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Knit directory: bentsen-rausch-2019/

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Ignored files:
    Ignored:    .Rproj.user/
    Ignored:    analysis/figure/
    Ignored:    test_files/

Untracked files:
    Untracked:  analysis/figure_6.Rmd
    Untracked:  analysis/figure_7.Rmd
    Untracked:  analysis/supp1.Rmd
    Untracked:  code/sc_functions.R
    Untracked:  data/bulk/
    Untracked:  data/fgf_filtered_nuclei.RDS
    Untracked:  data/figures/
    Untracked:  data/filtglia.RDS
    Untracked:  data/glia/
    Untracked:  data/lps1.txt
    Untracked:  data/mcao1.txt
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    Untracked:  data/mcaod7.txt
    Untracked:  data/mouse_data/
    Untracked:  data/neur_astro_induce.xlsx
    Untracked:  data/neuron/
    Untracked:  data/synaptic_activity_induced.xlsx
    Untracked:  output/agrp_pcgenes.csv
    Untracked:  output/all_wc_markers.csv
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    Untracked:  output/bulk/
    Untracked:  output/fig.RData
    Untracked:  output/fig4_part2.RData
    Untracked:  output/glia/
    Untracked:  output/glial_markergenes.csv
    Untracked:  output/integrated_all_markergenes.csv
    Untracked:  output/integrated_neuronmarkers.csv
    Untracked:  output/neuron/
    Untracked:  wc_de.pdf

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    Modified:   analysis/13_olig_pseudotime.Rmd
    Modified:   analysis/15_tany_wgcna_pseudo.Rmd
    Modified:   analysis/9_wc_processing.Rmd
    Modified:   analysis/figure_1.Rmd
    Modified:   analysis/index.Rmd

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Load libraries

library(DESeq2)
Loading required package: S4Vectors
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':

    anyDuplicated, append, as.data.frame, basename, cbind,
    colMeans, colnames, colSums, dirname, do.call, duplicated,
    eval, evalq, Filter, Find, get, grep, grepl, intersect,
    is.unsorted, lapply, lengths, Map, mapply, match, mget, order,
    paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind,
    Reduce, rowMeans, rownames, rowSums, sapply, setdiff, sort,
    table, tapply, union, unique, unsplit, which, which.max,
    which.min

Attaching package: 'S4Vectors'
The following object is masked from 'package:base':

    expand.grid
Loading required package: IRanges
Loading required package: GenomicRanges
Loading required package: GenomeInfoDb
Loading required package: SummarizedExperiment
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.
Loading required package: DelayedArray
Loading required package: matrixStats

Attaching package: 'matrixStats'
The following objects are masked from 'package:Biobase':

    anyMissing, rowMedians
Loading required package: BiocParallel

Attaching package: 'DelayedArray'
The following objects are masked from 'package:matrixStats':

    colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges
The following objects are masked from 'package:base':

    aperm, apply
library(Seurat)

Attaching package: 'Seurat'
The following object is masked from 'package:SummarizedExperiment':

    Assays
library(future.apply)
Loading required package: future

Attaching package: 'future'
The following object is masked from 'package:SummarizedExperiment':

    values
The following object is masked from 'package:GenomicRanges':

    values
The following object is masked from 'package:IRanges':

    values
The following object is masked from 'package:S4Vectors':

    values
library(ggplot2)
library(ggpubr)
Loading required package: magrittr
library(RColorBrewer)
library(here)
here() starts at /nfsdata/projects/dylan/bentsen-rausch-2019
library(reshape2)
library(tidyverse)
── Attaching packages ────────────────────────────────── tidyverse 1.2.1 ──
✔ tibble  2.1.3          ✔ purrr   0.3.2     
✔ tidyr   0.8.3          ✔ dplyr   0.8.3     
✔ readr   1.3.1.9000     ✔ stringr 1.4.0     
✔ tibble  2.1.3          ✔ forcats 0.4.0     
── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::collapse()   masks IRanges::collapse()
✖ dplyr::combine()    masks Biobase::combine(), BiocGenerics::combine()
✖ dplyr::count()      masks matrixStats::count()
✖ dplyr::desc()       masks IRanges::desc()
✖ tidyr::expand()     masks S4Vectors::expand()
✖ tidyr::extract()    masks magrittr::extract()
✖ dplyr::filter()     masks stats::filter()
✖ dplyr::first()      masks S4Vectors::first()
✖ dplyr::lag()        masks stats::lag()
✖ ggplot2::Position() masks BiocGenerics::Position(), base::Position()
✖ purrr::reduce()     masks GenomicRanges::reduce(), IRanges::reduce()
✖ dplyr::rename()     masks S4Vectors::rename()
✖ purrr::set_names()  masks magrittr::set_names()
✖ purrr::simplify()   masks DelayedArray::simplify()
✖ dplyr::slice()      masks IRanges::slice()
library(cowplot)

