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inst/doc/CiteFuse.R
In CiteFuse: CiteFuse: multi-modal analysis of CITE-seq data
## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE,
collapse = TRUE, comment = "#>",
fig.height = 5, fig.width = 5,
fig.retina = 1,
dpi = 60)
## ----eval=FALSE---------------------------------------------------------------
# if (!requireNamespace("BiocManager", quietly = TRUE)) {
# install.packages("BiocManager")
# }
# BiocManager::install("CiteFuse")
## -----------------------------------------------------------------------------
library(CiteFuse)
library(scater)
library(SingleCellExperiment)
library(DT)
## -----------------------------------------------------------------------------
data("CITEseq_example", package = "CiteFuse")
names(CITEseq_example)
lapply(CITEseq_example, dim)
## -----------------------------------------------------------------------------
sce_citeseq <- preprocessing(CITEseq_example)
sce_citeseq
## -----------------------------------------------------------------------------
sce_citeseq <- normaliseExprs(sce = sce_citeseq,
altExp_name = "HTO",
transform = "log")
## ----fig.height=6, fig.width=6------------------------------------------------
sce_citeseq <- scater::runTSNE(sce_citeseq,
altexp = "HTO",
name = "TSNE_HTO",
pca = TRUE)
visualiseDim(sce_citeseq,
dimNames = "TSNE_HTO") + labs(title = "tSNE (HTO)")
sce_citeseq <- scater::runUMAP(sce_citeseq,
altexp = "HTO",
name = "UMAP_HTO")
visualiseDim(sce_citeseq,
dimNames = "UMAP_HTO") + labs(title = "UMAP (HTO)")
## -----------------------------------------------------------------------------
sce_citeseq <- crossSampleDoublets(sce_citeseq)
## -----------------------------------------------------------------------------
table(sce_citeseq$doubletClassify_between_label)
table(sce_citeseq$doubletClassify_between_class)
## ----fig.height=6, fig.width=6------------------------------------------------
visualiseDim(sce_citeseq,
dimNames = "TSNE_HTO",
colour_by = "doubletClassify_between_label")
## ----fig.height=8, fig.width=6------------------------------------------------
plotHTO(sce_citeseq, 1:4)
## -----------------------------------------------------------------------------
sce_citeseq <- withinSampleDoublets(sce_citeseq,
minPts = 10)
## -----------------------------------------------------------------------------
table(sce_citeseq$doubletClassify_within_label)
table(sce_citeseq$doubletClassify_within_class)
## ----fig.height=6, fig.width=6------------------------------------------------
visualiseDim(sce_citeseq,
dimNames = "TSNE_HTO",
colour_by = "doubletClassify_within_label")
## -----------------------------------------------------------------------------
sce_citeseq <- sce_citeseq[, sce_citeseq$doubletClassify_within_class == "Singlet" & sce_citeseq$doubletClassify_between_class == "Singlet"]
sce_citeseq
## -----------------------------------------------------------------------------
sce_citeseq <- scater::logNormCounts(sce_citeseq)
sce_citeseq <- normaliseExprs(sce_citeseq, altExp_name = "ADT", transform = "log")
system.time(sce_citeseq <- CiteFuse(sce_citeseq))
## -----------------------------------------------------------------------------
SNF_W_clust <- spectralClustering(metadata(sce_citeseq)[["SNF_W"]], K = 20)
