Nothing
R/plotSmoothers.R
In tradeSeq: trajectory-based differential expression analysis for sequencing data
Defines functions
.plotSmoothers_conditions
.plotSmoothers_sce
.plotSmoothers
.plotSmoothers <- function(model, nPoints = 100, lwd = 2, size = 2/3,
xlab = "Pseudotime",
ylab = "Log(expression + 1)",
border = FALSE,
alpha = 1,
sample = 1) {
data <- model$model
y <- data$y
#construct time variable based on cell assignments.
nCurves <- length(model$smooth)
col <- timeAll <- rep(0, nrow(data))
for (jj in seq_len(nCurves)) {
for (ii in seq_len(nrow(data))) {
if (data[ii, paste0("l", jj)] == 1) {
timeAll[ii] <- data[ii, paste0("t", jj)]
col[ii] <- jj
} else {
next
}
}
}
# plot raw data
df <- data.frame("time" = timeAll,
"gene_count" = y,
"lineage" = as.character(col))
rows <- sample(seq_len(nrow(df)), nrow(df) * sample, replace = FALSE)
df <- df[rows, ]
p <- ggplot(df, aes(x = time, y = log1p(gene_count), col = lineage)) +
geom_point(size = size) +
labs(x = xlab, y = ylab) +
theme_classic() +
scale_color_viridis_d(alpha = alpha)
# predict and plot smoothers across the range
for (jj in seq_len(nCurves)) {
df <- .getPredictRangeDf(model$model, jj, nPoints = nPoints)
yhat <- predict(model, newdata = df, type = "response")
if (border) {
p <- p +
geom_line(data = data.frame("time" = df[, paste0("t", jj)],
"gene_count" = yhat,
"lineage" = as.character(jj)),
lwd = lwd + 1, colour = "white") +
geom_line(data = data.frame("time" = df[, paste0("t", jj)],
"gene_count" = yhat,
"lineage" = as.character(jj)),
lwd = lwd)
} else {
p <- p +
geom_line(data = data.frame("time" = df[, paste0("t", jj)],
"gene_count" = yhat,
"lineage" = as.character(jj)),
lwd = lwd)
}
}
return(p)
}
.plotSmoothers_sce <- function(models, counts, gene, nPoints = 100, lwd = 2,
size = 2/3, xlab = "Pseudotime",
ylab = "Log(expression + 1)", border = FALSE,
alpha = 2/3, sample = 1, pointCol = NULL,
plotLineages=TRUE)
{
#input is singleCellExperiment object.
if (length(gene) > 1) stop("Only provide a single gene's ID with the ",
"gene argument.")
# check if all gene IDs provided are present in the models object.
if (is(gene, "character")) {
if (!all(gene %in% names(models))) {
stop("The gene ID is not present in the models object.")
}
id <- which(names(models) %in% gene)
} else id <- gene
dm <- colData(models)$tradeSeq$dm # design matrix
y <- unname(counts[id,])
X <- colData(models)$tradeSeq$X # linear predictor
slingshotColData <- colData(models)$slingshot
pseudotime <- slingshotColData[,grep(x = colnames(slingshotColData),
pattern = "pseudotime")]
if (is.null(dim(pseudotime))) pseudotime <- matrix(pseudotime, ncol = 1)
nCurves <- length(grep(x = colnames(dm), pattern = "t[1-9]"))
betaMat <- rowData(models)$tradeSeq$beta[[1]]
beta <- betaMat[id,]
#construct time variable based on cell assignments.
lcol <- timeAll <- rep(0, nrow(dm))
for (jj in seq_len(nCurves)) {
for (ii in seq_len(nrow(dm))) {
if (dm[ii, paste0("l", jj)] == 1) {
timeAll[ii] <- dm[ii, paste0("t", jj)]
lcol[ii] <- jj
} else {
next
}
}
}
if(!is.null(pointCol)){
if(length(pointCol) == 1){
col <- colData(models)[,pointCol]
} else if(length(pointCol) == ncol(models)){
col <- pointCol
} else {
message(paste("pointCol should have length of either 1 or the number of cells,",
"reverting to default color scheme."))