********************************************************
Note: As of version 1.0.0, cowplot does not change the
  default ggplot2 theme anymore. To recover the previous
  behavior, execute:
  theme_set(theme_cowplot())
********************************************************

Attaching package: 'cowplot'
The following object is masked from 'package:ggpubr':

    get_legend
library(ggupset)
library(gProfileR)
library(here)
library(ggrepel)
library(ggsci)
plan(multiprocess, workers = 30)

Load updated functions

rundeseq <- function(pb) {
  future_lapply(pb, function(x) {
    tryCatch({
      trt <- ifelse(grepl("FGF", colnames(x)), yes = "F", no = "P")
      sample <- as.factor(sapply(strsplit(colnames(x), "_"), "[", 1))
      batch <- batch_df[match(sample, batch_df$samp), "batch"]
      meta <- data.frame(trt = trt, batch = factor(batch))
      dds <- DESeqDataSetFromMatrix(
        countData = x,
        colData = meta,
        design = ~ batch + trt
      )
      keep <- rowSums(counts(dds) >= 5) > 5
      dds <- dds[keep, ]
      dds <- DESeq(dds)
      res <- results(dds, contrast = c("trt", "F", "P"))
      return(list(dds, res))
    }, error = function(err) {
      print(err)
    })
  })
}

Load data

glia_sub <- readRDS(here("data/filtglia.RDS"))

Generate Pseudo Counts

batch_df <- data.frame(
  samp = c(7, 12, 29, 28, 4, 27, 37, 22, 6, 30, 20, 21, 35, 10, 3, 25, 36, 34),
  batch = rep(1:6, each = 3)
)
split_mats <- splitbysamp(glia_sub, split_by = "orig.ident")
names(split_mats) <- unique(Idents(glia_sub))
test <- replicate(100, gen_pseudo_counts(split_mats, ncells = 30))
names(test) <- paste0(rep(names(split_mats)), "_", rep(1:100, each = length(names(split_mats))))
res <- rundeseq(test)
Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
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Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

Warning in lfproc(x, y, weights = weights, cens = cens, base = base, geth =
geth, : Estimated rdf < 1.0; not estimating variance

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(glia_sub)), function(x) {
  y <- paste0("^", x)
  z <- unlist(degenes[grep(y, names(degenes))])
})

names(boxplot) <- unique(Idents(glia_sub))
genenum <- melt(boxplot)
colnames(genenum) <- c("number", "CellType")
write_csv(genenum, here("output/glia/wc_resamplingresults.csv"))
dge_re_wc <- ggplot(genenum, aes(x = reorder(CellType, -number), y = number, fill = CellType)) +
  geom_boxplot(notch = T) + theme_pubr() +
  theme(
    legend.position = "none", axis.text.x = element_text(angle = 45, hjust = 1)
  ) + 
  xlab(NULL) + ylab("Number DEG") + scale_fill_jco() + theme_figure
dge_re_wc
Warning: This manual palette can handle a maximum of 10 values. You have
supplied 13.

