Last updated: 2019-12-03

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Knit directory: bentsen-rausch-2019/

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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)

Generate Glial Plots

fgf.glia.sub<-readRDS(here("data/glia/glia_seur_filtered.RDS"))
fgf.glia.sub <- RenameIdents(fgf.glia.sub, "COP" = "OPC_COP")

fgf.glia.sub$group<-paste0(fgf.glia.sub$trt, "_", fgf.glia.sub$day)
data.frame(Embeddings(fgf.glia.sub, reduction = "umap")) %>% 
  mutate(group = fgf.glia.sub$group) %>%
  mutate(celltype = Idents(fgf.glia.sub)) %>%
  .[sample(nrow(.)),] %>%
  mutate(group = replace(group, group == "FGF_Day-5", "FGF_d5")) %>% 
  mutate(group = replace(group, group == "FGF_Day-1", "FGF_d1")) %>% 
  mutate(group = replace(group, group == "PF_Day-1", "Veh_d1")) %>%
  mutate(group = replace(group, group == "PF_Day-5", "Veh_d5")) -> umap_embed
colnames(umap_embed)[1:2] <- c("UMAP1", "UMAP2")

label.df <- data.frame(cluster=levels(umap_embed$celltype),label=levels(umap_embed$celltype))
label.df_2 <- umap_embed %>% 
  dplyr::group_by(celltype) %>% 
  dplyr::summarize(x = median(UMAP1), y = median(UMAP2))

p1<-ggplot(umap_embed, aes(x=UMAP1, y=UMAP2, colour=celltype)) + 
  geom_point(alpha=0.5, size=2)  + 
  geom_text_repel(data = label.df_2, aes(label = celltype, x=x, y=y), 
                  size=3, fontface="bold", inherit.aes = F, bg.colour="white")  +
  theme_pubr(legend="none") + ggsci::scale_color_igv() + theme_figure
p1

# Integration across timepoints and treatment

p2<-ggplot(umap_embed, aes(x=UMAP1, y=UMAP2, colour=group)) + 
  geom_point(alpha=.5, size=1) + 
  ggsci::scale_color_igv() + 
  theme_pubr(legend = "none") + theme_figure
p2

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(notch = T, alpha=1) + scale_fill_manual(values = colvec)  +
  ylab("Number DEG") + xlab(NULL)  + theme_pubr(legend="none") + theme_figure
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/nuclei/d1_%s_pseudobulk_dge.csv",y)))
})
               Astro          Olig           Micro          OPC_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,1478
plotlist<-mapply(x=volc_list, y=names(volc_list), 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)
plot_grid(plotlist = plotlist, ncol=3)

Version Author Date
9cf1e45 Full Name 2019-10-28 
ggsave(here("data/figures/supp/allvolplots.pdf"))
devolc_plot <- plot_grid(plotlist=plotlist[c("Astro","Tany")], ncol=2)

Go Term enrichment across cell-types

pos_genes <- lapply(dds_list[c("Tany","Astro","Epend")], function(x) {
  x[[2]] %>% na.omit() %>% data.frame() %>% add_rownames("gene") %>% 
    filter(padj<0.05, log2FoldChange>1) %>% pull("gene")
})
Warning: Deprecated, use tibble::rownames_to_column() instead.

Warning: Deprecated, use tibble::rownames_to_column() instead.

Warning: Deprecated, use tibble::rownames_to_column() instead.
neg_genes <- lapply(dds_list[c("Tany","Astro","Epend")], function(x) {
  x[[2]] %>% na.omit() %>% data.frame() %>% add_rownames("gene") %>% 
    filter(padj<0.05, log2FoldChange<(-1)) %>% pull("gene")
})
Warning: Deprecated, use tibble::rownames_to_column() instead.

Warning: Deprecated, use tibble::rownames_to_column() instead.

Warning: Deprecated, use tibble::rownames_to_column() instead.
pos_path <- lapply(pos_genes, function(x) {
  gprofiler(x, organism = "mmusculus", significant = T, 
                           src_filter = c("GO:BP","REAC", "KEGG"), hier_filtering = "strong",
                           min_isect_size = 3, 
                           sort_by_structure = T,exclude_iea = T, 
                           min_set_size = 10, max_set_size = 500,correction_method = "fdr") %>%
    arrange(p.value) 
})

neg_path <- lapply(neg_genes, function(x) {
  gprofiler(x, organism = "mmusculus", significant = T, 
                           src_filter = c("GO:BP","REAC", "KEGG"), hier_filtering = "strong",
                           min_isect_size = 3, 
                           sort_by_structure = T,exclude_iea = T, 
                           min_set_size = 10, max_set_size = 500,correction_method = "fdr") %>%
    arrange(p.value)
})

pos_go <- bind_rows(pos_path, .id="id")
write_csv(pos_go, here("output/glia/nucd1_pos_go.csv"))
neg_go <- bind_rows(neg_path, .id="id")
write_csv(neg_go, here("output/glia/nucd1_neg_go.csv"))