}
} else {
col <- lcol
}
# plot raw data
df <- data.frame("time" = timeAll,
"gene_count" = y,
"pCol" = as.character(col),
"lineage" = as.character(lcol))
rows <- sample(seq_len(nrow(df)), nrow(df) * sample, replace = FALSE)
df <- df[rows, ]
if(is.null(pointCol)){
p <- ggplot(df, aes(x = time, y = log1p(gene_count), col = lineage)) +
geom_point(size = size) +
labs(x = xlab, y = ylab) +
theme_classic() +
scale_color_viridis_d(alpha = alpha)
} else {
p <- ggplot(df, aes(x = time, y = log1p(gene_count), col = pCol)) +
geom_point(size = size, alpha = alpha) +
labs(x = xlab, y = ylab) +
theme_classic() +
scale_color_discrete() +
labs(col = "Cell labels")
}
# predict and plot smoothers across the range
if(plotLineages){
for (jj in seq_len(nCurves)) {
df <- .getPredictRangeDf(dm, jj, nPoints = nPoints)
Xdf <- predictGAM(lpmatrix = X,
df = df,
pseudotime = pseudotime)
yhat <- c(exp(t(Xdf %*% t(beta)) + df$offset))
if (border) {
p <- p +
geom_line(data = data.frame("time" = df[, paste0("t", jj)],
"gene_count" = yhat,
"lineage" = as.character(jj),
"pCol" = as.character(jj)),
lwd = lwd + 1, colour = "white") +
geom_line(data = data.frame("time" = df[, paste0("t", jj)],
"gene_count" = yhat,
"lineage" = as.character(jj),
"pCol" = as.character(jj)),
lwd = lwd, col=viridis::viridis(nCurves)[jj])
} else {
p <- p +
geom_line(data = data.frame("time" = df[, paste0("t", jj)],
"gene_count" = yhat,
"lineage" = as.character(jj),
"pCol" = as.character(jj)),
lwd = lwd, col=viridis::viridis(nCurves)[jj])
}
}
}
## TODO: add legend for different lineages
return(p)
}
.plotSmoothers_conditions <- function(models, counts, gene, nPoints = 100, lwd = 2,
size = 2/3,
xlab = "Pseudotime",
ylab = "Log(expression + 1)",
border = FALSE,
sample = 1,
alpha = 2/3,
pointCol = NULL,
plotLineages = TRUE)
{
#input is singleCellExperiment object.
if(length(gene) > 1) stop("Only provide a single gene's ID with the ",
"gene argument.")
# check if all gene IDs provided are present in the models object.
if (is(gene, "character")) {
if (!all(gene %in% names(models))) {
stop("The gene ID is not present in the models object.")
}
id <- which(names(models) %in% gene)
} else id <- gene
dm <- colData(models)$tradeSeq$dm # design matrix
y <- unname(counts[id,])
X <- colData(models)$tradeSeq$X # linear predictor
slingshotColData <- colData(models)$slingshot
pseudotime <- slingshotColData[,grep(x = colnames(slingshotColData),
pattern = "pseudotime")]
nCurves <- length(grep(x = colnames(dm), pattern = "t[1-9]"))
betaMat <- rowData(models)$tradeSeq$beta[[1]]
beta <- betaMat[id,]
conditions <- colData(models)$tradeSeq$conditions
nConditions <- nlevels(conditions)
# #construct time variable based on cell assignments.
lcol <- timeAll <- rep(0, nrow(dm))
for (jj in seq_len(nCurves)) {
for(kk in seq_len(nConditions)){
for (ii in seq_len(nrow(dm))) {
if (dm[ii, paste0("l", jj, kk)] == 1) {
timeAll[ii] <- dm[ii, paste0("t", jj)]
lcol[ii] <- paste0("Lineage ", jj, "_", levels(conditions)[kk])
} else {
next
}
}
}
}
if(!is.null(pointCol)){
if(length(pointCol) == 1){
col <- colData(models)[,pointCol]
} else if(length(pointCol) == ncol(models)){
col <- pointCol
} else {
message(paste("pointCol should have length of either 1 or the number of cells,",
"reverting to default color scheme."))
