
Res <- foreach(i = seq_len(nrow(index)), .export = c("predCor", "combpvalue")) %dopar% {
Interin1 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[index[i, 1]]), 1]
Interin2 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[index[i, 2]]), 1]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff) {
C1 <- targetSym[index[i, 1]]
C2 <- targetSym[index[i, 2]]
tarExpIdx1 <- which(ExpDataNames %in% targetSym[index[i, 1]])
tarExpIdx2 <- which(ExpDataNames %in% targetSym[index[i, 2]])
miRExpIdx <- which(ExpDataNames %in% intersect(Interin1, Interin2))
inputdata <- t(cbind(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2], ExpData[, miRExpIdx]))
C3 <- M3
C4 <- M5
C5 <- predCor(inputdata, num_perm, method = "PPC")
} else {
C1 <- NA; C2 <- NA; C3 <- NA; C4 <- NA; C5 <- NA
}
tmp <- c(C1, C2, C3, C4, C5)
return(tmp)
}
Res <- do.call(rbind, Res)
# Extract significant RNA-RNA pairs.
Res[, 4] <- p.adjust(as.numeric(Res[, 4]), method = padjustmethod)
Res[, 5] <- p.adjust(as.numeric(Res[, 5]), method = padjustmethod)
PPCceRInt <- Res[which(as.numeric(Res[, 4]) < padjustvaluecutoff & as.numeric(Res[, 5]) < padjustvaluecutoff), ]
if (is.vector(PPCceRInt)) {
names(PPCceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"p.adjusted_value of RNA competition")
} else {
colnames(PPCceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"p.adjusted_value of RNA competition")
}
return(PPCceRInt)
}
## 5. Hermes method
# Original version
hermes <- function(miRTarget, ExpData, minSharedmiR = 3, num_perm = 100, padjustvaluecutoff = 0.01,
padjustmethod = "BH") {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "all")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
m1 <- nrow(miRTargetCandidate)
n1 <- ncol(miRTargetCandidate)
colnames(ExpData) <- gsub("\\.", "-", colnames(ExpData))
ExpDataNames <- colnames(ExpData)
miR <- miRTargetCandidate[, 1]
tar <- miRTargetCandidate[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
# Initialize variables
ceRInt <- matrix(NA, m2 * (m2 - 1)/2, 2)
C <- matrix(NA, m2 * (m2 - 1)/2, 3)
for (i in seq_len(m2 - 1)) {
for (j in seq(i + 1, m2)) {
Interin1 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[i]), 1]
Interin2 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[j]), 1]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff) {
ceRInt[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- targetSym[i]
ceRInt[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- targetSym[j]
tarExpIdx1 <- which(ExpDataNames %in% targetSym[i])
tarExpIdx2 <- which(ExpDataNames %in% targetSym[j])
miRExpIdx <- which(ExpDataNames %in% intersect(Interin1, Interin2))
inputdata <- t(cbind(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2], ExpData[, miRExpIdx]))
C[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- M3
C[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- M5
C[(i - 1) * m2 + j - sum(seq_len(i)), 3] <- predCor(inputdata, num_perm, method = "CMI")
}
}
}
# Extract significant RNA-RNA pairs.
C[, 2] <- p.adjust(C[, 2], method = padjustmethod)
C[, 3] <- p.adjust(C[, 3], method = padjustmethod)
index <- which(C[, 2] < padjustvaluecutoff & C[, 3] < padjustvaluecutoff)
ceRInt <- ceRInt[index, ]
C <- C[index, ]
if (is.vector(C)) {
HermesceRInt <- c(ceRInt, C)
names(HermesceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"p.adjusted_value of RNA competition")
} else {
HermesceRInt <- cbind(ceRInt, C)
colnames(HermesceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"p.adjusted_value of RNA competition")
}
return(HermesceRInt)
}
# Parallel version
hermes_parallel <- function(miRTarget, ExpData, minSharedmiR = 3, num_perm = 100, padjustvaluecutoff = 0.01,
padjustmethod = "BH") {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "all")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
m1 <- nrow(miRTargetCandidate)
n1 <- ncol(miRTargetCandidate)
colnames(ExpData) <- gsub("\\.", "-", colnames(ExpData))
ExpDataNames <- colnames(ExpData)
miR <- miRTargetCandidate[, 1]
tar <- miRTargetCandidate[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
index <- t(combn(m2, 2))
Res <- foreach(i = seq_len(nrow(index)), .export = c("predCor", "combpvalue", "calCMI")) %dopar% {
Interin1 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[index[i, 1]]), 1]
Interin2 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[index[i, 2]]), 1]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff) {
C1 <- targetSym[index[i, 1]]
C2 <- targetSym[index[i, 2]]
tarExpIdx1 <- which(ExpDataNames %in% targetSym[index[i, 1]])
tarExpIdx2 <- which(ExpDataNames %in% targetSym[index[i, 2]])
miRExpIdx <- which(ExpDataNames %in% intersect(Interin1, Interin2))
inputdata <- t(cbind(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2], ExpData[, miRExpIdx]))
C3 <- M3
C4 <- M5
C5 <- predCor(inputdata, num_perm, method = "CMI")
} else {
C1 <- NA; C2 <- NA; C3 <- NA; C4 <- NA; C5 <- NA
}
tmp <- c(C1, C2, C3, C4, C5)
return(tmp)
}
Res <- do.call(rbind, Res)
# Extract significant RNA-RNA pairs.
Res[, 4] <- p.adjust(as.numeric(Res[, 4]), method = padjustmethod)
Res[, 5] <- p.adjust(as.numeric(Res[, 5]), method = padjustmethod)
HermesceRInt <- Res[which(as.numeric(Res[, 4]) < padjustvaluecutoff & as.numeric(Res[, 5]) < padjustvaluecutoff), ]
if (is.vector(HermesceRInt)) {
names(HermesceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"p.adjusted_value of RNA competition")
} else {
colnames(HermesceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"p.adjusted_value of RNA competition")
}
return(HermesceRInt)
}
## 6. MuTaME method
# Original version
muTaME <- function(miRTarget, mres, minSharedmiR = 3, padjustvaluecutoff = 0.01, padjustmethod = "BH",
scorecutoff = 0.5) {
miRTarget <- as.matrix(miRTarget)
m1 <- nrow(miRTarget)
n1 <- ncol(miRTarget)
miR <- miRTarget[, 1]
tar <- miRTarget[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
# Initialize variables
ceRInt <- matrix(NA, m2 * (m2 - 1)/2, 2)
C <- matrix(NA, m2 * (m2 - 1)/2, 8)
for (i in seq_len(m2 - 1)) {
for (j in seq(i + 1, m2)) {
Interin1 <- miRTarget[which(miRTarget[, 2] %in% targetSym[i]), 1]
Interin2 <- miRTarget[which(miRTarget[, 2] %in% targetSym[j]), 1]
cm <- intersect(Interin1, Interin2)
SharedMREs <- mres[mres[, 2] %in% c(targetSym[i], targetSym[j]) & mres[, 1] %in% cm, ]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff & nrow(SharedMREs) > 0) {
ceRInt[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- targetSym[i]
ceRInt[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- targetSym[j]
C[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- M3
C[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- M5
# Score 1 for the fraction of coomon miRNAs
C[(i - 1) * m2 + j - sum(seq_len(i)), 3] <- log(M3/min(M1, M2))
# Score 2 for the density of the MREs for all shared miRNAs
C[(i - 1) * m2 + j - sum(seq_len(i)), 4] <- sum(sapply(cm, function(miR) {
MREs <- SharedMREs[SharedMREs[, 1] == miR, ]
if (nrow(MREs) <= 0) return(1)
MREslr <- MREs[, c("gap_l", "gap_r")]
D <- abs(max(MREslr[, 1]) - min(MREslr[, 2]))
return(log(nrow(MREs)/D))
}))
# Score 3 for the distribution of MREs of the putative RNA-RNA pairs
C[(i - 1) * m2 + j - sum(seq_len(i)), 5] <- sum(sapply(cm, function(miR) {
positions <- SharedMREs[SharedMREs[, 1] == miR, c("gap_l", "gap_r")]
if (nrow(positions) <= 0) return(1)
return(log(abs(max(positions[, 1]) - min(positions[, 2]))^2/sum((positions[, 2] -
positions[, 1])^2)))
}))
# Score 4 for the relation between the overall number of MREs for a putative miRNA sponge,
# compared with the number of miRNAs that yield these MREs
B <- nrow(SharedMREs)
if (B == length(unique(SharedMREs[, 1]))) {
C[(i - 1) * m2 + j - sum(seq_len(i)), 6] <- log(1/B)
} else {
C[(i - 1) * m2 + j - sum(seq_len(i)), 6] <- log((B - length(unique(SharedMREs[,
1])) - 1)/B)
}
C[(i - 1) * m2 + j - sum(seq_len(i)), 7] <- C[(i - 1) * m2 + j - sum(seq_len(i)),
3] + C[(i - 1) * m2 + j - sum(seq_len(i)), 4] + C[(i - 1) * m2 + j - sum(seq_len(i)),
5] + C[(i - 1) * m2 + j - sum(seq_len(i)), 6]
}
}
}
# Extract RNA-RNA pair based on four scores.
ceRInt <- ceRInt[apply(ceRInt, 1, function(x) !all(is.na(x))), ]
C <- C[apply(C, 1, function(x) !all(is.na(x))), ]
C[, 8] <- (C[, 7] - min(C[, 7]))/(max(C[, 7]) - min(C[, 7]))
C[, 2] <- p.adjust(C[, 2], method = padjustmethod)
index <- which(C[, 2] < padjustvaluecutoff & C[, 8] > scorecutoff)
ceRInt <- ceRInt[index, ]
C <- C[index, ]
if (is.vector(C)) {
MuTaMEceRInt <- c(ceRInt, C)
names(MuTaMEceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"Score 1", "Score 2", "Score 3", "Score 4", "Combined score", "Normalized score")
} else {
MuTaMEceRInt <- cbind(ceRInt, C)
colnames(MuTaMEceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"Score 1", "Score 2", "Score 3", "Score 4", "Combined score", "Normalized score")
}
return(MuTaMEceRInt)
}
# Parallel version
muTaME_parallel <- function(miRTarget, mres, minSharedmiR = 3, padjustvaluecutoff = 0.01, padjustmethod = "BH",
scorecutoff = 0.5) {
miRTarget <- as.matrix(miRTarget)
m1 <- nrow(miRTarget)
n1 <- ncol(miRTarget)
miR <- miRTarget[, 1]
tar <- miRTarget[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
index <- t(combn(m2, 2))
Res <- foreach(i = seq_len(nrow(index))) %dopar% {
Interin1 <- miRTarget[which(miRTarget[, 2] %in% targetSym[index[i, 1]]), 1]
Interin2 <- miRTarget[which(miRTarget[, 2] %in% targetSym[index[i, 2]]), 1]
cm <- intersect(Interin1, Interin2)
SharedMREs <- mres[mres[, 2] %in% c(targetSym[index[i, 1]], targetSym[index[i, 2]]) & mres[, 1] %in% cm, ]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff & nrow(SharedMREs) > 0) {
C1 <- targetSym[index[i, 1]]
C2 <- targetSym[index[i, 2]]
C3 <- M3
C4 <- M5
# Score 1 for the fraction of coomon miRNAs
C5 <- log(M3/min(M1, M2))
# Score 2 for the density of the MREs for all shared miRNAs
C6 <- sum(sapply(cm, function(miR) {
MREs <- SharedMREs[SharedMREs[, 1] == miR, ]
if (nrow(MREs) <= 0) return(1)
MREslr <- MREs[, c("gap_l", "gap_r")]
D <- abs(max(MREslr[, 1]) - min(MREslr[, 2]))
return(log(nrow(MREs)/D))
}))
# Score 3 for the distribution of MREs of the putative RNA-RNA pairs
C7 <- sum(sapply(cm, function(miR) {
positions <- SharedMREs[SharedMREs[, 1] == miR, c("gap_l", "gap_r")]
if (nrow(positions) <= 0) return(1)
return(log(abs(max(positions[, 1]) - min(positions[, 2]))^2/sum((positions[, 2] -
positions[, 1])^2)))
}))
# Score 4 for the relation between the overall number of MREs for a putative miRNA sponge,
# compared with the number of miRNAs that yield these MREs
B <- nrow(SharedMREs)
if (B == length(unique(SharedMREs[, 1]))) {
C8 <- log(1/B)
} else {
C8 <- log((B - length(unique(SharedMREs[, 1])) - 1)/B)
}
C9 <- C5 + C6 + C7 + C8
} else {
C1 <- NA; C2 <- NA; C3 <- NA; C4 <- NA; C5 <- NA; C6 <- NA; C7 <- NA; C8 <- NA; C9 <- NA
}
tmp <- c(C1, C2, C3, C4, C5, C6, C7, C8, C9)
return(tmp)
}
Res <- do.call(rbind, Res)
# Extract RNA-RNA pair based on four scores.
Res <- Res[apply(Res, 1, function(x) !all(is.na(x))), ]
C10 <- (as.numeric(Res[, 9]) - min(as.numeric(Res[, 9])))/(max(as.numeric(Res[, 9])) - min(as.numeric(Res[, 9])))
Res <- cbind(Res, C10)
Res[, 4] <- p.adjust(as.numeric(Res[, 4]), method = padjustmethod)
MuTaMEceRInt <- Res[which(as.numeric(Res[, 4]) < padjustvaluecutoff & as.numeric(Res[, 10]) > scorecutoff), ]
if (is.vector(MuTaMEceRInt)) {
names(MuTaMEceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"Score 1", "Score 2", "Score 3", "Score 4", "Combined score", "Normalized score")
} else {
colnames(MuTaMEceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"Score 1", "Score 2", "Score 3", "Score 4", "Combined score", "Normalized score")
}
return(MuTaMEceRInt)
}
## 7. CERNIA method
# Original version
cernia <- function(miRTarget, ExpData, mres, consider.miR.expr = "TRUE", minSharedmiR = 3, poscorcutoff = 0,
padjustvaluecutoff = 0.01, padjustmethod = "BH", scorecutoff = 0.5) {
if (consider.miR.expr == "TRUE") {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "all")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
} else {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "target")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
}
m1 <- nrow(miRTargetCandidate)
n1 <- ncol(miRTargetCandidate)
colnames(ExpData) <- gsub("\\.", "-", colnames(ExpData))
ExpDataNames <- colnames(ExpData)
miR <- miRTargetCandidate[, 1]
tar <- miRTargetCandidate[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
# Initialize variables
ceRInt <- matrix(NA, m2 * (m2 - 1)/2, 2)
C <- matrix(NA, m2 * (m2 - 1)/2, 11)
for (i in seq_len(m2 - 1)) {
for (j in seq(i + 1, m2)) {
Interin1 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[i]), 1]
Interin2 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[j]), 1]
cm <- intersect(Interin1, Interin2)
SharedMREs <- mres[mres[, 2] %in% c(targetSym[i], targetSym[j]) & mres[, 1] %in% cm, ]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
tarExpIdx1 <- which(ExpDataNames %in% targetSym[i])
tarExpIdx2 <- which(ExpDataNames %in% targetSym[j])
M6 <- cor.test(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2])$estimate
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff & M6 > poscorcutoff &
nrow(SharedMREs) > 0) {
ceRInt[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- targetSym[i]
ceRInt[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- targetSym[j]
C[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- M3
C[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- M5
# Score 1 for the fraction of coomon miRNAs
C[(i - 1) * m2 + j - sum(seq_len(i)), 3] <- log(M3/min(M1, M2))
# Score 2 for the density of the MREs for all shared miRNAs
C[(i - 1) * m2 + j - sum(seq_len(i)), 4] <- sum(sapply(cm, function(miR) {
MREs <- SharedMREs[SharedMREs[, 1] == miR, ]
if (nrow(MREs) <= 0) return(1)
MREslr <- MREs[, c("gap_l", "gap_r")]
D <- abs(max(MREslr[, 1]) - min(MREslr[, 2]))
return(log(nrow(MREs)/D))
}))
# Score 3 for the distribution of MREs of the putative RNA-RNA pairs
C[(i - 1) * m2 + j - sum(seq_len(i)), 5] = sum(sapply(cm, function(miR) {
positions <- SharedMREs[SharedMREs[, 1] == miR, c("gap_l", "gap_r")]
if (nrow(positions) <= 0) return(1)
return(log(abs(max(positions[, 1]) - min(positions[, 2]))^2/sum((positions[, 2] -
positions[, 1])^2)))
}))
# Score 4 for the relation between the overall number of MREs for a putative miRNA sponge,
# compared with the number of miRNAs that yield these MREs
B <- nrow(SharedMREs)
