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PBMC_single_cell_ms.R
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PBMC_single_cell_ms.R
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#!/usr/bin/Rscript
# -*- coding: utf-8 -*-
# devtools::install_github('ZJUFanLab/scCATCH')
if !require("Matrix") install.packages("Matrix")
if !require("Seurat") install.packages("Seurat")
if !require("dplyr") install.packages("dplyr")
if !require("cowplot") install.packages("cowplot")
if !require("VISION") install.packages("VISION")
if !require("geneLenDataBase") install.packages("geneLenDataBase")
if !require("clusterProfiler") install.packages("clusterProfiler")
if !require("fgsea") install.packages("fgsea")
# PARAMS
WORKDIR="/mnt/Data10tb/student9_data/GSE138266_RAW"
setwd(WORKDIR)
data.dir = "/mnt/Data10tb/student9_data/GSE138266_RAW/PBMC_cont_3"
# LOAD DATA
pbmc_ms_1.data <- Read10X("/mnt/Data10tb/student9_data/GSE138266_RAW/PBMC_MS_1")
pbmc_ms_2.data <- Read10X("/mnt/Data10tb/student9_data/GSE138266_RAW/PBMC_MS_2")
pbmc_ms_3.data <- Read10X("/mnt/Data10tb/student9_data/GSE138266_RAW/PBMC_MS_3")
pbmc_ms_4.data <- Read10X("/mnt/Data10tb/student9_data/GSE138266_RAW/PBMC_MS_4")
pbmc_ms_5.data <- Read10X("/mnt/Data10tb/student9_data/GSE138266_RAW/PBMC_MS_5")
pbmc_cont_1.data <- Read10X("/mnt/Data10tb/student9_data/GSE138266_RAW/PBMC_cont_1")
pbmc_cont_2.data <- Read10X("/mnt/Data10tb/student9_data/GSE138266_RAW/PBMC_cont_2")
pbmc_cont_3.data <- Read10X("/mnt/Data10tb/student9_data/GSE138266_RAW/PBMC_cont_3")
pbmc_cont_4.data <- Read10X("/mnt/Data10tb/student9_data/GSE138266_RAW/PBMC_cont_4")
pbmc_cont_5.data <- Read10X("/mnt/Data10tb/student9_data/GSE138266_RAW/PBMC_cont_5")
pbmc_ms_1.data <- CreateSeuratObject(counts = pbmc_ms_1.data, min.cells = 3, min.features = 200, project = "pbmc_ms_1")
pbmc_ms_2.data <- CreateSeuratObject(counts = pbmc_ms_2.data, min.cells = 3, min.features = 200, project = "pbmc_ms_2")
pbmc_ms_3.data <- CreateSeuratObject(counts = pbmc_ms_3.data, min.cells = 3, min.features = 200, project = "pbmc_ms_3")
pbmc_ms_4.data <- CreateSeuratObject(counts = pbmc_ms_4.data, min.cells = 3, min.features = 200, project = "pbmc_ms_4")
pbmc_ms_5.data <- CreateSeuratObject(counts = pbmc_ms_5.data, min.cells = 3, min.features = 200, project = "pbmc_ms_5")
pbmc_cont_1.data <- CreateSeuratObject(counts = pbmc_cont_1.data, min.cells = 3, min.features = 200, project = "pbmc_cont_1")
pbmc_cont_2.data <- CreateSeuratObject(counts = pbmc_cont_2.data, min.cells = 3, min.features = 200, project = "pbmc_cont_2")
pbmc_cont_3.data <- CreateSeuratObject(counts = pbmc_cont_3.data, min.cells = 3, min.features = 200, project = "pbmc_cont_3")
pbmc_cont_4.data <- CreateSeuratObject(counts = pbmc_cont_4.data, min.cells = 3, min.features = 200, project = "pbmc_cont_4")
pbmc_cont_5.data <- CreateSeuratObject(counts = pbmc_cont_5.data, min.cells = 3, min.features = 200, project = "pbmc_cont_5")