plot(SNF_W_clust$eigen_values)
which.max(abs(diff(SNF_W_clust$eigen_values)))
## -----------------------------------------------------------------------------
SNF_W_clust <- spectralClustering(metadata(sce_citeseq)[["SNF_W"]], K = 5)
sce_citeseq$SNF_W_clust <- as.factor(SNF_W_clust$labels)
SNF_W1_clust <- spectralClustering(metadata(sce_citeseq)[["ADT_W"]], K = 5)
sce_citeseq$ADT_clust <- as.factor(SNF_W1_clust$labels)
SNF_W2_clust <- spectralClustering(metadata(sce_citeseq)[["RNA_W"]], K = 5)
sce_citeseq$RNA_clust <- as.factor(SNF_W2_clust$labels)
## ----fig.height=8, fig.width=8------------------------------------------------
sce_citeseq <- reducedDimSNF(sce_citeseq,
method = "tSNE",
dimNames = "tSNE_joint")
g1 <- visualiseDim(sce_citeseq, dimNames = "tSNE_joint", colour_by = "SNF_W_clust") +
labs(title = "tSNE (SNF clustering)")
g2 <- visualiseDim(sce_citeseq, dimNames = "tSNE_joint", colour_by = "ADT_clust") +
labs(title = "tSNE (ADT clustering)")
g3 <- visualiseDim(sce_citeseq, dimNames = "tSNE_joint", colour_by = "RNA_clust") +
labs(title = "tSNE (RNA clustering)")
library(gridExtra)
grid.arrange(g3, g2, g1, ncol = 2)
## ----fig.height=8-------------------------------------------------------------
g1 <- visualiseDim(sce_citeseq, dimNames = "tSNE_joint",
colour_by = "hg19_CD8A",
data_from = "assay",
assay_name = "logcounts") +
labs(title = "tSNE: hg19_CD8A (RNA expression)")
g2 <- visualiseDim(sce_citeseq,dimNames = "tSNE_joint",
colour_by = "CD8",
data_from = "altExp",
altExp_assay_name = "logcounts") +
labs(title = "tSNE: CD8 (ADT expression)")
grid.arrange(g1, g2, ncol = 2)
## ----fig.height=8, fig.width=8------------------------------------------------
SNF_W_louvain <- igraphClustering(sce_citeseq, method = "louvain")
table(SNF_W_louvain)
sce_citeseq$SNF_W_louvain <- as.factor(SNF_W_louvain)
visualiseDim(sce_citeseq, dimNames = "tSNE_joint", colour_by = "SNF_W_louvain") +
labs(title = "tSNE (SNF louvain clustering)")
## ----fig.height = 6, fig.width = 6--------------------------------------------
visualiseKNN(sce_citeseq, colour_by = "SNF_W_louvain")
## ----fig.height = 4, fig.width = 8--------------------------------------------
visualiseExprs(sce_citeseq,
plot = "boxplot",
group_by = "SNF_W_louvain",
feature_subset = c("hg19_CD2", "hg19_CD4", "hg19_CD8A", "hg19_CD19"))
visualiseExprs(sce_citeseq,
plot = "violin",
group_by = "SNF_W_louvain",
feature_subset = c("hg19_CD2", "hg19_CD4", "hg19_CD8A", "hg19_CD19"))
visualiseExprs(sce_citeseq,
plot = "jitter",
group_by = "SNF_W_louvain",
feature_subset = c("hg19_CD2", "hg19_CD4", "hg19_CD8A", "hg19_CD19"))
visualiseExprs(sce_citeseq,
plot = "density",
group_by = "SNF_W_louvain",
feature_subset = c("hg19_CD2", "hg19_CD4", "hg19_CD8A", "hg19_CD19"))
## ----fig.height = 4, fig.width = 6--------------------------------------------
visualiseExprs(sce_citeseq,
altExp_name = "ADT",
group_by = "SNF_W_louvain",
plot = "violin", n = 5)
visualiseExprs(sce_citeseq, altExp_name = "ADT",
plot = "jitter",
group_by = "SNF_W_louvain",
feature_subset = c("CD2", "CD8", "CD4", "CD19"))
visualiseExprs(sce_citeseq, altExp_name = "ADT",
plot = "density",