if (B == length(unique(SharedMREs[, 1]))) {
C[(i - 1) * m2 + j - sum(seq_len(i)), 6] <- log(1/B)
} else {
C[(i - 1) * m2 + j - sum(seq_len(i)), 6] <- log((B - length(unique(SharedMREs[,
1])) - 1)/B)
}
# Score 5 for the density of the hybridization energies related to MREs for all shared miRNAs
SharedMREs <- mres[mres[, 2] %in% c(targetSym[i], targetSym[j]) & mres[, 1] %in%
cm, ]
C[(i - 1) * m2 + j - sum(seq_len(i)), 7] <- sum(sapply(cm, function(miR) {
MREs <- SharedMREs[SharedMREs[, 1] == miR, ]
if (nrow(MREs) <= 0) return(1)
MREslr <- MREs[, c("gap_l", "gap_r")]
D <- abs(max(MREslr[, 1]) - min(MREslr[, 2]))
return(log(sum(abs(MREs[, 3]))/D))
}))
# Score 6 for the DT-Hybrid recommendation scores
cerna_recommendations <- dtHybrid(miRTargetCandidate)
C[(i - 1) * m2 + j - sum(seq_len(i)), 8] <- cerna_recommendations[targetSym[i],
targetSym[j]]
# Score 7 for the pairwise Peason correlation between putative RNA-RNA pair expression data
C[(i - 1) * m2 + j - sum(seq_len(i)), 9] <- log(M6)
C[(i - 1) * m2 + j - sum(seq_len(i)), 10] <- C[(i - 1) * m2 + j - sum(seq_len(i)),
3] + C[(i - 1) * m2 + j - sum(seq_len(i)), 4] + C[(i - 1) * m2 + j - sum(seq_len(i)),
5] + C[(i - 1) * m2 + j - sum(seq_len(i)), 6] + C[(i - 1) * m2 + j - sum(seq_len(i)),
7] + C[(i - 1) * m2 + j - sum(seq_len(i)), 8] + C[(i - 1) * m2 + j - sum(seq_len(i)),
9]
}
}
}
# Extract RNA-RNA pair based on seven scores
ceRInt <- ceRInt[apply(ceRInt, 1, function(x) !all(is.na(x))), ]
C <- C[apply(C, 1, function(x) !all(is.na(x))), ]
C[, 11] <- (C[, 10] - min(C[, 10]))/(max(C[, 10]) - min(C[, 10]))
C[, 2] <- p.adjust(C[, 2], method = padjustmethod)
index <- which(C[, 2] < padjustvaluecutoff & C[, 11] > scorecutoff)
ceRInt <- ceRInt[index, ]
C <- C[index, ]
if (is.vector(C)) {
CERNIAceRInt <- c(ceRInt, C)
names(CERNIAceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"Score 1", "Score 2", "Score 3", "Score 4", "Score 5", "Score 6", "Score 7", "Combined score",
"Normalized score")
} else {
CERNIAceRInt <- cbind(ceRInt, C)
colnames(CERNIAceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"Score 1", "Score 2", "Score 3", "Score 4", "Score 5", "Score 6", "Score 7", "Combined score",
"Normalized score")
}
return(CERNIAceRInt)
}
# Parallel version
cernia_parallel <- function(miRTarget, ExpData, mres, consider.miR.expr = "TRUE", minSharedmiR = 3, poscorcutoff = 0,
padjustvaluecutoff = 0.01, padjustmethod = "BH", scorecutoff = 0.5) {
if (consider.miR.expr == "TRUE") {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "all")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
} else {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "target")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
}
m1 <- nrow(miRTargetCandidate)
n1 <- ncol(miRTargetCandidate)
colnames(ExpData) <- gsub("\\.", "-", colnames(ExpData))
ExpDataNames <- colnames(ExpData)
miR <- miRTargetCandidate[, 1]
tar <- miRTargetCandidate[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
index <- t(combn(m2, 2))
Res <- foreach(i = seq_len(nrow(index)), .export = c("dtHybrid", "makeCluster", "detectCores", "recommendation",
"graphWeights", "parMM", "stopCluster", "clusterApply")) %dopar% {
Interin1 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[index[i, 1]]), 1]
Interin2 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[index[i, 2]]), 1]
cm <- intersect(Interin1, Interin2)
SharedMREs <- mres[mres[, 2] %in% c(targetSym[index[i, 1]], targetSym[index[i, 2]]) & mres[, 1] %in% cm, ]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
tarExpIdx1 <- which(ExpDataNames %in% targetSym[index[i, 1]])
tarExpIdx2 <- which(ExpDataNames %in% targetSym[index[i, 2]])
M6 <- cor.test(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2])$estimate
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff & M6 > poscorcutoff &
nrow(SharedMREs) > 0) {
C1 <- targetSym[index[i, 1]]
C2 <- targetSym[index[i, 2]]
C3 <- M3
C4 <- M5
# Score 1 for the fraction of coomon miRNAs
C5 <- log(M3/min(M1, M2))
# Score 2 for the density of the MREs for all shared miRNAs
C6 <- sum(sapply(cm, function(miR) {
MREs <- SharedMREs[SharedMREs[, 1] == miR, ]
if (nrow(MREs) <= 0) return(1)
MREslr <- MREs[, c("gap_l", "gap_r")]
D <- abs(max(MREslr[, 1]) - min(MREslr[, 2]))
return(log(nrow(MREs)/D))
}))
# Score 3 for the distribution of MREs of the putative RNA-RNA pairs
C7 = sum(sapply(cm, function(miR) {
positions <- SharedMREs[SharedMREs[, 1] == miR, c("gap_l", "gap_r")]
if (nrow(positions) <= 0) return(1)
return(log(abs(max(positions[, 1]) - min(positions[, 2]))^2/sum((positions[, 2] -
positions[, 1])^2)))
}))
# Score 4 for the relation between the overall number of MREs for a putative miRNA sponge,
# compared with the number of miRNAs that yield these MREs
B <- nrow(SharedMREs)
if (B == length(unique(SharedMREs[, 1]))) {
C8 <- log(1/B)
} else {
C8 <- log((B - length(unique(SharedMREs[, 1])) - 1)/B)
}
# Score 5 for the density of the hybridization energies related to MREs for all shared miRNAs
SharedMREs <- mres[mres[, 2] %in% c(targetSym[index[i, 1]], targetSym[index[i, 2]]) & mres[, 1] %in%
cm, ]
C9 <- sum(sapply(cm, function(miR) {
MREs <- SharedMREs[SharedMREs[, 1] == miR, ]
if (nrow(MREs) <= 0) return(1)
MREslr <- MREs[, c("gap_l", "gap_r")]
D <- abs(max(MREslr[, 1]) - min(MREslr[, 2]))
return(log(sum(abs(MREs[, 3]))/D))
}))
# Score 6 for the DT-Hybrid recommendation scores
cerna_recommendations <- dtHybrid(miRTargetCandidate)
C10 <- cerna_recommendations[targetSym[index[i, 1]], targetSym[index[i, 2]]]
# Score 7 for the pairwise Pearson correlation between putative RNA-RNA pair expression data
C11 <- log(M6)
C12 <- C5 + C6 + C7 + C8 + C9 + C10 + C11
} else {
C1 <- NA; C2 <- NA; C3 <- NA; C4 <- NA; C5 <- NA; C6 <- NA; C7 <- NA; C8 <- NA; C9 <- NA; C10 <- NA; C11 <- NA; C12 <- NA
}
tmp <- c(C1, C2, C3, C4, C5, C6, C7, C8, C9, C10, C11, C12)
return(tmp)
}
Res <- do.call(rbind, Res)
# Extract RNA-RNA pair based on seven scores
Res <- Res[apply(Res, 1, function(x) !all(is.na(x))), ]
C13 <- (as.numeric(Res[, 12]) - min(as.numeric(Res[, 12])))/(max(as.numeric(Res[, 12])) - min(as.numeric(Res[, 12])))
Res <- cbind(Res, C13)
Res[, 4] <- p.adjust(as.numeric(Res[, 4]), method = padjustmethod)
CERNIAceRInt <- Res[which(as.numeric(Res[, 4]) < padjustvaluecutoff & as.numeric(Res[, 13]) > scorecutoff), ]
if (is.vector(CERNIAceRInt)) {
names(CERNIAceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"Score 1", "Score 2", "Score 3", "Score 4", "Score 5", "Score 6", "Score 7", "Combined score",
"Normalized score")
} else {
colnames(CERNIAceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"Score 1", "Score 2", "Score 3", "Score 4", "Score 5", "Score 6", "Score 7", "Combined score",
"Normalized score")
}
return(CERNIAceRInt)
}
## 8. Sparse Partial correlation ON Gene Expression (SPONGE) method
sponge_parallel <- function(miRTarget, ExpData, minSharedmiR = 3, poscorcutoff = 0, padjustvaluecutoff = 0.01,
padjustmethod = "BH", senscorcutoff = 0, null_model) {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "all")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
m1 <- nrow(miRTargetCandidate)
n1 <- ncol(miRTargetCandidate)
colnames(ExpData) <- gsub("\\.", "-", colnames(ExpData))
ExpDataNames <- colnames(ExpData)
miR <- miRTargetCandidate[, 1]
tar <- miRTargetCandidate[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
index <- t(combn(m2, 2))
Res <- foreach(i = seq_len(nrow(index)), .packages = c("SPONGE")) %dopar% {
Interin1 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[index[i, 1]]), 1]
Interin2 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[index[i, 2]]), 1]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff) {
C1 <- targetSym[index[i, 1]]
C2 <- targetSym[index[i, 2]]
tarExpIdx1 <- which(ExpDataNames %in% targetSym[index[i, 1]])
tarExpIdx2 <- which(ExpDataNames %in% targetSym[index[i, 2]])
miRExpIdx <- which(ExpDataNames %in% intersect(Interin1, Interin2))
# Calculate sensitivity correlation of each RNA-RNA pair
M6 <- cor.test(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2])$estimate
M7 <- cor.test(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2])$p.value
M8 <- corpcor::pcor.shrink(cbind(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2],
ExpData[, miRExpIdx]), verbose = FALSE)[1, 2]
M9 <- M6 - M8
C3 <- M3
C4 <- M5
C5 <- M6
C6 <- M7
C7 <- M8
C8 <- M9
} else {
C1 <- NA; C2 <- NA; C3 <- NA; C4 <- NA; C5 <- NA; C6 <- NA; C7 <- NA; C8 <- NA
}
tmp <- c(C1, C2, C3, C4, C5, C6, C7, C8)
return(tmp)
}
Res <- do.call(rbind, Res)
# Extract RNA-RNA pairs with sensitivity correlation more than senscorcutoff.
Res[, 4] <- p.adjust(as.numeric(Res[, 4]), method = padjustmethod)
Res[, 6] <- p.adjust(as.numeric(Res[, 6]), method = padjustmethod)
Res <- Res[which(as.numeric(Res[, 4]) < padjustvaluecutoff & as.numeric(Res[, 5]) > poscorcutoff &
as.numeric(Res[, 6]) < padjustvaluecutoff & as.numeric(Res[, 8]) > senscorcutoff), ]
sponge_result <- data.frame(geneA = Res[, 1],
geneB = Res[, 2],
df = as.numeric(Res[, 3]),
cor = as.numeric(Res[, 5]),
pcor = as.numeric(Res[, 7]),
mscor = as.numeric(Res[, 8]))
sponge_result_null_model <- sponge_compute_p_values(sponge_result = sponge_result, null_model = null_model)
sponge_result_fdr <- sponge_result_null_model[which(sponge_result_null_model$p.adj < padjustvaluecutoff), ]
Res_fdr <- Res[which(sponge_result_null_model$p.adj < padjustvaluecutoff), ]
SPONGEceRInt <- cbind(Res_fdr, sponge_result_fdr[, 8])
if (is.vector(SPONGEceRInt)) {
names(SPONGEceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"correlation", "p.adjusted_value of positive correlation", "partial correlation",
"sensitivity correlation", "p.adjusted_value of sensitivity correlation")
} else {
colnames(SPONGEceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"correlation", "p.adjusted_value of positive correlation", "partial correlation",
"sensitivity correlation", "p.adjusted_value of sensitivity correlation")
}
return(SPONGEceRInt)
}
## 9. Integrate method for miRNA sponge interactions by integrating different methods.
integrateMethod <- function(Interlist, Intersect_num) {
if (length(Interlist) >= 2 & length(Interlist) >= Intersect_num) {
Combcase <- t(combn(length(Interlist), Intersect_num))
Combnum <- dim(Combcase)[1]
Integrate_Inter <- list()
for (i in seq_len(Combnum)) {
Interin <- do.call(rbind, lapply(Combcase[i, ], function(i) Interlist[[i]]))
Interin_paste <- paste(Interin[, 1], Interin[, 2], sep = "-")
Interin_table <- table(Interin_paste)
Interin_names <- names(Interin_table)[which(Interin_table == Intersect_num)]
Integrate_Inter[[i]] <- Interin[which(Interin_paste %in% Interin_names), ]
}
Integrate_res <- unique(do.call(rbind, Integrate_Inter))
return(Integrate_res)
} else {
stop("Please check your input!\n")
}
}
## Consolidating 15 functions: miRHomology, miRHomology_parallel, pc, pc_parallel, sppc, sppc_parallel,
## ppc, ppc_parallel, hermes, hermes_parallel, muTaME, muTaME_parallel, cernia, cernia_parallel and sponge_parallel.
spongeMethod <- function(miRTarget,
ExpData = NULL,
mres = NULL,
consider.miR.expr = "TRUE",
minSharedmiR = 3,
poscorcutoff = 0,
num_perm = 100,
padjustvaluecutoff = 0.01,
padjustmethod = "BH",
senscorcutoff = 0.3,
scorecutoff = 0.5,
null_model,
method = c("miRHomology", "miRHomology_parallel",
"pc", "pc_parallel", "sppc", "sppc_parallel",
"ppc", "ppc_parallel",
"hermes", "hermes_parallel",
"muTaME", "muTaME_parallel",
"cernia", "cernia_parallel",
"sponge_parallel"),
num.cores = 2) {
if (method == "miRHomology_parallel" | method == "pc_parallel" | method == "sppc_parallel" |
method == "ppc_parallel" | method == "hermes_parallel" | method == "muTaME_parallel" |
method == "cernia_parallel" | method == "sponge_parallel") {
# get number of cores to run
cores <- makeCluster(num.cores)
registerDoParallel(cores)
}
if (method == "miRHomology") {
ceRInt <- miRHomology(miRTarget, minSharedmiR, padjustvaluecutoff, padjustmethod)
} else if (method == "miRHomology_parallel"){
ceRInt <- miRHomology_parallel(miRTarget, minSharedmiR, padjustvaluecutoff, padjustmethod)
} else if (method == "pc") {
ceRInt <- pc(miRTarget, ExpData, consider.miR.expr, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod)
} else if (method == "pc_parallel") {
ceRInt <- pc_parallel(miRTarget, ExpData, consider.miR.expr, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod)
} else if (method == "sppc") {
ceRInt <- sppc(miRTarget, ExpData, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, senscorcutoff)
} else if (method == "sppc_parallel") {
ceRInt <- sppc_parallel(miRTarget, ExpData, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, senscorcutoff)
} else if (method == "ppc") {
ceRInt <- ppc(miRTarget, ExpData, minSharedmiR, num_perm, padjustvaluecutoff,
padjustmethod)
} else if (method == "ppc_parallel") {
ceRInt <- ppc_parallel(miRTarget, ExpData, minSharedmiR, num_perm, padjustvaluecutoff,
padjustmethod)
} else if (method == "hermes") {
ceRInt <- hermes(miRTarget, ExpData, minSharedmiR, num_perm, padjustvaluecutoff,
padjustmethod)
} else if (method == "hermes_parallel") {
ceRInt <- hermes_parallel(miRTarget, ExpData, minSharedmiR, num_perm, padjustvaluecutoff,
padjustmethod)
} else if (method == "muTaME") {
ceRInt <- muTaME(miRTarget, mres, minSharedmiR, padjustvaluecutoff,
padjustmethod, scorecutoff)
} else if (method == "muTaME_parallel") {
ceRInt <- muTaME_parallel(miRTarget, mres, minSharedmiR, padjustvaluecutoff,
padjustmethod, scorecutoff)
} else if (method == "cernia") {
ceRInt <- cernia(miRTarget, ExpData, mres, consider.miR.expr, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, scorecutoff)
} else if (method == "cernia_parallel") {
ceRInt <- cernia_parallel(miRTarget, ExpData, mres, consider.miR.expr, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, scorecutoff)
} else if (method == "sponge_parallel") {
ceRInt <- sponge_parallel(miRTarget, ExpData, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, senscorcutoff, null_model)
}
if (method == "miRHomology_parallel" | method == "pc_parallel" | method == "sppc_parallel" |
method == "ppc_parallel" | method == "hermes_parallel" | method == "muTaME_parallel" |
method == "cernia_parallel" | method == "sponge_parallel") {
# shut down the workers
stopCluster(cores)
stopImplicitCluster()
}
return(ceRInt)