pbmc <- merge(pbmc_ms_1.data, c(pbmc_ms_2.data, pbmc_ms_3.data, pbmc_ms_4.data, pbmc_ms_5.data, pbmc_cont_1.data, pbmc_cont_2.data, pbmc_cont_3.data, pbmc_cont_4.data, pbmc_cont_5.data),
add.cell.ids = c("pbmc_ms_1", "pbmc_ms_2", "pbmc_ms_3", "pbmc_ms_4", "pbmc_ms_5",
"pbmc_cont_1", "pbmc_cont_2", "pbmc_cont_3", "pbmc_cont_4", "pbmc_cont_5"))
rm(csf_ms_1.data, csf_ms_2.data, csf_ms_3.data, csf_ms_4.data,
csf_ms_5.data,csf_ms_6.data, csf_cont_1.data, csf_cont_2.data,
csf_cont_3.data, csf_cont_4.data, csf_cont_5.data, csf_cont_6.data,
pbmc_ms_1.data, pbmc_ms_2.data, pbmc_ms_3.data, pbmc_ms_4.data,
pbmc_ms_5.data, pbmc_cont_1.data, pbmc_cont_2.data,
pbmc_cont_3.data, pbmc_cont_4.data, pbmc_cont_5.data)
# run garbage collect to free up memory
gc()
pbmc.list <- SplitObject(pbmc)
rm(pbmc)
pbmc.list$pbmc_ms_1$stim <- "MS"
pbmc.list$pbmc_ms_2$stim <- "MS"
pbmc.list$pbmc_ms_3$stim <- "MS"
pbmc.list$pbmc_ms_4$stim <- "MS"
pbmc.list$pbmc_ms_5$stim <- "MS"
pbmc.list$pbmc_cont_1$stim <- "CONT"
pbmc.list$pbmc_cont_2$stim <- "CONT"
pbmc.list$pbmc_cont_3$stim <- "CONT"
pbmc.list$pbmc_cont_4$stim <- "CONT"
pbmc.list$pbmc_cont_5$stim <- "CONT"
for (i in 1:length(pbmc.list)) {
# filtering
pbmc.list[[i]][["percent.mt"]] <- PercentageFeatureSet(pbmc.list[[i]], pattern = "^MT-")
pbmc.list[[i]][["percent.Ribo"]] <- PercentageFeatureSet(pbmc.list[[i]], pattern = "^RP[SL]")
pbmc.list[[i]][["percent.hemoglob"]] <- PercentageFeatureSet(pbmc.list[[i]], pattern = "^HB[^(P)]")
pbmc.list[[i]] <- subset(pbmc.list[[i]], subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 10 & percent.hemoglob <10 & percent.Ribo < 10)
# normalization
pbmc.list[[i]] <- SCTransform(pbmc.list[[i]], verbose = FALSE)
}
options(future.globals.maxSize= 14000000000)
pbmc.features <- SelectIntegrationFeatures(object.list = pbmc.list, nfeatures = 3000)
pbmc.list <- PrepSCTIntegration(object.list = pbmc.list, anchor.features = pbmc.features,
verbose = FALSE)
pbmc.anchors <- FindIntegrationAnchors(object.list = pbmc.list, normalization.method = "SCT",
anchor.features = pbmc.features, verbose = FALSE)
pbmc.integrated <- IntegrateData(anchorset = pbmc.anchors, normalization.method = "SCT",
verbose = FALSE)
#clastering
pbmc.integrated <- RunPCA(pbmc.integrated, verbose = FALSE)
pbmc.integrated <- RunUMAP(pbmc.integrated, dims = 1:30)
pbmc.integrated <- FindNeighbors(pbmc.integrated, reduction = "pca", dims = 1:30)
pbmc.integrated <- FindClusters(pbmc.integrated, resolution = 0.5)
p1 <- DimPlot(pbmc.integrated, reduction = "umap", group.by = "stim")
p2 <- DimPlot(pbmc.integrated, reduction = "umap", label = TRUE, repel = TRUE)
p1 + p2
# find markers for every cluster compared to all remaining cells, report only the positive ones
pbmc.markers <- FindAllMarkers(pbmc.integrated, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