group_by = "SNF_W_louvain",
feature_subset = c("CD2", "CD8", "CD4", "CD19"))
## ----fig.height = 4, fig.width = 8--------------------------------------------
visualiseExprs(sce_citeseq, altExp_name = "ADT",
plot = "pairwise",
feature_subset = c("CD4", "CD8"))
visualiseExprs(sce_citeseq, altExp_name = "ADT",
plot = "pairwise",
feature_subset = c("CD45RA", "CD4", "CD8"),
threshold = rep(4, 3))
## -----------------------------------------------------------------------------
# DE will be performed for RNA if altExp_name = "none"
sce_citeseq <- DEgenes(sce_citeseq,
altExp_name = "none",
group = sce_citeseq$SNF_W_louvain,
return_all = TRUE,
exprs_pct = 0.5)
sce_citeseq <- selectDEgenes(sce_citeseq,
altExp_name = "none")
datatable(format(do.call(rbind, metadata(sce_citeseq)[["DE_res_RNA_filter"]]),
digits = 2))
## -----------------------------------------------------------------------------
sce_citeseq <- DEgenes(sce_citeseq,
altExp_name = "ADT",
group = sce_citeseq$SNF_W_louvain,
return_all = TRUE,
exprs_pct = 0.5)
sce_citeseq <- selectDEgenes(sce_citeseq,
altExp_name = "ADT")
datatable(format(do.call(rbind, metadata(sce_citeseq)[["DE_res_ADT_filter"]]),
digits = 2))
## ----fig.height = 10, fig.width = 10------------------------------------------
rna_DEgenes <- metadata(sce_citeseq)[["DE_res_RNA_filter"]]
adt_DEgenes <- metadata(sce_citeseq)[["DE_res_ADT_filter"]]
rna_DEgenes <- lapply(rna_DEgenes, function(x){
x$name <- gsub("hg19_", "", x$name)
x})
DEbubblePlot(list(RNA = rna_DEgenes, ADT = adt_DEgenes))
## ----fig.height = 8, fig.width = 8--------------------------------------------
rna_list <- c("hg19_CD4",
"hg19_CD8A",
"hg19_HLA-DRB1",
"hg19_ITGAX",
"hg19_NCAM1",
"hg19_CD27",
"hg19_CD19")
adt_list <- c("CD4", "CD8", "MHCII (HLA-DR)", "CD11c", "CD56", "CD27", "CD19")
rna_DEgenes_all <- metadata(sce_citeseq)[["DE_res_RNA"]]
adt_DEgenes_all <- metadata(sce_citeseq)[["DE_res_ADT"]]
feature_list <- list(RNA = rna_list, ADT = adt_list)
de_list <- list(RNA = rna_DEgenes_all, ADT = adt_DEgenes_all)
DEcomparisonPlot(de_list = de_list,
feature_list = feature_list)
## ----fig.height = 8, fig.width = 8--------------------------------------------
set.seed(2020)
sce_citeseq <- importanceADT(sce_citeseq,
group = sce_citeseq$SNF_W_louvain,
subsample = TRUE)
visImportance(sce_citeseq, plot = "boxplot")
visImportance(sce_citeseq, plot = "heatmap")
sort(metadata(sce_citeseq)[["importanceADT"]], decreasing = TRUE)[1:20]
## -----------------------------------------------------------------------------
subset_adt <- names(which(metadata(sce_citeseq)[["importanceADT"]] > 5))
subset_adt
system.time(sce_citeseq <- CiteFuse(sce_citeseq,
ADT_subset = subset_adt,
metadata_names = c("W_SNF_adtSubset1",
"W_ADT_adtSubset1",
"W_RNA")))
SNF_W_clust_adtSubset1 <- spectralClustering(metadata(sce_citeseq)[["W_SNF_adtSubset1"]], K = 5)
sce_citeseq$SNF_W_clust_adtSubset1 <- as.factor(SNF_W_clust_adtSubset1$labels)
library(mclust)
adjustedRandIndex(sce_citeseq$SNF_W_clust_adtSubset1, sce_citeseq$SNF_W_clust)
## ----fig.height = 8, fig.width = 8--------------------------------------------
RNA_feature_subset <- unique(as.character(unlist(lapply(rna_DEgenes_all, "[[", "name"))))