Version Author Date
3b5cbe7 Full Name 2019-10-28

Generate Pseudo Counts

split_mats <- lapply(unique(Idents(glia_sub)), function(x) {
  sub <- subset(glia_sub, idents = x)
  DefaultAssay(sub) <- "SCT"
  list_sub <- SplitObject(sub, split.by = "orig.ident")
  return(list_sub)
})
names(split_mats) <- unique(Idents(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")
    sample <- as.factor(sapply(strsplit(colnames(x), "_"), "[", 1))
    batch <- batch_df[match(sample, batch_df$samp), "batch"]
    meta <- data.frame(trt = trt, batch = factor(batch))
    dds <- DESeqDataSetFromMatrix(
      countData = x,
      colData = meta,
      design = ~ batch + trt
    )
    keep <- rowSums(counts(dds) >= 5) > 5
    dds <- dds[keep, ]
    dds <- DESeq(dds)
    res <- results(dds, contrast = c("trt", "F", "P"))
    return(list(dds, res))
  }, error = function(err) {
    print(err)
  })
})

Generate Volcano Plots

volc_list <- lapply(dds_list, function(x) {
  x[[2]] %>%
    na.omit() %>%
    data.frame() %>%
    add_rownames("gene") %>%
    mutate(siglog = ifelse(padj < 0.05 & abs(log2FoldChange) > .5, yes = T, no = F)) %>%
    mutate(onlysig = ifelse(padj < 0.05 & abs(log2FoldChange) < .5, yes = T, no = F)) %>%
    mutate(onlylog = ifelse(padj > 0.05 & abs(log2FoldChange) > .5, 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 = case_when(
      min(padj) > 0.05 ~ "",
      min_rank(padj) <= 10 ~ gene,
      TRUE ~ NA_character_
    )) %>%
    dplyr::select(gene, log2FoldChange, padj, col, label)
})

mapply(x = volc_list, y = names(volc_list), function(x, y) {
  write_csv(x, path = sprintf(here("output/glia/wc_%s_pseudobulk_dge.csv"), y))
})
               Peri          Astro           Tany           Endo          
gene           Character,970 Character,10979 Character,9879 Character,3122
log2FoldChange Numeric,970   Numeric,10979   Numeric,9879   Numeric,3122  
padj           Numeric,970   Numeric,10979   Numeric,9879   Numeric,3122  
col            Character,970 Character,10979 Character,9879 Character,3122
label          Character,970 Character,10979 Character,9879 Character,3122
               OPC            Olig           Epend          COP          
gene           Character,9158 Character,6372 Character,6047 Character,548
log2FoldChange Numeric,9158   Numeric,6372   Numeric,6047   Numeric,548  
padj           Numeric,9158   Numeric,6372   Numeric,6047   Numeric,548  
col            Character,9158 Character,6372 Character,6047 Character,548
label          Character,9158 Character,6372 Character,6047 Character,548
               Micro         VLMC           Macro         SMC          
gene           Character,897 Character,1497 Character,872 Character,237
log2FoldChange Numeric,897   Numeric,1497   Numeric,872   Numeric,237  
padj           Numeric,897   Numeric,1497   Numeric,872   Numeric,237  
col            Character,897 Character,1497 Character,872 Character,237
label          Character,897 Character,1497 Character,872 Character,237
               ABC         
gene           Character,22
log2FoldChange Numeric,22  
padj           Numeric,22  
col            Character,22
label          Character,22
volc_list <- volc_list[as.logical(unlist(lapply(volc_list, function(x) !min(x$padj > 0.05))))]
plotlist <- mapply(x = volc_list, y = names(volc_list), function(x, y) {
  tryCatch({
    ggplot(x, aes(y = (-log10(padj)), x = log2FoldChange, fill = factor(col), label = label)) +
      xlab(expression(bold(Log[2]~Fold~Change))) + ylab(expression(bold(-log[10]~pvalue))) +
      geom_point(shape = 21, size = 3, alpha = 0.75) + geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
      geom_vline(xintercept = c(-.5, .5), linetype = "dashed") + geom_text_repel(bg.colour="white", fontface="bold", size=3) + theme_pubr() + 
      theme(legend.position = "none")  + 
      scale_fill_manual(values = c("1" = "red", "2" = "blue", "3" = "darkgreen", "4" = "grey")) + theme_figure
  },
  error = function(err) {
    print(err)
  }
  )
}, SIMPLIFY = F)

astro_volc <- plot_grid(plotlist[[1]]) 
plot_grid(dge_re_wc, astro_volc)

Version Author Date
3b5cbe7 Full Name 2019-10-28 
ggsave("wc_de.pdf", w = 20, h = 20)

Arrange Figure

mid <- plot_grid(dge_re_wc, astro_volc, labels = c("c","d"), scale=0.9, nrow=1)
Warning: This manual palette can handle a maximum of 10 values. You have
supplied 13.
mid

Version Author Date
3b5cbe7 Full Name 2019-10-28 
save(mid, file = here("data/figures/fig4/fig4_wc_deg.RData"))

Overlap across celltypes

res_glia <- 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.

