Glial DGE
Last updated: 2019-10-28
Checks: 6 1
Knit directory: fgf_alldata/
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Load Libraries
library(Seurat)
library(DESeq2)
library(future.apply)
library(cowplot)
library(tidyverse)
library(ggrepel)
library(reshape2)
library(ggpubr)
library(here)
library(wesanderson)
library(ggupset)
library(ggcorrplot)
library(gProfileR)
plan(multiprocess, workers=40)
options(future.globals.maxSize = 4000 * 1024^2)Functions
source(here("code/sc_functions.R"))Generate Glial Plots
fgf.glia.sub<-readRDS(here("data/glia/glia_seur_filtered.RDS"))Start Plotting
tsne_embed<-data.frame(Embeddings(fgf.glia.sub, reduction = "umap"))
fgf.glia.sub$group<-paste0(fgf.glia.sub$trt, "_", fgf.glia.sub$day)
tsne_embed$group<-fgf.glia.sub$group
tsne_embed$celltype<-Idents(fgf.glia.sub)
tsne_embed<-tsne_embed[sample(nrow(tsne_embed)),]
label.df <- data.frame(cluster=levels(tsne_embed$celltype),label=levels(tsne_embed$celltype))
label.df_2 <- tsne_embed %>%
dplyr::group_by(celltype) %>%
dplyr::summarize(x = median(UMAP_1), y = median(UMAP_2))
p1<-ggplot(tsne_embed, aes(x=UMAP_1, y=UMAP_2, colour=celltype)) +
geom_point(alpha=0.75, size=2) +
geom_label(data = label.df_2, aes(label = celltype, x=x, y=y), size=3, fontface="bold", inherit.aes = F, nudge_x = 1) +
theme_pubr() + theme(legend.position = "none") + ggsci::scale_color_igv()
p2<-ggplot(tsne_embed, aes(x=UMAP_1, y=UMAP_2, colour=group)) +
geom_point(alpha=.75, size=2) +
ggsci::scale_color_igv() +
theme_pubr(legend = "none")Get colors for matching
g <- ggplot_build(p1)
cols<-data.frame(colours = as.character(unique(g$data[[1]]$colour)),
label = as.character(unique(g$plot$data[, g$plot$labels$colour])))
colvec<-as.character(cols$colours)
names(colvec)<-as.character(cols$label)Generate Pseudo Counts
fgf.glia.sub<-ScaleData(fgf.glia.sub, verbose=F)
split_mats<-splitbysamp(fgf.glia.sub, split_by="sample")
names(split_mats)<-unique(Idents(fgf.glia.sub))
pb<-replicate(100, gen_pseudo_counts(split_mats, ncells=10))
names(pb)<-paste0(rep(names(split_mats)),rep(1:100, each=length(names(split_mats))))Generate DESeq2 Objects
res<-rundeseq(pb)Identify most responsive cell types
degenes<-lapply(res, function(x) {
tryCatch({
y<-x[[2]]
y<-na.omit(y)
data.frame(y)%>%filter(padj<0.1)%>%nrow()},
error=function(err) {NA})
})
boxplot<-lapply(unique(Idents(fgf.glia.sub)), function(x) {
y<-paste0("^",x)
z<-unlist(degenes[grep(y, names(degenes))])
})
names(boxplot)<-unique(Idents(fgf.glia.sub))
genenum<-melt(boxplot)
colnames(genenum)<-c("number","CellType")
genenum <- write_csv(genenum, path = here("output/glia/glia_resampling_output.csv"))
deplot_re <- ggplot(genenum, aes(x=reorder(CellType, -number), y=number, fill=CellType)) +
geom_boxplot(outlier.shape = NA, notch = T, alpha=1) + scale_fill_manual(values = colvec) + theme_pubr() +
theme(axis.text.x = element_text(angle=45, hjust=1), legend.position = "none") +
ylab("Differentially Expressed\n Genes") + xlab(NULL)
deplot_re
Generate Pseudo Counts
split_mats<-lapply(unique(Idents(fgf.glia.sub)), function(x){
sub<-subset(fgf.glia.sub, idents=x)
DefaultAssay(sub)<-"SCT"
list_sub<-SplitObject(sub, split.by="sample")
return(list_sub)
})
names(split_mats)<-unique(Idents(fgf.glia.sub))
pseudo_counts<-lapply(split_mats, function(x){
lapply(x, function(y) {
DefaultAssay(y) <- "SCT"
mat<-GetAssayData(y, slot="counts")