ADT_feature_subset <- unique(as.character(unlist(lapply(adt_DEgenes_all, "[[", "name"))))
geneADTnetwork(sce_citeseq,
RNA_feature_subset = RNA_feature_subset,
ADT_feature_subset = ADT_feature_subset,
cor_method = "pearson",
network_layout = igraph::layout_with_fr)
## -----------------------------------------------------------------------------
data("lr_pair_subset", package = "CiteFuse")
head(lr_pair_subset)
sce_citeseq <- normaliseExprs(sce = sce_citeseq,
altExp_name = "ADT",
transform = "zi_minMax")
sce_citeseq <- normaliseExprs(sce = sce_citeseq,
altExp_name = "none",
exprs_value = "logcounts",
transform = "minMax")
sce_citeseq <- ligandReceptorTest(sce = sce_citeseq,
ligandReceptor_list = lr_pair_subset,
cluster = sce_citeseq$SNF_W_louvain,
RNA_exprs_value = "minMax",
use_alt_exp = TRUE,
altExp_name = "ADT",
altExp_exprs_value = "zi_minMax",
num_permute = 1000)
## ----fig.height=10, fig.width=8-----------------------------------------------
visLigandReceptor(sce_citeseq,
type = "pval_heatmap",
receptor_type = "ADT")
## ----fig.height=12, fig.width=6-----------------------------------------------
visLigandReceptor(sce_citeseq,
type = "pval_dotplot",
receptor_type = "ADT")
## ----fig.height=8, fig.width=8------------------------------------------------
visLigandReceptor(sce_citeseq,
type = "group_network",
receptor_type = "ADT")
## ----fig.height=8, fig.width=8------------------------------------------------
visLigandReceptor(sce_citeseq,
type = "group_heatmap",
receptor_type = "ADT")
## ----fig.height=8, fig.width=8------------------------------------------------
visLigandReceptor(sce_citeseq,
type = "lr_network",
receptor_type = "ADT")
## -----------------------------------------------------------------------------
data("sce_ctcl_subset", package = "CiteFuse")
## -----------------------------------------------------------------------------
visualiseExprsList(sce_list = list(control = sce_citeseq,
ctcl = sce_ctcl_subset),
plot = "boxplot",
altExp_name = "none",
exprs_value = "logcounts",
feature_subset = c("hg19_S100A10", "hg19_CD8A"),
group_by = c("SNF_W_louvain", "SNF_W_louvain"))
visualiseExprsList(sce_list = list(control = sce_citeseq,
ctcl = sce_ctcl_subset),
plot = "boxplot",
altExp_name = "ADT",
feature_subset = c("CD19", "CD8"),
group_by = c("SNF_W_louvain", "SNF_W_louvain"))
## -----------------------------------------------------------------------------
de_res <- DEgenesCross(sce_list = list(control = sce_citeseq,
ctcl = sce_ctcl_subset),
colData_name = c("SNF_W_louvain", "SNF_W_louvain"),
group_to_test = c("2", "6"))
de_res_filter <- selectDEgenes(de_res = de_res)
de_res_filter
## ----eval = FALSE-------------------------------------------------------------
# tmpdir <- tempdir()
# download.file("http://cf.10xgenomics.com/samples/cell-exp/3.1.0/connect_5k_pbmc_NGSC3_ch1/connect_5k_pbmc_NGSC3_ch1_filtered_feature_bc_matrix.tar.gz", file.path(tmpdir, "/5k_pbmc_NGSC3_ch1_filtered_feature_bc_matrix.tar.gz"))
# untar(file.path(tmpdir, "5k_pbmc_NGSC3_ch1_filtered_feature_bc_matrix.tar.gz"),
# exdir = tmpdir)
# sce_citeseq_10X <- readFrom10X(file.path(tmpdir, "filtered_feature_bc_matrix/"))
# sce_citeseq_10X
## -----------------------------------------------------------------------------
sessionInfo()
