library(Seurat)
library(SingleCellExperiment)
library(scuttle)
library(limma)
library(tidyverse)
library(muscat)
library(princurve)
library(ggplot2)
library(msigdbr)
library(fgsea)
library(GSEAtraining)
library(EnhancedVolcano)4 scRNA: Pseudobulk Differential Expression (Muscat Workflow)
4.1 Environment Setup
4.2 Loading in an Intergrated Object
# Replace the path with working directory
merged <- readRDS("/jsimonlab/users/bshim/Shuntaro_scRNA_TCR/analysis_052124/gated_altered_threshold_1_integrated.rds")4.3 Sample-level Analysis: Pseudobulk Methods with Muscat
merged[['RNA']] <- JoinLayers(merged[['RNA']])
DefaultAssay(merged) <- "RNA"
dim(merged)[1] 61578 68740# Using Raw Counts
merged.sce <- as.SingleCellExperiment(merged,assay="RNA")
# Compute log2 CPM consistent
merged.sce <- computeLibraryFactors(merged.sce)
merged.sce <- logNormCounts(merged.sce)
(mergedCondition.sce <- prepSCE(merged.sce,
kid = "RNA_snn_res.0.3",
gid = "Condition",
sid = "Sample",
drop=T))class: SingleCellExperiment
dim: 61578 68740
metadata(1): experiment_info
assays(2): counts logcounts
rownames(61578): 5-8S-rRNA 5S-rRNA ... ZZEF1 ZZZ3
rowData names(0):
colnames(68740): Pool142_1_R_TTGGCAAGTCGGGTCT
Pool142_1_R_AAGCCGCGTCCCTTGT ... Pool144_3_NR_AACTCTTTCGCATGGC
Pool144_3_NR_CCCATACGTACGACCC
colData names(3): cluster_id sample_id group_id
reducedDimNames(4): PCA UMAP.UNINTEGRATED INTEGRATED.HARMONY
UMAP.HARMONY
mainExpName: RNA
altExpNames(0):pb <- aggregateData(mergedCondition.sce,
assay = "counts",fun="sum",
by = c("cluster_id", "sample_id"))
# one sheet per subpopulation
assayNames(pb)[1] "0" "1" "2" "3" "4" "5"# pseudobulks for 1st subpopulation
t(head(assay(pb))) 5-8S-rRNA 5S-rRNA 7SK A1BG A1BG-AS1 A1CF
Pool142_1_R 0 0 384.9131 97.8000 24.0 0.00000000
Pool142_2_NR 0 0 616.2380 174.1250 57.5 0.00000000
Pool142_3_NR 0 0 485.1923 81.2250 37.0 0.00000000
Pool142_4_NR 0 0 377.6047 102.7000 44.0 0.00000000
Pool142_5_R 0 0 1445.6681 299.9457 105.5 0.00000000
Pool142_6_R 0 0 1491.8766 247.2000 69.0 0.01012797
Pool142_7_NR 0 0 2347.5901 226.9000 56.0 0.00000000
Pool144_1_R 0 0 491.6196 252.0000 61.5 0.00000000
Pool144_2_NR 0 0 614.5267 219.9000 68.0 0.00000000
Pool144_3_NR 0 0 136.0976 22.0000 6.0 0.000000004.4 Constructing Design Matrix and Running DESeq based Pseudobulk Differential Expression
set.seed(61217)
ei <- metadata(mergedCondition.sce)$experiment_info
# Meta Data
batch = factor(c("A", "A", "A", "A", "A", "A", "A", "B", "B", "B"))
Sex = factor(c("Male", "Female", "Female", "Male", "Male", "Male", "Female", "Female", "Female", "Female"))
mm <- model.matrix(~0 + ei$group_id + Sex)
dim_name <- levels(ei$group_id)
dim_name[3] <- "Sex"
dimnames(mm) <- list(ei$sample_id, dim_name)
contrast <- makeContrasts("R-NR", levels = mm)
res <- pbDS(pb, design = mm, contrast = contrast, method="DESeq2", min_cells = 50)
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|======================================================================| 100%tbl_all <- res$table[[1]]
sort_for_sig <- function(u) {
u <- dplyr::filter(u, p_adj.loc < 0.05, abs(logFC) > 1)
dplyr::arrange(u, p_adj.loc)
}
# filter FDR < 5%, abs(logFC) > 1 & sort by adj. p-value
tbl_fil_all <- lapply(tbl_all, sort_for_sig)
# nb. of DS genes & % of total by cluster
n_de <- vapply(tbl_fil_all, nrow, numeric(1))
p_de <- format(n_de / nrow(mergedCondition.sce) * 100, digits = 3)
data.frame("#DS" = n_de, "%DS" = p_de, check.names = FALSE) #DS %DS
0 2 0.00325
1 14 0.02274
2 3 0.00487
3 1 0.00162
4 70 0.11368
5 34 0.05521# view top 2 hits in each cluster
top2 <- bind_rows(lapply(tbl_fil_all
