Additional Enrichments for revisions
Briana Mittleman
12/13/2020
Last updated: 2020-12-13
Checks: 7 0
Knit directory: Comparative_APA/analysis/
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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/additionalGSEA.Rmd) and HTML (docs/additionalGSEA.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | e57e4b5 | brimittleman | 2020-12-13 | add analysis for FGSEA |
In this analysis I will look for gene set enrichments for categories requested by reviewers.
library(tidyverse)── Attaching packages ────────────────────────────────── tidyverse 1.3.0 ──✓ ggplot2 3.2.1 ✓ purrr 0.3.4
✓ tibble 2.1.3 ✓ dplyr 0.8.3
✓ tidyr 1.1.0 ✓ stringr 1.4.0
✓ readr 1.3.1 ✓ forcats 0.4.0── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()library(workflowr)This is workflowr version 1.6.2
Run ?workflowr for help getting startedlibrary(fgsea)Gene sets:
Hset=gmtPathways("../data/gsea/h.all.v7.1.symbols.gmt")
C3=gmtPathways("../data/gsea/c3.all.v7.1.symbols.gmt")
C5=gmtPathways("../data/gsea/c5.all.v7.1.symbols.gmt")DE genes withing dAPA
First I will look at the differential APA genes that are differentially expressed. I will ask the question if DE genes are enriched within dAPA genes.
#load DE genes
nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F)
DE= read.table("../data/DiffExpression/DEtested_allres.txt",header=F, stringsAsFactors = F,col.names = c('Gene_stable_ID', 'logFC' ,'AveExpr', 't', 'P.Value', 'adj.P.Val', 'B')) %>% inner_join(nameID, by="Gene_stable_ID") %>% dplyr::select(-Gene_stable_ID, -Source_of_gene_name) %>% rename("gene"=Gene.name)
#loaddAPA
dAPA=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% filter(SigPAU2=='Yes') %>% select(gene) %>% unique()
DEwithdAPA=DE %>% inner_join(dAPA, by="gene") %>% arrange(t)
DEwithdAPAVec= setNames(DEwithdAPA$t, DEwithdAPA$gene)Run FGSEA
FGSEA_Funtion=function(path,stat){
fgseaResDiffUsed <- fgsea(pathways = path,
stats = stat,
minSize = 15,
maxSize = 500,
nperm = 100000)
fgseaResTidy <- fgseaResDiffUsed %>%
as_tibble() %>%
arrange(desc(NES)) %>%
select(-leadingEdge, -ES, -nMoreExtreme) %>%
arrange(padj)
return(fgseaResTidy)
}
DE_halmark=FGSEA_Funtion(Hset,DEwithdAPAVec)Warning in fgsea(pathways = path, stats = stat, minSize = 15, maxSize
= 500, : You are trying to run fgseaSimple. It is recommended to use
fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm
argument in the fgsea function call.ggplot(DE_halmark, aes(reorder(pathway, NES), NES)) +
geom_col(aes(fill=padj<0.05)) +
coord_flip() +
labs(x="Pathway", y="Normalized Enrichment Score",
title="Hallmark pathways NES from GSEA") +
theme_minimal()
DE_C3=FGSEA_Funtion(C3,DEwithdAPAVec)Warning in fgsea(pathways = path, stats = stat, minSize = 15, maxSize
= 500, : You are trying to run fgseaSimple. It is recommended to use
fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm
argument in the fgsea function call.head(DE_C3)# A tibble: 6 x 5
pathway pval padj NES size
<chr> <dbl> <dbl> <dbl> <int>
1 AUTS2_TARGET_GENES 0.000253 0.162 1.90 70
2 TAF9B_TARGET_GENES 0.000401 0.162 1.84 78
3 ZFHX3_TARGET_GENES 0.000305 0.162 1.64 195
4 THAP1_TARGET_GENES 0.000269 0.162 1.60 229
5 MTA1_TARGET_GENES 0.000542 0.176 1.60 215