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## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE,
collapse = TRUE, comment = "#>",
fig.height = 5, fig.width = 5,
fig.retina = 1,
dpi = 60)
## ----eval=FALSE---------------------------------------------------------------
# if (!requireNamespace("BiocManager", quietly = TRUE)) {
# install.packages("BiocManager")
# }
# BiocManager::install("CiteFuse")
## -----------------------------------------------------------------------------
library(CiteFuse)
library(scater)
library(SingleCellExperiment)
library(DT)
## -----------------------------------------------------------------------------
data("CITEseq_example", package = "CiteFuse")
names(CITEseq_example)
lapply(CITEseq_example, dim)
## -----------------------------------------------------------------------------
sce_citeseq <- preprocessing(CITEseq_example)
sce_citeseq
## -----------------------------------------------------------------------------
sce_citeseq <- normaliseExprs(sce = sce_citeseq,
altExp_name = "HTO",
transform = "log")
## ----fig.height=6, fig.width=6------------------------------------------------
sce_citeseq <- scater::runTSNE(sce_citeseq,
altexp = "HTO",
name = "TSNE_HTO",
pca = TRUE)
visualiseDim(sce_citeseq,
dimNames = "TSNE_HTO") + labs(title = "tSNE (HTO)")
sce_citeseq <- scater::runUMAP(sce_citeseq,
altexp = "HTO",
name = "UMAP_HTO")
visualiseDim(sce_citeseq,
dimNames = "UMAP_HTO") + labs(title = "UMAP (HTO)")
## -----------------------------------------------------------------------------
sce_citeseq <- crossSampleDoublets(sce_citeseq)
## -----------------------------------------------------------------------------
table(sce_citeseq$doubletClassify_between_label)
table(sce_citeseq$doubletClassify_between_class)
## ----fig.height=6, fig.width=6------------------------------------------------
visualiseDim(sce_citeseq,
dimNames = "TSNE_HTO",
colour_by = "doubletClassify_between_label")
## ----fig.height=8, fig.width=6------------------------------------------------
plotHTO(sce_citeseq, 1:4)
## -----------------------------------------------------------------------------
sce_citeseq <- withinSampleDoublets(sce_citeseq,
minPts = 10)
## -----------------------------------------------------------------------------
table(sce_citeseq$doubletClassify_within_label)
table(sce_citeseq$doubletClassify_within_class)
## ----fig.height=6, fig.width=6------------------------------------------------
visualiseDim(sce_citeseq,
dimNames = "TSNE_HTO",
colour_by = "doubletClassify_within_label")
## -----------------------------------------------------------------------------
sce_citeseq <- sce_citeseq[, sce_citeseq$doubletClassify_within_class == "Singlet" & sce_citeseq$doubletClassify_between_class == "Singlet"]
sce_citeseq
## -----------------------------------------------------------------------------
sce_citeseq <- scater::logNormCounts(sce_citeseq)
sce_citeseq <- normaliseExprs(sce_citeseq, altExp_name = "ADT", transform = "log")
system.time(sce_citeseq <- CiteFuse(sce_citeseq))
## -----------------------------------------------------------------------------
SNF_W_clust <- spectralClustering(metadata(sce_citeseq)[["SNF_W"]], K = 20)