, top_n, 2, p_adj.loc))
format(top2[, -ncol(top2)], digits = 2) gene cluster_id baseMean logFC lfcSE stat p_val p_adj.loc
1 ERAP2 0 811 -1.7 0.35 -4.7 2.5e-06 0.01449
2 ENSG00000253853 0 36 2.8 0.63 4.5 6.3e-06 0.02180
3 TRBV10-3 1 320 1.3 0.32 4.1 3.4e-05 0.02368
4 SLC27A2 1 34 -1.7 0.43 -4.0 6.5e-05 0.04132
5 ERAP2 2 362 -1.7 0.31 -5.5 4.5e-08 0.00031
6 IFNGR2 2 481 -1.0 0.21 -4.8 1.9e-06 0.00579
7 SULT1A1 3 21 2.8 0.65 4.4 1.2e-05 0.03768
8 CCDC167 4 76 -1.3 0.42 -3.0 2.4e-03 0.04728
9 ROMO1 4 113 -1.3 0.42 -3.0 2.6e-03 0.04912
10 RRM2 5 609 -1.3 0.36 -3.5 3.9e-04 0.04320
11 RPL13P12 5 49 -20.9 5.95 -3.5 4.4e-04 0.04745
p_adj.glb
1 0.00567
2 0.01004
3 0.02850
4 0.04392
5 0.00028
6 0.00448
7 0.01568
8 0.28105
9 0.29274
10 0.11146
11 0.117664.5 Running Paired Pseudobulk Experiment
# List of all possible 2 Male and 2 Female Pairs
sample_pairs <- list(
c("Pool144_1_R", "Pool142_1_R", "Pool142_4_NR", "Pool142_2_NR"),
c("Pool144_1_R", "Pool142_5_R", "Pool142_4_NR", "Pool142_2_NR"),
c("Pool144_1_R", "Pool142_6_R", "Pool142_4_NR", "Pool142_2_NR"),
c("Pool144_1_R", "Pool142_1_R", "Pool142_4_NR", "Pool142_3_NR"),
c("Pool144_1_R", "Pool142_5_R", "Pool142_4_NR", "Pool142_3_NR"),
c("Pool144_1_R", "Pool142_6_R", "Pool142_4_NR", "Pool142_3_NR"),
c("Pool144_1_R", "Pool142_1_R", "Pool142_4_NR", "Pool142_7_NR"),
c("Pool144_1_R", "Pool142_5_R", "Pool142_4_NR", "Pool142_7_NR"),
c("Pool144_1_R", "Pool142_6_R", "Pool142_4_NR", "Pool142_7_NR"),
c("Pool144_1_R", "Pool142_1_R", "Pool142_4_NR", "Pool144_2_NR"),
c("Pool144_1_R", "Pool142_5_R", "Pool142_4_NR", "Pool144_2_NR"),
c("Pool144_1_R", "Pool142_6_R", "Pool142_4_NR", "Pool144_2_NR")
)
cluster_1_gene <- list()
cluster_4_gene <- list()
cluster_2_gene <- list()
cluster_3_gene <- list()
cluster_5_gene <- list()
cluster_0_gene <- list()
i=1
cluster_1_all <- list()
cluster_4_all <- list()
cluster_2_all <- list()
cluster_3_all <- list()
cluster_5_all <- list()
cluster_0_all <- list()
for (pair in sample_pairs) {
# subset only pair samples
subset_df <- subset(merged, Sample %in% pair)
subset_df[['RNA']] <- JoinLayers(subset_df[['RNA']])
merged.sce <- as.SingleCellExperiment(subset_df,assay="RNA")
# Compute log2 CPM consistent
merged.sce <- computeLibraryFactors(merged.sce)
merged.sce <- logNormCounts(merged.sce)
(mergedCondition.sce <- prepSCE(merged.sce,
kid = "RNA_snn_res.0.3",
gid = "Condition",
sid = "Sample",
drop=T))
pb <- aggregateData(mergedCondition.sce,
assay = "counts",fun="sum",
by = c("cluster_id", "sample_id"))
ei <- metadata(mergedCondition.sce)$experiment_info
mm <- model.matrix(~0 + ei$group_id)
dim_name <- levels(ei$group_id)
dimnames(mm) <- list(ei$sample_id, dim_name)
contrast <- makeContrasts("R-NR", levels = mm)
res <- pbDS(pb, design = mm, contrast = contrast, method="DESeq2", min_cells = 50)
# access results table for 1st comparison
tbl <- res$table[[1]]
# one data.frame per cluster
names(tbl)
tbl_fil <- lapply(tbl, function(u) {
u <- dplyr::filter(u, p_adj.loc < 0.05)
dplyr::arrange(u, p_adj.loc)
})
# Getting the statistically significant genes
cluster_1_gene[[i]] <- tbl_fil$'1'
cluster_4_gene[[i]] <- tbl_fil$'4'
cluster_2_gene[[i]] <- tbl_fil$'2'
cluster_3_gene[[i]] <- tbl_fil$'3'
cluster_5_gene[[i]] <- tbl_fil$'5'
cluster_0_gene[[i]] <- tbl_fil$'0'
i = i + 1
# outputting DE object with all genes
cluster_1_all[[i]] <- tbl$'1'
cluster_4_all[[i]] <- tbl$'4'
cluster_2_all[[i]] <- tbl$'2'
cluster_3_all[[i]] <- tbl$'3'
cluster_5_all[[i]] <- tbl$'5'
cluster_0_all[[i]] <- tbl$'0'
}
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# Paired analysis