}
## Inferring sample-specific miRNA sponge interaction network using sample
## control variable strategy.
sponge_sample_specific <- function(miRTarget,
ExpData = NULL,
mres = NULL,
consider.miR.expr = "TRUE",
minSharedmiR = 3,
poscorcutoff = 0,
num_perm = 100,
padjustvaluecutoff = 0.01,
padjustmethod = "BH",
senscorcutoff = 0.3,
scorecutoff = 0.5,
null_model,
method = c("pc", "pc_parallel", "sppc",
"sppc_parallel", "ppc", "ppc_parallel",
"hermes", "hermes_parallel", "cernia",
"cernia_parallel", "sponge_parallel"),
num.cores = 2) {
# get number of cores to run
cores <- makeCluster(num.cores)
registerDoParallel(cores)
if (method == "pc") {
ceRInt.all <- pc(miRTarget, ExpData, consider.miR.expr, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod)
ceRInt.single <- foreach(i = seq(nrow(ExpData)), .export = c("pc", "querymiRTargetbinding")) %dopar% {
pc(miRTarget, ExpData[-i, ], consider.miR.expr, minSharedmiR, poscorcutoff, padjustvaluecutoff, padjustmethod)
}
} else if (method == "pc_parallel") {
ceRInt.all <- pc_parallel(miRTarget, ExpData, consider.miR.expr, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod)
ceRInt.single <- foreach(i = seq(nrow(ExpData)), .export = c("pc_parallel", "querymiRTargetbinding")) %dopar% {
pc_parallel(miRTarget, ExpData[-i, ], consider.miR.expr, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod)
}
} else if (method == "sppc") {
ceRInt.all <- sppc(miRTarget, ExpData, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, senscorcutoff)
ceRInt.single <- foreach(i = seq(nrow(ExpData)), .export = c("sppc", "querymiRTargetbinding")) %dopar% {
sppc(miRTarget, ExpData[-i, ], minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, senscorcutoff)
}
} else if (method == "sppc_parallel") {
ceRInt.all <- sppc_parallel(miRTarget, ExpData, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, senscorcutoff)
ceRInt.single <- foreach(i = seq(nrow(ExpData)), .export = c("sppc_parallel", "querymiRTargetbinding")) %dopar% {
sppc_parallel(miRTarget, ExpData[-i, ], minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, senscorcutoff)
}
} else if (method == "ppc") {
ceRInt.all <- ppc(miRTarget, ExpData, minSharedmiR, num_perm, padjustvaluecutoff,
padjustmethod)
ceRInt.single <- foreach(i = seq(nrow(ExpData)), .export = c("ppc", "querymiRTargetbinding", "predCor", "combpvalue")) %dopar% {
ppc(miRTarget, ExpData[-i, ], minSharedmiR, num_perm, padjustvaluecutoff,
padjustmethod)
}
} else if (method == "ppc_parallel") {
ceRInt.all <- ppc_parallel(miRTarget, ExpData, minSharedmiR, num_perm, padjustvaluecutoff,
padjustmethod)
ceRInt.single <- foreach(i = seq(nrow(ExpData)), .export = c("ppc_parallel", "querymiRTargetbinding", "predCor", "combpvalue")) %dopar% {
ppc_parallel(miRTarget, ExpData[-i, ], minSharedmiR, num_perm, padjustvaluecutoff,
padjustmethod)
}
} else if (method == "hermes") {
ceRInt.all <- hermes(miRTarget, ExpData, minSharedmiR, num_perm, padjustvaluecutoff,
padjustmethod)
ceRInt.single <- foreach(i = seq(nrow(ExpData)), .export = c("hermes", "querymiRTargetbinding", "predCor", "combpvalue", "calCMI")) %dopar% {
hermes(miRTarget, ExpData[-i, ], minSharedmiR, num_perm, padjustvaluecutoff,
padjustmethod)
}
} else if (method == "hermes_parallel") {
ceRInt.all <- hermes_parallel(miRTarget, ExpData, minSharedmiR, num_perm, padjustvaluecutoff,
padjustmethod)
ceRInt.single <- foreach(i = seq(nrow(ExpData)), .export = c("hermes_parallel", "querymiRTargetbinding", "predCor", "combpvalue", "calCMI")) %dopar% {
hermes_parallel(miRTarget, ExpData[-i, ], minSharedmiR, num_perm, padjustvaluecutoff,
padjustmethod)
}
} else if (method == "cernia") {
ceRInt.all <- cernia(miRTarget, ExpData, mres, consider.miR.expr, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, scorecutoff)
ceRInt.single <- foreach(i = seq(nrow(ExpData)), .export = c("cernia", "querymiRTargetbinding", "dtHybrid", "makeCluster",
"detectCores", "recommendation", "graphWeights", "parMM", "stopCluster", "clusterApply")) %dopar% {
cernia(miRTarget, ExpData[-i, ], mres, consider.miR.expr, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, scorecutoff)
}
} else if (method == "cernia_parallel") {
ceRInt.all <- cernia_parallel(miRTarget, ExpData, mres, consider.miR.expr, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, scorecutoff)
ceRInt.single <- foreach(i = seq(nrow(ExpData)), .export = c("cernia_parallel", "querymiRTargetbinding", "dtHybrid", "makeCluster",
"detectCores", "recommendation", "graphWeights", "parMM", "stopCluster", "clusterApply")) %dopar% {
cernia_parallel(miRTarget, ExpData[-i, ], mres, consider.miR.expr, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, scorecutoff)
}
} else if (method == "sponge_parallel") {
ceRInt.all <- sponge_parallel(miRTarget, ExpData, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, senscorcutoff, null_model)
ceRInt.single <- foreach(i = seq(nrow(ExpData)), .packages = c("SPONGE"), .export = c("sponge_parallel", "querymiRTargetbinding")) %dopar% {
sponge_parallel(miRTarget, ExpData[-i, ], minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, senscorcutoff, null_model)
}
}
# shut down the workers
stopCluster(cores)
stopImplicitCluster()
ceRInt.all.graph <- make_graph(c(t(ceRInt.all[, 1:2])), directed = FALSE)
ceRInt.single.graph <- lapply(seq(ceRInt.single), function(i) make_graph(c(t(ceRInt.single[[i]][, 1:2])), directed = FALSE))
ceRInt <- lapply(seq(ceRInt.single), function(i) as_data_frame((ceRInt.all.graph %m% ceRInt.single.graph[[i]]) %u%
(ceRInt.single.graph[[i]] %m% ceRInt.all.graph)))
names(ceRInt) <- rownames(ExpData)
return(ceRInt)
}
## Identifying sample-sample correlation network in terms of
## sample-specific miRNA sponge networks.
sample_cor_network <- function(ceRNet,
genes_num,
method = "Simpson",
simcutoff = 0.5,
padjustvaluecutoff = 0.01,
padjustmethod = "BH",
num.cores = 2){
# get number of cores to run
cores <- makeCluster(num.cores)
registerDoParallel(cores)
ceRNet.graph <- lapply(seq(ceRNet), function(i) graph_from_data_frame(ceRNet[[i]], directed = FALSE))
m <- length(ceRNet.graph)
index <- t(combn(m, 2))
background <- genes_num * (genes_num - 1)/2
Res <- foreach(i = seq_len(nrow(index)), .packages = c("igraph")) %dopar% {
net1 <- ceRNet.graph[[index[i, 1]]]
net2 <- ceRNet.graph[[index[i, 2]]]
overlap <- length(E(net1 %s% net2))
if (method == "Simpson") {
sim <- overlap/min(length(E(net1)), length(E(net2)))
} else if (method == "Jaccard") {
sim <- overlap/length(E(net1 %u% net2))
} else if (method == "Lin") {
sim <- 2 * overlap/(length(E(net1)) + length(E(net2)))
}
sim.pvalue <- 1 - phyper(overlap - 1, length(E(net2)), background - length(E(net2)), length(E(net1)))
sample_1 <- names(ceRNet)[index[i, 1]]
sample_2 <- names(ceRNet)[index[i, 2]]
tmp <- c(sample_1, sample_2, sim, sim.pvalue)
return(tmp)
}
# shut down the workers
stopCluster(cores)
stopImplicitCluster()
Res <- do.call(rbind, Res)
Res[, 4] <- p.adjust(as.numeric(Res[, 4]), method = padjustmethod)
Res <- Res[which(as.numeric(Res[, 3]) > simcutoff & as.numeric(Res[, 4]) < padjustvaluecutoff), ]
if (is.vector(Res)) {
names(Res) <- c("sample_1", "sample_2", "similarity", "p.adjusted_value of similarity")
} else {
colnames(Res) <- c("sample_1", "sample_2", "similarity", "p.adjusted_value of similarity")
}
return(Res)
}
## Validation of computationally predicted miRNA sponge interactions.
spongeValidate <- function(spongenetwork, directed = FALSE, Groundtruth) {
spongenetwork_graph <- graph_from_data_frame(spongenetwork, directed = directed)
Groundtruth_graph <- graph_from_data_frame(Groundtruth, directed = directed)
Validated_interactions <- as_data_frame(spongenetwork_graph %s% Groundtruth_graph)
colnames(Validated_interactions) <- c("sponge_1", "sponge_2")
return(Validated_interactions)
}
## netModule function for identifying miRNA sponge modules from network. Possible methods
## include FN, MCL, LINKCOMM, MCODE, betweenness, infomap, prop, eigen, louvain, walktrap.
netModule <- function(spongenetwork, method = "MCL", directed = FALSE, modulesize = 3, save = FALSE) {
if (method == "FN" | method == "MCL" | method == "MCODE") {
spongenetwork_Cluster <- cluster(graph_from_data_frame(spongenetwork, directed = directed),
method = method, directed = directed)
} else if (method == "LINKCOMM") {
edgelist <- get.edgelist(graph_from_data_frame(spongenetwork, directed = directed))
spongenetwork_Cluster <- getLinkCommunities(edgelist, directed = directed)$nodeclusters
}
if (method == "FN" | method == "MCL") {
spongenetwork_Cluster_result <- lapply(seq_len(max(spongenetwork_Cluster)),
function(i) rownames(as.matrix(spongenetwork_Cluster))[which(spongenetwork_Cluster == i)])
size <- unlist(lapply(seq_len(max(spongenetwork_Cluster)), function(i) length(spongenetwork_Cluster_result[[i]])))
spongenetwork_Cluster_result <- lapply(which(size >= modulesize), function(i) spongenetwork_Cluster_result[[i]])
} else if (method == "LINKCOMM") {
spongenetwork_Cluster_result <- lapply(seq_len(max(c(spongenetwork_Cluster$cluster))),
function(i) as.character(spongenetwork_Cluster$node[which(c(spongenetwork_Cluster$cluster) == i)]))
size <- unlist(lapply(seq_len(max(c(spongenetwork_Cluster$cluster))), function(i) length
(spongenetwork_Cluster_result[[i]])))
spongenetwork_Cluster_result <- lapply(which(size >= modulesize), function(i) spongenetwork_Cluster_result[[i]])
} else if (method == "MCODE") {
spongenetwork_Cluster <- spongenetwork_Cluster + 1
spongenetwork_Cluster_result <- lapply(seq_len(max(spongenetwork_Cluster)),
function(i) rownames(as.matrix(spongenetwork_Cluster))[which(spongenetwork_Cluster == i)])
size <- unlist(lapply(seq_len(max(spongenetwork_Cluster)), function(i) length(spongenetwork_Cluster_result[[i]])))
spongenetwork_Cluster_result <- lapply(which(size >= modulesize), function(i) spongenetwork_Cluster_result[[i]])
} else if (method == "betweenness") {
spongenetwork_Cluster_result <- cluster_edge_betweenness(graph_from_data_frame(spongenetwork, directed = directed))
size <- unlist(lapply(seq_len(length(spongenetwork_Cluster_result)), function(i) length(spongenetwork_Cluster_result[[i]])))
spongenetwork_Cluster_result <- lapply(which(size >= modulesize), function(i) spongenetwork_Cluster_result[[i]])
} else if (method == "infomap") {
spongenetwork_Cluster_result <- cluster_infomap(graph_from_data_frame(spongenetwork, directed = directed))
size <- unlist(lapply(seq_len(length(spongenetwork_Cluster_result)), function(i) length(spongenetwork_Cluster_result[[i]])))
spongenetwork_Cluster_result <- lapply(which(size >= modulesize), function(i) spongenetwork_Cluster_result[[i]])
} else if (method == "prop") {
spongenetwork_Cluster_result <- cluster_label_prop(graph_from_data_frame(spongenetwork, directed = directed))
size <- unlist(lapply(seq_len(length(spongenetwork_Cluster_result)), function(i) length(spongenetwork_Cluster_result[[i]])))
spongenetwork_Cluster_result <- lapply(which(size >= modulesize), function(i) spongenetwork_Cluster_result[[i]])
} else if (method == "eigen") {
spongenetwork_Cluster_result <- cluster_leading_eigen(graph_from_data_frame(spongenetwork, directed = directed))
size <- unlist(lapply(seq_len(length(spongenetwork_Cluster_result)), function(i) length(spongenetwork_Cluster_result[[i]])))
spongenetwork_Cluster_result <- lapply(which(size >= modulesize), function(i) spongenetwork_Cluster_result[[i]])
} else if (method == "louvain") {
spongenetwork_Cluster_result <- cluster_louvain(graph_from_data_frame(spongenetwork, directed = directed))
size <- unlist(lapply(seq_len(length(spongenetwork_Cluster_result)), function(i) length(spongenetwork_Cluster_result[[i]])))
spongenetwork_Cluster_result <- lapply(which(size >= modulesize), function(i) spongenetwork_Cluster_result[[i]])
} else if (method == "walktrap") {
spongenetwork_Cluster_result <- cluster_walktrap(graph_from_data_frame(spongenetwork, directed = directed))
size <- unlist(lapply(seq_len(length(spongenetwork_Cluster_result)), function(i) length(spongenetwork_Cluster_result[[i]])))
spongenetwork_Cluster_result <- lapply(which(size >= modulesize), function(i) spongenetwork_Cluster_result[[i]])
}
if (save) {
fileName <- paste("spongenetwork_Cluster_", method, ".txt", sep = "")
for (i in seq_len(length(spongenetwork_Cluster_result))) {
cat(c(i, "\t", length(spongenetwork_Cluster_result[[i]])), file = fileName, sep = "", append = TRUE)
for (j in seq_len(length(spongenetwork_Cluster_result[[i]]))) {
cat(c("\t", spongenetwork_Cluster_result[[i]][j]), file = fileName, sep = "", append = TRUE)
}
if (i != length(spongenetwork_Cluster_result)) {
cat("\n", file = fileName, sep = "", append = TRUE)
}
}
}
return(spongenetwork_Cluster_result)
}
## Disease enrichment analysis of modules
moduleDEA <- function(Modulelist, OrgDb = "org.Hs.eg.db", padjustvaluecutoff = 0.05,
padjustedmethod = "BH") {
entrezIDs <- lapply(seq_along(Modulelist), function(i) bitr(Modulelist[[i]], fromType = "SYMBOL",
toType = "ENTREZID", OrgDb = OrgDb)$ENTREZID)
entrezIDs <- lapply(seq_along(Modulelist), function(i) as.character(entrezIDs[[i]]))
enrichDOs <- lapply(seq_along(Modulelist), function(i) enrichDO(entrezIDs[[i]], pvalueCutoff = padjustvaluecutoff,
pAdjustMethod = padjustedmethod))
enrichDGNs <- lapply(seq_along(Modulelist), function(i) enrichDGN(entrezIDs[[i]], pvalueCutoff = padjustvaluecutoff,
pAdjustMethod = padjustedmethod))
enrichNCGs <- lapply(seq_along(Modulelist), function(i) enrichNCG(entrezIDs[[i]], pvalueCutoff = padjustvaluecutoff,
pAdjustMethod = padjustedmethod))
return(list(enrichDOs, enrichDGNs, enrichNCGs))
}
## Functional GO, KEGG and Reactome enrichment analysis of modules
moduleFEA <- function(Modulelist, ont = "BP", KEGGorganism = "hsa", Reactomeorganism = "human",
OrgDb = "org.Hs.eg.db", padjustvaluecutoff = 0.05, padjustedmethod = "BH") {
entrezIDs <- lapply(seq_along(Modulelist), function(i) bitr(Modulelist[[i]], fromType = "SYMBOL",
toType = "ENTREZID", OrgDb = OrgDb)$ENTREZID)
entrezIDs <- lapply(seq_along(Modulelist), function(i) as.character(entrezIDs[[i]]))
enrichGOs <- lapply(seq_along(Modulelist), function(i) enrichGO(entrezIDs[[i]], OrgDb = OrgDb,
ont = ont, pvalueCutoff = padjustvaluecutoff, pAdjustMethod = padjustedmethod))
enrichKEGGs <- lapply(seq_along(Modulelist), function(i) enrichKEGG(entrezIDs[[i]], organism = KEGGorganism,
pvalueCutoff = padjustvaluecutoff, pAdjustMethod = padjustedmethod))
enrichReactomes <- lapply(seq_along(Modulelist), function(i) enrichPathway(entrezIDs[[i]], organism = Reactomeorganism,
pvalueCutoff = padjustvaluecutoff, pAdjustMethod = padjustedmethod))
return(list(enrichGOs, enrichKEGGs, enrichReactomes))
}
## Survival analysis of modules
moduleSurvival <- function(Modulelist, ExpData, SurvData, devidePercentage = 0.5, plot = FALSE) {
colnames(ExpData) <- gsub("\\.", "-", colnames(ExpData))
ExpDataNames <- colnames(ExpData)
myfit <- list()
LogRank <- list()
for (i in seq_along(Modulelist)) {
Interin_Data <- cbind(SurvData[, seq(2, 3)], ExpData[, which(ExpDataNames %in% Modulelist[[i]])])
Interin_Data <- na.omit(Interin_Data)
try_mm <- try(coxph(survival::Surv(time, status) ~ ., data = data.frame(Interin_Data)),
silent = TRUE)
if ("try-error" %in% class(try_mm))
next
mm <- coxph(survival::Surv(time, status) ~ ., data = data.frame(Interin_Data))
Risk_score <- predict(mm, newdata = data.frame(Interin_Data), type = "risk")
group <- rep("NA", dim(Interin_Data)[1])
group[Risk_score > quantile(Risk_score, probs = devidePercentage)] <- "High"
group[Risk_score <= quantile(Risk_score, probs = devidePercentage)] <- "Low"
Data <- cbind(Interin_Data[, seq_len(2)], group)
myfit[[i]] <- survfit(survival::Surv(time, status) ~ group, data = Data)
sdf <- survdiff(survival::Surv(time, status) ~ group, data = Data)
sdf.p.val <- 1 - pchisq(sdf$chisq, length(sdf$n) - 1)
HR <- (sdf$obs[1]/sdf$exp[1])/(sdf$obs[2]/sdf$exp[2])
HRlow95 <- exp(log(HR) - qnorm(0.975) * sqrt(1/sdf$exp[1] + 1/sdf$exp[2]))
HRup95 <- exp(log(HR) + qnorm(0.975) * sqrt(1/sdf$exp[1] + 1/sdf$exp[2]))
LogRank[[i]] <- c(sdf$chisq, sdf.p.val, HR, HRlow95, HRup95)
}
if (plot) {
for (i in seq_along(myfit)) {
if (!is.null(LogRank[[i]])) {
dev.new()
plot(myfit[[i]], lty = 1, col = c("red", "green"), main = paste("Module", i), xlab = "Time (Months)",
ylab = "Probability of survival")
legend("topright", legend = c("High risk group", "Low risk group"), lty = seq_len(2),
col = c("red", "green"))
}
}
}
LogRank_res <- do.call(rbind, LogRank)
if (length(myfit) >= 1) {
colnames(LogRank_res) <- c("Chi-square", "p-value", "HR", "HRlow95", "HRup95")
names(LogRank) <- seq_along(Modulelist)
LogRank[sapply(LogRank, is.null)] <- NULL
rownames(LogRank_res) <- paste("Module", names(LogRank))
}
return(LogRank_res)
}
zhangjunpeng411/miRspongeR documentation built on Aug. 26, 2024, 6:48 a.m.