plot(SNF_W_clust$eigen_values)
which.max(abs(diff(SNF_W_clust$eigen_values)))
## -----------------------------------------------------------------------------
SNF_W_clust <- spectralClustering(metadata(sce_citeseq)[["SNF_W"]], K = 5)
sce_citeseq$SNF_W_clust <- as.factor(SNF_W_clust$labels)
SNF_W1_clust <- spectralClustering(metadata(sce_citeseq)[["ADT_W"]], K = 5)
sce_citeseq$ADT_clust <- as.factor(SNF_W1_clust$labels)
SNF_W2_clust <- spectralClustering(metadata(sce_citeseq)[["RNA_W"]], K = 5)
sce_citeseq$RNA_clust <- as.factor(SNF_W2_clust$labels)
## ----fig.height=8, fig.width=8------------------------------------------------
sce_citeseq <- reducedDimSNF(sce_citeseq,
method = "tSNE",
dimNames = "tSNE_joint")
g1 <- visualiseDim(sce_citeseq, dimNames = "tSNE_joint", colour_by = "SNF_W_clust") +
labs(title = "tSNE (SNF clustering)")
g2 <- visualiseDim(sce_citeseq, dimNames = "tSNE_joint", colour_by = "ADT_clust") +
labs(title = "tSNE (ADT clustering)")
g3 <- visualiseDim(sce_citeseq, dimNames = "tSNE_joint", colour_by = "RNA_clust") +
labs(title = "tSNE (RNA clustering)")
library(gridExtra)
grid.arrange(g3, g2, g1, ncol = 2)
## ----fig.height=8-------------------------------------------------------------
g1 <- visualiseDim(sce_citeseq, dimNames = "tSNE_joint",
colour_by = "hg19_CD8A",
data_from = "assay",
assay_name = "logcounts") +
labs(title = "tSNE: hg19_CD8A (RNA expression)")
g2 <- visualiseDim(sce_citeseq,dimNames = "tSNE_joint",
colour_by = "CD8",
data_from = "altExp",
altExp_assay_name = "logcounts") +
labs(title = "tSNE: CD8 (ADT expression)")
grid.arrange(g1, g2, ncol = 2)
## ----fig.height=8, fig.width=8------------------------------------------------
SNF_W_louvain <- igraphClustering(sce_citeseq, method = "louvain")
table(SNF_W_louvain)
sce_citeseq$SNF_W_louvain <- as.factor(SNF_W_louvain)
visualiseDim(sce_citeseq, dimNames = "tSNE_joint", colour_by = "SNF_W_louvain") +
labs(title = "tSNE (SNF louvain clustering)")
## ----fig.height = 6, fig.width = 6--------------------------------------------
visualiseKNN(sce_citeseq, colour_by = "SNF_W_louvain")
## ----fig.height = 4, fig.width = 8--------------------------------------------
visualiseExprs(sce_citeseq,
plot = "boxplot",
group_by = "SNF_W_louvain",
feature_subset = c("hg19_CD2", "hg19_CD4", "hg19_CD8A", "hg19_CD19"))
visualiseExprs(sce_citeseq,
plot = "violin",
group_by = "SNF_W_louvain",
feature_subset = c("hg19_CD2", "hg19_CD4", "hg19_CD8A", "hg19_CD19"))
visualiseExprs(sce_citeseq,
plot = "jitter",
group_by = "SNF_W_louvain",
feature_subset = c("hg19_CD2", "hg19_CD4", "hg19_CD8A", "hg19_CD19"))
visualiseExprs(sce_citeseq,
plot = "density",
group_by = "SNF_W_louvain",
feature_subset = c("hg19_CD2", "hg19_CD4", "hg19_CD8A", "hg19_CD19"))
## ----fig.height = 4, fig.width = 6--------------------------------------------
visualiseExprs(sce_citeseq,
altExp_name = "ADT",
group_by = "SNF_W_louvain",
plot = "violin", n = 5)
visualiseExprs(sce_citeseq, altExp_name = "ADT",
plot = "jitter",
group_by = "SNF_W_louvain",
feature_subset = c("CD2", "CD8", "CD4", "CD19"))
visualiseExprs(sce_citeseq, altExp_name = "ADT",
plot = "density",
group_by = "SNF_W_louvain",
feature_subset = c("CD2", "CD8", "CD4", "CD19"))
