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Downstream.v3.R
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Downstream.v3.R
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# Set up environment, activate library components
library("ggsci")
library("cowplot")
library("dplyr")
library("Matrix")
library("reticulate")
library("Seurat")
library("reshape2")
library("ggplot2")
library("celldex")
# library("harmony")
# library("future")
# plan(strategy = "multicore", workers = 3)
## Define functions
## enricher - https://guangchuangyu.github.io/2015/05/use-clusterprofiler-as-an-universal-enrichment-analysis-tool/
# WRITE ANNOTATION
# Function for msigdb and SingleR annotation
# spec: default is "human", the other species available is "mouse"
# category default is "H" for the hallmark gene sets, but can also be defined as "C2" or "C7" for those msigdb respective sets.
# Note: in msigdbr output, gene_symbol is comprised of mouse genes, while human_gene_symbol is needed for the human genes.
DGEA <- function(data,
spec = "human",
category = "H",
subcategory = NULL,
direction = "up") {
if (spec == "human") {
hpca.se <- celldex::HumanPrimaryCellAtlasData()
} else if (spec == "mouse") {
mrsd.se <- MouseRNAseqData()
}
# Preparing clusterProfiler to perform hypergeometric test on msigdb signatures
if (spec == "human") {
if (category == "H") {
m_t2g.h <- msigdbr(species = "Homo sapiens", category = "H") %>%
dplyr::select(gs_name, human_gene_symbol)
m_t2n.h <- msigdbr(species = "Homo sapiens", category = "H") %>%
dplyr::select(gs_id, gs_name)
# msigdb signature to use
msig.gene.set = m_t2g.h
msig.name = m_t2n.h
}
else if (category == "C2") {
if (is.null(subcategory)) {
m_t2g.c2 <- msigdbr(species = "Homo sapiens", category = "C2") %>%
dplyr::select(gs_name, human_gene_symbol)
m_t2n.c2 <- msigdbr(species = "Homo sapiens", category = "C2") %>%
dplyr::select(gs_id, gs_name)
# msigdb signature to use
msig.gene.set = m_t2g.c2
msig.name = m_t2n.c2
}
else if (subcategory == "CP") {
m_t2g.c2 <- msigdbr(species = "Homo sapiens", category = "C2",
subcategory = "CP") %>%
dplyr::select(gs_name, human_gene_symbol)
m_t2n.c2 <- msigdbr(species = "Homo sapiens", category = "C2",
subcategory = "CP") %>%
dplyr::select(gs_id, gs_name)
# msigdb signature to use
msig.gene.set = m_t2g.c2
msig.name = m_t2n.c2
}
}
else if (category == "C7") {
m_t2g.c7 <- msigdbr(species = "Homo sapiens", category = "C7") %>%
dplyr::select(gs_name, human_gene_symbol)
m_t2n.c7 <- msigdbr(species = "Homo sapiens", category = "C7") %>%
dplyr::select(gs_id, gs_name)
# m_t2g=rbind(m_t2g.c7,m_t2g.c7)
# msigdb signature to use
msig.gene.set = m_t2g.c7
msig.name = m_t2n.c7
}
}
else if (spec == "mouse") {
if (category == "H") {
m_t2g.h <- msigdbr(species = "Mus musculus", category = "H") %>%
dplyr::select(gs_name, gene_symbol)
m_t2n.h <- msigdbr(species = "Mus musculus", category = "H") %>%
dplyr::select(gs_id, gs_name)
#m_t2g=rbind(m_t2g.c2,m_t2g.c6)
# msigdb signature to use
msig.gene.set = m_t2g.h
msig.name = m_t2n.h
}
else if (category == "C2") {
m_t2g.c2 <- msigdbr(species = "Mus musculus", category = "C2") %>%
dplyr::select(gs_name, gene_symbol)
m_t2n.c2 <- msigdbr(species = "Mus musculus", category = "C2") %>%
dplyr::select(gs_id, gs_name)
# msigdb signature to use
msig.gene.set = m_t2g.c2
msig.name = m_t2n.c2
}
else if (category == "C7") {
m_t2g.c7 <- msigdbr(species = "Mus musculus", category = "C7") %>%
dplyr::select(gs_name, gene_symbol)
m_t2n.c7 <- msigdbr(species = "Mus musculus", category = "C7") %>%
dplyr::select(gs_id, gs_name)
#m_t2g=rbind(m_t2g.c7,m_t2g.c7)
# msigdb signature to use
msig.gene.set = m_t2g.c7
msig.name = m_t2n.c7
}
}
# getting log normalized data for specific cluster
clust.ids = sort(unique(data@active.ident))
new.cluster.ids = rep(NA, length(clust.ids))
# store top 30 pathway enrichment analysis
em = NULL
for (i in 1:length(clust.ids)){
clust <- GetAssayData(subset(x = data,idents=clust.ids[i]),slot="data")
label <- rep(clust.ids[i],dim(clust)[2])
# getting common genes
if (spec == "human") {
common <- intersect(rownames(clust), rownames(hpca.se))
# use only differential markers
if (direction == "up") {
cluster.markers <- FindMarkers(data, ident.1 =clust.ids[i],
logfc.threshold = 0.25, only.pos = TRUE) #logfc.threshold
}
else if (direction == "down") {
cluster.markers <- FindMarkers(data, ident.1 = clust.ids[i],
logfc.threshold = 0.25, only.pos = FALSE)
cluster.markers <- subset(cluster.markers, cluster.markers[["avg_log2FC"]] < 0)
}
common <- intersect(common, rownames(cluster.markers))
hpca.se.common <- hpca.se[common,]
# pred.hpca <- SingleR(test = clust, ref = hpca.se.common, labels = hpca.se$label.main,
# method="cluster",clusters=label)
# new.cluster.ids[i]=pred.hpca$labels
tmp <- enricher(rownames(cluster.markers), TERM2GENE = msig.gene.set, TERM2NAME = msig.name)