counts <- Matrix::rowSums(mat)
}) %>% do.call(rbind, .) %>% t() %>% as.data.frame()
})
names(pseudo_counts)<-names(split_mats)Generate DESeq2 Objects
dds_list<-lapply(pseudo_counts, function(x){
tryCatch({
trt<-ifelse(grepl("FGF", colnames(x)), yes="F", no="P")
number<-sapply(strsplit(colnames(x),"_"),"[",1)
day<-ifelse(as.numeric(as.character(number))>10, yes="5", no="1")
meta<-data.frame(trt=trt, day=factor(day))
dds <- DESeqDataSetFromMatrix(countData = x,
colData = meta,
design = ~ 0 + trt)
dds$group<-factor(paste0(dds$trt, "_", dds$day))
design(dds) <- ~ 0 + group
keep <- rowSums(counts(dds) >= 5) > 5
dds <- dds[keep,]
dds<-DESeq(dds)
res_5<-results(dds, contrast = c("group","F_5","P_5"))
res_1<-results(dds, contrast = c("group","F_1","P_1"))
f_5_1<-results(dds, contrast = c("group","F_5","F_1"))
p_5_1<-results(dds, contrast = c("group","P_5","P_1"))
return(list(dds, res_1, res_5,f_5_1, p_5_1))
}, error=function(err) {print(err)})
})Volcano Plot of DE genes
volc_list<-lapply(dds_list, function(x) {
x[[2]] %>% na.omit() %>% data.frame() %>% add_rownames("gene") %>%
mutate(siglog=ifelse(padj<0.05&abs(log2FoldChange)>1, yes=T, no=F)) %>%
mutate(onlysig=ifelse(padj<0.05&abs(log2FoldChange)<1, yes=T, no=F)) %>%
mutate(onlylog=ifelse(padj>0.05&abs(log2FoldChange)>1, yes=T, no=F)) %>%
mutate(col=ifelse(siglog==T, yes="1", no =
ifelse(onlysig==T, yes="2", no =
ifelse(onlylog==T, yes="3", no="4")))) %>%
arrange(padj) %>% mutate(label=ifelse(min_rank(padj) < 15, gene, "")) %>%
dplyr::select(gene, log2FoldChange, padj, col, label)
})Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.mapply(x=volc_list, y = names(volc_list), function(x,y) {
write_csv(x, path = here(sprintf("output/glia/%s_glia_pseudobulk_dge.csv", y)))
}) Astro Olig Micro COP
gene Character,8681 Character,9346 Character,6068 Character,2820
log2FoldChange Numeric,8681 Numeric,9346 Numeric,6068 Numeric,2820
padj Numeric,8681 Numeric,9346 Numeric,6068 Numeric,2820
col Character,8681 Character,9346 Character,6068 Character,2820
label Character,8681 Character,9346 Character,6068 Character,2820
Tany VLMC Endo Epend
gene Character,3731 Character,732 Character,956 Character,1478
log2FoldChange Numeric,3731 Numeric,732 Numeric,956 Numeric,1478
padj Numeric,3731 Numeric,732 Numeric,956 Numeric,1478
col Character,3731 Character,732 Character,956 Character,1478
label Character,3731 Character,732 Character,956 Character,1478plotlist<-mapply(x=volc_list[c("Astro","Tany","Epend", "VLMC")], y= c("Astro","Tany","Epend", "VLMC"), function(x,y){
ggplot(x, aes(y=(-log10(padj)), x=log2FoldChange, colour=factor(col), label=label)) +
xlab(expression(Log[2]*~Fold*~Change)) + ylab(expression(-Log[10]*~pvalue)) +
geom_point(size=3, alpha=0.75) + geom_hline(yintercept = -log10(0.05), linetype="dashed") +
geom_vline(xintercept = c(-1,1), linetype="dashed") + geom_text_repel(colour="black") + theme_pubr() +
theme(legend.position = "none", title = element_text(vjust=0.5)) +
scale_colour_manual(values = wes_palette("Royal1", 3, type="discrete")[c(2,1,3)]) + ggtitle(y)}, SIMPLIFY = FALSE)
devolc_plot <- plot_grid(plotlist=plotlist[c(1,2)], ncol=2)
devolc_plot
Overlap
res_glia_1<-lapply(dds_list, function(x) {
data.frame(x[[2]]) %>% add_rownames("gene") %>% na.omit(x) %>%
filter(padj<0.05) %>% arrange(padj) %>% select(gene) -> x
})Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.