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, .id = "id")
resglia %>%
  group_by(gene) %>%
  summarize(Celltype = list(id)) -> resglia

ggplot(resglia, aes(x = Celltype)) +
  geom_bar() + theme_pubr() +
  scale_x_upset(n_intersections = 10)
Warning: Removed 98 rows containing non-finite values (stat_count).

Version Author Date
f4dd96b Full Name 2019-10-29
3b5cbe7 Full Name 2019-10-28

GO terms up

res_up <- lapply(dds_list, function(x) {
  data.frame(x[[2]]) %>%
    add_rownames("gene") %>%
    na.omit(x) %>%
    filter(padj < 0.05) %>%
    filter(log2FoldChange > .5) %>%
    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.

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.
goup <- lapply(names(dds_list), function(x) {
  gprofiler(res_up[[x]]$gene,
    organism = "mmusculus", significant = T, custom_bg = rownames(dds_list[[x]][[1]]),
    src_filter = c("GO:BP", "GO:MF", "REAC", "KEGG"),
    hier_filtering = "strong",
    min_isect_size = 3,
    sort_by_structure = T, exclude_iea = T,
    min_set_size = 10, max_set_size = 300, correction_method = "fdr"
  ) %>% arrange(p.value)
})

names(goup) <- names(dds_list)

bind_rows(goup, .id = "id") %>%
  group_by(id) %>%
  top_n(5, -p.value) %>%
  ggplot(aes(x = str_wrap(term.name, 30), y = -log10(p.value), fill = domain)) + geom_col() + 
  facet_wrap(. ~ id, scales = "free_y") +
  coord_flip() + theme_pubr()

Version Author Date
f4dd96b Full Name 2019-10-29
3b5cbe7 Full Name 2019-10-28 
mapply(x = goup, y = names(goup), function(x, y) {
  write_csv(x, path = sprintf(here("output/glia/wc_up_goterm_%s.csv"), y))
})
                Peri        Astro        Tany        Endo       
query.number    Logical,0   Integer,50   Logical,0   Integer,2  
significant     Logical,0   Logical,50   Logical,0   Logical,2  
p.value         Logical,0   Numeric,50   Logical,0   Numeric,2  
term.size       Logical,0   Integer,50   Logical,0   Integer,2  
query.size      Logical,0   Integer,50   Logical,0   Integer,2  
overlap.size    Logical,0   Integer,50   Logical,0   Integer,2  
precision       Logical,0   Numeric,50   Logical,0   Numeric,2  
recall          Logical,0   Numeric,50   Logical,0   Numeric,2  
term.id         Logical,0   Character,50 Logical,0   Character,2
domain          Logical,0   Character,50 Logical,0   Character,2
subgraph.number Logical,0   Integer,50   Logical,0   Integer,2  
term.name       Character,0 Character,50 Character,0 Character,2
relative.depth  Logical,0   Integer,50   Logical,0   Integer,2  
intersection    Logical,0   Character,50 Logical,0   Character,2
                OPC          Olig        Epend       COP         
query.number    Integer,37   Integer,4   Integer,5   Integer,42  
significant     Logical,37   Logical,4   Logical,5   Logical,42  
p.value         Numeric,37   Numeric,4   Numeric,5   Numeric,42  
term.size       Integer,37   Integer,4   Integer,5   Integer,42  
query.size      Integer,37   Integer,4   Integer,5   Integer,42  
overlap.size    Integer,37   Integer,4   Integer,5   Integer,42  
precision       Numeric,37   Numeric,4   Numeric,5   Numeric,42  
recall          Numeric,37   Numeric,4   Numeric,5   Numeric,42  
term.id         Character,37 Character,4 Character,5 Character,42
domain          Character,37 Character,4 Character,5 Character,42
subgraph.number Integer,37   Integer,4   Integer,5   Integer,42  
term.name       Character,37 Character,4 Character,5 Character,42
relative.depth  Integer,37   Integer,4   Integer,5   Integer,42  
intersection    Character,37 Character,4 Character,5 Character,42
                Micro        VLMC        Macro       SMC        
query.number    Integer,12   Logical,0   Logical,0   Logical,0  
significant     Logical,12   Logical,0   Logical,0   Logical,0  
p.value         Numeric,12   Logical,0   Logical,0   Logical,0  
term.size       Integer,12   Logical,0   Logical,0   Logical,0  
query.size      Integer,12   Logical,0   Logical,0   Logical,0  
overlap.size    Integer,12   Logical,0   Logical,0   Logical,0  
precision       Numeric,12   Logical,0   Logical,0   Logical,0  
recall          Numeric,12   Logical,0   Logical,0   Logical,0  
term.id         Character,12 Logical,0   Logical,0   Logical,0  
domain          Character,12 Logical,0   Logical,0   Logical,0  
subgraph.number Integer,12   Logical,0   Logical,0   Logical,0  
term.name       Character,12 Character,0 Character,0 Character,0
relative.depth  Integer,12   Logical,0   Logical,0   Logical,0  
intersection    Character,12 Logical,0   Logical,0   Logical,0  
                ABC        
query.number    Logical,0  
significant     Logical,0  
p.value         Logical,0  
term.size       Logical,0  
query.size      Logical,0  
overlap.size    Logical,0  
precision       Logical,0  
recall          Logical,0  
term.id         Logical,0  
domain          Logical,0  
subgraph.number Logical,0  
term.name       Character,0
relative.depth  Logical,0  
intersection    Logical,0