top_pbmc.markers <- pbmc.markers %>% group_by(cluster) %>% top_n(n = 3, wt = avg_logFC)
FeaturePlot(pbmc.integrated, features = top_chf_ms.markers$gene)
###vision
signatures <- c("/mnt/Data10tb/student9_data/GSE138266_RAW/Signatures/c7.all.v7.2.symbols.gmt")
vision.obj_pbmc <- Vision(pbmc.integrated, signatures = signatures, projection_methods = "UMAP")
vision.obj_pbmc <- analyze(vision.obj_pbmc)
viewResults(vision.obj_pbmc)
# vision.obj_csf_ms <- addProjection(vis, "UMAP", projection)
# finding cell types with scCATCH( only for pbmc becouse this there is no CSF tissue in scCATCH)
library(scCATCH)
pbmc.markers_annot<- scCATCH(object = pbmc.markers,species = 'Human',tissue = c('Blood','Peripheral blood','Bone marrow'))
colnames(pbmc.markers_annot)[3] <- 'scCATCH_cell_type'
# pbmc_cont.markers_annot<- scCATCH(object = pbmc_cont.markers,species = 'Human',tissue = c('Blood','Peripheral blood','Bone marrow'))
# colnames(pbmc_cont.markers_annot)[3] <- 'scCATCH_cell_type'
#annotatig cell types to clasters
pbmc.cluster.ids <- pbmc.markers_annot$scCATCH_cell_type
names(pbmc.cluster.ids) <- levels(pbmc.integrated)
pbmc.integrated <- RenameIdents(pbmc.integrated, pbmc.cluster.ids)
DimPlot(pbmc.integrated, reduction = "umap", label = TRUE, pt.size = 0.5) + NoLegend()
DimPlot(pbmc.integrated, reduction = "umap", split.by = "stim")
#####diff exp analysis
DefaultAssay(pbmc.integrated) <- "RNA"
#
# naive.t.cells <- subset(pbmc.integrated, idents = "Naive T Cell")
# Idents(naive.t.cells) <- "stim"
# avg.t.cells <- AverageExpression(naive.t.cells, verbose = FALSE)$RNA
# avg.t.cells$gene <- rownames(avg.t.cells)
# library(ggplot2)
# p3 <- ggplot(avg.t.cells, aes(CTRL, STIM)) + geom_point() + ggtitle("CD4 Naive T Cells")
###########
# Find differentially expressed features between claster and all
# BiocManager::install('multtest')
# install.packages('metap')
# library(metap)
VSall_naiveTcell.markers <- FindConservedMarkers(pbmc.integrated, ident.1 = "Naive T Cell", grouping.var = "stim", verbose = FALSE)
# head(naiveTcell.markers)
VSall_mono.markers <- FindConservedMarkers(pbmc.integrated, ident.1 = "Monocyte", grouping.var = "stim", verbose = FALSE)
VSall_memoryTcell.markers <- FindConservedMarkers(pbmc.integrated, ident.1 = "Memory T Cell", grouping.var = "stim", verbose = FALSE)
VSall_regulatoryTcell.markers <- FindConservedMarkers(pbmc.integrated, ident.1 = "Regulatory T Cell", grouping.var = "stim", verbose = FALSE)
VSall_transitionalBcell.markers <- FindConservedMarkers(pbmc.integrated, ident.1 = "Transitional B Cell", grouping.var = "stim", verbose = FALSE)
VSall_plasmacytoiddendriticcell.markers <- FindConservedMarkers(pbmc.integrated, ident.1 = "Plasmacytoid Dendritic Cell", grouping.var = "stim", verbose = FALSE)
VSall_basophil.markers <- FindConservedMarkers(pbmc.integrated, ident.1 = "Basophil", grouping.var = "stim", verbose = FALSE)