Warning: Deprecated, use tibble::rownames_to_column() instead.resglia<-bind_rows(res_glia_1, .id="id")
resglia %>%
dplyr::group_by(gene) %>%
dplyr::summarize(Celltype = list(id)) -> resglia
upset <- ggplot(resglia, aes(x=Celltype)) +
geom_bar(fill=c(rep("black",3),"red","red","red", rep("black",4))) + theme_pubr() +
scale_x_upset(n_intersections = 10)
upsetWarning: Removed 13 rows containing non-finite values (stat_count).
top <- plot_grid(p1, deplot_re, labels=c("A","B"), scale=0.9)
bot <- plot_grid(devolc_plot, upset, axis="t", align="h", rel_widths = c(2,1), labels=c("C","D"))Warning: Removed 13 rows containing non-finite values (stat_count).fig <- plot_grid(top, bot, ncol=1, align="hv", axis="tblr", rel_heights = c(1,1.25,2.5))
fig
Correlation
library(ggcorrplot)
ranks<-lapply(dds_list, function(x) {
x<-data.frame(x[[2]])
x<-na.omit(x)
y <- (-log10(x$pvalue))*(x$log2FoldChange)
z <- rownames(x)
df<-data.frame(order=y,gene=z)
df<-df[order(-df$order),]
})
corframe<-Reduce(function(x, y) merge(x, y, all=T, by=c("gene")), ranks)Warning in merge.data.frame(x, y, all = T, by = c("gene")): column names
'order.x', 'order.y' are duplicated in the result
Warning in merge.data.frame(x, y, all = T, by = c("gene")): column names
'order.x', 'order.y' are duplicated in the resultWarning in merge.data.frame(x, y, all = T, by = c("gene")): column names
'order.x', 'order.y', 'order.x', 'order.y' are duplicated in the result
Warning in merge.data.frame(x, y, all = T, by = c("gene")): column names
'order.x', 'order.y', 'order.x', 'order.y' are duplicated in the resultWarning in merge.data.frame(x, y, all = T, by = c("gene")): column names
'order.x', 'order.y', 'order.x', 'order.y', 'order.x', 'order.y' are
duplicated in the resultcolnames(corframe)<-c("gene",names(ranks))
corframe<-corframe[,-1]
dim(corframe[complete.cases(corframe),])[1] 363 8plotcor <- cor(corframe, method = "spearman", use="complete.obs")
ggcorrplot(plotcor, hc.order = T, type="lower") +
ggsci::scale_fill_gsea(limit = c(0,1))
sessionInfo()R version 3.5.3 (2019-03-11)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Storage
Matrix products: default
BLAS/LAPACK: /usr/lib64/libopenblas-r0.3.3.so
locale:
[1] LC_CTYPE=en_DK.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_DK.UTF-8 LC_COLLATE=en_DK.UTF-8
[5] LC_MONETARY=en_DK.UTF-8 LC_MESSAGES=en_DK.UTF-8
[7] LC_PAPER=en_DK.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_DK.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] gProfileR_0.6.7 ggcorrplot_0.1.3
[3] ggupset_0.1.0.9000 wesanderson_0.3.6.9000
[5] here_0.1 ggpubr_0.2.1