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Defines functions moduleSurvival moduleFEA moduleDEA netModule spongeValidate sample_cor_network sponge_sample_specific spongeMethod integrateMethod sponge_parallel cernia_parallel cernia muTaME_parallel muTaME hermes_parallel hermes ppc_parallel ppc sppc_parallel sppc pc_parallel pc miRHomology_parallel miRHomology mcode mcode.post.process mcode.fluff.complex mcode.find.complexex mcode.find.complex mcode.vertex.weighting cluster.save cluster dtHybrid recommendation graphWeights parMM predCor combpvalue calCMI querymiRTargetbinding
Documented in integrateMethod moduleDEA moduleFEA moduleSurvival netModule querymiRTargetbinding sample_cor_network spongeMethod sponge_sample_specific spongeValidate
## Query miRNA-target interactions by combining expression data and putative miRNA-target
## interactions
querymiRTargetbinding <- function(ExpData, miRTarget, type = c("all", "miRNA", "target")) {
colnames(ExpData) <- gsub("\\.", "-", colnames(ExpData))
ExpDataNames <- colnames(ExpData)
miRTarget <- as.matrix(miRTarget)
if (type == "all") {
miRTargetCandidate <- miRTarget[intersect(which(miRTarget[, 1] %in% ExpDataNames),
which(miRTarget[, 2] %in% ExpDataNames)), ]
} else if (type == "miRNA") {
miRTargetCandidate <- miRTarget[which(miRTarget[, 1] %in% ExpDataNames), ]
} else if (type == "target") {
miRTargetCandidate <- miRTarget[which(miRTarget[, 2] %in% ExpDataNames), ]
}
return(miRTargetCandidate)
}
## Internal functions (calCMI, combpvalue, predCor) of hermes method are from the website:
## http://califano.c2b2.columbia.edu/hermes Copyright Columbia University in the City of New
## York. You may not use this file except in compliance with the License. You may obtain a copy
## of the License at http://califano.c2b2.columbia.edu/hermes-license
## calCMI: Calculate conditional mutual information
calCMI <- function(dmat) {
# rank data
N <- nrow(dmat)
cdim <- ncol(dmat)
idat <- apply(dmat, 2, order)
ydat <- idat
sapply(seq_len(cdim), function(i) ydat[idat[, i], i] <- seq_len(N))
ddim <- 2^cdim
dim2 <- 2 * cdim
# init
rm(dmat, idat)
poc <- vector()
kon <- vector()
cmi <- 0
npar <- 1
poc[1] <- 1
kon[1] <- N
poradi <- seq_len(N)
NN <- matrix(0, nrow = 1, ncol = ddim)
marg <- matrix(0, nrow = 8 * ddim, ncol = dim2)
marg[1, ] <- c(rep(1, cdim), rep(N, cdim))
Imm <- matrix(c(0, 1), ncol = 1) # Binary matrix for all combinations
for (d in seq(2, cdim)) {
Imm <- rbind(cbind(matrix(0, nrow = nrow(Imm), ncol = 1), Imm),
cbind(matrix(1, nrow = nrow(Imm), ncol = 1), Imm))
}
chi2 <- c(0, 7.81, 13.9, 25, 42)
run <- 0
# partition
while (npar > 0) {
run <- run + 1
apoc <- poc[npar]
akon <- kon[npar]
apor <- poradi[seq(apoc, akon)]
Nex <- length(apor)
margave <- floor((marg[npar, seq_len(cdim)] + marg[npar, seq(cdim + 1, dim2)])/2)
J <- (ydat[apor, ] <= (matrix(1, nrow = Nex, ncol = 1) %*% margave)) * 1
cI <- matrix(0, nrow = Nex, ncol = ddim)
amarg <- matrix(1, nrow = ddim, ncol = 1) %*%
subset(marg, subset = seq_len(nrow(marg)) %in% npar)
for (d in seq_len(ddim)) {
cI[, d] <- matrix(1, nrow = Nex, ncol = 1)
for (k in seq_len(cdim)) {
if (Imm[d, k] == 1) {
cI[, d] <- 1 * (cI[, d] & (!J[, k]))
amarg[d, k] <- margave[k] + 1
} else {
cI[, d] <- 1 * (cI[, d] & J[, k])
amarg[d, k + cdim] <- margave[k]
}
}
}
NN <- colSums(cI)
tst <- ddim * sum((NN - Nex/ddim * matrix(1, nrow = 1, ncol = ddim))^2)/Nex
if ((tst > chi2[cdim]) | (run == 1)) {
# decide partition or not
npar <- npar - 1
for (ind in seq_len(ddim)) {
if (NN[ind] > ddim) {
npar <- npar + 1
akon <- apoc + NN[ind] - 1
poc[npar] <- apoc
kon[npar] <- akon
marg[npar, ] <- amarg[ind, ]
poradi[seq(apoc, akon)] <- apor[which(cI[, ind] != 0, arr.ind = TRUE)]
apoc <- akon + 1
} else {
if (NN[ind] > 0) {
Nxx <- apply(amarg[ind, seq(cdim + 1, dim2)] - amarg[ind, seq_len(cdim)] + matrix(1,
nrow = 1, ncol = cdim), 2, prod)
Nz <- amarg[ind, 6] - amarg[ind, 3] + 1
Jx <- 1 * ((ydat[, 1] >= amarg[ind, 1]) & (ydat[, 1] <= amarg[ind, 4]))
Jy <- 1 * ((ydat[, 2] >= amarg[ind, 2]) & (ydat[, 2] <= amarg[ind, 5]))
Jz <- 1 * ((ydat[, 3] >= amarg[ind, 3]) & (ydat[, 3] <= amarg[ind, 6]))
Nxz <- sum(1 * (Jx & Jz))
Nyz <- sum(1 * (Jy & Jz))
cond <- (NN[ind] * Nz)/(Nxz * Nyz)
if (is.infinite(cond))
cond <- 1
if (cond == 0)
cond <- 1
cmi <- cmi + NN[ind] * log(cond)
}
}
}
} else {
Nxx <- apply(marg[npar, seq(cdim + 1, dim2)] - marg[npar, seq_len(cdim)] + matrix(1,
nrow = 1, ncol = cdim), 2, prod)
Nz <- marg[npar, 6] - marg[npar, 3] + 1
Jx <- 1 * ((ydat[, 1] >= marg[npar, 1]) & (ydat[, 1] <= marg[npar, 4]))
Jy <- 1 * ((ydat[, 2] >= marg[npar, 2]) & (ydat[, 2] <= marg[npar, 5]))
Jz <- 1 * ((ydat[, 3] >= marg[npar, 3]) & (ydat[, 3] <= marg[npar, 6]))
Nxz <- sum(1 * (Jx & Jz))
Nyz <- sum(1 * (Jy & Jz))
cond <- (Nex * Nz)/(Nxz * Nyz)
if (is.infinite(cond))
cond <- 1
if (cond == 0)
cond <- 1
cmi <- cmi + Nex * log(cond)
npar <- npar - 1
}
}
# normalize
cmi <- cmi/N
return(cmi)
}
## combpvalue: Combine p-values using Fisher's method
combpvalue <- function(p_values) {
# calculate chi-square statistic and combined p-value
Q <- -2 * sum(log(p_values))
Degree <- length(p_values)
Combp <- pchisq(Q, 2 * Degree, lower.tail = FALSE)
return(Combp)
}
## predCor: Predict competing endogenous RNAs via evidence for competition for miRNA regulation
## based on conditional mutual information (CMI) or partial pearson correlation (PPC)
predCor <- function(expr, num_perm, method = c("CMI", "PPC")) {
exprRownames <- rownames(expr)
expr <- as.matrix(expr)
# generate random sequence
num_sample <- ncol(expr)
perm_seq <- t(sapply(seq_len(num_perm), function(i) sample(num_sample)))
# identify mediator candidate
num_cand <- nrow(expr) - 2
# evaluate significance of triplet
tri_id <- matrix(0, nrow = 2 * num_cand, ncol = 3)
tri_cor <- matrix(0, nrow = 2 * num_cand, ncol = 1)
tri_pval <- rep(1, 2 * num_cand)
for (i in seq_len(2)) {
for (j in seq_len(num_cand)) {
idx_tar <- i # target index
idx_miR <- j + 2 # miRNA index
idx_mod <- 2/i # modulator index
idx_tri <- (i - 1) * num_cand + j # triplet index
tri_id[idx_tri, ] <- c(exprRownames[idx_tar], exprRownames[idx_miR], exprRownames[idx_mod])
# calculate Cor( target ; miRNA | modulator )
data <- t(expr[c(idx_tar, idx_miR, idx_mod), ])
# construct null distribution
rand_exp <- lapply(seq_len(num_perm), function(i) expr[idx_mod, ][perm_seq[i, ]])
nulldata <- lapply(seq_len(num_perm), function(i) cbind(t(expr[c(idx_tar, idx_miR),
]), rand_exp[[i]]))
if (method == "CMI") {
tri_cor[idx_tri, ] <- calCMI(data)
null <- unlist(lapply(seq_len(num_perm), function(i) calCMI(nulldata[[i]])))
} else if (method == "PPC") {
tri_cor[idx_tri, ] <- corpcor::pcor.shrink(data, verbose = FALSE)[1, 2]
null <- unlist(lapply(seq_len(num_perm), function(i) corpcor::pcor.shrink(nulldata[[i]],
verbose = FALSE)[1, 2]))
}
# calculate p-value
tri_pval[idx_tri] <- max(1, sum(tri_cor[idx_tri, ] <= null))/num_perm
}
}
# evaluate significance of interaction
tri_idx <- order(tri_pval)
tri_pval <- tri_pval[tri_idx]
tri_id <- tri_id[tri_idx, ]
tri_cor <- tri_cor[tri_idx]
pcomb <- as.vector(sapply(seq_along(tri_pval), function(i) combpvalue(tri_pval[seq_len(i)])[1]))
# identify final mediators
min_pval <- min(pcomb)
return(min_pval)
}
## Internal functions (parMM, graphWeights, recommendation, dtHybrid) of cernia method are from
## the website: https://github.com/dsardina/cernia Copyright 2016 Rosalba Giugno Licensed under
## the Apache License, Version 2.0 (the 'License'); you may not use this file except in
## compliance with the License. You may obtain a copy of the License at
## http://www.apache.org/licenses/LICENSE-2.0
## Unless required by applicable law or agreed to in writing, software distributed under the
## License is distributed on an 'AS IS' BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,
## either express or implied. See the License for the specific language governing permissions and
## limitations under the License.
## Matrix multiplication in parallel
parMM <- function(cl, A, B) {
if (!all(is.na(cl)) && is.object(cl)) {
nA <- nrow(A)
ncl <- length(cl)
# Split an indice equally
i <- seq_len(nA)
if (ncl == 1) {
splitIndices <- i
} else {
fuzz <- min((nA - 1)/1000, 0.4 * nA/ncl)
breaks <- seq(1 - fuzz, nA + fuzz, length = ncl + 1)
splitIndices <- structure(split(i, cut(i, breaks)), names = NULL)
}
# Split a matrix equally according to the rows of the matrix
splitRows <- lapply(splitIndices, function(i) A[i, , drop = FALSE])
fun <- get(as.character("rbind"), envir = .GlobalEnv, mode = "function")
R <- do.call("fun", lapply(clusterApply(cl = cl, x = splitRows, get("%*%"), B), enquote))
} else {
R <- A %*% B
}
return(R)
}
## The first step of DT-Hybrid recommendation algorithm: generating the weights for each pair of
## target nodes
graphWeights <- function(n, m, A, lambda = 0.5, alpha = 0.5, S = NA, S1 = NA, cl = NA) {
if (nrow(A) != n || ncol(A) != m) {
stop("The matrix A should be an n by m matrix.")
}
has.similarity <- (!all(is.na(S)) && is.matrix(S) && !all(is.na(S1)) && is.matrix(S1))
if (has.similarity) {
if (nrow(S1) != m || ncol(S1) != m) {
stop("The matrix S1 should be an m by m matrix.")
}
if (nrow(S) != n || ncol(S) != n) {
stop("The matrix S should be an n by n matrix.")
}
}
Ky <- diag(1/colSums(A))
Ky[is.infinite(Ky) | is.na(Ky)] <- 0 #BugFix: 1/0=Infinite replaced with 0
kx <- rowSums(A)
Nx <- 1/(matrix(kx, nrow = n, ncol = n, byrow = TRUE)^(lambda) * matrix(kx, nrow = n, ncol = n,
byrow = FALSE)^(1 - lambda))
Nx[is.infinite(Nx) | is.na(Nx)] <- 0 #BugFix: 1/0=Infinite replaced with 0
kx[is.infinite(kx) | is.na(kx)] <- 0 #BugFix: 1/0=Infinite replaced with 0
W <- t(parMM(cl, A, Ky))
W <- parMM(cl, A, W)
W <- Nx * W
rownames(W) <- rownames(A)
colnames(W) <- rownames(A)
if (has.similarity) {
X5 <- parMM(cl, A, S1)
X6 <- parMM(cl, X5, t(A))
X7 <- parMM(cl, A, matrix(1, nrow = m, ncol = m))
X8 <- parMM(cl, X7, t(A))
S2 <- X6/X8
W <- W * (1 + (alpha * S) + ((1 - alpha) * S2))
}
W[is.nan(W)] <- 0 #This should never happen
return(W)
}
## The second step of DT-Hybrid recommendation algorithm: generating ecommendation scores of each
## RNA-RNA pair
recommendation <- function(A, lambda = 0.5, alpha = 0.5, S = NA, S1 = NA, cl = NA) {
n <- nrow(A)
m <- ncol(A)
W <- graphWeights(n = n, m = m, A = A, lambda = lambda, alpha = alpha, S = S, S1 = S1, cl = cl)
R <- parMM(cl, W, A)
return(R)
}
## Make projection from bipartite network using DT-hybrid sources
dtHybrid <- function(miRTarget) {
# Extract miRs and their targets
mir <- unique(miRTarget[, 1])
tar <- unique(miRTarget[, 2])
# Create the matrix of the miRTarget
A <- matrix(nrow = length(tar), ncol = length(mir), data = 0)
colnames(A) <- mir
rownames(A) <- tar
for (i in seq_len(nrow(miRTarget))) {
A[which(tar %in% as.character(miRTarget[i, 2])),
which(mir %in% as.character(miRTarget[i, 1]))] <- 1
}
# Make projection from bipartite network using DT-hybrid sources
cl <- makeCluster(detectCores() - 2)
M <- recommendation(A, cl = cl)
W <- graphWeights(nrow(M), ncol(M), M, cl = cl)
stopCluster(cl)
return(W)
}
## Internal function cluster from ProNet package
## (https://github.com/cran/ProNet) with GPL-2 license.
cluster <- function(graph, method="MCL", expansion = 2, inflation = 2,
hcmethod = "average", directed = FALSE, outfile = NULL, ...)
{
#method<-match.arg(method)
if(method=="FN"){
graph <- simplify(graph)
fc <- fastgreedy.community(graph, merges = TRUE, modularity = TRUE)
membership <- membership(fc)
if(!is.null(V(graph)$name)){
names(membership) <- V(graph)$name
}
if(!is.null(outfile)){
cluster.save(cbind(names(membership),membership),outfile=outfile)
}else{
return(membership)
}
}else if(method=="LINKCOMM"){
edgelist <- get.edgelist(graph)
if(!is.null(E(graph)$weight)){
edgelist <- cbind(edgelist,E(graph)$weight)
}
lc <- getLinkCommunities(edgelist,plot=FALSE,directed=directed,hcmethod=hcmethod)
if(!is.null(outfile)){
cluster.save(lc$nodeclusters,outfile=outfile)
}else{
return(lc$nodeclusters)
}
}else if(method=="MCL"){
adj <- matrix(rep(0,length(V(graph))^2),nrow=length(V(graph)),ncol=length(V(graph)))
for(i in seq_along(V(graph))){
neighbors <- neighbors(graph,v=V(graph)$name[i],mode="all")
j <- match(neighbors$name,V(graph)$name,nomatch=0)
adj[i,j] = 1
}
lc <- mcl(adj,addLoops=TRUE,expansion=expansion,inflation=inflation,allow1=TRUE,max.iter=100,ESM=FALSE)
lc$name <- V(graph)$name
lc$Cluster <- lc$Cluster
if(!is.null(outfile)){
cluster.save(cbind(lc$name,lc$Cluster),outfile=outfile)
}else{
result <- lc$Cluster
names(result) <- V(graph)$name
return(result)
}
}else if(method=="MCODE"){
compx <- mcode(graph,vwp=0.9,haircut=T,fluff=T,fdt=0.1)
index <- which(!is.na(compx$score))
membership <- rep(0,vcount(graph))
for(i in seq_along(index)){
membership[compx$COMPLEX[[index[i]]]]<-i
}
if(!is.null(V(graph)$name)) names(membership)<-V(graph)$name
if(!is.null(outfile)){
cluster.save(cbind(names(membership),membership),outfile=outfile)
invisible(NULL)
}else{
return(membership)
}
}
}
## Internal function cluster.save from ProNet package
## (https://github.com/cran/ProNet) with GPL-2 license.
cluster.save <- function(membership, outfile){
wd <- dirname(outfile)
wd <- ifelse(wd==".",paste(wd,"/",sep=""),wd)
filename <- basename(outfile)
if((filename=="")||(grepl(":",filename))){
filename <- "membership.txt"
}else if(grepl("\\.",filename)){
filename <- sub("\\.(?:.*)",".txt", filename)
}
write.table(membership,file=paste(wd,filename,sep="/"),
row.names=FALSE,col.names=c("node","cluster"),quote =FALSE)
}
## Internal function mcode.vertex.weighting from ProNet package
## (https://github.com/cran/ProNet) with GPL-2 license.
mcode.vertex.weighting<-function(graph, neighbors){
stopifnot(is.igraph(graph))
weight <- lapply(seq_len(vcount(graph)),
function(i){
subg<-induced.subgraph(graph,neighbors[[i]])
core<-graph.coreness(subg)
k<-max(core)
### k-coreness
kcore<-induced.subgraph(subg,which(core==k))
if(vcount(kcore)>1){
if(any(is.loop(kcore))){
k*ecount(kcore)/choose(vcount(kcore)+1,2)
}else{
k*ecount(kcore)/choose(vcount(kcore),2)
}
}else{
0
}
}
)
return(unlist(weight))
}
## Internal function mcode.find.complex from ProNet package
## (https://github.com/cran/ProNet) with GPL-2 license.
mcode.find.complex <- function(neighbors, neighbors.indx, vertex.weight,
vwp, seed.vertex, seen)
{
res<-.C("complex",as.integer(neighbors),as.integer(neighbors.indx),
as.single(vertex.weight),as.single(vwp),as.integer(seed.vertex),
seen=as.integer(seen),COMPLEX=as.integer(rep(0,length(seen))), PACKAGE = "miRspongeR"
)
return(list(seen=res$seen,COMPLEX=which(res$COMPLEX!=0)))
}
## Internal function mcode.find.complexex from ProNet package
## (https://github.com/cran/ProNet) with GPL-2 license.
mcode.find.complexex <- function(graph, neighbors, vertex.weight, vwp)
{
seen<-rep(0,vcount(graph))
neighbors<-lapply(neighbors,function(item){item[-1]})
neighbors.indx<-cumsum(unlist(lapply(neighbors,length)))
neighbors.indx<-c(0,neighbors.indx)
neighbors<-unlist(neighbors)-1
COMPLEX<-list()
n<-1
w.order<-order(vertex.weight,decreasing=TRUE)
for(i in w.order){
if(!(seen[i])){
res<-mcode.find.complex(neighbors,neighbors.indx,vertex.weight,vwp,i-1,seen)
if(length(res$COMPLEX)>1){
COMPLEX[[n]]<-res$COMPLEX
seen<-res$seen
n<-n+1
}
}
}
rm(neighbors)
return(list(COMPLEX=COMPLEX,seen=seen))