# dif exp between each claster by condition
pbmc.integrated$celltype.stim <- paste(Idents(pbmc.integrated), pbmc.integrated$stim, sep = "_")
pbmc.integrated$celltype <- Idents(pbmc.integrated)
Idents(pbmc.integrated) <- "celltype.stim"
# 1 cluster
naiveTcell.markers <- FindMarkers(pbmc.integrated, ident.1 = "Naive T Cell_MS", ident.2 = "Naive T Cell_CONT",min.pct = 0.4, logfc.threshold = 0.25, verbose = FALSE)
# 2 cluster
mono.markers <- FindMarkers(pbmc.integrated, ident.1 = "Monocyte_MS", ident.2 = "Monocyte_CONT",min.pct = 0.4, logfc.threshold = 0.25, verbose = FALSE)
# 3 cluster
memoryTcell.markers <- FindMarkers(pbmc.integrated, ident.1 = "Memory T Cell_MS", ident.2 = "Memory T Cell_CONT",min.pct = 0.4, logfc.threshold = 0.25, verbose = FALSE)
# 4
regulatoryTcell.markers <- FindMarkers(pbmc.integrated, ident.1 = "Regulatory T Cell_MS", ident.2 = "Regulatory T Cell_CONT",min.pct = 0.4, logfc.threshold = 0.25, verbose = FALSE)
# 5 cluster
transitionalBcell.markers <- FindMarkers(pbmc.integrated, ident.1 = "Transitional B Cell_MS", ident.2 = "Transitional B Cell_CONT",min.pct = 0.4, logfc.threshold = 0.25, verbose = FALSE)
# 6 cluster
plasmacytoiddendriticcell.markers <- FindMarkers(pbmc.integrated, ident.1 = "Plasmacytoid Dendritic Cell_MS", ident.2 = "Plasmacytoid Dendritic Cell_CONT",min.pct = 0.25, logfc.threshold = 0.25, verbose = FALSE)
# 7 cluster
basophil.markers <- FindMarkers(pbmc.integrated, ident.1 = "Basophil_MS", ident.2 = "Basophil_CONT",min.pct = 0.25, logfc.threshold = 0.25, verbose = FALSE)
# fiter de gene markers
# filter(mono.markers, p_val_adj <= 0.01, pct.1 >= 0.4, pct.2 >= 0.4, abs(avg_log2FC) > 3)
top_pos_mono.markers <- filter(mono.markers, p_val_adj <= 0.01, pct.1 >= 0.4, pct.2 >= 0.4, avg_log2FC > 2) %>% top_n(n = 30, wt = abs(avg_log2FC))
top_neg_mono.markers <- filter(mono.markers, p_val_adj <= 0.01, pct.1 >= 0.4, pct.2 >= 0.4, avg_log2FC < 0) %>% top_n(n = 10, wt = abs(avg_log2FC))
top_pos_memoryT.markers <- filter(memoryTcell.markers, p_val_adj <= 0.01, pct.1 >= 0.4, pct.2 >= 0.4, avg_log2FC > 0) %>% top_n(n = 30, wt = abs(avg_log2FC))
top_neg_memoryT.markers <- filter(memoryTcell.markers, p_val_adj <= 0.01, pct.1 >= 0.4, pct.2 >= 0.4, avg_log2FC < 0) %>% top_n(n = 10, wt = abs(avg_log2FC))
top_pos_naiveTcell.markers <- filter(naiveTcell.markers, p_val_adj <= 0.01, pct.1 >= 0.4, pct.2 >= 0.4, avg_log2FC > 0) %>% top_n(n = 30, wt = abs(avg_log2FC))
top_neg_naiveTcell.markers <- filter(naiveTcell.markers, p_val_adj <= 0.01, pct.1 >= 0.4, pct.2 >= 0.4, avg_log2FC < 0) %>% top_n(n = 10, wt = abs(avg_log2FC))
top_pos_plasm.markers <- filter(plasmacytoiddendriticcell.markers, p_val_adj <= 0.01, pct.1 >= 0.4, pct.2 >= 0.4, avg_log2FC > 0) %>% top_n(n = 30, wt = abs(avg_log2FC))
top_neg_plasm.markers <- filter(plasmacytoiddendriticcell.markers, p_val_adj <= 0.01, pct.1 >= 0.4, pct.2 >= 0.4, avg_log2FC < 0) %>% top_n(n = 10, wt = abs(avg_log2FC))