}
} else {
col <- lcol
}
# plot raw data
df <- data.frame("time" = timeAll,
"gene_count" = y,
"pCol" = as.character(col),
"lineage" = as.character(lcol))
rows <- sample(seq_len(nrow(df)), nrow(df) * sample, replace = FALSE)
df <- df[rows, ]
if(is.null(pointCol)){
p <- ggplot(df, aes(x = time, y = log1p(gene_count), col = lineage)) +
geom_point(size = size) +
labs(x = xlab, y = ylab) +
theme_classic() +
scale_color_viridis_d(alpha = alpha)
} else {
p <- ggplot(df, aes(x = time, y = log1p(gene_count), col = pCol)) +
geom_point(size = size, alpha = alpha) +
labs(x = xlab, y = ylab) +
theme_classic() +
scale_color_discrete() +
labs(col = "Cell labels")
}
# predict and plot smoothers across the range
if(plotLineages){
for (jj in seq_len(nCurves)) {
for(kk in seq_len(nConditions)){
df <- .getPredictRangeDf(dm, lineageId = jj, conditionId = kk,
nPoints = nPoints)
Xdf <- predictGAM(lpmatrix = X,
df = df,
pseudotime = pseudotime,
conditions = conditions)
yhat <- c(exp(t(Xdf %*% t(beta)) + df$offset))
if(border){
p <- p +
geom_line(data = data.frame("time" = df[, paste0("t", jj)],
"gene_count" = yhat,
"lineage" = as.character(paste0(jj, kk))),
lwd = lwd + 1, colour = "white") +
geom_line(data = data.frame("time" = df[, paste0("t", jj)],
"gene_count" = yhat,
"lineage" = as.character(paste0(jj, kk))),
lwd = lwd,
col = viridis::viridis(nCurves * nConditions)[
jj * nConditions - (nConditions - kk)])
} else {
p <- p +
geom_line(data = data.frame("time" = df[, paste0("t", jj)],
"gene_count" = yhat,
"lineage" = as.character(paste0(jj, kk))),
lwd = lwd,
col = viridis::viridis(nCurves * nConditions)[
jj * nConditions - (nConditions - kk)])
}
}
}
}
return(p)
}
#' @import mgcv
setOldClass("gam")
#' @description Plot the smoothers estimated by \code{tradeSeq}.
#' @param models Either the \code{SingleCellExperiment} object obtained after
#' running \code{fitGAM}, or the specific GAM model for the corresponding gene,
#' if working with the list output of \code{tradeSeq}.
#' @param counts The matrix of gene expression counts.
#' @param gene Gene name or row in count matrix of gene to plot.
#' @param nPoints The number of points used to extrapolate the fit.
#' Defaults to 100.
#' @param lwd Line width of the smoother. Passed to \code{\link{geom_line}}.
#' @param size Character expansion of the data points. Passed to \code{\link{geom_point}}.
#' @param xlab x-axis label. Passed to \code{\link{labs}}.
#' @param ylab y-axis label. Passed to \code{\link{labs}}.
#' @param border Logical: should a white border be drawn around the mean smoother.
#' @param alpha Numeric between 0 and 1, determines the transparency of data points,
#' see \code{scale_color_viridis_d}.
#' @param sample Numeric between 0 and 1, use to subsample the cells when there
#' are too many so that it can plot faster.
#' @param pointCol Plotting colors for each cell. Can be either character vector of
#' length 1, denoting a variable in the \code{colData(models)} to color cells by,
#' or a vector of length equal to the number of cells.
#' @param plotLineages Logical, should the mean smoothers for each lineage
#' be plotted?