## ----fig.height = 4, fig.width = 8--------------------------------------------
visualiseExprs(sce_citeseq, altExp_name = "ADT",
plot = "pairwise",
feature_subset = c("CD4", "CD8"))
visualiseExprs(sce_citeseq, altExp_name = "ADT",
plot = "pairwise",
feature_subset = c("CD45RA", "CD4", "CD8"),
threshold = rep(4, 3))
## -----------------------------------------------------------------------------
# DE will be performed for RNA if altExp_name = "none"
sce_citeseq <- DEgenes(sce_citeseq,
altExp_name = "none",
group = sce_citeseq$SNF_W_louvain,
return_all = TRUE,
exprs_pct = 0.5)
sce_citeseq <- selectDEgenes(sce_citeseq,
altExp_name = "none")
datatable(format(do.call(rbind, metadata(sce_citeseq)[["DE_res_RNA_filter"]]),
digits = 2))
## -----------------------------------------------------------------------------
sce_citeseq <- DEgenes(sce_citeseq,
altExp_name = "ADT",
group = sce_citeseq$SNF_W_louvain,
return_all = TRUE,
exprs_pct = 0.5)
sce_citeseq <- selectDEgenes(sce_citeseq,
altExp_name = "ADT")
datatable(format(do.call(rbind, metadata(sce_citeseq)[["DE_res_ADT_filter"]]),
digits = 2))
## ----fig.height = 10, fig.width = 10------------------------------------------
rna_DEgenes <- metadata(sce_citeseq)[["DE_res_RNA_filter"]]
adt_DEgenes <- metadata(sce_citeseq)[["DE_res_ADT_filter"]]
rna_DEgenes <- lapply(rna_DEgenes, function(x){
x$name <- gsub("hg19_", "", x$name)
x})
DEbubblePlot(list(RNA = rna_DEgenes, ADT = adt_DEgenes))
## ----fig.height = 8, fig.width = 8--------------------------------------------
rna_list <- c("hg19_CD4",
"hg19_CD8A",
"hg19_HLA-DRB1",
"hg19_ITGAX",
"hg19_NCAM1",
"hg19_CD27",
"hg19_CD19")
adt_list <- c("CD4", "CD8", "MHCII (HLA-DR)", "CD11c", "CD56", "CD27", "CD19")
rna_DEgenes_all <- metadata(sce_citeseq)[["DE_res_RNA"]]
adt_DEgenes_all <- metadata(sce_citeseq)[["DE_res_ADT"]]
feature_list <- list(RNA = rna_list, ADT = adt_list)
de_list <- list(RNA = rna_DEgenes_all, ADT = adt_DEgenes_all)
DEcomparisonPlot(de_list = de_list,
feature_list = feature_list)
## ----fig.height = 8, fig.width = 8--------------------------------------------
set.seed(2020)
sce_citeseq <- importanceADT(sce_citeseq,
group = sce_citeseq$SNF_W_louvain,
subsample = TRUE)
visImportance(sce_citeseq, plot = "boxplot")
visImportance(sce_citeseq, plot = "heatmap")
sort(metadata(sce_citeseq)[["importanceADT"]], decreasing = TRUE)[1:20]
## -----------------------------------------------------------------------------
subset_adt <- names(which(metadata(sce_citeseq)[["importanceADT"]] > 5))
subset_adt
system.time(sce_citeseq <- CiteFuse(sce_citeseq,
ADT_subset = subset_adt,
metadata_names = c("W_SNF_adtSubset1",
"W_ADT_adtSubset1",
"W_RNA")))
SNF_W_clust_adtSubset1 <- spectralClustering(metadata(sce_citeseq)[["W_SNF_adtSubset1"]], K = 5)
sce_citeseq$SNF_W_clust_adtSubset1 <- as.factor(SNF_W_clust_adtSubset1$labels)
library(mclust)
adjustedRandIndex(sce_citeseq$SNF_W_clust_adtSubset1, sce_citeseq$SNF_W_clust)
## ----fig.height = 8, fig.width = 8--------------------------------------------
RNA_feature_subset <- unique(as.character(unlist(lapply(rna_DEgenes_all, "[[", "name"))))
ADT_feature_subset <- unique(as.character(unlist(lapply(adt_DEgenes_all, "[[", "name"))))