# If Found in more than 2 significant pairs, i would consider that as significant
# In this case, I will take lowest logFC and highest padj
generate_de_objects <- function(x) {
# bind rows of DE objects from each pairs
temp_0_all <- bind_rows(x)
temp_0_all <- temp_0_all %>%
filter(!is.na(p_adj.loc)) %>%
group_by(gene) %>%
summarize(
logFC = if (sum(p_adj.loc < 0.05) >= 2) { # Check if there are 2 or more items with p_adj.loc < 0.05
# Get the index of the logFC with the minimum absolute value where p_adj.loc < 0.05
min_logFC_index <- which.min(abs(logFC[p_adj.loc < 0.05]))
logFC[p_adj.loc < 0.05][min_logFC_index]
} else {
# Get the minimum of logFC overall
sorted_logFC <- sort(logFC, na.last = TRUE) # Sort logFC values, ignoring NA
sorted_logFC[1]
},
# case of more than 2
p_adj.loc = if (sum(p_adj.loc < 0.05) >= 2) { # Check if there are 2 or more items with p_adj.loc < 0.05
max(p_adj.loc[p_adj.loc < 0.05], na.rm = TRUE) # Take the maximum p_adj.loc where p_adj.loc < 0.05
} else {
# Get the maximum of p_adj.loc overall
sorted_p_adj <- sort(p_adj.loc, decreasing = TRUE, na.last = TRUE) # Sort p_adj.loc values in descending order, ignoring NA
if (sorted_p_adj[1] < 0.05) {
# if the largest pvalue is not significant,
sorted_p_adj[1] = 1
}
sorted_p_adj[1] # Select the largest value
}
) %>%
ungroup()
temp_0_all <- drop_na(temp_0_all)
temp_0_all <- temp_0_all %>%
mutate(across(everything(), ~ ifelse(is.infinite(.), 1, .)))
return(temp_0_all)
}4.7 Function to Combine DE Objects of Sample-Level / Pairwise Analysis
# We are combining the DE object from the paired and the conventional DE analysis with sex as covariate.
# We consider the conventional DE analysis as a priority over the paired analysis
# Within the paired DE object, if there is a padj and logFC from the conventional analysis, replace the logFC and padj with it
update_tbl <- function(x,num) {
temp <- tbl[[num]]
updated_tbl <- temp %>%
left_join(x %>% select(gene, logFC, p_adj.loc), by = "gene", suffix = c("", ".new")) %>%
mutate(
logFC = ifelse(!is.na(logFC.new), logFC.new, logFC), # Replace logFC if there's a match
p_adj.loc = ifelse(!is.na(p_adj.loc.new), p_adj.loc.new, p_adj.loc) # Replace p_adj.loc if there's a match
) %>%
select(-logFC.new, -p_adj.loc.new)
}4.8 Generating Final DE Objects
vis_c0 <- generate_de_objects(cluster_0_all)
vis_c0_sig <- vis_c0[vis_c0$p_adj.loc<0.05,]
vis_c1 <- generate_de_objects(cluster_1_all)
vis_c1_sig <- vis_c1[vis_c1$p_adj.loc<0.05,]
vis_c2 <- generate_de_objects(cluster_2_all)
vis_c2_sig <- vis_c2[vis_c2$p_adj.loc<0.05,]
vis_c3 <- generate_de_objects(cluster_3_all)
vis_c3_sig <- vis_c3[vis_c3$p_adj.loc<0.05,]
vis_c4 <- generate_de_objects(cluster_4_all)
vis_c4_sig <- vis_c4[vis_c4$p_adj.loc<0.05,]
# Conducted on pseudobulk de on all samples
# Lacking statistically significant outputs from the paired analysis but only in the conventional DE analysis
vis_c5 <- tbl_all$"5"
vis_c5 <- vis_c5 %>% filter(!is.na(p_adj.loc)) %>% select(gene,p_adj.loc, logFC)
vis_c0_up <- update_tbl(vis_c0_sig, 1)
vis_c0_up <- vis_c0_up %>% filter(!is.na(p_adj.loc)) %>% select(gene,p_adj.loc, logFC)
vis_c1_up <- update_tbl(vis_c1_sig, 2)
vis_c1_up <- vis_c1_up %>% filter(!is.na(p_adj.loc)) %>% select(gene,p_adj.loc, logFC)
vis_c2_up <- update_tbl(vis_c2_sig, 3)
vis_c2_up <- vis_c2_up %>% filter(!is.na(p_adj.loc)) %>% select(gene,p_adj.loc, logFC)
vis_c3_up <- update_tbl(vis_c3_sig, 4)
vis_c3_up <- vis_c3_up %>% filter(!is.na(p_adj.loc)) %>% select(gene,p_adj.loc, logFC)
vis_c4_up <- update_tbl(vis_c4_sig, 5)