}
## Internal function mcode.fluff.complex from ProNet package
## (https://github.com/cran/ProNet) with GPL-2 license.
mcode.fluff.complex <- function(graph, vertex.weight, fdt=0.8, complex.g, seen)
{
seq_complex.g<-seq_along(complex.g)
for(i in seq_complex.g){
node.neighbor<-unlist(neighborhood(graph,1,complex.g[i]))
if(length(node.neighbor)>1){
subg<-induced.subgraph(graph,node.neighbor)
if(graph.density(subg, loops=FALSE)>fdt){
complex.g<-c(complex.g,node.neighbor)
}
}
}
return(unique(complex.g))
}
## Internal function mcode.post.process from ProNet package
## (https://github.com/cran/ProNet) with GPL-2 license.
mcode.post.process<-function(graph, vertex.weight, haircut, fluff, fdt=0.8,
set.complex.g, seen)
{
indx<-unlist(lapply(set.complex.g,
function(complex.g){
if(length(complex.g)<=2)
0
else
1
}
))
set.complex.g<-set.complex.g[indx!=0]
set.complex.g<-lapply(set.complex.g,
function(complex.g){
coreness<-graph.coreness(induced.subgraph(graph,complex.g))
if(fluff){
complex.g<-mcode.fluff.complex(graph,vertex.weight,fdt,complex.g,seen)
if(haircut){
## coreness needs to be recalculated
coreness<-graph.coreness(induced.subgraph(graph,complex.g))
complex.g<-complex.g[coreness>1]
}
}else if(haircut){
complex.g<-complex.g[coreness>1]
}
return(complex.g)
})
set.complex.g<-set.complex.g[lapply(set.complex.g,length)>2]
return(set.complex.g)
}
## Internal function mcode from ProNet package
## (https://github.com/cran/ProNet) with GPL-2 license.
mcode <- function(graph, vwp=0.5, haircut=FALSE, fluff=FALSE, fdt=0.8, loops=TRUE)
{
stopifnot(is.igraph(graph))
if(vwp>1 | vwp <0){
stop("vwp must be between 0 and 1")
}
if(!loops){
graph<-simplify(graph,remove.multiple=FALSE,remove.loops=TRUE)
}
neighbors<-neighborhood(graph,1)
W<-mcode.vertex.weighting(graph,neighbors)
res<-mcode.find.complexex(graph,neighbors=neighbors,vertex.weight=W,vwp=vwp)
COMPLEX<-mcode.post.process(graph,vertex.weight=W,haircut=haircut,fluff=fluff,
fdt=fdt,res$COMPLEX,res$seen)
score<-unlist(lapply(COMPLEX,
function(complex.g){
complex.g<-induced.subgraph(graph,complex.g)
if(any(is.loop(complex.g)))
score<-ecount(complex.g)/choose(vcount(complex.g)+1,2)*vcount(complex.g)
else
score<-ecount(complex.g)/choose(vcount(complex.g),2)*vcount(complex.g)
return(score)
}
))
order_score<-order(score,decreasing=TRUE)
return(list(COMPLEX=COMPLEX[order_score],score=score[order_score]))
}
## Utility methods for identifying miRNA sponge interactions For input expression data, the
## columns are genes and the rows are samples. For input miRTarget, the miRNA-target
## interactions could be miRNA-mRNA, miRNA-lncRNA, miRNA-circRNA, miRNA-pseudogene, etc. For
## input mres, each row contains five elements: Mirna, Target, energy, gap_l, gap_r.
## 1. miRHomology
# Original version
miRHomology <- function(miRTarget, minSharedmiR = 3, padjustvaluecutoff = 0.01, padjustmethod = "BH") {
miRTarget <- as.matrix(miRTarget)
m1 <- nrow(miRTarget)
n1 <- ncol(miRTarget)
miR <- miRTarget[, 1]
tar <- miRTarget[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
# Initialize variables
ceRInt <- matrix(NA, m2 * (m2 - 1)/2, 2)
C <- matrix(NA, m2 * (m2 - 1)/2, 2)
for (i in seq_len(m2 - 1)) {
for (j in seq(i + 1, m2)) {
Interin1 <- miRTarget[which(miRTarget[, 2] %in% targetSym[i]), 1]
Interin2 <- miRTarget[which(miRTarget[, 2] %in% targetSym[j]), 1]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff) {
ceRInt[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- targetSym[i]
ceRInt[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- targetSym[j]
C[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- M3
C[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- M5
}
}
}
# Extract RNA-RNA pair based on the homology of sharing miRNAs
C[, 2] <- p.adjust(C[, 2], method = padjustmethod)
index <- which(C[, 2] < padjustvaluecutoff)
ceRInt <- ceRInt[index, ]
C <- C[index, ]
if (is.vector(C)) {
miRHomologyceRInt <- c(ceRInt, C)
names(miRHomologyceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs")
} else {
miRHomologyceRInt <- cbind(ceRInt, C)
colnames(miRHomologyceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs")
}
return(miRHomologyceRInt)
}
# Parallel version
miRHomology_parallel <- function(miRTarget, minSharedmiR = 3, padjustvaluecutoff = 0.01, padjustmethod = "BH") {
miRTarget <- as.matrix(miRTarget)
m1 <- nrow(miRTarget)
n1 <- ncol(miRTarget)
miR <- miRTarget[, 1]
tar <- miRTarget[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
index <- t(combn(m2, 2))
Res <- foreach(i = seq_len(nrow(index))) %dopar% {
Interin1 <- miRTarget[which(miRTarget[, 2] %in% targetSym[index[i, 1]]), 1]
Interin2 <- miRTarget[which(miRTarget[, 2] %in% targetSym[index[i, 2]]), 1]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff) {
C1 <- targetSym[index[i, 1]]
C2 <- targetSym[index[i, 2]]
C3 <- M3
C4 <- M5
} else {
C1 <- NA; C2 <- NA; C3 <- NA; C4 <- NA
}
tmp <- c(C1, C2, C3, C4)
return(tmp)
}
Res <- do.call(rbind, Res)
# Extract RNA-RNA pair based on the homology of sharing miRNAs
Res[, 4] <- p.adjust(as.numeric(Res[, 4]), method = padjustmethod)
miRHomologyceRInt <- Res[which(as.numeric(Res[, 4]) < padjustvaluecutoff), ]
if (is.vector(miRHomologyceRInt)) {
names(miRHomologyceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs")
} else {
colnames(miRHomologyceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs")
}
return(miRHomologyceRInt)
}
## 2. Positive Correlation (PC) method
# Original version
pc <- function(miRTarget, ExpData, consider.miR.expr = "TRUE", minSharedmiR = 3, poscorcutoff = 0, padjustvaluecutoff = 0.01,
padjustmethod = "BH") {
if (consider.miR.expr == "TRUE") {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "all")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
} else {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "target")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
}
m1 <- nrow(miRTargetCandidate)
n1 <- ncol(miRTargetCandidate)
colnames(ExpData) <- gsub("\\.", "-", colnames(ExpData))
ExpDataNames <- colnames(ExpData)
miR <- miRTargetCandidate[, 1]
tar <- miRTargetCandidate[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
# Initialize variables
ceRInt <- matrix(NA, m2 * (m2 - 1)/2, 2)
C <- matrix(NA, m2 * (m2 - 1)/2, 4)
for (i in seq_len(m2 - 1)) {
for (j in seq(i + 1, m2)) {
Interin1 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[i]), 1]
Interin2 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[j]), 1]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff) {
ceRInt[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- targetSym[i]
ceRInt[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- targetSym[j]
tarExpIdx1 <- which(ExpDataNames %in% targetSym[i])
tarExpIdx2 <- which(ExpDataNames %in% targetSym[j])
# Calculate Pearson correlation of each RNA-RNA pair
M6 <- cor.test(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2])$estimate
M7 <- cor.test(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2])$p.value
C[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- M3
C[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- M5
C[(i - 1) * m2 + j - sum(seq_len(i)), 3] <- M6
C[(i - 1) * m2 + j - sum(seq_len(i)), 4] <- M7
}
}
}
# Extract positive correlated RNA-RNA pairs.
C[, 2] <- p.adjust(C[, 2], method = padjustmethod)
C[, 4] <- p.adjust(C[, 4], method = padjustmethod)
index <- which(C[, 2] < padjustvaluecutoff & C[, 3] > poscorcutoff & C[, 4] < padjustvaluecutoff)
ceRInt <- ceRInt[index, ]
C <- C[index, ]
if (is.vector(C)) {
PCceRInt <- c(ceRInt, C)
names(PCceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"correlation", "p.adjusted_value of positive correlation")
} else {
PCceRInt <- cbind(ceRInt, C)
colnames(PCceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"correlation", "p.adjusted_value of positive correlation")
}
return(PCceRInt)
}
# Parallel version
pc_parallel <- function(miRTarget, ExpData, consider.miR.expr = "TRUE", minSharedmiR = 3, poscorcutoff = 0, padjustvaluecutoff = 0.01,
padjustmethod = "BH") {
if (consider.miR.expr == "TRUE") {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "all")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
} else {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "target")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
}
m1 <- nrow(miRTargetCandidate)
n1 <- ncol(miRTargetCandidate)
colnames(ExpData) <- gsub("\\.", "-", colnames(ExpData))
ExpDataNames <- colnames(ExpData)
miR <- miRTargetCandidate[, 1]
tar <- miRTargetCandidate[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
index <- t(combn(m2, 2))
Res <- foreach(i = seq_len(nrow(index))) %dopar% {
Interin1 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[index[i, 1]]), 1]
Interin2 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[index[i, 2]]), 1]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff) {
C1 <- targetSym[index[i, 1]]
C2 <- targetSym[index[i, 2]]
tarExpIdx1 <- which(ExpDataNames %in% targetSym[index[i, 1]])
tarExpIdx2 <- which(ExpDataNames %in% targetSym[index[i, 2]])
# Calculate Pearson correlation of each RNA-RNA pair
M6 <- cor.test(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2])$estimate
M7 <- cor.test(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2])$p.value
C3 <- M3
C4 <- M5
C5 <- M6
C6 <- M7
} else {
C1 <- NA; C2 <- NA; C3 <- NA; C4 <- NA; C5 <- NA; C6 <- NA
}
tmp <- c(C1, C2, C3, C4, C5, C6)
return(tmp)
}
Res <- do.call(rbind, Res)
# Extract positive correlated RNA-RNA pairs.
Res[, 4] <- p.adjust(as.numeric(Res[, 4]), method = padjustmethod)
Res[, 6] <- p.adjust(as.numeric(Res[, 6]), method = padjustmethod)
PCceRInt <- Res[which(as.numeric(Res[, 4]) < padjustvaluecutoff & as.numeric(Res[, 5]) > poscorcutoff & as.numeric(Res[, 6]) < padjustvaluecutoff), ]
if (is.vector(PCceRInt)) {
names(PCceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"correlation", "p.adjusted_value of positive correlation")
} else {
colnames(PCceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"correlation", "p.adjusted_value of positive correlation")
}
return(PCceRInt)
}
## 3. Sensitivity Partial Pearson Correlation (SPPC) method
# Original version
sppc <- function(miRTarget, ExpData, minSharedmiR = 3, poscorcutoff = 0, padjustvaluecutoff = 0.01,
padjustmethod = "BH", senscorcutoff = 0.3) {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "all")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
m1 <- nrow(miRTargetCandidate)
n1 <- ncol(miRTargetCandidate)
colnames(ExpData) <- gsub("\\.", "-", colnames(ExpData))
ExpDataNames <- colnames(ExpData)
miR <- miRTargetCandidate[, 1]
tar <- miRTargetCandidate[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
# Initialize variables
ceRInt <- matrix(NA, m2 * (m2 - 1)/2, 2)
C <- matrix(NA, m2 * (m2 - 1)/2, 5)
for (i in seq_len(m2 - 1)) {
for (j in seq(i + 1, m2)) {
Interin1 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[i]), 1]
Interin2 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[j]), 1]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff) {
ceRInt[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- targetSym[i]
ceRInt[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- targetSym[j]
tarExpIdx1 <- which(ExpDataNames %in% targetSym[i])
tarExpIdx2 <- which(ExpDataNames %in% targetSym[j])
miRExpIdx <- which(ExpDataNames %in% intersect(Interin1, Interin2))
# Calculate sensitivity correlation of each RNA-RNA pair
M6 <- cor.test(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2])$estimate
M7 <- cor.test(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2])$p.value
M8 <- M6 - corpcor::pcor.shrink(cbind(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2],
ExpData[, miRExpIdx]), verbose = FALSE)[1, 2]
C[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- M3
C[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- M5
C[(i - 1) * m2 + j - sum(seq_len(i)), 3] <- M6
C[(i - 1) * m2 + j - sum(seq_len(i)), 4] <- M7
C[(i - 1) * m2 + j - sum(seq_len(i)), 5] <- M8
}
}
}
# Extract RNA-RNA pairs with sensitivity correlation more than senscorcutoff.
C[, 2] <- p.adjust(C[, 2], method = padjustmethod)
C[, 4] <- p.adjust(C[, 4], method = padjustmethod)
index <- which(C[, 2] < padjustvaluecutoff & C[, 3] > poscorcutoff & C[, 4] < padjustvaluecutoff &
C[, 5] > senscorcutoff)
ceRInt <- ceRInt[index, ]
C <- C[index, ]
if (is.vector(C)) {
SPPCceRInt <- c(ceRInt, C)
names(SPPCceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"correlation", "p.adjusted_value of positive correlation", "sensitivity correlation")
} else {
SPPCceRInt <- cbind(ceRInt, C)
colnames(SPPCceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"correlation", "p.adjusted_value of positive correlation", "sensitivity correlation")
}
return(SPPCceRInt)
}
# Parallel version
sppc_parallel <- function(miRTarget, ExpData, minSharedmiR = 3, poscorcutoff = 0, padjustvaluecutoff = 0.01,
padjustmethod = "BH", senscorcutoff = 0.3) {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "all")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
m1 <- nrow(miRTargetCandidate)
n1 <- ncol(miRTargetCandidate)
colnames(ExpData) <- gsub("\\.", "-", colnames(ExpData))
ExpDataNames <- colnames(ExpData)
miR <- miRTargetCandidate[, 1]
tar <- miRTargetCandidate[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
index <- t(combn(m2, 2))
Res <- foreach(i = seq_len(nrow(index))) %dopar% {
Interin1 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[index[i, 1]]), 1]
Interin2 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[index[i, 2]]), 1]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff) {
C1 <- targetSym[index[i, 1]]
C2 <- targetSym[index[i, 2]]
tarExpIdx1 <- which(ExpDataNames %in% targetSym[index[i, 1]])
tarExpIdx2 <- which(ExpDataNames %in% targetSym[index[i, 2]])
miRExpIdx <- which(ExpDataNames %in% intersect(Interin1, Interin2))
# Calculate sensitivity correlation of each RNA-RNA pair
M6 <- cor.test(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2])$estimate
M7 <- cor.test(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2])$p.value
M8 <- M6 - corpcor::pcor.shrink(cbind(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2],
ExpData[, miRExpIdx]), verbose = FALSE)[1, 2]
C3 <- M3
C4 <- M5
C5 <- M6
C6 <- M7
C7 <- M8
} else {
C1 <- NA; C2 <- NA; C3 <- NA; C4 <- NA; C5 <- NA; C6 <- NA; C7 <- NA
}
tmp <- c(C1, C2, C3, C4, C5, C6, C7)
return(tmp)
}
Res <- do.call(rbind, Res)
# Extract RNA-RNA pairs with sensitivity correlation more than senscorcutoff.
Res[, 4] <- p.adjust(as.numeric(Res[, 4]), method = padjustmethod)
Res[, 6] <- p.adjust(as.numeric(Res[, 6]), method = padjustmethod)
SPPCceRInt <- Res[which(as.numeric(Res[, 4]) < padjustvaluecutoff & as.numeric(Res[, 5]) > poscorcutoff &
as.numeric(Res[, 6]) < padjustvaluecutoff & as.numeric(Res[, 7]) > senscorcutoff), ]
if (is.vector(SPPCceRInt)) {
names(SPPCceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"correlation", "p.adjusted_value of positive correlation", "sensitivity correlation")
} else {
colnames(SPPCceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"correlation", "p.adjusted_value of positive correlation", "sensitivity correlation")
}
return(SPPCceRInt)
}
## 4. Partial Pearson Correlation (PPC) method
# Original version
ppc <- function(miRTarget, ExpData, minSharedmiR = 3, num_perm = 100, padjustvaluecutoff = 0.01,
padjustmethod = "BH") {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "all")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
m1 <- nrow(miRTargetCandidate)
n1 <- ncol(miRTargetCandidate)
colnames(ExpData) <- gsub("\\.", "-", colnames(ExpData))
ExpDataNames <- colnames(ExpData)
miR <- miRTargetCandidate[, 1]
tar <- miRTargetCandidate[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
# Initialize variables
ceRInt <- matrix(NA, m2 * (m2 - 1)/2, 2)
C <- matrix(NA, m2 * (m2 - 1)/2, 3)
for (i in seq_len(m2 - 1)) {
for (j in seq(i + 1, m2)) {
Interin1 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[i]), 1]
Interin2 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[j]), 1]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff) {
ceRInt[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- targetSym[i]
ceRInt[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- targetSym[j]
tarExpIdx1 <- which(ExpDataNames %in% targetSym[i])
tarExpIdx2 <- which(ExpDataNames %in% targetSym[j])
miRExpIdx <- which(ExpDataNames %in% intersect(Interin1, Interin2))
inputdata <- t(cbind(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2], ExpData[, miRExpIdx]))
C[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- M3
C[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- M5
C[(i - 1) * m2 + j - sum(seq_len(i)), 3] <- predCor(inputdata, num_perm, method = "PPC")