GO term plot of upregulated genes

pos_go %>% dplyr::group_by(id) %>% dplyr::slice(1:5) %>% dplyr::pull(term.id) -> go_id
pos_go %>% filter(term.id%in%go_id)  %>% 
  ggplot(aes(x=fct_relevel(id,"Tany","Epend","Astro"), y=str_to_title(str_wrap(term.name, 40), locale = "en"))) +
  geom_point(aes(size=(-log10(p.value)), fill=domain), shape=21,  alpha=0.5) +
  scale_size(name = expression(bold(-log[10]*pvalue)), range=c(3,8)) + 
  ggsci::scale_fill_npg(name = "Database", labels=c("GO:BP","KEGG","REAC")) + 
  xlab(NULL) + ylab(NULL) + theme_bw() + theme_figure +
  theme(axis.text.y = element_text(size=7, face="bold")) + 
  guides(fill = guide_legend(override.aes = list(size=4), title.theme = element_text(face="bold", size=8), 
                             label.theme = element_text(face="bold", size=8)),
         size =  guide_legend(title.theme = element_text(size=8), 
                              label.theme = element_text(face="bold", size=8)))  -> pos_go_plot
pos_go_plot

Version Author Date
9cf1e45 Full Name 2019-10-28

Go term plot of downregulated genes

neg_go %>% dplyr::group_by(id) %>% dplyr::slice(1:5) %>% ggplot(aes(x=fct_relevel(id,"Tany","Epend","Astro"), y=str_wrap(term.name, 30))) + 
  geom_point(aes(size=(-log10(p.value)), fill=domain), shape=21, alpha=0.75) +
  scale_size(name = expression(log[10]*pvalue), range=c(3,10)) + ggsci::scale_fill_npg(name = "Database") + xlab(NULL) + ylab(NULL) + 
  theme_figure + guides(fill = guide_legend(override.aes = list(size=6))) -> neg_go_plot
neg_go_plot
Warning: Unknown levels in `f`: Astro

Warning: Unknown levels in `f`: Astro

Version Author Date
9cf1e45 Full Name 2019-10-28

Overlap between cell-types

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),"#E64B35B2","#E64B35B2","#E64B35B2", rep("black",4))) + theme_pubr() +
    scale_x_upset(n_intersections = 10) + xlab(NULL) + ylab("Number DEG") + theme_figure
upset
Warning: Removed 13 rows containing non-finite values (stat_count).

Version Author Date
9cf1e45 Full Name 2019-10-28

Generate part of figure

top <- plot_grid(p1, deplot_re, labels=c("a","b"), scale=0.95, align="hv", axis="tb")
mid <- plot_grid(pos_go_plot, upset, axis="t", scale=0.95, align="hv", labels=c("c","d"), rel_widths = c(1,1))
Warning: Removed 13 rows containing non-finite values (stat_count).
fig3_top <- plot_grid(top, mid, ncol=1, align="hv", axis="tblr", rel_heights = c(1,1.1))
fig3_top

ggsave2(fig3_top, filename = here("data/figures/fig3/fig3_top.png"), h=7, w=12)
save(fig3_top, file = here("data/figures/fig3/fig3_top.RData"))

Correlation (Supplementary figure)

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 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 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 result
Warning 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 result
colnames(corframe)<-c("gene",names(ranks))
corframe<-corframe[,-1]
dim(corframe[complete.cases(corframe),])
[1] 363   8
plotcor <- 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_0.8.0.9000          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          labeling_0.3          
 [22] Rdpack_0.11-0          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             whisker_0.3-2         
 [73] fitdistrplus_1.0-14    hms_0.5.0              lsei_1.2-0            
 [76] evaluate_0.14          xtable_1.8-4           XML_3.98-1.20         
 [79] readxl_1.3.1           gridExtra_2.3          compiler_3.5.3        
 [82] KernSmooth_2.23-15     crayon_1.3.4           R.oo_1.22.0           
 [85] htmltools_0.3.6        Formula_1.2-3          geneplotter_1.60.0    
 [88] RcppParallel_4.4.3     lubridate_1.7.4        DBI_1.0.0             
 [91] MASS_7.3-51.4          Matrix_1.2-17          cli_1.1.0             
 [94] R.methodsS3_1.7.1      gdata_2.18.0           metap_1.1             
 [97] igraph_1.2.4.1         pkgconfig_2.0.2        foreign_0.8-71        
[100] plotly_4.9.0           xml2_1.2.0             annotate_1.60.1       
[103] XVector_0.22.0         bibtex_0.4.2           rvest_0.3.4           
[106] digest_0.6.20          sctransform_0.2.0      RcppAnnoy_0.0.12      
[109] tsne_0.1-3             rmarkdown_1.13         cellranger_1.1.0      
[112] leiden_0.3.1           htmlTable_1.13.1       uwot_0.1.3            
[115] gtools_3.8.1           nlme_3.1-140           jsonlite_1.6          
[118] viridisLite_0.3.0      pillar_1.4.2           ggsci_2.9             
[121] lattice_0.20-38        httr_1.4.1             survival_2.44-1.1     
[124] glue_1.3.1             png_0.1-7              bit_1.1-14            
[127] stringi_1.4.3          blob_1.1.1             latticeExtra_0.6-28   
[130] caTools_1.17.1.2       memoise_1.1.0          irlba_2.3.3           
[133] ape_5.3