vis_c4_up <- vis_c4_up %>% filter(!is.na(p_adj.loc)) %>% select(gene,p_adj.loc, logFC)4.9 Plotting Volcano Plots
plot_volcano <- function(x, num, subtitle, gene_list) {
x <- na.omit(x)
x$gene <- gsub("-", "_", x$gene)
title <- paste("Cluster", num, sep="")
temp <- x[x$gene %in% gene_list,]
gene_list <- c(temp[(temp$p_adj.loc < 0.05 & abs(temp$logFC) > 0.5),]$gene)
# Add gene_names to top genes
# top 5 by highest logFC
top_logFC <- x[order(-abs(x$logFC)), ][1:8, "gene"]
# top 5 by lowest adjusted p-value
top_padj <- x[order(x$p_adj.loc), ][1:8, "gene"]
# combine & ensure uniqueness
highlight_genes <- unique(c(gene_list, top_logFC, top_padj))
p1 <- EnhancedVolcano(x,
x = 'logFC',
y = 'p_adj.loc',
lab = x$gene,
selectLab = highlight_genes,
xlab = bquote(~Log[2]~ 'fold change'),
pCutoff = 0.05,
pointSize = 6.0,
labSize = 8.0,
title = title,
subtitle = subtitle,
FCcutoff = 0,
labCol = 'black',
labFace = 'italic',
col = c('black', 'pink', 'purple', 'red3'),
colAlpha = 4/5,
legendPosition = 'bottom',
legendLabSize = 14,
legendIconSize = 4.0,
drawConnectors = TRUE,
colConnectors = "black",
widthConnectors = 1.0,
arrowheads = FALSE, boxedLabels = TRUE,
box.padding=1.0,
titleLabSize = 24,
subtitleLabSize = 20,
axisLabSize = 18)
p1 + theme(
plot.title = element_text(size = 24, face = "bold"),
plot.subtitle = element_text(size = 20, face = "italic"),
axis.title = element_text(size = 18, face = "bold")
)
return(p1)
}4.10 Generating Volcano Plots For Each Cluster
all_vis <- bind_rows(
c0 = vis_c0_up,
c1 = vis_c1_up,
c2 = vis_c2_up,
c3 = vis_c3_up,
c4 = vis_c4_up,
c5 = vis_c5,
.id = "cluster"
)
#subset all_vis to vis_c0 to 5 _up
vis_c0_up <- all_vis[all_vis$cluster == "c0",]
vis_c1_up <- all_vis[all_vis$cluster == "c1",]
vis_c2_up <- all_vis[all_vis$cluster == "c2",]
vis_c3_up <- all_vis[all_vis$cluster == "c3",]
vis_c4_up <- all_vis[all_vis$cluster == "c4",]
vis_c5_up <- all_vis[all_vis$cluster == "c5",]
p1 <- plot_volcano(vis_c0_up,1,"Activated", c("ERAP2", "ARMC9", "ETV7", "HLA-K", "HDAC9", "TMEM204", "STAT1","IFNGR2","IRF1", "SOCS1", "SOCS3", "IFITM3"))
p2 <- plot_volcano(vis_c1_up,2, "Exhausted/Cytotoxic", c("IFITM3", "HDAC9", "ERAP2", "CPA5", "LMNA", "ITGB1", "FOXP1", "STAT1", "STAT1", "IFNGR2","IRF1", "SOCS1", "SOCS3", "IFITM3"))
p3 <- plot_volcano(vis_c2_up,3, "Naive", c("ERAP2", "TBC1D4", "DDIT4", "SOCS1", "IRF1", "STAT1", "STAT1", "IFNGR2","IRF1", "SOCS1", "SOCS3", "IFITM3"))
p4 <- plot_volcano(vis_c3_up,4, "MHC_Class_II",c("ERAP2", "HLA-K", "STAT1", "FOXP1", "TNFRSF4", "STAT1", "IFNGR2","IRF1", "SOCS1", "SOCS3", "IFITM3"))
p5 <- plot_volcano(vis_c4_up,5, "Effector", c("MIF", "TIMP1", "PPDPF", "IKZF3", "ETV7", "FOXP1", "STAT1", "STAT1", "IFNGR2","IRF1", "SOCS1", "SOCS3", "IFITM3"))
p6 <- plot_volcano(vis_c5_up,6, "Replicating", c("TNR", "UBE2C", "H3C2", "H1-5", "STAT1", "STAT1", "IFNGR2","IRF1", "SOCS1", "SOCS3", "IFITM3"))
p1
p2
p3
p4
p5
p6
4.11 Curating Gene Lists for Pathway Analysis
all_vis <- read.csv("/jsimonlab/users/bshim/Shuntaro_scRNA_TCR/analysis_052124/all_clusters_de.csv")
all_vis_split <- all_vis %>%
group_by(cluster) %>%
group_split()
all_vis_split_ranks <- lapply(all_vis_split,
FUN = function(x) { x %>%
dplyr::select(gene,logFC) %>%
dplyr::arrange(logFC) %>%
deframe()
}
)
names(all_vis_split_ranks) <- paste0("Cluster",
sapply(all_vis_split,
FUN = function(x) { x %>%
pull(cluster) %>%
unique()
}
)
)4.12 Buliding a Pathway Database for Pathway Analysis
c2 <- msigdbr::msigdbr(species="Homo sapiens",
category = "C2")Warning: The `category` argument of `msigdbr()` is deprecated as of msigdbr 10.0.0.