}
}
}
# Extract significant RNA-RNA pairs.
C[, 2] <- p.adjust(C[, 2], method = padjustmethod)
C[, 3] <- p.adjust(C[, 3], method = padjustmethod)
index <- which(C[, 2] < padjustvaluecutoff & C[, 3] < padjustvaluecutoff)
ceRInt <- ceRInt[index, ]
C <- C[index, ]
if (is.vector(C)) {
PPCceRInt <- c(ceRInt, C)
names(PPCceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"p.adjusted_value of RNA competition")
} else {
PPCceRInt <- cbind(ceRInt, C)
colnames(PPCceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"p.adjusted_value of RNA competition")
}
return(PPCceRInt)
}
# Parallel version
ppc_parallel <- function(miRTarget, ExpData, minSharedmiR = 3, num_perm = 100, padjustvaluecutoff = 0.01,
padjustmethod = "BH") {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "all")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
m1 <- nrow(miRTargetCandidate)
n1 <- ncol(miRTargetCandidate)
colnames(ExpData) <- gsub("\\.", "-", colnames(ExpData))
ExpDataNames <- colnames(ExpData)
miR <- miRTargetCandidate[, 1]
tar <- miRTargetCandidate[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
index <- t(combn(m2, 2))
Res <- foreach(i = seq_len(nrow(index)), .export = c("predCor", "combpvalue")) %dopar% {
Interin1 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[index[i, 1]]), 1]
Interin2 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[index[i, 2]]), 1]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff) {
C1 <- targetSym[index[i, 1]]
C2 <- targetSym[index[i, 2]]
tarExpIdx1 <- which(ExpDataNames %in% targetSym[index[i, 1]])
tarExpIdx2 <- which(ExpDataNames %in% targetSym[index[i, 2]])
miRExpIdx <- which(ExpDataNames %in% intersect(Interin1, Interin2))
inputdata <- t(cbind(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2], ExpData[, miRExpIdx]))
C3 <- M3
C4 <- M5
C5 <- predCor(inputdata, num_perm, method = "PPC")
} else {
C1 <- NA; C2 <- NA; C3 <- NA; C4 <- NA; C5 <- NA
}
tmp <- c(C1, C2, C3, C4, C5)
return(tmp)
}
Res <- do.call(rbind, Res)
# Extract significant RNA-RNA pairs.
Res[, 4] <- p.adjust(as.numeric(Res[, 4]), method = padjustmethod)
Res[, 5] <- p.adjust(as.numeric(Res[, 5]), method = padjustmethod)
PPCceRInt <- Res[which(as.numeric(Res[, 4]) < padjustvaluecutoff & as.numeric(Res[, 5]) < padjustvaluecutoff), ]
if (is.vector(PPCceRInt)) {
names(PPCceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"p.adjusted_value of RNA competition")
} else {
colnames(PPCceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"p.adjusted_value of RNA competition")
}
return(PPCceRInt)
}
## 5. Hermes method
# Original version
hermes <- function(miRTarget, ExpData, minSharedmiR = 3, num_perm = 100, padjustvaluecutoff = 0.01,
padjustmethod = "BH") {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "all")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
m1 <- nrow(miRTargetCandidate)
n1 <- ncol(miRTargetCandidate)
colnames(ExpData) <- gsub("\\.", "-", colnames(ExpData))
ExpDataNames <- colnames(ExpData)
miR <- miRTargetCandidate[, 1]
tar <- miRTargetCandidate[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
# Initialize variables
ceRInt <- matrix(NA, m2 * (m2 - 1)/2, 2)
C <- matrix(NA, m2 * (m2 - 1)/2, 3)
for (i in seq_len(m2 - 1)) {
for (j in seq(i + 1, m2)) {
Interin1 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[i]), 1]
Interin2 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[j]), 1]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff) {
ceRInt[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- targetSym[i]
ceRInt[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- targetSym[j]
tarExpIdx1 <- which(ExpDataNames %in% targetSym[i])
tarExpIdx2 <- which(ExpDataNames %in% targetSym[j])
miRExpIdx <- which(ExpDataNames %in% intersect(Interin1, Interin2))
inputdata <- t(cbind(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2], ExpData[, miRExpIdx]))
C[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- M3
C[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- M5
C[(i - 1) * m2 + j - sum(seq_len(i)), 3] <- predCor(inputdata, num_perm, method = "CMI")
}
}
}
# Extract significant RNA-RNA pairs.
C[, 2] <- p.adjust(C[, 2], method = padjustmethod)
C[, 3] <- p.adjust(C[, 3], method = padjustmethod)
index <- which(C[, 2] < padjustvaluecutoff & C[, 3] < padjustvaluecutoff)
ceRInt <- ceRInt[index, ]
C <- C[index, ]
if (is.vector(C)) {
HermesceRInt <- c(ceRInt, C)
names(HermesceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"p.adjusted_value of RNA competition")
} else {
HermesceRInt <- cbind(ceRInt, C)
colnames(HermesceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"p.adjusted_value of RNA competition")
}
return(HermesceRInt)
}
# Parallel version
hermes_parallel <- function(miRTarget, ExpData, minSharedmiR = 3, num_perm = 100, padjustvaluecutoff = 0.01,
padjustmethod = "BH") {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "all")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
m1 <- nrow(miRTargetCandidate)
n1 <- ncol(miRTargetCandidate)
colnames(ExpData) <- gsub("\\.", "-", colnames(ExpData))
ExpDataNames <- colnames(ExpData)
miR <- miRTargetCandidate[, 1]
tar <- miRTargetCandidate[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
index <- t(combn(m2, 2))
Res <- foreach(i = seq_len(nrow(index)), .export = c("predCor", "combpvalue", "calCMI")) %dopar% {
Interin1 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[index[i, 1]]), 1]
Interin2 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[index[i, 2]]), 1]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff) {
C1 <- targetSym[index[i, 1]]
C2 <- targetSym[index[i, 2]]
tarExpIdx1 <- which(ExpDataNames %in% targetSym[index[i, 1]])
tarExpIdx2 <- which(ExpDataNames %in% targetSym[index[i, 2]])
miRExpIdx <- which(ExpDataNames %in% intersect(Interin1, Interin2))
inputdata <- t(cbind(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2], ExpData[, miRExpIdx]))
C3 <- M3
C4 <- M5
C5 <- predCor(inputdata, num_perm, method = "CMI")
} else {
C1 <- NA; C2 <- NA; C3 <- NA; C4 <- NA; C5 <- NA
}
tmp <- c(C1, C2, C3, C4, C5)
return(tmp)
}
Res <- do.call(rbind, Res)
# Extract significant RNA-RNA pairs.
Res[, 4] <- p.adjust(as.numeric(Res[, 4]), method = padjustmethod)
Res[, 5] <- p.adjust(as.numeric(Res[, 5]), method = padjustmethod)
HermesceRInt <- Res[which(as.numeric(Res[, 4]) < padjustvaluecutoff & as.numeric(Res[, 5]) < padjustvaluecutoff), ]
if (is.vector(HermesceRInt)) {
names(HermesceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"p.adjusted_value of RNA competition")
} else {
colnames(HermesceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"p.adjusted_value of RNA competition")
}
return(HermesceRInt)
}
## 6. MuTaME method
# Original version
muTaME <- function(miRTarget, mres, minSharedmiR = 3, padjustvaluecutoff = 0.01, padjustmethod = "BH",
scorecutoff = 0.5) {
miRTarget <- as.matrix(miRTarget)
m1 <- nrow(miRTarget)
n1 <- ncol(miRTarget)
miR <- miRTarget[, 1]
tar <- miRTarget[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
# Initialize variables
ceRInt <- matrix(NA, m2 * (m2 - 1)/2, 2)
C <- matrix(NA, m2 * (m2 - 1)/2, 8)
for (i in seq_len(m2 - 1)) {
for (j in seq(i + 1, m2)) {
Interin1 <- miRTarget[which(miRTarget[, 2] %in% targetSym[i]), 1]
Interin2 <- miRTarget[which(miRTarget[, 2] %in% targetSym[j]), 1]
cm <- intersect(Interin1, Interin2)
SharedMREs <- mres[mres[, 2] %in% c(targetSym[i], targetSym[j]) & mres[, 1] %in% cm, ]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff & nrow(SharedMREs) > 0) {
ceRInt[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- targetSym[i]
ceRInt[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- targetSym[j]
C[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- M3
C[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- M5
# Score 1 for the fraction of coomon miRNAs
C[(i - 1) * m2 + j - sum(seq_len(i)), 3] <- log(M3/min(M1, M2))
# Score 2 for the density of the MREs for all shared miRNAs
C[(i - 1) * m2 + j - sum(seq_len(i)), 4] <- sum(sapply(cm, function(miR) {
MREs <- SharedMREs[SharedMREs[, 1] == miR, ]
if (nrow(MREs) <= 0) return(1)
MREslr <- MREs[, c("gap_l", "gap_r")]
D <- abs(max(MREslr[, 1]) - min(MREslr[, 2]))
return(log(nrow(MREs)/D))
}))
# Score 3 for the distribution of MREs of the putative RNA-RNA pairs
C[(i - 1) * m2 + j - sum(seq_len(i)), 5] <- sum(sapply(cm, function(miR) {
positions <- SharedMREs[SharedMREs[, 1] == miR, c("gap_l", "gap_r")]
if (nrow(positions) <= 0) return(1)
return(log(abs(max(positions[, 1]) - min(positions[, 2]))^2/sum((positions[, 2] -
positions[, 1])^2)))
}))
# Score 4 for the relation between the overall number of MREs for a putative miRNA sponge,
# compared with the number of miRNAs that yield these MREs
B <- nrow(SharedMREs)
if (B == length(unique(SharedMREs[, 1]))) {
C[(i - 1) * m2 + j - sum(seq_len(i)), 6] <- log(1/B)
} else {
C[(i - 1) * m2 + j - sum(seq_len(i)), 6] <- log((B - length(unique(SharedMREs[,
1])) - 1)/B)
}
C[(i - 1) * m2 + j - sum(seq_len(i)), 7] <- C[(i - 1) * m2 + j - sum(seq_len(i)),
3] + C[(i - 1) * m2 + j - sum(seq_len(i)), 4] + C[(i - 1) * m2 + j - sum(seq_len(i)),
5] + C[(i - 1) * m2 + j - sum(seq_len(i)), 6]
}
}
}
# Extract RNA-RNA pair based on four scores.
ceRInt <- ceRInt[apply(ceRInt, 1, function(x) !all(is.na(x))), ]
C <- C[apply(C, 1, function(x) !all(is.na(x))), ]
C[, 8] <- (C[, 7] - min(C[, 7]))/(max(C[, 7]) - min(C[, 7]))
C[, 2] <- p.adjust(C[, 2], method = padjustmethod)
index <- which(C[, 2] < padjustvaluecutoff & C[, 8] > scorecutoff)
ceRInt <- ceRInt[index, ]
C <- C[index, ]
if (is.vector(C)) {
MuTaMEceRInt <- c(ceRInt, C)
names(MuTaMEceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"Score 1", "Score 2", "Score 3", "Score 4", "Combined score", "Normalized score")
} else {
MuTaMEceRInt <- cbind(ceRInt, C)
colnames(MuTaMEceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"Score 1", "Score 2", "Score 3", "Score 4", "Combined score", "Normalized score")
}
return(MuTaMEceRInt)
}
# Parallel version
muTaME_parallel <- function(miRTarget, mres, minSharedmiR = 3, padjustvaluecutoff = 0.01, padjustmethod = "BH",
scorecutoff = 0.5) {
miRTarget <- as.matrix(miRTarget)
m1 <- nrow(miRTarget)
n1 <- ncol(miRTarget)
miR <- miRTarget[, 1]
tar <- miRTarget[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
index <- t(combn(m2, 2))
Res <- foreach(i = seq_len(nrow(index))) %dopar% {
Interin1 <- miRTarget[which(miRTarget[, 2] %in% targetSym[index[i, 1]]), 1]
Interin2 <- miRTarget[which(miRTarget[, 2] %in% targetSym[index[i, 2]]), 1]
cm <- intersect(Interin1, Interin2)
SharedMREs <- mres[mres[, 2] %in% c(targetSym[index[i, 1]], targetSym[index[i, 2]]) & mres[, 1] %in% cm, ]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff & nrow(SharedMREs) > 0) {
C1 <- targetSym[index[i, 1]]
C2 <- targetSym[index[i, 2]]
C3 <- M3
C4 <- M5
# Score 1 for the fraction of coomon miRNAs
C5 <- log(M3/min(M1, M2))
# Score 2 for the density of the MREs for all shared miRNAs
C6 <- sum(sapply(cm, function(miR) {
MREs <- SharedMREs[SharedMREs[, 1] == miR, ]
if (nrow(MREs) <= 0) return(1)
MREslr <- MREs[, c("gap_l", "gap_r")]
D <- abs(max(MREslr[, 1]) - min(MREslr[, 2]))
return(log(nrow(MREs)/D))
}))
# Score 3 for the distribution of MREs of the putative RNA-RNA pairs
C7 <- sum(sapply(cm, function(miR) {
positions <- SharedMREs[SharedMREs[, 1] == miR, c("gap_l", "gap_r")]
if (nrow(positions) <= 0) return(1)
return(log(abs(max(positions[, 1]) - min(positions[, 2]))^2/sum((positions[, 2] -
positions[, 1])^2)))
}))
# Score 4 for the relation between the overall number of MREs for a putative miRNA sponge,
# compared with the number of miRNAs that yield these MREs
B <- nrow(SharedMREs)
if (B == length(unique(SharedMREs[, 1]))) {
C8 <- log(1/B)
} else {
C8 <- log((B - length(unique(SharedMREs[, 1])) - 1)/B)
}
C9 <- C5 + C6 + C7 + C8
} else {
C1 <- NA; C2 <- NA; C3 <- NA; C4 <- NA; C5 <- NA; C6 <- NA; C7 <- NA; C8 <- NA; C9 <- NA
}
tmp <- c(C1, C2, C3, C4, C5, C6, C7, C8, C9)
return(tmp)
}
Res <- do.call(rbind, Res)