#' @return A \code{\link{ggplot}} object
#' @examples
#' data(gamList, package = "tradeSeq")
#' plotSmoothers(gamList[[4]])
#' @import ggplot2
#' @import mgcv
#' @import viridis
#' @importFrom methods is
#' @rdname plotSmoothers
#' @export
setMethod(f = "plotSmoothers",
signature = c(models = "gam"),
definition = function(models,
nPoints = 100,
lwd = 2,
size = 2/3,
xlab = "Pseudotime",
ylab = "Log(expression + 1)",
border = TRUE,
alpha = 1,
sample = 1){
.plotSmoothers(model = models,
nPoints = nPoints,
lwd = lwd,
size = size,
xlab = xlab,
ylab = ylab,
border = border,
alpha = alpha,
sample = sample)
}
)
#' @rdname plotSmoothers
#' @import SingleCellExperiment
#' @export
setMethod(f = "plotSmoothers",
signature = c(models = "SingleCellExperiment"),
definition = function(models,
counts,
gene,
nPoints = 100,
lwd = 2,
size = 2/3,
xlab = "Pseudotime",
ylab = "Log(expression + 1)",
border = TRUE,
alpha = 1,
sample = 1,
pointCol = NULL,
plotLineages = TRUE){
conditions <- suppressWarnings(!is.null(models$tradeSeq$conditions))
if(conditions){
.plotSmoothers_conditions(models = models,
counts = counts,
gene = gene,
nPoints = nPoints,
lwd = lwd,
size = size,
xlab = xlab,
ylab = ylab,
border = border,
alpha = alpha,
sample = sample,
pointCol = pointCol,
plotLineages = plotLineages)
} else {
.plotSmoothers_sce(models = models,
counts = counts,
gene = gene,
nPoints = nPoints,
lwd = lwd,
size = size,
xlab = xlab,
ylab = ylab,
border = border,
alpha = alpha,
sample = sample,
pointCol = pointCol,
plotLineages = plotLineages)
}
}
)
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tradeSeq documentation built on Nov. 8, 2020, 7:51 p.m.
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Defines functions .plotSmoothers_conditions .plotSmoothers_sce .plotSmoothers
.plotSmoothers <- function(model, nPoints = 100, lwd = 2, size = 2/3,
xlab = "Pseudotime",
ylab = "Log(expression + 1)",
border = FALSE,
alpha = 1,
sample = 1) {
data <- model$model
y <- data$y
#construct time variable based on cell assignments.
nCurves <- length(model$smooth)
col <- timeAll <- rep(0, nrow(data))
for (jj in seq_len(nCurves)) {
for (ii in seq_len(nrow(data))) {
if (data[ii, paste0("l", jj)] == 1) {
timeAll[ii] <- data[ii, paste0("t", jj)]
col[ii] <- jj
} else {
next
}
}
}
# plot raw data
df <- data.frame("time" = timeAll,
"gene_count" = y,
"lineage" = as.character(col))
rows <- sample(seq_len(nrow(df)), nrow(df) * sample, replace = FALSE)
df <- df[rows, ]
p <- ggplot(df, aes(x = time, y = log1p(gene_count), col = lineage)) +
geom_point(size = size) +
labs(x = xlab, y = ylab) +
theme_classic() +
scale_color_viridis_d(alpha = alpha)
# predict and plot smoothers across the range
for (jj in seq_len(nCurves)) {
df <- .getPredictRangeDf(model$model, jj, nPoints = nPoints)
yhat <- predict(model, newdata = df, type = "response")
if (border) {
p <- p +
geom_line(data = data.frame("time" = df[, paste0("t", jj)],
"gene_count" = yhat,
"lineage" = as.character(jj)),
lwd = lwd + 1, colour = "white") +
geom_line(data = data.frame("time" = df[, paste0("t", jj)],
"gene_count" = yhat,
"lineage" = as.character(jj)),
lwd = lwd)
} else {
p <- p +
geom_line(data = data.frame("time" = df[, paste0("t", jj)],
"gene_count" = yhat,
"lineage" = as.character(jj)),
lwd = lwd)
}
}
return(p)
}
.plotSmoothers_sce <- function(models, counts, gene, nPoints = 100, lwd = 2,
size = 2/3, xlab = "Pseudotime",
ylab = "Log(expression + 1)", border = FALSE,
alpha = 2/3, sample = 1, pointCol = NULL,
plotLineages=TRUE)
{
#input is singleCellExperiment object.