[7] magrittr_1.5 reshape2_1.4.3
[9] ggrepel_0.8.1 forcats_0.4.0
[11] stringr_1.4.0 dplyr_0.8.3
[13] purrr_0.3.2 readr_1.3.1.9000
[15] tidyr_0.8.3 tibble_2.1.3
[17] ggplot2_3.2.1 tidyverse_1.2.1
[19] cowplot_1.0.0 future.apply_1.3.0
[21] future_1.14.0 DESeq2_1.22.2
[23] SummarizedExperiment_1.12.0 DelayedArray_0.8.0
[25] BiocParallel_1.16.6 matrixStats_0.54.0
[27] Biobase_2.42.0 GenomicRanges_1.34.0
[29] GenomeInfoDb_1.18.2 IRanges_2.16.0
[31] S4Vectors_0.20.1 BiocGenerics_0.28.0
[33] Seurat_3.0.3.9036
loaded via a namespace (and not attached):
[1] reticulate_1.13 R.utils_2.9.0 tidyselect_0.2.5
[4] RSQLite_2.1.1 AnnotationDbi_1.44.0 htmlwidgets_1.3
[7] grid_3.5.3 Rtsne_0.15 munsell_0.5.0
[10] codetools_0.2-16 ica_1.0-2 withr_2.1.2
[13] colorspace_1.4-1 highr_0.8 knitr_1.23
[16] rstudioapi_0.10 ROCR_1.0-7 ggsignif_0.5.0
[19] gbRd_0.4-11 listenv_0.7.0 Rdpack_0.11-0
[22] labeling_0.3 git2r_0.25.2 GenomeInfoDbData_1.2.0
[25] bit64_0.9-7 rprojroot_1.3-2 vctrs_0.2.0
[28] generics_0.0.2 xfun_0.8 R6_2.4.0
[31] rsvd_1.0.2 locfit_1.5-9.1 bitops_1.0-6
[34] assertthat_0.2.1 SDMTools_1.1-221.1 scales_1.0.0
[37] nnet_7.3-12 gtable_0.3.0 npsurv_0.4-0
[40] globals_0.12.4 workflowr_1.4.0 rlang_0.4.0
[43] zeallot_0.1.0 genefilter_1.64.0 splines_3.5.3
[46] lazyeval_0.2.2 acepack_1.4.1 broom_0.5.2
[49] checkmate_1.9.4 yaml_2.2.0 modelr_0.1.4
[52] backports_1.1.4 Hmisc_4.2-0 tools_3.5.3
[55] gplots_3.0.1.1 RColorBrewer_1.1-2 ggridges_0.5.1
[58] Rcpp_1.0.2 plyr_1.8.4 base64enc_0.1-3
[61] zlibbioc_1.28.0 RCurl_1.95-4.12 rpart_4.1-15
[64] pbapply_1.4-1 zoo_1.8-6 haven_2.1.0
[67] cluster_2.1.0 fs_1.3.1 data.table_1.12.2
[70] lmtest_0.9-37 RANN_2.6.1 fitdistrplus_1.0-14
[73] hms_0.5.0 lsei_1.2-0 evaluate_0.14
[76] xtable_1.8-4 XML_3.98-1.20 readxl_1.3.1
[79] gridExtra_2.3 compiler_3.5.3 KernSmooth_2.23-15
[82] crayon_1.3.4 R.oo_1.22.0 htmltools_0.3.6
[85] Formula_1.2-3 geneplotter_1.60.0 RcppParallel_4.4.3
[88] lubridate_1.7.4 DBI_1.0.0 MASS_7.3-51.4
[91] Matrix_1.2-17 cli_1.1.0 R.methodsS3_1.7.1
[94] gdata_2.18.0 metap_1.1 igraph_1.2.4.1
[97] pkgconfig_2.0.2 foreign_0.8-71 plotly_4.9.0
[100] xml2_1.2.0 annotate_1.60.1 XVector_0.22.0
[103] bibtex_0.4.2 rvest_0.3.4 digest_0.6.20
[106] sctransform_0.2.0 RcppAnnoy_0.0.12 tsne_0.1-3
[109] rmarkdown_1.13 cellranger_1.1.0 leiden_0.3.1
[112] htmlTable_1.13.1 uwot_0.1.3 gtools_3.8.1
[115] nlme_3.1-140 jsonlite_1.6 viridisLite_0.3.0
[118] pillar_1.4.2 ggsci_2.9 lattice_0.20-38
[121] httr_1.4.1 survival_2.44-1.1 glue_1.3.1
[124] png_0.1-7 bit_1.1-14 stringi_1.4.3
[127] blob_1.1.1 latticeExtra_0.6-28 caTools_1.17.1.2
[130] memoise_1.1.0 irlba_2.3.3 ape_5.3