# Extract RNA-RNA pair based on four scores.
Res <- Res[apply(Res, 1, function(x) !all(is.na(x))), ]
C10 <- (as.numeric(Res[, 9]) - min(as.numeric(Res[, 9])))/(max(as.numeric(Res[, 9])) - min(as.numeric(Res[, 9])))
Res <- cbind(Res, C10)
Res[, 4] <- p.adjust(as.numeric(Res[, 4]), method = padjustmethod)
MuTaMEceRInt <- Res[which(as.numeric(Res[, 4]) < padjustvaluecutoff & as.numeric(Res[, 10]) > scorecutoff), ]
if (is.vector(MuTaMEceRInt)) {
names(MuTaMEceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"Score 1", "Score 2", "Score 3", "Score 4", "Combined score", "Normalized score")
} else {
colnames(MuTaMEceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"Score 1", "Score 2", "Score 3", "Score 4", "Combined score", "Normalized score")
}
return(MuTaMEceRInt)
}
## 7. CERNIA method
# Original version
cernia <- function(miRTarget, ExpData, mres, consider.miR.expr = "TRUE", minSharedmiR = 3, poscorcutoff = 0,
padjustvaluecutoff = 0.01, padjustmethod = "BH", scorecutoff = 0.5) {
if (consider.miR.expr == "TRUE") {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "all")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
} else {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "target")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
}
m1 <- nrow(miRTargetCandidate)
n1 <- ncol(miRTargetCandidate)
colnames(ExpData) <- gsub("\\.", "-", colnames(ExpData))
ExpDataNames <- colnames(ExpData)
miR <- miRTargetCandidate[, 1]
tar <- miRTargetCandidate[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
# Initialize variables
ceRInt <- matrix(NA, m2 * (m2 - 1)/2, 2)
C <- matrix(NA, m2 * (m2 - 1)/2, 11)
for (i in seq_len(m2 - 1)) {
for (j in seq(i + 1, m2)) {
Interin1 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[i]), 1]
Interin2 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[j]), 1]
cm <- intersect(Interin1, Interin2)
SharedMREs <- mres[mres[, 2] %in% c(targetSym[i], targetSym[j]) & mres[, 1] %in% cm, ]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
tarExpIdx1 <- which(ExpDataNames %in% targetSym[i])
tarExpIdx2 <- which(ExpDataNames %in% targetSym[j])
M6 <- cor.test(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2])$estimate
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff & M6 > poscorcutoff &
nrow(SharedMREs) > 0) {
ceRInt[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- targetSym[i]
ceRInt[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- targetSym[j]
C[(i - 1) * m2 + j - sum(seq_len(i)), 1] <- M3
C[(i - 1) * m2 + j - sum(seq_len(i)), 2] <- M5
# Score 1 for the fraction of coomon miRNAs
C[(i - 1) * m2 + j - sum(seq_len(i)), 3] <- log(M3/min(M1, M2))
# Score 2 for the density of the MREs for all shared miRNAs
C[(i - 1) * m2 + j - sum(seq_len(i)), 4] <- sum(sapply(cm, function(miR) {
MREs <- SharedMREs[SharedMREs[, 1] == miR, ]
if (nrow(MREs) <= 0) return(1)
MREslr <- MREs[, c("gap_l", "gap_r")]
D <- abs(max(MREslr[, 1]) - min(MREslr[, 2]))
return(log(nrow(MREs)/D))
}))
# Score 3 for the distribution of MREs of the putative RNA-RNA pairs
C[(i - 1) * m2 + j - sum(seq_len(i)), 5] = sum(sapply(cm, function(miR) {
positions <- SharedMREs[SharedMREs[, 1] == miR, c("gap_l", "gap_r")]
if (nrow(positions) <= 0) return(1)
return(log(abs(max(positions[, 1]) - min(positions[, 2]))^2/sum((positions[, 2] -
positions[, 1])^2)))
}))
# Score 4 for the relation between the overall number of MREs for a putative miRNA sponge,
# compared with the number of miRNAs that yield these MREs
B <- nrow(SharedMREs)
if (B == length(unique(SharedMREs[, 1]))) {
C[(i - 1) * m2 + j - sum(seq_len(i)), 6] <- log(1/B)
} else {
C[(i - 1) * m2 + j - sum(seq_len(i)), 6] <- log((B - length(unique(SharedMREs[,
1])) - 1)/B)
}
# Score 5 for the density of the hybridization energies related to MREs for all shared miRNAs
SharedMREs <- mres[mres[, 2] %in% c(targetSym[i], targetSym[j]) & mres[, 1] %in%
cm, ]
C[(i - 1) * m2 + j - sum(seq_len(i)), 7] <- sum(sapply(cm, function(miR) {
MREs <- SharedMREs[SharedMREs[, 1] == miR, ]
if (nrow(MREs) <= 0) return(1)
MREslr <- MREs[, c("gap_l", "gap_r")]
D <- abs(max(MREslr[, 1]) - min(MREslr[, 2]))
return(log(sum(abs(MREs[, 3]))/D))
}))
# Score 6 for the DT-Hybrid recommendation scores
cerna_recommendations <- dtHybrid(miRTargetCandidate)
C[(i - 1) * m2 + j - sum(seq_len(i)), 8] <- cerna_recommendations[targetSym[i],
targetSym[j]]
# Score 7 for the pairwise Peason correlation between putative RNA-RNA pair expression data
C[(i - 1) * m2 + j - sum(seq_len(i)), 9] <- log(M6)
C[(i - 1) * m2 + j - sum(seq_len(i)), 10] <- C[(i - 1) * m2 + j - sum(seq_len(i)),
3] + C[(i - 1) * m2 + j - sum(seq_len(i)), 4] + C[(i - 1) * m2 + j - sum(seq_len(i)),
5] + C[(i - 1) * m2 + j - sum(seq_len(i)), 6] + C[(i - 1) * m2 + j - sum(seq_len(i)),
7] + C[(i - 1) * m2 + j - sum(seq_len(i)), 8] + C[(i - 1) * m2 + j - sum(seq_len(i)),
9]
}
}
}
# Extract RNA-RNA pair based on seven scores
ceRInt <- ceRInt[apply(ceRInt, 1, function(x) !all(is.na(x))), ]
C <- C[apply(C, 1, function(x) !all(is.na(x))), ]
C[, 11] <- (C[, 10] - min(C[, 10]))/(max(C[, 10]) - min(C[, 10]))
C[, 2] <- p.adjust(C[, 2], method = padjustmethod)
index <- which(C[, 2] < padjustvaluecutoff & C[, 11] > scorecutoff)
ceRInt <- ceRInt[index, ]
C <- C[index, ]
if (is.vector(C)) {
CERNIAceRInt <- c(ceRInt, C)
names(CERNIAceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"Score 1", "Score 2", "Score 3", "Score 4", "Score 5", "Score 6", "Score 7", "Combined score",
"Normalized score")
} else {
CERNIAceRInt <- cbind(ceRInt, C)
colnames(CERNIAceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"Score 1", "Score 2", "Score 3", "Score 4", "Score 5", "Score 6", "Score 7", "Combined score",
"Normalized score")
}
return(CERNIAceRInt)
}
# Parallel version
cernia_parallel <- function(miRTarget, ExpData, mres, consider.miR.expr = "TRUE", minSharedmiR = 3, poscorcutoff = 0,
padjustvaluecutoff = 0.01, padjustmethod = "BH", scorecutoff = 0.5) {
if (consider.miR.expr == "TRUE") {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "all")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
} else {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "target")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
}
m1 <- nrow(miRTargetCandidate)
n1 <- ncol(miRTargetCandidate)
colnames(ExpData) <- gsub("\\.", "-", colnames(ExpData))
ExpDataNames <- colnames(ExpData)
miR <- miRTargetCandidate[, 1]
tar <- miRTargetCandidate[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
index <- t(combn(m2, 2))
Res <- foreach(i = seq_len(nrow(index)), .export = c("dtHybrid", "makeCluster", "detectCores", "recommendation",
"graphWeights", "parMM", "stopCluster", "clusterApply")) %dopar% {
Interin1 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[index[i, 1]]), 1]
Interin2 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[index[i, 2]]), 1]
cm <- intersect(Interin1, Interin2)
SharedMREs <- mres[mres[, 2] %in% c(targetSym[index[i, 1]], targetSym[index[i, 2]]) & mres[, 1] %in% cm, ]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
tarExpIdx1 <- which(ExpDataNames %in% targetSym[index[i, 1]])
tarExpIdx2 <- which(ExpDataNames %in% targetSym[index[i, 2]])
M6 <- cor.test(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2])$estimate
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff & M6 > poscorcutoff &
nrow(SharedMREs) > 0) {
C1 <- targetSym[index[i, 1]]
C2 <- targetSym[index[i, 2]]
C3 <- M3
C4 <- M5
# Score 1 for the fraction of coomon miRNAs
C5 <- log(M3/min(M1, M2))
# Score 2 for the density of the MREs for all shared miRNAs
C6 <- sum(sapply(cm, function(miR) {
MREs <- SharedMREs[SharedMREs[, 1] == miR, ]
if (nrow(MREs) <= 0) return(1)
MREslr <- MREs[, c("gap_l", "gap_r")]
D <- abs(max(MREslr[, 1]) - min(MREslr[, 2]))
return(log(nrow(MREs)/D))
}))
# Score 3 for the distribution of MREs of the putative RNA-RNA pairs
C7 = sum(sapply(cm, function(miR) {
positions <- SharedMREs[SharedMREs[, 1] == miR, c("gap_l", "gap_r")]
if (nrow(positions) <= 0) return(1)
return(log(abs(max(positions[, 1]) - min(positions[, 2]))^2/sum((positions[, 2] -
positions[, 1])^2)))
}))
# Score 4 for the relation between the overall number of MREs for a putative miRNA sponge,
# compared with the number of miRNAs that yield these MREs
B <- nrow(SharedMREs)
if (B == length(unique(SharedMREs[, 1]))) {
C8 <- log(1/B)
} else {
C8 <- log((B - length(unique(SharedMREs[, 1])) - 1)/B)
}
# Score 5 for the density of the hybridization energies related to MREs for all shared miRNAs
SharedMREs <- mres[mres[, 2] %in% c(targetSym[index[i, 1]], targetSym[index[i, 2]]) & mres[, 1] %in%
cm, ]
C9 <- sum(sapply(cm, function(miR) {
MREs <- SharedMREs[SharedMREs[, 1] == miR, ]
if (nrow(MREs) <= 0) return(1)
MREslr <- MREs[, c("gap_l", "gap_r")]
D <- abs(max(MREslr[, 1]) - min(MREslr[, 2]))
return(log(sum(abs(MREs[, 3]))/D))
}))
# Score 6 for the DT-Hybrid recommendation scores
cerna_recommendations <- dtHybrid(miRTargetCandidate)
C10 <- cerna_recommendations[targetSym[index[i, 1]], targetSym[index[i, 2]]]
# Score 7 for the pairwise Pearson correlation between putative RNA-RNA pair expression data
C11 <- log(M6)
C12 <- C5 + C6 + C7 + C8 + C9 + C10 + C11
} else {
C1 <- NA; C2 <- NA; C3 <- NA; C4 <- NA; C5 <- NA; C6 <- NA; C7 <- NA; C8 <- NA; C9 <- NA; C10 <- NA; C11 <- NA; C12 <- NA
}
tmp <- c(C1, C2, C3, C4, C5, C6, C7, C8, C9, C10, C11, C12)
return(tmp)
}
Res <- do.call(rbind, Res)
# Extract RNA-RNA pair based on seven scores
Res <- Res[apply(Res, 1, function(x) !all(is.na(x))), ]
C13 <- (as.numeric(Res[, 12]) - min(as.numeric(Res[, 12])))/(max(as.numeric(Res[, 12])) - min(as.numeric(Res[, 12])))
Res <- cbind(Res, C13)
Res[, 4] <- p.adjust(as.numeric(Res[, 4]), method = padjustmethod)
CERNIAceRInt <- Res[which(as.numeric(Res[, 4]) < padjustvaluecutoff & as.numeric(Res[, 13]) > scorecutoff), ]
if (is.vector(CERNIAceRInt)) {
names(CERNIAceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"Score 1", "Score 2", "Score 3", "Score 4", "Score 5", "Score 6", "Score 7", "Combined score",
"Normalized score")
} else {
colnames(CERNIAceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"Score 1", "Score 2", "Score 3", "Score 4", "Score 5", "Score 6", "Score 7", "Combined score",
"Normalized score")
}
return(CERNIAceRInt)
}
## 8. Sparse Partial correlation ON Gene Expression (SPONGE) method
sponge_parallel <- function(miRTarget, ExpData, minSharedmiR = 3, poscorcutoff = 0, padjustvaluecutoff = 0.01,
padjustmethod = "BH", senscorcutoff = 0, null_model) {
miRTargetCandidate <- querymiRTargetbinding(ExpData, miRTarget, type = "all")
miRTargetCandidate <- as.matrix(miRTargetCandidate)
m1 <- nrow(miRTargetCandidate)
n1 <- ncol(miRTargetCandidate)
colnames(ExpData) <- gsub("\\.", "-", colnames(ExpData))
ExpDataNames <- colnames(ExpData)
miR <- miRTargetCandidate[, 1]
tar <- miRTargetCandidate[, 2]
miRSym <- unique(miR)
targetSym <- unique(tar)
m2 <- length(targetSym)
index <- t(combn(m2, 2))
Res <- foreach(i = seq_len(nrow(index)), .packages = c("SPONGE")) %dopar% {
Interin1 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[index[i, 1]]), 1]
Interin2 <- miRTargetCandidate[which(miRTargetCandidate[, 2] %in% targetSym[index[i, 2]]), 1]
M1 <- length(Interin1)
M2 <- length(Interin2)
M3 <- length(intersect(Interin1, Interin2))
M4 <- length(miRSym)
M5 <- 1 - phyper(M3 - 1, M2, M4 - M2, M1)
if (M3 >= minSharedmiR & M5 < padjustvaluecutoff) {
C1 <- targetSym[index[i, 1]]
C2 <- targetSym[index[i, 2]]
tarExpIdx1 <- which(ExpDataNames %in% targetSym[index[i, 1]])
tarExpIdx2 <- which(ExpDataNames %in% targetSym[index[i, 2]])
miRExpIdx <- which(ExpDataNames %in% intersect(Interin1, Interin2))
# Calculate sensitivity correlation of each RNA-RNA pair
M6 <- cor.test(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2])$estimate
M7 <- cor.test(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2])$p.value
M8 <- corpcor::pcor.shrink(cbind(ExpData[, tarExpIdx1], ExpData[, tarExpIdx2],
ExpData[, miRExpIdx]), verbose = FALSE)[1, 2]
M9 <- M6 - M8
C3 <- M3
C4 <- M5
C5 <- M6
C6 <- M7
C7 <- M8
C8 <- M9
} else {
C1 <- NA; C2 <- NA; C3 <- NA; C4 <- NA; C5 <- NA; C6 <- NA; C7 <- NA; C8 <- NA
}
tmp <- c(C1, C2, C3, C4, C5, C6, C7, C8)
return(tmp)
}
Res <- do.call(rbind, Res)
# Extract RNA-RNA pairs with sensitivity correlation more than senscorcutoff.
Res[, 4] <- p.adjust(as.numeric(Res[, 4]), method = padjustmethod)
Res[, 6] <- p.adjust(as.numeric(Res[, 6]), method = padjustmethod)
Res <- Res[which(as.numeric(Res[, 4]) < padjustvaluecutoff & as.numeric(Res[, 5]) > poscorcutoff &
as.numeric(Res[, 6]) < padjustvaluecutoff & as.numeric(Res[, 8]) > senscorcutoff), ]
sponge_result <- data.frame(geneA = Res[, 1],
geneB = Res[, 2],
df = as.numeric(Res[, 3]),
cor = as.numeric(Res[, 5]),
pcor = as.numeric(Res[, 7]),
mscor = as.numeric(Res[, 8]))
sponge_result_null_model <- sponge_compute_p_values(sponge_result = sponge_result, null_model = null_model)
sponge_result_fdr <- sponge_result_null_model[which(sponge_result_null_model$p.adj < padjustvaluecutoff), ]
Res_fdr <- Res[which(sponge_result_null_model$p.adj < padjustvaluecutoff), ]
SPONGEceRInt <- cbind(Res_fdr, sponge_result_fdr[, 8])
if (is.vector(SPONGEceRInt)) {
names(SPONGEceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"correlation", "p.adjusted_value of positive correlation", "partial correlation",
"sensitivity correlation", "p.adjusted_value of sensitivity correlation")
} else {
colnames(SPONGEceRInt) <- c("sponge_1", "sponge_2", "#shared miRNAs", "p.adjusted_value of shared miRNAs",
"correlation", "p.adjusted_value of positive correlation", "partial correlation",
"sensitivity correlation", "p.adjusted_value of sensitivity correlation")
}
return(SPONGEceRInt)
}
## 9. Integrate method for miRNA sponge interactions by integrating different methods.
integrateMethod <- function(Interlist, Intersect_num) {
if (length(Interlist) >= 2 & length(Interlist) >= Intersect_num) {
Combcase <- t(combn(length(Interlist), Intersect_num))
Combnum <- dim(Combcase)[1]
Integrate_Inter <- list()
for (i in seq_len(Combnum)) {
Interin <- do.call(rbind, lapply(Combcase[i, ], function(i) Interlist[[i]]))
Interin_paste <- paste(Interin[, 1], Interin[, 2], sep = "-")
Interin_table <- table(Interin_paste)
Interin_names <- names(Interin_table)[which(Interin_table == Intersect_num)]
Integrate_Inter[[i]] <- Interin[which(Interin_paste %in% Interin_names), ]
}
Integrate_res <- unique(do.call(rbind, Integrate_Inter))
return(Integrate_res)
} else {
stop("Please check your input!\n")
}
}
## Consolidating 15 functions: miRHomology, miRHomology_parallel, pc, pc_parallel, sppc, sppc_parallel,
## ppc, ppc_parallel, hermes, hermes_parallel, muTaME, muTaME_parallel, cernia, cernia_parallel and sponge_parallel.
spongeMethod <- function(miRTarget,
ExpData = NULL,
mres = NULL,
consider.miR.expr = "TRUE",
minSharedmiR = 3,
poscorcutoff = 0,
num_perm = 100,
padjustvaluecutoff = 0.01,
padjustmethod = "BH",
senscorcutoff = 0.3,
scorecutoff = 0.5,
null_model,
method = c("miRHomology", "miRHomology_parallel",
"pc", "pc_parallel", "sppc", "sppc_parallel",
"ppc", "ppc_parallel",
"hermes", "hermes_parallel",
"muTaME", "muTaME_parallel",
"cernia", "cernia_parallel",
"sponge_parallel"),
num.cores = 2) {
if (method == "miRHomology_parallel" | method == "pc_parallel" | method == "sppc_parallel" |
method == "ppc_parallel" | method == "hermes_parallel" | method == "muTaME_parallel" |
method == "cernia_parallel" | method == "sponge_parallel") {
# get number of cores to run
cores <- makeCluster(num.cores)
registerDoParallel(cores)
}
if (method == "miRHomology") {
ceRInt <- miRHomology(miRTarget, minSharedmiR, padjustvaluecutoff, padjustmethod)
} else if (method == "miRHomology_parallel"){
ceRInt <- miRHomology_parallel(miRTarget, minSharedmiR, padjustvaluecutoff, padjustmethod)
} else if (method == "pc") {
ceRInt <- pc(miRTarget, ExpData, consider.miR.expr, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod)
} else if (method == "pc_parallel") {
ceRInt <- pc_parallel(miRTarget, ExpData, consider.miR.expr, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod)
} else if (method == "sppc") {
ceRInt <- sppc(miRTarget, ExpData, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, senscorcutoff)
} else if (method == "sppc_parallel") {
ceRInt <- sppc_parallel(miRTarget, ExpData, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, senscorcutoff)
} else if (method == "ppc") {
ceRInt <- ppc(miRTarget, ExpData, minSharedmiR, num_perm, padjustvaluecutoff,
padjustmethod)
} else if (method == "ppc_parallel") {
ceRInt <- ppc_parallel(miRTarget, ExpData, minSharedmiR, num_perm, padjustvaluecutoff,
padjustmethod)
} else if (method == "hermes") {
ceRInt <- hermes(miRTarget, ExpData, minSharedmiR, num_perm, padjustvaluecutoff,
padjustmethod)
} else if (method == "hermes_parallel") {
ceRInt <- hermes_parallel(miRTarget, ExpData, minSharedmiR, num_perm, padjustvaluecutoff,
padjustmethod)
} else if (method == "muTaME") {
ceRInt <- muTaME(miRTarget, mres, minSharedmiR, padjustvaluecutoff,
padjustmethod, scorecutoff)
} else if (method == "muTaME_parallel") {
ceRInt <- muTaME_parallel(miRTarget, mres, minSharedmiR, padjustvaluecutoff,
padjustmethod, scorecutoff)
} else if (method == "cernia") {
ceRInt <- cernia(miRTarget, ExpData, mres, consider.miR.expr, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, scorecutoff)
} else if (method == "cernia_parallel") {
ceRInt <- cernia_parallel(miRTarget, ExpData, mres, consider.miR.expr, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, scorecutoff)
} else if (method == "sponge_parallel") {
ceRInt <- sponge_parallel(miRTarget, ExpData, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, senscorcutoff, null_model)
}
if (method == "miRHomology_parallel" | method == "pc_parallel" | method == "sppc_parallel" |
method == "ppc_parallel" | method == "hermes_parallel" | method == "muTaME_parallel" |
method == "cernia_parallel" | method == "sponge_parallel") {
# shut down the workers
stopCluster(cores)
stopImplicitCluster()
}
return(ceRInt)