GO terms down

res_down <- lapply(dds_list, function(x) {
  data.frame(x[[2]]) %>%
    add_rownames("gene") %>%
    na.omit(x) %>%
    filter(padj < 0.05) %>%
    filter(log2FoldChange < (-0.5)) %>%
    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.

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.
godown <- lapply(names(dds_list), function(x) {
  gprofiler(res_down[[x]]$gene,
    organism = "mmusculus", significant = T, custom_bg = rownames(dds_list[[x]][[1]]),
    src_filter = c("GO:BP", "GO:MF", "REAC", "KEGG"),
    hier_filtering = "strong",
    min_isect_size = 3,
    sort_by_structure = T, exclude_iea = T,
    min_set_size = 10, max_set_size = 300, correction_method = "fdr"
  ) %>% arrange(p.value)
})

names(godown) <- names(dds_list)
bind_rows(godown, .id = "id") %>%
  group_by(id) %>%
  top_n(5, -p.value) %>%
  ggplot(aes(x = str_wrap(term.name, 30), y = -log10(p.value), fill = domain)) + geom_col() + facet_wrap(. ~ id, scales = "free_y") +
  coord_flip() + theme_pubr()

Version Author Date
3b5cbe7 Full Name 2019-10-28 
mapply(x = godown, y = names(godown), function(x, y) {
  write_csv(x, path = sprintf(here("output/glia/wc_down_goterm_%s.csv"), y))
})
                Peri        Astro        Tany         Endo        
query.number    Logical,0   Integer,66   Integer,57   Integer,10  
significant     Logical,0   Logical,66   Logical,57   Logical,10  
p.value         Logical,0   Numeric,66   Numeric,57   Numeric,10  
term.size       Logical,0   Integer,66   Integer,57   Integer,10  
query.size      Logical,0   Integer,66   Integer,57   Integer,10  
overlap.size    Logical,0   Integer,66   Integer,57   Integer,10  
precision       Logical,0   Numeric,66   Numeric,57   Numeric,10  
recall          Logical,0   Numeric,66   Numeric,57   Numeric,10  
term.id         Logical,0   Character,66 Character,57 Character,10
domain          Logical,0   Character,66 Character,57 Character,10
subgraph.number Logical,0   Integer,66   Integer,57   Integer,10  
term.name       Character,0 Character,66 Character,57 Character,10
relative.depth  Logical,0   Integer,66   Integer,57   Integer,10  
intersection    Logical,0   Character,66 Character,57 Character,10
                OPC         Olig         Epend       COP        
query.number    Logical,0   Integer,37   Integer,7   Logical,0  
significant     Logical,0   Logical,37   Logical,7   Logical,0  
p.value         Logical,0   Numeric,37   Numeric,7   Logical,0  
term.size       Logical,0   Integer,37   Integer,7   Logical,0  
query.size      Logical,0   Integer,37   Integer,7   Logical,0  
overlap.size    Logical,0   Integer,37   Integer,7   Logical,0  
precision       Logical,0   Numeric,37   Numeric,7   Logical,0  
recall          Logical,0   Numeric,37   Numeric,7   Logical,0  
term.id         Logical,0   Character,37 Character,7 Logical,0  
domain          Logical,0   Character,37 Character,7 Logical,0  
subgraph.number Logical,0   Integer,37   Integer,7   Logical,0  
term.name       Character,0 Character,37 Character,7 Character,0
relative.depth  Logical,0   Integer,37   Integer,7   Logical,0  