if (length(gene) > 1) stop("Only provide a single gene's ID with the ",
"gene argument.")
# check if all gene IDs provided are present in the models object.
if (is(gene, "character")) {
if (!all(gene %in% names(models))) {
stop("The gene ID is not present in the models object.")
}
id <- which(names(models) %in% gene)
} else id <- gene
dm <- colData(models)$tradeSeq$dm # design matrix
y <- unname(counts[id,])
X <- colData(models)$tradeSeq$X # linear predictor
slingshotColData <- colData(models)$slingshot
pseudotime <- slingshotColData[,grep(x = colnames(slingshotColData),
pattern = "pseudotime")]
if (is.null(dim(pseudotime))) pseudotime <- matrix(pseudotime, ncol = 1)
nCurves <- length(grep(x = colnames(dm), pattern = "t[1-9]"))
betaMat <- rowData(models)$tradeSeq$beta[[1]]
beta <- betaMat[id,]
#construct time variable based on cell assignments.
lcol <- timeAll <- rep(0, nrow(dm))
for (jj in seq_len(nCurves)) {
for (ii in seq_len(nrow(dm))) {
if (dm[ii, paste0("l", jj)] == 1) {
timeAll[ii] <- dm[ii, paste0("t", jj)]
lcol[ii] <- jj
} else {
next
}
}
}
if(!is.null(pointCol)){
if(length(pointCol) == 1){
col <- colData(models)[,pointCol]
} else if(length(pointCol) == ncol(models)){
col <- pointCol
} else {
message(paste("pointCol should have length of either 1 or the number of cells,",
"reverting to default color scheme."))
}
} else {
col <- lcol
}
# plot raw data
df <- data.frame("time" = timeAll,
"gene_count" = y,
"pCol" = as.character(col),
"lineage" = as.character(lcol))
rows <- sample(seq_len(nrow(df)), nrow(df) * sample, replace = FALSE)
df <- df[rows, ]
if(is.null(pointCol)){
p <- ggplot(df, aes(x = time, y = log1p(gene_count), col = lineage)) +
geom_point(size = size) +
labs(x = xlab, y = ylab) +
theme_classic() +
scale_color_viridis_d(alpha = alpha)
} else {
p <- ggplot(df, aes(x = time, y = log1p(gene_count), col = pCol)) +
geom_point(size = size, alpha = alpha) +
labs(x = xlab, y = ylab) +
theme_classic() +
scale_color_discrete() +
labs(col = "Cell labels")
}
# predict and plot smoothers across the range
if(plotLineages){
for (jj in seq_len(nCurves)) {
df <- .getPredictRangeDf(dm, jj, nPoints = nPoints)
Xdf <- predictGAM(lpmatrix = X,
df = df,
pseudotime = pseudotime)
yhat <- c(exp(t(Xdf %*% t(beta)) + df$offset))
if (border) {
p <- p +
geom_line(data = data.frame("time" = df[, paste0("t", jj)],
"gene_count" = yhat,
"lineage" = as.character(jj),
"pCol" = as.character(jj)),
lwd = lwd + 1, colour = "white") +
geom_line(data = data.frame("time" = df[, paste0("t", jj)],
"gene_count" = yhat,
"lineage" = as.character(jj),
"pCol" = as.character(jj)),
lwd = lwd, col=viridis::viridis(nCurves)[jj])
} else {
p <- p +
geom_line(data = data.frame("time" = df[, paste0("t", jj)],
"gene_count" = yhat,
"lineage" = as.character(jj),
"pCol" = as.character(jj)),
lwd = lwd, col=viridis::viridis(nCurves)[jj])
}
}
}
## TODO: add legend for different lineages
return(p)
}
.plotSmoothers_conditions <- function(models, counts, gene, nPoints = 100, lwd = 2,
size = 2/3,
xlab = "Pseudotime",
ylab = "Log(expression + 1)",
border = FALSE,
sample = 1,
alpha = 2/3,
pointCol = NULL,
plotLineages = TRUE)
{
#input is singleCellExperiment object.