ℹ Please use the `collection` argument instead.immunesig <- msigdbr::msigdbr(species="Homo sapiens",
category = "C7",
subcategory = "IMMUNESIGDB")Warning: The `subcategory` argument of `msigdbr()` is deprecated as of msigdbr 10.0.0.
ℹ Please use the `subcollection` argument instead.go_pathway <- msigdbr::msigdbr(species="Homo sapiens",
category = "C5")
reactome <- msigdbr::msigdbr(
species = "Homo sapiens",
category = "C2",
subcategory = "CP:REACTOME"
)
hallmark <- msigdbr::msigdbr(species="Homo sapiens",
category = "H")
# Get updated KEGG MEDICUS collection from GSEAtraining package
km <- get_msigdb(version = "2024.1.Hs",
collection = "c2.cp.kegg_medicus",
gene_id_type = "symbols")
# Combine into named list
# pathwaysToTest <- bind_rows(c2,immunesig,hallmark)
pathwaysToTest <- bind_rows(c2, immunesig, hallmark)
pathwaysToTest_split <- pathwaysToTest %>%
dplyr::select(gs_name,gene_symbol) %>%
group_by(gs_name) %>%
group_split()
names(pathwaysToTest_split) <- sapply(pathwaysToTest_split,
FUN = function(x) { x %>%
pull(gs_name) %>%
unique()
}
)
pathwaysToTest_split_list <- sapply(pathwaysToTest_split,
simplify = FALSE,
USE.NAMES = TRUE,
FUN = function(x) { x %>%
pull(gene_symbol) %>%
unique()
}
)
# Add KEGG MEDICUS to the list
pathwaysToTest_split_list <- c(pathwaysToTest_split_list, km)4.13 Screening All Genes to Detect Pathways That are Statistically Significant
set.seed(123)
fgseaRes <- sapply(all_vis_split_ranks,
simplify = FALSE,
USE.NAMES = TRUE,
FUN = function(x) {
fgsea(pathways = pathwaysToTest_split_list,
stats = x,
minSize = 5, # Note this is the intersection of genes in the ranked list and genes in the pathway
maxSize = 1000,
nproc = 1
)
}
)Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.03% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
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|======================================================================| 100%Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.02% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
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|======================================================================| 100%Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.02% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
minSize, : There were 13 pathways for which P-values were not calculated
properly due to unbalanced (positive and negative) gene-level statistic values.
For such pathways pval, padj, NES, log2err are set to NA. You can try to
increase the value of the argument nPermSimple (for example set it nPermSimple
= 10000)
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|
|======================================================================| 100%Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
minSize, : For some of the pathways the P-values were likely overestimated. For
such pathways log2err is set to NA.Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
minSize, : There were 4 pathways for which P-values were not calculated
properly due to unbalanced (positive and negative) gene-level statistic values.
For such pathways pval, padj, NES, log2err are set to NA. You can try to
increase the value of the argument nPermSimple (for example set it nPermSimple
= 10000)
|
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|======================================================================| 100%Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
minSize, : For some of the pathways the P-values were likely overestimated. For
such pathways log2err is set to NA.Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
minSize, : For some pathways, in reality P-values are less than 1e-50. You can
set the `eps` argument to zero for better estimation.4.14 Screening pathways that are phenotype specific
fgsea_df <- bind_rows(fgseaRes, .id = "sub.cluster") %>%
as_tibble() %>%
mutate(leadingEdgeGenes = map_chr(leadingEdge, toString)) %>%
dplyr::select(-leadingEdge) %>%
dplyr::arrange(sub.cluster, padj,NES)
