Expression of TDP-HA cluster markers in ALS brain RNA-seq
Katharina Hembach
21.06.2021
Last updated: 2021-06-22
Checks: 7 0
Knit directory: neural_scRNAseq/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 8e552ba | khembach | 2021-06-22 | cluster 12 marker expression in brain RNA-seq (Prudencio et al.) |
Load packages
library(ggplot2)
library(dplyr)
library(readxl)
library(edgeR)
library(tidyr)metadat <- read_excel(file.path("data", "Prudencio_et_al",
"GSE67196_Petrucelli2015_ALS_genes.rawcount.xlsx"),
sheet = "metadata")
dat <- read_excel(file.path("data", "Prudencio_et_al",
"GSE67196_Petrucelli2015_ALS_genes.rawcount.xlsx"),
sheet = "GSE67196_Petrucelli2015_ALS_gen")What is the sum of counts per sample? How different are the samples?
colSums(dat[,-c(1,2)]) ALS001_fcx ALS002_fcx ALS006_fcx ALS007_fcx ALS011_fcx ALS012_fcx
26554860 41454722 42360282 23538490 32640786 37958110
ALS013_fcx ALS014_fcx ALS024_fcx ALS034_fcx ALS057_fcx ALS062_fcx
85167674 41173910 29136444 36695030 34223326 52580520
ALS063_fcx ALS070_fcx ALS071_fcx ALS072_fcx ALS073_fcx ALS074_fcx
56301898 33040420 41906874 47039564 19736212 32053046
ALS075_fcx ALS086_fcx ALS090_fcx ALS091_fcx ALS093_fcx ALS094_fcx
42280538 40875180 41202516 28834020 38194838 38893108
ALS095_fcx ALS097_fcx ALS100_fcx ALS001_cereb ALS002_cereb ALS006_cereb
33657802 49963292 36320214 59447166 69885944 25674652
ALS007_cereb ALS011_cereb ALS012_cereb ALS013_cereb ALS014_cereb ALS024_cereb
31319694 38067046 43907402 68151038 33665914 39854022
ALS034_cereb ALS057_cereb ALS062_cereb ALS063_cereb ALS070_cereb ALS071_cereb
56249184 63180188 53565152 62135672 42210478 49287260
ALS072_cereb ALS073_cereb ALS074_cereb ALS075_cereb ALS086_cereb ALS090_cereb
42509026 36439888 60039372 53721930 59513110 63361344
ALS091_cereb ALS093_cereb ALS094_cereb ALS095_cereb ALS097_cereb
60207348 63138550 56130312 40510656 62747986 ## generate DGE object
dge <- DGEList(counts = dat[,-c(1,2)], genes = dat[,c(1,2)], samples = metadat)
dge <- calcNormFactors(dge)
cpm <- cpm(dge)We want to compare the gene expression of the cluster 12 markers in controls and ALS patients.
markers <- list(up = c("NPTX2", "FGF18", "TDP43-HA", "PCED1A", "MEF2A", "DYNC2H1",
"APOE", "GADD45A", "BCAM", "DDIT3"),
down = c("VGF", "SCG2", "GAP43", "C4orf48", "HINT1", "LY6H",
"TUBA1A", "TMSB4X", "TUBB2B", "STMN2"))
lapply(markers, function(x) x[x %in% dge$genes$GeneID])$up
[1] "NPTX2" "FGF18" "MEF2A" "DYNC2H1" "APOE" "GADD45A" "BCAM"
[8] "DDIT3"
$down
[1] "VGF" "SCG2" "GAP43" "C4orf48" "HINT1" "LY6H" "TUBA1A"
[8] "TMSB4X" "TUBB2B" "STMN2" lapply(markers, function(x) x[!x %in% dge$genes$GeneID])$up
[1] "TDP43-HA" "PCED1A"
$down
character(0)ind <- lapply(markers, function(x) match(x, dge$genes$GeneID))
markers <- lapply(names(ind), function(x) markers[[x]][!is.na(ind[[x]])])
names(markers) <- names(ind)
ind <- lapply(ind, function(x) x[!is.na(x)])
## data frame with cpms, marker label (up/down), sample group and tissue
markers_cpm <- data.frame(gene = unlist(markers),
direction = c(rep(names(markers)[[1]], length(markers[[1]])),
rep(names(markers)[[2]], length(markers[[2]]))))
markers_cpm <- cbind(markers_cpm, cpm[unlist(ind),])Prepare data frame for plotting.
## long format and join metadata to add group and tissue info
markers_cpm_long <- markers_cpm %>%
tidyr::pivot_longer(cols = -c(1,2),
names_to = "sample_name", values_to = "cpm")
markers_cpm_long <- markers_cpm_long %>% dplyr::left_join(metadat) %>%
dplyr::mutate(group = factor(group, levels = c("control", "c9ALS", "sALS")))Marker gene expression
We plot the expression of the different markers in the experimental groups and tissues.
for (d in names(markers)){
for(g in markers[[d]]){
cat("## ", g, " - ", d, "\n")
p <- markers_cpm_long %>% dplyr::filter(gene == g) %>%
ggplot(aes(x = group, y = cpm)) +
geom_boxplot() +
geom_point(alpha = 0.6) +
facet_wrap(vars(tissue)) +
scale_y_log10() +
theme_bw() +
ggtitle(paste0(g, " - ", d))
print(p)
cat("\n\n")
}
}NPTX2 - up
FGF18 - up
MEF2A - up
DYNC2H1 - up
APOE - up
GADD45A - up
BCAM - up
DDIT3 - up
VGF - down
SCG2 - down
GAP43 - down
C4orf48 - down
HINT1 - down
LY6H - down
TUBA1A - down
TMSB4X - down
TUBB2B - down
STMN2 - down
sessionInfo()R version 4.0.5 (2021-03-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS
Matrix products: default
BLAS: /usr/local/R/R-4.0.5/lib/libRblas.so
LAPACK: /usr/local/R/R-4.0.5/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] tidyr_1.1.0 edgeR_3.30.3 limma_3.44.3 readxl_1.3.1
[5] dplyr_1.0.2 ggplot2_3.3.2 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 cellranger_1.1.0 pillar_1.4.6 compiler_4.0.5
[5] later_1.1.0.1 git2r_0.27.1 tools_4.0.5 digest_0.6.25
[9] lattice_0.20-41 evaluate_0.14 lifecycle_1.0.0 tibble_3.0.3
[13] gtable_0.3.0 pkgconfig_2.0.3 rlang_0.4.10 yaml_2.2.1
[17] xfun_0.15 withr_2.4.1 stringr_1.4.0 knitr_1.29
[21] generics_0.0.2 fs_1.5.0 vctrs_0.3.4 locfit_1.5-9.4
[25] rprojroot_1.3-2 grid_4.0.5 tidyselect_1.1.0 glue_1.4.2
[29] R6_2.4.1 rmarkdown_2.3 farver_2.0.3 purrr_0.3.4
[33] magrittr_1.5 whisker_0.4 codetools_0.2-16 backports_1.1.9
[37] scales_1.1.1 promises_1.1.1 ellipsis_0.3.1 htmltools_0.5.0
[41] colorspace_1.4-1 httpuv_1.5.4 stringi_1.4.6 munsell_0.5.0
[45] crayon_1.3.4