if(length(gene) > 1) stop("Only provide a single gene's ID with the ",
"gene argument.")
# check if all gene IDs provided are present in the models object.
if (is(gene, "character")) {
if (!all(gene %in% names(models))) {
stop("The gene ID is not present in the models object.")
}
id <- which(names(models) %in% gene)
} else id <- gene
dm <- colData(models)$tradeSeq$dm # design matrix
y <- unname(counts[id,])
X <- colData(models)$tradeSeq$X # linear predictor
slingshotColData <- colData(models)$slingshot
pseudotime <- slingshotColData[,grep(x = colnames(slingshotColData),
pattern = "pseudotime")]
nCurves <- length(grep(x = colnames(dm), pattern = "t[1-9]"))
betaMat <- rowData(models)$tradeSeq$beta[[1]]
beta <- betaMat[id,]
conditions <- colData(models)$tradeSeq$conditions
nConditions <- nlevels(conditions)
# #construct time variable based on cell assignments.
lcol <- timeAll <- rep(0, nrow(dm))
for (jj in seq_len(nCurves)) {
for(kk in seq_len(nConditions)){
for (ii in seq_len(nrow(dm))) {
if (dm[ii, paste0("l", jj, kk)] == 1) {
timeAll[ii] <- dm[ii, paste0("t", jj)]
lcol[ii] <- paste0("Lineage ", jj, "_", levels(conditions)[kk])
} else {
next
}
}
}
}
if(!is.null(pointCol)){
if(length(pointCol) == 1){
col <- colData(models)[,pointCol]
} else if(length(pointCol) == ncol(models)){
col <- pointCol
} else {
message(paste("pointCol should have length of either 1 or the number of cells,",
"reverting to default color scheme."))
}
} else {
col <- lcol
}
# plot raw data
df <- data.frame("time" = timeAll,
"gene_count" = y,
"pCol" = as.character(col),
"lineage" = as.character(lcol))
rows <- sample(seq_len(nrow(df)), nrow(df) * sample, replace = FALSE)
df <- df[rows, ]
if(is.null(pointCol)){
p <- ggplot(df, aes(x = time, y = log1p(gene_count), col = lineage)) +
geom_point(size = size) +
labs(x = xlab, y = ylab) +
theme_classic() +
scale_color_viridis_d(alpha = alpha)
} else {
p <- ggplot(df, aes(x = time, y = log1p(gene_count), col = pCol)) +
geom_point(size = size, alpha = alpha) +
labs(x = xlab, y = ylab) +
theme_classic() +
scale_color_discrete() +
labs(col = "Cell labels")
}
# predict and plot smoothers across the range
if(plotLineages){
for (jj in seq_len(nCurves)) {
for(kk in seq_len(nConditions)){
df <- .getPredictRangeDf(dm, lineageId = jj, conditionId = kk,
nPoints = nPoints)
Xdf <- predictGAM(lpmatrix = X,
df = df,
pseudotime = pseudotime,
conditions = conditions)
yhat <- c(exp(t(Xdf %*% t(beta)) + df$offset))
if(border){
p <- p +
geom_line(data = data.frame("time" = df[, paste0("t", jj)],
"gene_count" = yhat,
"lineage" = as.character(paste0(jj, kk))),
lwd = lwd + 1, colour = "white") +
geom_line(data = data.frame("time" = df[, paste0("t", jj)],
"gene_count" = yhat,
"lineage" = as.character(paste0(jj, kk))),
lwd = lwd,
col = viridis::viridis(nCurves * nConditions)[
jj * nConditions - (nConditions - kk)])
} else {
p <- p +
geom_line(data = data.frame("time" = df[, paste0("t", jj)],
"gene_count" = yhat,
"lineage" = as.character(paste0(jj, kk))),
lwd = lwd,
col = viridis::viridis(nCurves * nConditions)[
jj * nConditions - (nConditions - kk)])
}
}
}
}
return(p)
}
#' @import mgcv
setOldClass("gam")
#' @description Plot the smoothers estimated by \code{tradeSeq}.