# pathways with specific NES signs.
get_top_sig <- function(df, cluster, nes_sign) {
df %>%
filter(sign(NES) == nes_sign,sub.cluster == cluster) %>%
arrange(padj)
}
Il12_TH1_Paths <- read.csv("/jsimonlab/users/bshim/Shuntaro_scRNA_TCR/publication_figure/gsea_filtered_IL12_TH1_IFNG.csv")
topSigPathways_c1_IL12 <- get_top_sig(Il12_TH1_Paths, "Clusterc1", -1)
topSigPathways_c4_IL12 <- get_top_sig(Il12_TH1_Paths, "Clusterc4", 1)4.15 Plotting top 10 pathways from each cluster
##NES negatives are upregulated in NR
##NES positives are upregulated in R
top_n <- 10 # change as needed
neg_pathways <- fgsea_df %>%
filter(NES < 0, padj < 0.05) %>%
group_by(pathway) %>%
filter(n_distinct(sub.cluster) > 0) %>%
ungroup() %>%
group_by(sub.cluster) %>%
arrange(padj) %>%
slice_head(n=top_n) %>%
ungroup()
pos_pathways <- fgsea_df %>%
filter(NES > 0, padj < 0.05) %>%
group_by(pathway) %>%
filter(n_distinct(sub.cluster) > 0) %>%
ungroup() %>%
group_by(sub.cluster) %>%
arrange(padj) %>%
slice_head(n=top_n) %>%
ungroup()
pathways_c1 <- neg_pathways$pathway %>% unique()
pathways_c4 <- pos_pathways$pathway %>% unique()
p_c2 <- fgsea_df %>%
as_tibble() %>%
mutate(
cluster = as.integer(gsub("^Clusterc", "", sub.cluster)),
cluster_label = paste0("Cluster", cluster + 1)
) %>%
mutate(logP = ifelse(padj < 0.05, -log10(padj), 0)) %>%
dplyr::filter(pathway %in% pathways_c1) %>%
ggplot(aes(x = cluster_label, y = pathway)) +
geom_point(aes(color = NES, size = logP, alpha = I(ifelse(logP < -log10(0.1), 0, 1)))) +
scale_size_continuous("-log10 adj p", range = c(3, 15), breaks = c(1.3, 5, 10, 20, 30)) +
scale_color_gradient2(low = "blue", mid = "white", high = "red", limits=c(-3,3), midpoint = 0, oob = scales::squish) +
theme_bw() +
scale_y_discrete(position = "right") +
ylab("") +
xlab("") +
theme(
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
strip.text.y.left = element_text(angle = 0),
axis.title = element_text(size = 40),
axis.text = element_text(size = 40),
plot.title = element_text(size = 40, face = "bold", hjust = 0.5),
legend.title = element_text(size = 40, face = "bold"),
legend.text = element_text(size = 40)
) +
guides(
color = guide_colorbar(
title = "NES\n(Responder ↑ | ↓ Non-responder)",
title.position = "top",
barwidth = 5,
barheight = 35,
title.theme = element_text(
size = 40, face = "bold",
margin = margin(b = 20)
),
label.theme = element_text(
size = 40,
margin = margin(t = 25),
lineheight = 15
)
)
)
p_c5 <- fgsea_df %>%
as_tibble() %>%
mutate(
cluster = as.integer(gsub("^Clusterc", "", sub.cluster)),
cluster_label = paste0("Cluster", cluster + 1)
) %>%
mutate(logP = ifelse(padj < 0.05, -log10(padj), 0)) %>%
dplyr::filter(pathway %in% pathways_c4) %>%
ggplot(aes(x = cluster_label, y = pathway)) +
geom_point(aes(color = NES, size = logP, alpha = I(ifelse(logP < -log10(0.1), 0, 1)))) +
scale_size_continuous("-log10 adj p", range = c(3, 15), breaks = c(1.3, 5, 10, 20, 30)) +
scale_color_gradient2(low = "blue", mid = "white", high = "red", limits=c(-3,3), midpoint = 0, oob = scales::squish) +
theme_bw() +
scale_y_discrete(position = "right") +
ylab("") +
xlab("") +
theme(
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
strip.text.y.left = element_text(angle = 0),
axis.title = element_text(size = 35),
axis.text = element_text(size = 35),
plot.title = element_text(size = 40, face = "bold", hjust = 0.5),
legend.title = element_text(size = 40, face = "bold"),
legend.text = element_text(size = 40)
) +
guides(
color = guide_colorbar(
title = "NES\n(Responder ↑ | ↓ Non-responder)",
title.position = "top",
barwidth = 5,
barheight = 35,
title.theme = element_text(
size = 40, face = "bold",
margin = margin(b = 20)
),
label.theme = element_text(
size = 40,
margin = margin(t = 25),
lineheight = 15
)
)
)
p_c2Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).