em[[i]]=tmp@result[,c("ID", "p.adjust")] #pvalue/p.adjust
}
else if (spec == "mouse") {
common <- intersect(rownames(clust), rownames(mrsd.se))
# use only differential markers
if (direction == "up") {
cluster.markers <- FindMarkers(data, ident.1 =clust.ids[i],
logfc.threshold = 0.25, only.pos = TRUE) #logfc.threshold
}
else if (direction == "down") {
cluster.markers <- FindMarkers(data, ident.1 = clust.ids[i],
logfc.threshold = 0.25, only.pos = FALSE)
cluster.markers <- subset(cluster.markers, cluster.markers[["avg_log2FC"]] < 0)
}
common <- intersect(common,rownames(cluster.markers))
mrsd.se.common <- mrsd.se[common,]
# pred.hpca <- SingleR(test = clust, ref = hpca.se.common, labels = hpca.se$label.main,
# method="cluster",clusters=label)
# new.cluster.ids[i]=pred.hpca$labels
tmp <- enricher(rownames(cluster.markers), TERM2GENE = msig.gene.set, TERM2NAME = msig.name)
em[[i]]=tmp@result[,c("ID", "p.adjust")] #pvalue/p.adjust
}
}
# heatmap of enrichment
library(pheatmap)
# get top 10 enrichments
em.table.top10 = lapply(em,function(x) x[1:10,])
# create dataframe for heatmap
em.hm = NULL
em.hm = data.frame(gene_set=unique(unlist(lapply(em.table.top10,function(x) rownames(x)))))
for (i in 1:length(em.hm$gene_set)){
for (j in 1:length(clust.ids)){
em.hm[i,j+1]=em[[j]]$p.adjust[match(em.hm$gene_set[i],em[[j]]$ID)]
}
}
rownames(em.hm)=em.hm[,1]
em.hm=em.hm[,-1]
em.hm[is.na(em.hm)]=1
#colnames(em.hm)=new.cluster.ids
colnames(em.hm)=as.character(clust.ids)
return(em.hm)
}
# Determine average Silhouette scores for each specified resolution.
# (calcualted using the silhouette() function (package clustter))
#References: https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html,
#https://github.com/satijalab/seurat/issues/1985
# The following variables can be defined:
#' @param sobject A Seurat object containing all of the cells for analysis (required)
#' @param res A character vector of resolutions to investigate (required)
# This function returns a list containing the following objects:
# - input Seurat object [1],
# - list of calculated silhouette scores for each specified resolution [2],
# - list of specified resolution as found in sobject metadata [3] and
# - data frame of means of silhouette scores calculated for each specified resolution
#Example:
# sobject.nRes <- nRes(sobject,
# res = seq(from = 0.1, to = 0.3, by = 0.1))
nRes <- function(sobject, res) {
sobject <- FindClusters(sobject, resolution = res)
ResolutionList <- paste("RNA_snn_res.", res, sep = "")
# ResolutionList <- grep("_snn_res", colnames(sobject@meta.data), value = TRUE)
ResolutionList <- sort(ResolutionList)
library(cluster, quietly = TRUE)
dist.matrix <- dist(x = Embeddings(object = sobject[["pca"]])[, 1:20])
values <- list()
silscore <- list()
for (resolution in ResolutionList) {
clusters <- sobject@meta.data[[resolution]]
sil <- silhouette(x = as.numeric(x = as.factor(x = clusters)), dist = dist.matrix)
# sobject$sil <- sil[, 3]
values[[resolution]] <- sil[, 3]
silscore[[resolution]] <- sil
}
a <- names(values)
means <- data.frame(x = a, y = 0)
for (i in 1:length(values)) {
means[i,2] <- mean(values[[i]])
}
bestc <- means[which.max(means$y),]
bestc <- bestc[,1]
bplot <- boxplot(values, plot = TRUE,
main=(paste(bestc,"is the resolution with highest Sil score")),
xlab="Resolution", ylab="Sil Score",
col="gold")
return.list <- list(sobject, silscore, ResolutionList, means)
}
#silhouette plot to visualize silhouette score distribution of cells in each cluster.
# The following variables can be defined:
#' @param sobject.nRes Output list from 'nRes' function (required; refer documentation above)
#' @param res Desired resolution to use to generate silhouette plot (required)
# RStudio does not plot silhouette plot properly.
# This function does not return anything to the R interpreter instead plots the silhouette plot in a separate plot device
#Example:
# plot <- pSil(sobject.nRes , 0.15)
library(RColorBrewer)
n <- 60
qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual',]
col_vector = unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))
pSil <- function(sobject.nRes, res) {
sobject <- sobject.nRes[[1]]
silscore <- sobject.nRes[[2]]
ResolutionList <- sobject.nRes[[3]]
sobject <-FindClusters(sobject, resolution = res)
res <- paste("RNA_snn_res.", res, sep = "")
k <- length(levels(sobject@meta.data[[res]]))
windows()
col= pal_npg("nrc")(n)
resolution <- res
n <- match(res, ResolutionList)
plot(silscore[[n]], main = paste("res = ", resolution), do.n.k=FALSE,
col = col[1:k]) # with cluster-wise coloring; choose col_vector for more than 10 colors
# abline(v = mean(sil[, 3]), col=c("black"), lty=c(5,2), lwd=c(1, 3))
}