Last updated: 2019-07-26
Checks: 4 3
Knit directory: MAGL/
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---|---|---|
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File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | 08bf260 | HelenaLC | 2019-06-17 | update |
html | 08bf260 | HelenaLC | 2019-06-17 | update |
library(limma)
library(magrittr)
library(msigdbr)
library(dplyr)
library(reshape2)
library(SingleCellExperiment)
library(pheatmap)
res <- readRDS(file.path("output", "MAGL-DS_res.rds"))
m_df <- msigdbr(
species = "Mus musculus") %>%
dplyr::filter(gs_cat %in% c("H", "C5", "C7"))
dat <- lapply(res$data, function(u) {
ss <- strsplit(rownames(u), ".", fixed=TRUE)
u$genes <- data.frame(
ensembl_id = sapply(ss, .subset, 1),
symbol = sapply(ss, .subset, 2))
return(u)
})
Loading required package: edgeR
Attaching package: 'edgeR'
The following object is masked from 'package:SingleCellExperiment':
cpm
sapply(dat, function(u)
table(u$genes$symbol %in% m_df$gene_symbol))
Astrocytes Endothelial Microglia Oligodendrocytes OPC CPE cells
FALSE 1082 1085 1077 1081 1070 1078
TRUE 9960 9964 9919 9942 9934 9922
Excit. Neuron Inhib. Neuron
FALSE 1091 1090
TRUE 9972 9972
sets <- split(m_df$gene_symbol, m_df$gs_name)
n <- vapply(sets, length, numeric(1))
sets <- sets[n >= 20 & n <= 1000]
length(sets)
[1] 8978
camera
on ea. clustergs_dat <- mapply(function(uu, vv) {
inds <- ids2indices(sets, uu$genes$symbol, remove.empty = TRUE)
mm <- uu$design[colnames(uu),]
v <- voom(uu, mm)
f <- lmFit(v, mm)
f <- eBayes(f)
cf <- contrasts.fit(f, coefficients = 2)
cf <- eBayes(cf)
list(indices = inds, voom = v, design = mm,
cluster_id = vv, contrasts.fit = cf)
}, dat, names(dat), SIMPLIFY = FALSE)
gs_df <- lapply(gs_dat, function(u)
camera(u$voom, u$indices, u$design) %>%
rownames_to_column("geneset")) %>%
bind_rows(.id = "cluster_id")
cats <- gs_df %>%
dplyr::filter(FDR < 1e-20) %>%
pull(geneset) %>% unique
length(cats)
[1] 68
gs_df %>%
dplyr::filter(geneset %in% cats) %>%
dplyr::mutate(neg_log10_fdr = -log10(FDR)) %>%
acast(cluster_id ~ geneset, value.var = "neg_log10_fdr") %>%
set_colnames(gsub("/*([^_]*)_(.*)", "\\2", colnames(.))) %>%
set_colnames(strtrim(colnames(.), 30)) %>%
pheatmap(fontsize = 8, border_color = NA, color = colorRampPalette(
c("aliceblue", "cornflowerblue", "violet", "red"))(50))
Version | Author | Date |
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08bf260 | HelenaLC | 2019-06-17 |
cats_by_cluster <- gs_df %>%
group_by(cluster_id) %>%
top_n(10, dplyr::desc(FDR)) %>%
group_split %>%
lapply(pull, geneset) %>%
set_names(names(res$data))
lapply(names(res$data), function(k) {
cat("## ", k, "\n")
lapply(cats_by_cluster[[k]], function(c)
barcodeplot(
statistics = gs_dat[[k]]$contrasts.fit$t[, 1],
index = gs_dat[[k]]$indices[[c]],
quantiles=c(-1, 1) * qt(0.95, df = 14),
main = c, cex.main = 0.8))
cat("\n\n")
})
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[[1]] NULL
[[2]] NULL
[[3]] NULL
[[4]] NULL
[[5]] NULL
[[6]] NULL
[[7]] NULL
[[8]] NULL
saveRDS(gs_dat, file.path("output", "MAGL-geneset_data.rds"))
saveRDS(gs_df, file.path("output", "MAGL-geneset_df.rds"))
write.csv(dplyr::filter(gs_df, FDR < 0.05),
file.path("output", "MAGL-geneset_res.csv"),
quote = FALSE, row.names = FALSE)
sessionInfo()
R version 3.6.0 (2019-04-26)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.5 LTS
Matrix products: default
BLAS: /usr/local/R/R-3.6.0/lib/libRblas.so
LAPACK: /usr/local/R/R-3.6.0/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_CA.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_CA.UTF-8 LC_COLLATE=en_CA.UTF-8
[5] LC_MONETARY=en_CA.UTF-8 LC_MESSAGES=en_CA.UTF-8
[7] LC_PAPER=en_CA.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 parallel stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] edgeR_3.26.5 pheatmap_1.0.12
[3] SingleCellExperiment_1.6.0 SummarizedExperiment_1.14.0
[5] DelayedArray_0.10.0 BiocParallel_1.18.0
[7] matrixStats_0.54.0 Biobase_2.44.0
[9] GenomicRanges_1.36.0 GenomeInfoDb_1.20.0
[11] IRanges_2.18.1 S4Vectors_0.22.0
[13] BiocGenerics_0.30.0 reshape2_1.4.3
[15] msigdbr_6.2.1 tibble_2.1.3
[17] dplyr_0.8.3 magrittr_1.5
[19] limma_3.40.2
loaded via a namespace (and not attached):
[1] locfit_1.5-9.1 tidyselect_0.2.5 xfun_0.8
[4] purrr_0.3.2 lattice_0.20-38 colorspace_1.4-1
[7] htmltools_0.3.6 yaml_2.2.0 rlang_0.4.0
[10] pillar_1.4.2 glue_1.3.1 RColorBrewer_1.1-2
[13] GenomeInfoDbData_1.2.1 plyr_1.8.4 stringr_1.4.0
[16] zlibbioc_1.30.0 munsell_0.5.0 gtable_0.3.0
[19] workflowr_1.4.0 codetools_0.2-16 evaluate_0.14
[22] knitr_1.23 highr_0.8 Rcpp_1.0.1
[25] backports_1.1.4 scales_1.0.0 XVector_0.24.0
[28] fs_1.3.1 digest_0.6.20 stringi_1.4.3
[31] rprojroot_1.3-2 grid_3.6.0 tools_3.6.0
[34] bitops_1.0-6 RCurl_1.95-4.12 crayon_1.3.4
[37] whisker_0.3-2 pkgconfig_2.0.2 Matrix_1.2-17
[40] assertthat_0.2.1 rmarkdown_1.14 rstudioapi_0.10
[43] R6_2.4.0 git2r_0.26.1 compiler_3.6.0