p_c5
4.16 Building IL12/TH1 Specific Pathway Lists
IL12 <- Il12_TH1_Paths %>%
as_tibble() %>%
mutate(
cluster = as.integer(gsub("^Clusterc", "", sub.cluster)),
cluster_label = paste0("Cluster", cluster + 1),
logP = ifelse(padj < 0.1, -log10(padj), 0)
) %>%
filter(pathway %in% topSigPathways_c1_IL12$pathway) %>%
{
# Grab example pathway name (to use for dummy rows) -> this is to fill in clusters with no significant IL12 / TH1 Pathways
ex_pathway <- .$pathway[1]
# Define all desired clusters
all_clusters <- paste0("Clusterc", 0:5)
# Find which clusters are missing
# Detect missing ones
missing_clusters <- setdiff(all_clusters, unique(.$sub.cluster))
# Build dummy rows for missing clusters
dummy_rows <- tibble(
sub.cluster = missing_clusters,
pathway = ex_pathway,
padj = 1,
NES = 0,
cluster = as.integer(gsub("^Clusterc", "", missing_clusters)),
cluster_label = paste0("Cluster", as.integer(gsub("^Clusterc", "", missing_clusters)) + 1),
logP = 0
)
# Combine original + dummy
bind_rows(., dummy_rows)
}4.17 Plotting IL12 / TH1 specific pathways
p <- IL12 %>%
ggplot(aes(x = cluster_label, y = pathway)) +
geom_point(aes(color = NES, size = logP, alpha = I(ifelse(logP < -log10(0.1), 0, 1)))) +
scale_size_continuous("-log10 adj p", range = c(3, 15), breaks = c(1.3, 5, 10, 20, 30)) +
scale_color_gradient2(low = "blue", mid = "white", high = "red", limits=c(-3,3), midpoint = 0, oob = scales::squish) +
theme_bw() +
scale_y_discrete(position = "right") +
ylab("") +
xlab("") +
theme(
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
strip.text.y.left = element_text(angle = 0),
axis.title = element_text(size = 55),
axis.text = element_text(size = 55),
plot.title = element_text(size = 55, face = "bold", hjust = 0.5),
legend.title = element_text(size = 60, face = "bold"),
legend.text = element_text(size = 60)
) +
guides(
color = guide_colorbar(
title = "NES\n(Responder ↑ | ↓ Non-responder)",
title.position = "top",
barwidth = 5,
barheight = 35,
title.theme = element_text(
size = 50, face = "bold",
margin = margin(b = 20)
),
label.theme = element_text(
size = 60,
margin = margin(t = 25),
lineheight = 15
)
)
)
p
sessionInfo()R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Rocky Linux 8.10 (Green Obsidian)
Matrix products: default
BLAS/LAPACK: /usr/lib64/libopenblasp-r0.3.15.so; LAPACK version 3.9.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: America/New_York
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] EnhancedVolcano_1.13.2 ggrepel_0.9.6
[3] GSEAtraining_3.20.0 fgsea_1.28.0
[5] msigdbr_10.0.2 princurve_2.1.6
[7] muscat_1.18.0 lubridate_1.9.4
[9] forcats_1.0.0 stringr_1.5.1
[11] dplyr_1.1.4 purrr_1.0.2
[13] readr_2.1.5 tidyr_1.3.1
[15] tibble_3.2.1 ggplot2_3.5.1
[17] tidyverse_2.0.0 limma_3.58.1
[19] scuttle_1.12.0 SingleCellExperiment_1.24.0
[21] SummarizedExperiment_1.32.0 Biobase_2.62.0
[23] GenomicRanges_1.54.1 GenomeInfoDb_1.38.8
[25] IRanges_2.36.0 S4Vectors_0.40.2
[27] BiocGenerics_0.48.1 MatrixGenerics_1.14.0
[29] matrixStats_1.5.0 Seurat_5.1.0
[31] SeuratObject_5.0.2 sp_2.2-0
loaded via a namespace (and not attached):
[1] R.methodsS3_1.8.2 dichromat_2.0-0.1
[3] progress_1.2.3 goftest_1.2-3
[5] DT_0.33 Biostrings_2.72.1
[7] TH.data_1.1-3 vctrs_0.6.5
[9] spatstat.random_3.3-2 digest_0.6.37
[11] png_0.1-8 corpcor_1.6.10