}
## Inferring sample-specific miRNA sponge interaction network using sample
## control variable strategy.
sponge_sample_specific <- function(miRTarget,
ExpData = NULL,
mres = NULL,
consider.miR.expr = "TRUE",
minSharedmiR = 3,
poscorcutoff = 0,
num_perm = 100,
padjustvaluecutoff = 0.01,
padjustmethod = "BH",
senscorcutoff = 0.3,
scorecutoff = 0.5,
null_model,
method = c("pc", "pc_parallel", "sppc",
"sppc_parallel", "ppc", "ppc_parallel",
"hermes", "hermes_parallel", "cernia",
"cernia_parallel", "sponge_parallel"),
num.cores = 2) {
# get number of cores to run
cores <- makeCluster(num.cores)
registerDoParallel(cores)
if (method == "pc") {
ceRInt.all <- pc(miRTarget, ExpData, consider.miR.expr, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod)
ceRInt.single <- foreach(i = seq(nrow(ExpData)), .export = c("pc", "querymiRTargetbinding")) %dopar% {
pc(miRTarget, ExpData[-i, ], consider.miR.expr, minSharedmiR, poscorcutoff, padjustvaluecutoff, padjustmethod)
}
} else if (method == "pc_parallel") {
ceRInt.all <- pc_parallel(miRTarget, ExpData, consider.miR.expr, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod)
ceRInt.single <- foreach(i = seq(nrow(ExpData)), .export = c("pc_parallel", "querymiRTargetbinding")) %dopar% {
pc_parallel(miRTarget, ExpData[-i, ], consider.miR.expr, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod)
}
} else if (method == "sppc") {
ceRInt.all <- sppc(miRTarget, ExpData, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, senscorcutoff)
ceRInt.single <- foreach(i = seq(nrow(ExpData)), .export = c("sppc", "querymiRTargetbinding")) %dopar% {
sppc(miRTarget, ExpData[-i, ], minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, senscorcutoff)
}
} else if (method == "sppc_parallel") {
ceRInt.all <- sppc_parallel(miRTarget, ExpData, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, senscorcutoff)
ceRInt.single <- foreach(i = seq(nrow(ExpData)), .export = c("sppc_parallel", "querymiRTargetbinding")) %dopar% {
sppc_parallel(miRTarget, ExpData[-i, ], minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, senscorcutoff)
}
} else if (method == "ppc") {
ceRInt.all <- ppc(miRTarget, ExpData, minSharedmiR, num_perm, padjustvaluecutoff,
padjustmethod)
ceRInt.single <- foreach(i = seq(nrow(ExpData)), .export = c("ppc", "querymiRTargetbinding", "predCor", "combpvalue")) %dopar% {
ppc(miRTarget, ExpData[-i, ], minSharedmiR, num_perm, padjustvaluecutoff,
padjustmethod)
}
} else if (method == "ppc_parallel") {
ceRInt.all <- ppc_parallel(miRTarget, ExpData, minSharedmiR, num_perm, padjustvaluecutoff,
padjustmethod)
ceRInt.single <- foreach(i = seq(nrow(ExpData)), .export = c("ppc_parallel", "querymiRTargetbinding", "predCor", "combpvalue")) %dopar% {
ppc_parallel(miRTarget, ExpData[-i, ], minSharedmiR, num_perm, padjustvaluecutoff,
padjustmethod)
}
} else if (method == "hermes") {
ceRInt.all <- hermes(miRTarget, ExpData, minSharedmiR, num_perm, padjustvaluecutoff,
padjustmethod)
ceRInt.single <- foreach(i = seq(nrow(ExpData)), .export = c("hermes", "querymiRTargetbinding", "predCor", "combpvalue", "calCMI")) %dopar% {
hermes(miRTarget, ExpData[-i, ], minSharedmiR, num_perm, padjustvaluecutoff,
padjustmethod)
}
} else if (method == "hermes_parallel") {
ceRInt.all <- hermes_parallel(miRTarget, ExpData, minSharedmiR, num_perm, padjustvaluecutoff,
padjustmethod)
ceRInt.single <- foreach(i = seq(nrow(ExpData)), .export = c("hermes_parallel", "querymiRTargetbinding", "predCor", "combpvalue", "calCMI")) %dopar% {
hermes_parallel(miRTarget, ExpData[-i, ], minSharedmiR, num_perm, padjustvaluecutoff,
padjustmethod)
}
} else if (method == "cernia") {
ceRInt.all <- cernia(miRTarget, ExpData, mres, consider.miR.expr, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, scorecutoff)
ceRInt.single <- foreach(i = seq(nrow(ExpData)), .export = c("cernia", "querymiRTargetbinding", "dtHybrid", "makeCluster",
"detectCores", "recommendation", "graphWeights", "parMM", "stopCluster", "clusterApply")) %dopar% {
cernia(miRTarget, ExpData[-i, ], mres, consider.miR.expr, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, scorecutoff)
}
} else if (method == "cernia_parallel") {
ceRInt.all <- cernia_parallel(miRTarget, ExpData, mres, consider.miR.expr, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, scorecutoff)
ceRInt.single <- foreach(i = seq(nrow(ExpData)), .export = c("cernia_parallel", "querymiRTargetbinding", "dtHybrid", "makeCluster",
"detectCores", "recommendation", "graphWeights", "parMM", "stopCluster", "clusterApply")) %dopar% {
cernia_parallel(miRTarget, ExpData[-i, ], mres, consider.miR.expr, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, scorecutoff)
}
} else if (method == "sponge_parallel") {
ceRInt.all <- sponge_parallel(miRTarget, ExpData, minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, senscorcutoff, null_model)
ceRInt.single <- foreach(i = seq(nrow(ExpData)), .packages = c("SPONGE"), .export = c("sponge_parallel", "querymiRTargetbinding")) %dopar% {
sponge_parallel(miRTarget, ExpData[-i, ], minSharedmiR, poscorcutoff,
padjustvaluecutoff, padjustmethod, senscorcutoff, null_model)
}
}
# shut down the workers
stopCluster(cores)
stopImplicitCluster()
ceRInt.all.graph <- make_graph(c(t(ceRInt.all[, 1:2])), directed = FALSE)
ceRInt.single.graph <- lapply(seq(ceRInt.single), function(i) make_graph(c(t(ceRInt.single[[i]][, 1:2])), directed = FALSE))
ceRInt <- lapply(seq(ceRInt.single), function(i) as_data_frame((ceRInt.all.graph %m% ceRInt.single.graph[[i]]) %u%
(ceRInt.single.graph[[i]] %m% ceRInt.all.graph)))
names(ceRInt) <- rownames(ExpData)
return(ceRInt)
}
## Identifying sample-sample correlation network in terms of
## sample-specific miRNA sponge networks.
sample_cor_network <- function(ceRNet,
genes_num,
method = "Simpson",
simcutoff = 0.5,
padjustvaluecutoff = 0.01,
padjustmethod = "BH",
num.cores = 2){
# get number of cores to run
cores <- makeCluster(num.cores)
registerDoParallel(cores)
ceRNet.graph <- lapply(seq(ceRNet), function(i) graph_from_data_frame(ceRNet[[i]], directed = FALSE))
m <- length(ceRNet.graph)
index <- t(combn(m, 2))
background <- genes_num * (genes_num - 1)/2
Res <- foreach(i = seq_len(nrow(index)), .packages = c("igraph")) %dopar% {
net1 <- ceRNet.graph[[index[i, 1]]]
net2 <- ceRNet.graph[[index[i, 2]]]
overlap <- length(E(net1 %s% net2))
if (method == "Simpson") {
sim <- overlap/min(length(E(net1)), length(E(net2)))
} else if (method == "Jaccard") {
sim <- overlap/length(E(net1 %u% net2))
} else if (method == "Lin") {
sim <- 2 * overlap/(length(E(net1)) + length(E(net2)))
}
sim.pvalue <- 1 - phyper(overlap - 1, length(E(net2)), background - length(E(net2)), length(E(net1)))
sample_1 <- names(ceRNet)[index[i, 1]]
sample_2 <- names(ceRNet)[index[i, 2]]
tmp <- c(sample_1, sample_2, sim, sim.pvalue)
return(tmp)
}
# shut down the workers
stopCluster(cores)
stopImplicitCluster()
Res <- do.call(rbind, Res)
Res[, 4] <- p.adjust(as.numeric(Res[, 4]), method = padjustmethod)
Res <- Res[which(as.numeric(Res[, 3]) > simcutoff & as.numeric(Res[, 4]) < padjustvaluecutoff), ]
if (is.vector(Res)) {
names(Res) <- c("sample_1", "sample_2", "similarity", "p.adjusted_value of similarity")
} else {
colnames(Res) <- c("sample_1", "sample_2", "similarity", "p.adjusted_value of similarity")
}
return(Res)
}
## Validation of computationally predicted miRNA sponge interactions.
spongeValidate <- function(spongenetwork, directed = FALSE, Groundtruth) {
spongenetwork_graph <- graph_from_data_frame(spongenetwork, directed = directed)
Groundtruth_graph <- graph_from_data_frame(Groundtruth, directed = directed)
Validated_interactions <- as_data_frame(spongenetwork_graph %s% Groundtruth_graph)
colnames(Validated_interactions) <- c("sponge_1", "sponge_2")
return(Validated_interactions)
}
## netModule function for identifying miRNA sponge modules from network. Possible methods
## include FN, MCL, LINKCOMM, MCODE, betweenness, infomap, prop, eigen, louvain, walktrap.
netModule <- function(spongenetwork, method = "MCL", directed = FALSE, modulesize = 3, save = FALSE) {
if (method == "FN" | method == "MCL" | method == "MCODE") {
spongenetwork_Cluster <- cluster(graph_from_data_frame(spongenetwork, directed = directed),
method = method, directed = directed)
} else if (method == "LINKCOMM") {
edgelist <- get.edgelist(graph_from_data_frame(spongenetwork, directed = directed))
spongenetwork_Cluster <- getLinkCommunities(edgelist, directed = directed)$nodeclusters
}
if (method == "FN" | method == "MCL") {
spongenetwork_Cluster_result <- lapply(seq_len(max(spongenetwork_Cluster)),
function(i) rownames(as.matrix(spongenetwork_Cluster))[which(spongenetwork_Cluster == i)])
size <- unlist(lapply(seq_len(max(spongenetwork_Cluster)), function(i) length(spongenetwork_Cluster_result[[i]])))
spongenetwork_Cluster_result <- lapply(which(size >= modulesize), function(i) spongenetwork_Cluster_result[[i]])
} else if (method == "LINKCOMM") {
spongenetwork_Cluster_result <- lapply(seq_len(max(c(spongenetwork_Cluster$cluster))),
function(i) as.character(spongenetwork_Cluster$node[which(c(spongenetwork_Cluster$cluster) == i)]))
size <- unlist(lapply(seq_len(max(c(spongenetwork_Cluster$cluster))), function(i) length
(spongenetwork_Cluster_result[[i]])))
spongenetwork_Cluster_result <- lapply(which(size >= modulesize), function(i) spongenetwork_Cluster_result[[i]])
} else if (method == "MCODE") {
spongenetwork_Cluster <- spongenetwork_Cluster + 1
spongenetwork_Cluster_result <- lapply(seq_len(max(spongenetwork_Cluster)),
function(i) rownames(as.matrix(spongenetwork_Cluster))[which(spongenetwork_Cluster == i)])
size <- unlist(lapply(seq_len(max(spongenetwork_Cluster)), function(i) length(spongenetwork_Cluster_result[[i]])))
spongenetwork_Cluster_result <- lapply(which(size >= modulesize), function(i) spongenetwork_Cluster_result[[i]])
} else if (method == "betweenness") {
spongenetwork_Cluster_result <- cluster_edge_betweenness(graph_from_data_frame(spongenetwork, directed = directed))
size <- unlist(lapply(seq_len(length(spongenetwork_Cluster_result)), function(i) length(spongenetwork_Cluster_result[[i]])))
spongenetwork_Cluster_result <- lapply(which(size >= modulesize), function(i) spongenetwork_Cluster_result[[i]])
} else if (method == "infomap") {
spongenetwork_Cluster_result <- cluster_infomap(graph_from_data_frame(spongenetwork, directed = directed))
size <- unlist(lapply(seq_len(length(spongenetwork_Cluster_result)), function(i) length(spongenetwork_Cluster_result[[i]])))
spongenetwork_Cluster_result <- lapply(which(size >= modulesize), function(i) spongenetwork_Cluster_result[[i]])
} else if (method == "prop") {
spongenetwork_Cluster_result <- cluster_label_prop(graph_from_data_frame(spongenetwork, directed = directed))
size <- unlist(lapply(seq_len(length(spongenetwork_Cluster_result)), function(i) length(spongenetwork_Cluster_result[[i]])))
spongenetwork_Cluster_result <- lapply(which(size >= modulesize), function(i) spongenetwork_Cluster_result[[i]])
} else if (method == "eigen") {
spongenetwork_Cluster_result <- cluster_leading_eigen(graph_from_data_frame(spongenetwork, directed = directed))
size <- unlist(lapply(seq_len(length(spongenetwork_Cluster_result)), function(i) length(spongenetwork_Cluster_result[[i]])))
spongenetwork_Cluster_result <- lapply(which(size >= modulesize), function(i) spongenetwork_Cluster_result[[i]])
} else if (method == "louvain") {
spongenetwork_Cluster_result <- cluster_louvain(graph_from_data_frame(spongenetwork, directed = directed))
size <- unlist(lapply(seq_len(length(spongenetwork_Cluster_result)), function(i) length(spongenetwork_Cluster_result[[i]])))
spongenetwork_Cluster_result <- lapply(which(size >= modulesize), function(i) spongenetwork_Cluster_result[[i]])
} else if (method == "walktrap") {
spongenetwork_Cluster_result <- cluster_walktrap(graph_from_data_frame(spongenetwork, directed = directed))
size <- unlist(lapply(seq_len(length(spongenetwork_Cluster_result)), function(i) length(spongenetwork_Cluster_result[[i]])))
spongenetwork_Cluster_result <- lapply(which(size >= modulesize), function(i) spongenetwork_Cluster_result[[i]])
}
if (save) {
fileName <- paste("spongenetwork_Cluster_", method, ".txt", sep = "")
for (i in seq_len(length(spongenetwork_Cluster_result))) {
cat(c(i, "\t", length(spongenetwork_Cluster_result[[i]])), file = fileName, sep = "", append = TRUE)
for (j in seq_len(length(spongenetwork_Cluster_result[[i]]))) {
cat(c("\t", spongenetwork_Cluster_result[[i]][j]), file = fileName, sep = "", append = TRUE)
}
if (i != length(spongenetwork_Cluster_result)) {
cat("\n", file = fileName, sep = "", append = TRUE)
}
}
}
return(spongenetwork_Cluster_result)
}
## Disease enrichment analysis of modules
moduleDEA <- function(Modulelist, OrgDb = "org.Hs.eg.db", padjustvaluecutoff = 0.05,
padjustedmethod = "BH") {
entrezIDs <- lapply(seq_along(Modulelist), function(i) bitr(Modulelist[[i]], fromType = "SYMBOL",
toType = "ENTREZID", OrgDb = OrgDb)$ENTREZID)
entrezIDs <- lapply(seq_along(Modulelist), function(i) as.character(entrezIDs[[i]]))
enrichDOs <- lapply(seq_along(Modulelist), function(i) enrichDO(entrezIDs[[i]], pvalueCutoff = padjustvaluecutoff,
pAdjustMethod = padjustedmethod))
enrichDGNs <- lapply(seq_along(Modulelist), function(i) enrichDGN(entrezIDs[[i]], pvalueCutoff = padjustvaluecutoff,
pAdjustMethod = padjustedmethod))
enrichNCGs <- lapply(seq_along(Modulelist), function(i) enrichNCG(entrezIDs[[i]], pvalueCutoff = padjustvaluecutoff,
pAdjustMethod = padjustedmethod))
return(list(enrichDOs, enrichDGNs, enrichNCGs))
}
## Functional GO, KEGG and Reactome enrichment analysis of modules
moduleFEA <- function(Modulelist, ont = "BP", KEGGorganism = "hsa", Reactomeorganism = "human",
OrgDb = "org.Hs.eg.db", padjustvaluecutoff = 0.05, padjustedmethod = "BH") {
entrezIDs <- lapply(seq_along(Modulelist), function(i) bitr(Modulelist[[i]], fromType = "SYMBOL",
toType = "ENTREZID", OrgDb = OrgDb)$ENTREZID)
entrezIDs <- lapply(seq_along(Modulelist), function(i) as.character(entrezIDs[[i]]))
enrichGOs <- lapply(seq_along(Modulelist), function(i) enrichGO(entrezIDs[[i]], OrgDb = OrgDb,
ont = ont, pvalueCutoff = padjustvaluecutoff, pAdjustMethod = padjustedmethod))
enrichKEGGs <- lapply(seq_along(Modulelist), function(i) enrichKEGG(entrezIDs[[i]], organism = KEGGorganism,
pvalueCutoff = padjustvaluecutoff, pAdjustMethod = padjustedmethod))
enrichReactomes <- lapply(seq_along(Modulelist), function(i) enrichPathway(entrezIDs[[i]], organism = Reactomeorganism,
pvalueCutoff = padjustvaluecutoff, pAdjustMethod = padjustedmethod))
return(list(enrichGOs, enrichKEGGs, enrichReactomes))
}
## Survival analysis of modules
moduleSurvival <- function(Modulelist, ExpData, SurvData, devidePercentage = 0.5, plot = FALSE) {
colnames(ExpData) <- gsub("\\.", "-", colnames(ExpData))
ExpDataNames <- colnames(ExpData)
myfit <- list()
LogRank <- list()
for (i in seq_along(Modulelist)) {
Interin_Data <- cbind(SurvData[, seq(2, 3)], ExpData[, which(ExpDataNames %in% Modulelist[[i]])])
Interin_Data <- na.omit(Interin_Data)
try_mm <- try(coxph(survival::Surv(time, status) ~ ., data = data.frame(Interin_Data)),
silent = TRUE)
if ("try-error" %in% class(try_mm))
next
mm <- coxph(survival::Surv(time, status) ~ ., data = data.frame(Interin_Data))
Risk_score <- predict(mm, newdata = data.frame(Interin_Data), type = "risk")
group <- rep("NA", dim(Interin_Data)[1])
group[Risk_score > quantile(Risk_score, probs = devidePercentage)] <- "High"
group[Risk_score <= quantile(Risk_score, probs = devidePercentage)] <- "Low"
Data <- cbind(Interin_Data[, seq_len(2)], group)
myfit[[i]] <- survfit(survival::Surv(time, status) ~ group, data = Data)
sdf <- survdiff(survival::Surv(time, status) ~ group, data = Data)
sdf.p.val <- 1 - pchisq(sdf$chisq, length(sdf$n) - 1)
HR <- (sdf$obs[1]/sdf$exp[1])/(sdf$obs[2]/sdf$exp[2])
HRlow95 <- exp(log(HR) - qnorm(0.975) * sqrt(1/sdf$exp[1] + 1/sdf$exp[2]))
HRup95 <- exp(log(HR) + qnorm(0.975) * sqrt(1/sdf$exp[1] + 1/sdf$exp[2]))
LogRank[[i]] <- c(sdf$chisq, sdf.p.val, HR, HRlow95, HRup95)
}
if (plot) {
for (i in seq_along(myfit)) {
if (!is.null(LogRank[[i]])) {
dev.new()
plot(myfit[[i]], lty = 1, col = c("red", "green"), main = paste("Module", i), xlab = "Time (Months)",
ylab = "Probability of survival")
legend("topright", legend = c("High risk group", "Low risk group"), lty = seq_len(2),
col = c("red", "green"))
}
}
}
LogRank_res <- do.call(rbind, LogRank)
if (length(myfit) >= 1) {
colnames(LogRank_res) <- c("Chi-square", "p-value", "HR", "HRlow95", "HRup95")
names(LogRank) <- seq_along(Modulelist)
LogRank[sapply(LogRank, is.null)] <- NULL
rownames(LogRank_res) <- paste("Module", names(LogRank))
}
return(LogRank_res)
}
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