top_pos_reg.markers <- filter(regulatoryTcell.markers, p_val_adj <= 0.01, pct.1 >= 0.4, pct.2 >= 0.4, avg_log2FC > 0) %>% top_n(n = 30, wt = abs(avg_log2FC))
top_neg_reg.markers <- filter(regulatoryTcell.markers, p_val_adj <= 0.01, pct.1 >= 0.4, pct.2 >= 0.4, avg_log2FC < 0) %>% top_n(n = 10, wt = abs(avg_log2FC))
top_pos_transB.markers <- filter(transitionalBcell.markers, p_val_adj <= 0.01, pct.1 >= 0.4, pct.2 >= 0.4, avg_log2FC > 0) %>% top_n(n = 30, wt = abs(avg_log2FC))
top_neg_transB.markers <- filter(transitionalBcell.markers, p_val_adj <= 0.01, pct.1 >= 0.4, pct.2 >= 0.4, avg_log2FC < 0) %>% top_n(n = 10, wt = abs(avg_log2FC))
# plot variable features
markers.to.plot <- c(row.names(top_pos_plasm.markers), row.names(top_neg_plasm.markers))
# plots <- VlnPlot(pbmc.integrated, features = markers.to.plot, split.by = "stim", group.by = "celltype",
# pt.size = 0, combine = FALSE, split.plot = TRUE)
# CombinePlots(plots = plots, ncol = 1)
DotPlot(pbmc.integrated, features = markers.to.plot,
split.by = "stim", idents = "Plasmacytoid Dendritic Cell") + RotatedAxis()
# DotPlot(pbmc.integrated, features = markers.to.plot, cols = c("blue", "red"), dot.scale = 8,
# ) + RotatedAxis()
# library(presto)
#
# markers<- presto::wilcoxauc(pbmc.integrated, assay = 'data', group_by = "celltype.stim")
#
# markers<- filter(markers, padj <= 0.01, pct_in >= 0.4, pct_out >= 0.4, logFC > 0)
#
# all_markers<- markers %>%
# select(-rank) %>%
# unclass() %>%
# stack() %>%
# pull(values) %>%
# unique() %>%
# .[!is.na(.)]
# DoHeatmap(pbmc, features = all_markers) + NoLegend()
#
# mat<- pbmc.integrated[["RNA"]]@data[all_markers, ] %>% as.matrix()
# GSEA analisys##############
pos_memoryT.markers <- filter(memoryTcell.markers, p_val_adj <= 0.01, pct.1 >= 0.4, pct.2 >= 0.4, avg_log2FC > 1)
ranks <- pos_memoryT.markers$avg_log2FC
namrangs <- row.names(pos_memoryT.markers)
write.csv(namrangs, row.names = FALSE)
library(msigdbr)
m_df<- msigdbr(species = "Homo sapiens", category = "C7")
head(m_df)
fgsea_sets<- m_df %>% split(x = .$gene_symbol, f = .$gs_name)
library(fgsea)
fgseaRes<- fgsea(fgsea_sets, stats = ranks, scoreType = "pos")
# only plot the top 20 pathways
ggplot(fgseaRes %>% filter(padj < 0.01) %>% head(n= 20), aes(reorder(pathway, NES), NES)) +
geom_col(aes(fill= NES < 7.5)) +
coord_flip() +
labs(x="Pathway", y="Normalized Enrichment Score",
title="Hallmark pathways NES from GSEA") +
theme_minimal()
#GO analisis
# BiocManager::install("geneLenDataBase")
library(goseq)
library(geneLenDataBase)
supportedOrganisms() %>% filter(str_detect(Genome, "hsa"))
library(clusterProfiler)
search_kegg_organism('hsa', by='kegg_code')
sigGenes <- shrinkLvV$Entrez[ shrinkLvV$FDR < 0.01 &
!is.na(shrinkLvV$FDR) &
abs(shrinkLvV$logFC) > 1 ]
sigGenes <- na.exclude(sigGenes)
kk <- enrichKEGG(gene = sigGenes, organism = 'hsa')
head(kk, n=10)