geneADTnetwork(sce_citeseq,
RNA_feature_subset = RNA_feature_subset,
ADT_feature_subset = ADT_feature_subset,
cor_method = "pearson",
network_layout = igraph::layout_with_fr)
## -----------------------------------------------------------------------------
data("lr_pair_subset", package = "CiteFuse")
head(lr_pair_subset)
sce_citeseq <- normaliseExprs(sce = sce_citeseq,
altExp_name = "ADT",
transform = "zi_minMax")
sce_citeseq <- normaliseExprs(sce = sce_citeseq,
altExp_name = "none",
exprs_value = "logcounts",
transform = "minMax")
sce_citeseq <- ligandReceptorTest(sce = sce_citeseq,
ligandReceptor_list = lr_pair_subset,
cluster = sce_citeseq$SNF_W_louvain,
RNA_exprs_value = "minMax",
use_alt_exp = TRUE,
altExp_name = "ADT",
altExp_exprs_value = "zi_minMax",
num_permute = 1000)
## ----fig.height=10, fig.width=8-----------------------------------------------
visLigandReceptor(sce_citeseq,
type = "pval_heatmap",
receptor_type = "ADT")
## ----fig.height=12, fig.width=6-----------------------------------------------
visLigandReceptor(sce_citeseq,
type = "pval_dotplot",
receptor_type = "ADT")
## ----fig.height=8, fig.width=8------------------------------------------------
visLigandReceptor(sce_citeseq,
type = "group_network",
receptor_type = "ADT")
## ----fig.height=8, fig.width=8------------------------------------------------
visLigandReceptor(sce_citeseq,
type = "group_heatmap",
receptor_type = "ADT")
## ----fig.height=8, fig.width=8------------------------------------------------
visLigandReceptor(sce_citeseq,
type = "lr_network",
receptor_type = "ADT")
## -----------------------------------------------------------------------------
data("sce_ctcl_subset", package = "CiteFuse")
## -----------------------------------------------------------------------------
visualiseExprsList(sce_list = list(control = sce_citeseq,
ctcl = sce_ctcl_subset),
plot = "boxplot",
altExp_name = "none",
exprs_value = "logcounts",
feature_subset = c("hg19_S100A10", "hg19_CD8A"),
group_by = c("SNF_W_louvain", "SNF_W_louvain"))
visualiseExprsList(sce_list = list(control = sce_citeseq,
ctcl = sce_ctcl_subset),
plot = "boxplot",
altExp_name = "ADT",
feature_subset = c("CD19", "CD8"),
group_by = c("SNF_W_louvain", "SNF_W_louvain"))
## -----------------------------------------------------------------------------
de_res <- DEgenesCross(sce_list = list(control = sce_citeseq,
ctcl = sce_ctcl_subset),
colData_name = c("SNF_W_louvain", "SNF_W_louvain"),
group_to_test = c("2", "6"))
de_res_filter <- selectDEgenes(de_res = de_res)
de_res_filter
## ----eval = FALSE-------------------------------------------------------------
# tmpdir <- tempdir()
# download.file("http://cf.10xgenomics.com/samples/cell-exp/3.1.0/connect_5k_pbmc_NGSC3_ch1/connect_5k_pbmc_NGSC3_ch1_filtered_feature_bc_matrix.tar.gz", file.path(tmpdir, "/5k_pbmc_NGSC3_ch1_filtered_feature_bc_matrix.tar.gz"))
# untar(file.path(tmpdir, "5k_pbmc_NGSC3_ch1_filtered_feature_bc_matrix.tar.gz"),
# exdir = tmpdir)
# sce_citeseq_10X <- readFrom10X(file.path(tmpdir, "filtered_feature_bc_matrix/"))
# sce_citeseq_10X
## -----------------------------------------------------------------------------
sessionInfo()
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