#' @param models Either the \code{SingleCellExperiment} object obtained after
#' running \code{fitGAM}, or the specific GAM model for the corresponding gene,
#' if working with the list output of \code{tradeSeq}.
#' @param counts The matrix of gene expression counts.
#' @param gene Gene name or row in count matrix of gene to plot.
#' @param nPoints The number of points used to extrapolate the fit.
#' Defaults to 100.
#' @param lwd Line width of the smoother. Passed to \code{\link{geom_line}}.
#' @param size Character expansion of the data points. Passed to \code{\link{geom_point}}.
#' @param xlab x-axis label. Passed to \code{\link{labs}}.
#' @param ylab y-axis label. Passed to \code{\link{labs}}.
#' @param border Logical: should a white border be drawn around the mean smoother.
#' @param alpha Numeric between 0 and 1, determines the transparency of data points,
#' see \code{scale_color_viridis_d}.
#' @param sample Numeric between 0 and 1, use to subsample the cells when there
#' are too many so that it can plot faster.
#' @param pointCol Plotting colors for each cell. Can be either character vector of
#' length 1, denoting a variable in the \code{colData(models)} to color cells by,
#' or a vector of length equal to the number of cells.
#' @param plotLineages Logical, should the mean smoothers for each lineage
#' be plotted?
#' @return A \code{\link{ggplot}} object
#' @examples
#' data(gamList, package = "tradeSeq")
#' plotSmoothers(gamList[[4]])
#' @import ggplot2
#' @import mgcv
#' @import viridis
#' @importFrom methods is
#' @rdname plotSmoothers
#' @export
setMethod(f = "plotSmoothers",
signature = c(models = "gam"),
definition = function(models,
nPoints = 100,
lwd = 2,
size = 2/3,
xlab = "Pseudotime",
ylab = "Log(expression + 1)",
border = TRUE,
alpha = 1,
sample = 1){
.plotSmoothers(model = models,
nPoints = nPoints,
lwd = lwd,
size = size,
xlab = xlab,
ylab = ylab,
border = border,
alpha = alpha,
sample = sample)
}
)
#' @rdname plotSmoothers
#' @import SingleCellExperiment
#' @export
setMethod(f = "plotSmoothers",
signature = c(models = "SingleCellExperiment"),
definition = function(models,
counts,
gene,
nPoints = 100,
lwd = 2,
size = 2/3,
xlab = "Pseudotime",
ylab = "Log(expression + 1)",
border = TRUE,
alpha = 1,
sample = 1,
pointCol = NULL,
plotLineages = TRUE){
conditions <- suppressWarnings(!is.null(models$tradeSeq$conditions))
if(conditions){
.plotSmoothers_conditions(models = models,
counts = counts,
gene = gene,
nPoints = nPoints,
lwd = lwd,
size = size,
xlab = xlab,
ylab = ylab,
border = border,
alpha = alpha,
sample = sample,
pointCol = pointCol,
plotLineages = plotLineages)
} else {
.plotSmoothers_sce(models = models,
counts = counts,
gene = gene,
nPoints = nPoints,
lwd = lwd,
size = size,
xlab = xlab,
ylab = ylab,
border = border,
alpha = alpha,
sample = sample,
pointCol = pointCol,
plotLineages = plotLineages)
}
}
)
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