intersection    Logical,0   Character,37 Character,7 Logical,0  
                Micro       VLMC        Macro       SMC        
query.number    Integer,4   Logical,0   Logical,0   Logical,0  
significant     Logical,4   Logical,0   Logical,0   Logical,0  
p.value         Numeric,4   Logical,0   Logical,0   Logical,0  
term.size       Integer,4   Logical,0   Logical,0   Logical,0  
query.size      Integer,4   Logical,0   Logical,0   Logical,0  
overlap.size    Integer,4   Logical,0   Logical,0   Logical,0  
precision       Numeric,4   Logical,0   Logical,0   Logical,0  
recall          Numeric,4   Logical,0   Logical,0   Logical,0  
term.id         Character,4 Logical,0   Logical,0   Logical,0  
domain          Character,4 Logical,0   Logical,0   Logical,0  
subgraph.number Integer,4   Logical,0   Logical,0   Logical,0  
term.name       Character,4 Character,0 Character,0 Character,0
relative.depth  Integer,4   Logical,0   Logical,0   Logical,0  
intersection    Character,4 Logical,0   Logical,0   Logical,0  
                ABC        
query.number    Logical,0  
significant     Logical,0  
p.value         Logical,0  
term.size       Logical,0  
query.size      Logical,0  
overlap.size    Logical,0  
precision       Logical,0  
recall          Logical,0  
term.id         Logical,0  
domain          Logical,0  
subgraph.number Logical,0  
term.name       Character,0
relative.depth  Logical,0  
intersection    Logical,0

Quantify Cell Numbers

colourCount <- length(unique(Idents(glia_sub)))
getPalette <- colorRampPalette(brewer.pal(9, "Set1"))

table(Idents(glia_sub), glia_sub$orig.ident) %>%
  prop.table(margin = 2) %>%
  as.data.frame.matrix() %>%
  rownames_to_column("celltype") %>%
  melt() %>%
  separate(variable, into = c(NA, "treat"), remove = F) %>%
  ggplot(aes(x = variable, y = value, fill = factor(celltype))) + geom_col() +
  facet_wrap(. ~ treat, scales = "free") + theme_pubr() +
  scale_fill_manual(values = getPalette(colourCount)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = "right")


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] ggsci_2.9                   ggrepel_0.8.0.9000         
 [3] gProfileR_0.6.7             ggupset_0.1.0.9000         
 [5] cowplot_1.0.0               forcats_0.4.0              
 [7] stringr_1.4.0               dplyr_0.8.3                
 [9] purrr_0.3.2                 readr_1.3.1.9000           
[11] tidyr_0.8.3                 tibble_2.1.3               
[13] tidyverse_1.2.1             reshape2_1.4.3             
[15] here_0.1                    RColorBrewer_1.1-2         
[17] ggpubr_0.2.1                magrittr_1.5               
[19] ggplot2_3.2.1               future.apply_1.3.0         
[21] future_1.14.0               Seurat_3.0.3.9036          
[23] DESeq2_1.22.2               SummarizedExperiment_1.12.0
[25] DelayedArray_0.8.0          BiocParallel_1.16.6        
[27] matrixStats_0.54.0          Biobase_2.42.0             
[29] GenomicRanges_1.34.0        GenomeInfoDb_1.18.2        
[31] IRanges_2.16.0              S4Vectors_0.20.1           
[33] BiocGenerics_0.28.0        

