Last updated: 2019-10-29

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Knit directory: fgf_alldata/

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html 9cf1e45 Full Name 2019-10-28 Build site.

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

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

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,1478
plotlist<-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

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

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)
upset
Warning: Removed 13 rows containing non-finite values (stat_count).

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

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

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 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))

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

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          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