[13] shape_1.4.6.1 deldir_2.0-4
[15] parallelly_1.43.0 MASS_7.3-60.0.1
[17] reshape2_1.4.4 httpuv_1.6.15
[19] foreach_1.5.2 withr_3.0.2
[21] xfun_0.52 survival_3.8-3
[23] memoise_2.0.1 ggbeeswarm_0.7.2
[25] emmeans_1.10.4 zoo_1.8-13
[27] GlobalOptions_0.1.2 gtools_3.9.5
[29] pbapply_1.7-2 R.oo_1.27.0
[31] prettyunits_1.2.0 KEGGREST_1.44.1
[33] promises_1.3.0 httr_1.4.7
[35] globals_0.16.3 fitdistrplus_1.2-2
[37] rstudioapi_0.17.1 miniUI_0.1.1.1
[39] generics_0.1.3 babelgene_22.9
[41] zlibbioc_1.48.2 ScaledMatrix_1.10.0
[43] polyclip_1.10-7 GenomeInfoDbData_1.2.11
[45] SparseArray_1.4.8 xtable_1.8-4
[47] doParallel_1.0.17 evaluate_1.0.3
[49] S4Arrays_1.4.1 hms_1.1.3
[51] irlba_2.3.5.1 colorspace_2.1-1
[53] ROCR_1.0-11 reticulate_1.31
[55] spatstat.data_3.1-2 magrittr_2.0.3
[57] lmtest_0.9-40 later_1.4.1
[59] viridis_0.6.5 lattice_0.22-7
[61] spatstat.geom_3.3-2 future.apply_1.11.3
[63] scattermore_1.2 cowplot_1.1.3.9000
[65] RcppAnnoy_0.0.22 pillar_1.10.1
[67] nlme_3.1-168 iterators_1.0.14
[69] SeuratWrappers_0.3.19 caTools_1.18.3
[71] compiler_4.3.2 beachmat_2.18.1
[73] RSpectra_0.16-2 stringi_1.8.7
[75] tensor_1.5 minqa_1.2.8
[77] plyr_1.8.9 crayon_1.5.3
[79] abind_1.4-8 scater_1.30.1
[81] blme_1.0-6 locfit_1.5-9.10
[83] bit_4.0.5 sandwich_3.1-1
[85] fastmatch_1.1-6 codetools_0.2-20
[87] multcomp_1.4-28 BiocSingular_1.18.0
[89] GetoptLong_1.0.5 plotly_4.10.4
[91] remaCor_0.0.18 mime_0.13
[93] splines_4.3.2 circlize_0.4.16
[95] Rcpp_1.1.0 fastDummies_1.7.4
[97] sparseMatrixStats_1.14.0 knitr_1.50
[99] blob_1.2.4 clue_0.3-66
[101] lme4_1.1-37 listenv_0.9.1
[103] DelayedMatrixStats_1.24.0 Rdpack_2.6.3
[105] estimability_1.5.1 Matrix_1.6-5
[107] statmod_1.5.0 tzdb_0.5.0
[109] fANCOVA_0.6-1 pkgconfig_2.0.3
[111] tools_4.3.2 cachem_1.0.8
[113] RhpcBLASctl_0.23-42 rbibutils_2.3
[115] RSQLite_2.3.7 viridisLite_0.4.2
[117] DBI_1.2.3 numDeriv_2016.8-1.1
[119] fastmap_1.2.0 rmarkdown_2.29
[121] scales_1.4.0 grid_4.3.2
[123] ica_1.0-3 broom_1.0.8
[125] patchwork_1.2.0 coda_0.19-4.1
[127] BiocManager_1.30.25 dotCall64_1.2
[129] RANN_2.6.2 farver_2.1.2
[131] reformulas_0.4.0 aod_1.3.3
[133] mgcv_1.9-3 msigdbdf_24.1.1
[135] cli_3.6.4 leiden_0.4.3.1
[137] lifecycle_1.0.4 uwot_0.2.3
[139] glmmTMB_1.1.9 mvtnorm_1.3-3
[141] backports_1.5.0 BiocParallel_1.36.0
[143] timechange_0.3.0 gtable_0.3.5
[145] rjson_0.2.23 ggridges_0.5.6
[147] progressr_0.14.0 parallel_4.3.2
[149] jsonlite_2.0.0 edgeR_4.0.16
[151] RcppHNSW_0.6.0 bitops_1.0-9
[153] bit64_4.0.5 assertthat_0.2.1
[155] Rtsne_0.17 spatstat.utils_3.1-1
[157] BiocNeighbors_1.20.2 spatstat.univar_3.0-1
[159] R.utils_2.13.0 pbkrtest_0.5.3
[161] lazyeval_0.2.2 shiny_1.9.1
[163] htmltools_0.5.8.1 sctransform_0.4.1
[165] glue_1.8.0 spam_2.11-1
[167] XVector_0.42.0 RCurl_1.98-1.17
[169] gridExtra_2.3 EnvStats_3.0.0
[171] boot_1.3-31 igraph_1.5.1
[173] variancePartition_1.34.0 TMB_1.9.17
[175] R6_2.6.1 DESeq2_1.42.1
[177] gplots_3.2.0 labeling_0.4.3
[179] cluster_2.1.8.1 nloptr_2.0.3
[181] DelayedArray_0.28.0 tidyselect_1.2.1
[183] vipor_0.4.7 AnnotationDbi_1.66.0
[185] future_1.34.0 rsvd_1.0.5
[187] KernSmooth_2.23-26 data.table_1.17.0
[189] htmlwidgets_1.6.4 ComplexHeatmap_2.18.0
[191] RColorBrewer_1.1-3 rlang_1.1.5
[193] spatstat.sparse_3.1-0 spatstat.explore_3.3-3
[195] lmerTest_3.1-3 remotes_2.5.0
[197] beeswarm_0.4.0