loaded via a namespace (and not attached):
  [1] readxl_1.3.1           backports_1.1.4        Hmisc_4.2-0           
  [4] workflowr_1.4.0        plyr_1.8.4             igraph_1.2.4.1        
  [7] lazyeval_0.2.2         splines_3.5.3          listenv_0.7.0         
 [10] digest_0.6.20          htmltools_0.3.6        gdata_2.18.0          
 [13] checkmate_1.9.4        memoise_1.1.0          cluster_2.1.0         
 [16] ROCR_1.0-7             globals_0.12.4         annotate_1.60.1       
 [19] modelr_0.1.4           RcppParallel_4.4.3     R.utils_2.9.0         
 [22] colorspace_1.4-1       rvest_0.3.4            blob_1.1.1            
 [25] haven_2.1.0            xfun_0.8               crayon_1.3.4          
 [28] RCurl_1.95-4.12        jsonlite_1.6           genefilter_1.64.0     
 [31] zeallot_0.1.0          survival_2.44-1.1      zoo_1.8-6             
 [34] ape_5.3                glue_1.3.1             gtable_0.3.0          
 [37] zlibbioc_1.28.0        XVector_0.22.0         leiden_0.3.1          
 [40] scales_1.0.0           DBI_1.0.0              bibtex_0.4.2          
 [43] Rcpp_1.0.2             metap_1.1              viridisLite_0.3.0     
 [46] xtable_1.8-4           htmlTable_1.13.1       reticulate_1.13       
 [49] foreign_0.8-71         bit_1.1-14             rsvd_1.0.2            
 [52] SDMTools_1.1-221.1     Formula_1.2-3          tsne_0.1-3            
 [55] htmlwidgets_1.3        httr_1.4.1             gplots_3.0.1.1        
 [58] acepack_1.4.1          ica_1.0-2              pkgconfig_2.0.2       
 [61] XML_3.98-1.20          R.methodsS3_1.7.1      nnet_7.3-12           
 [64] uwot_0.1.3             locfit_1.5-9.1         labeling_0.3          
 [67] tidyselect_0.2.5       rlang_0.4.0            AnnotationDbi_1.44.0  
 [70] cellranger_1.1.0       munsell_0.5.0          tools_3.5.3           
 [73] cli_1.1.0              generics_0.0.2         RSQLite_2.1.1         
 [76] broom_0.5.2            ggridges_0.5.1         evaluate_0.14         
 [79] yaml_2.2.0             npsurv_0.4-0           knitr_1.23            
 [82] bit64_0.9-7            fs_1.3.1               fitdistrplus_1.0-14   
 [85] caTools_1.17.1.2       RANN_2.6.1             pbapply_1.4-1         
 [88] nlme_3.1-140           whisker_0.3-2          R.oo_1.22.0           
 [91] xml2_1.2.0             compiler_3.5.3         rstudioapi_0.10       
 [94] plotly_4.9.0           png_0.1-7              ggsignif_0.5.0        
 [97] lsei_1.2-0             geneplotter_1.60.0     stringi_1.4.3         
[100] highr_0.8              lattice_0.20-38        Matrix_1.2-17         
[103] vctrs_0.2.0            pillar_1.4.2           Rdpack_0.11-0         
[106] lmtest_0.9-37          RcppAnnoy_0.0.12       data.table_1.12.2     
[109] bitops_1.0-6           irlba_2.3.3            gbRd_0.4-11           
[112] R6_2.4.0               latticeExtra_0.6-28    KernSmooth_2.23-15    
[115] gridExtra_2.3          codetools_0.2-16       MASS_7.3-51.4         
[118] gtools_3.8.1           assertthat_0.2.1       rprojroot_1.3-2       
[121] withr_2.1.2            sctransform_0.2.0      GenomeInfoDbData_1.2.0
[124] hms_0.5.0              grid_3.5.3             rpart_4.1-15          
[127] rmarkdown_1.13         Rtsne_0.15             git2r_0.25.2          
[130] lubridate_1.7.4        base64enc_0.1-3