6 ACCATTT_MIR522 0.000945 0.255 -1.92 17DE_C5=FGSEA_Funtion(C5,DEwithdAPAVec)Warning in fgsea(pathways = path, stats = stat, minSize = 15, maxSize
= 500, : You are trying to run fgseaSimple. It is recommended to use
fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm
argument in the fgsea function call.DE_C5_sig <- DE_C5 %>%
filter(padj < 0.05) %>%
select(pathway, padj, NES)
DE_C5# A tibble: 1,056 x 5
pathway pval padj NES size
<chr> <dbl> <dbl> <dbl> <int>
1 GO_RIBONUCLEOPROTEIN_COMPLEX 1.66e-5 0.00876 1.95 88
2 GO_MRNA_METABOLIC_PROCESS 1.62e-5 0.00876 1.91 112
3 GO_RNA_CATABOLIC_PROCESS 3.43e-5 0.0121 1.98 58
4 GO_NUCLEUS_ORGANIZATION 5.49e-5 0.0145 2.07 19
5 GO_RIBOSOME 1.23e-4 0.0222 2.04 40
6 GO_NUCLEAR_TRANSCRIBED_MRNA_CATABOLIC_PROC… 1.59e-4 0.0222 2.01 38
7 GO_RIBOSOMAL_SUBUNIT 1.42e-4 0.0222 1.98 34
8 GO_STRUCTURAL_MOLECULE_ACTIVITY 1.89e-4 0.0222 1.94 54
9 GO_RNA_BINDING 1.81e-4 0.0222 1.64 214
10 GO_NUCLEAR_TRANSCRIBED_MRNA_CATABOLIC_PROC… 2.16e-4 0.0228 2.03 29
# … with 1,046 more rowsdAPA and TE
For this I will have to use GOrilla because I only have FDRs. I will run the same analysis but with the differentially translated:
nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F)
colnames(nameID)=c("ENSG", "gene", "source")
translation=read.table("../data/Wang_ribo/Additionaltable5_translationComparisons.txt",stringsAsFactors = F, header = T) %>% inner_join(nameID,by="ENSG") %>% select(gene,HvC.pvalue, HvC.FDR)
translationandAPA=translation %>% inner_join(dAPA,by="gene") %>% arrange(HvC.FDR)dAPA and DP
ProtComp=read.csv("../data/Khan_prot/Khan_TableS4.csv", stringsAsFactors = F, header = T)%>% inner_join(nameID,by="ENSG") %>% select(gene.symbol ,HC.qvalues.protein) %>% rename("gene"=gene.symbol)
protandAPA=ProtComp %>% inner_join(dAPA,by="gene") %>% arrange(HC.qvalues.protein)dAPA, DP not DE
I will use the two set analysis in GORilla. I will compare the DP not DE genes with all of the dAPA genes.
DE_sig=DE %>% filter(adj.P.Val <0.05)
protandAPA_notDE= protandAPA %>% anti_join(DE_sig,by="gene") %>% filter(HC.qvalues.protein<0.05)
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.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] fgsea_1.15.1 workflowr_1.6.2 forcats_0.4.0 stringr_1.4.0
[5] dplyr_0.8.3 purrr_0.3.4 readr_1.3.1 tidyr_1.1.0
[9] tibble_2.1.3 ggplot2_3.2.1 tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 lubridate_1.7.4 lattice_0.20-38
[4] utf8_1.1.4 assertthat_0.2.1 rprojroot_1.3-2
[7] digest_0.6.20 R6_2.4.0 cellranger_1.1.0
[10] backports_1.1.4 reprex_0.3.0 evaluate_0.14
[13] httr_1.4.1 pillar_1.4.2 rlang_0.4.6
[16] lazyeval_0.2.2 readxl_1.3.1 rstudioapi_0.10
[19] data.table_1.13.2 whisker_0.3-2 Matrix_1.2-18
[22] rmarkdown_1.13 labeling_0.3 BiocParallel_1.18.0
[25] munsell_0.5.0 broom_0.5.2 compiler_3.6.1
[28] httpuv_1.5.1 modelr_0.1.8 xfun_0.8
[31] pkgconfig_2.0.2 htmltools_0.3.6 tidyselect_1.1.0
[34] gridExtra_2.3 fansi_0.4.0 crayon_1.3.4
[37] dbplyr_1.4.2 withr_2.1.2 later_0.8.0
[40] grid_3.6.1 nlme_3.1-140 jsonlite_1.6
[43] gtable_0.3.0 lifecycle_0.1.0 DBI_1.1.0
[46] git2r_0.26.1 magrittr_1.5 scales_1.1.0
[49] cli_2.2.0 stringi_1.4.3 farver_2.0.1
[52] fs_1.3.1 promises_1.0.1 xml2_1.3.2
[55] generics_0.0.2 vctrs_0.3.0 fastmatch_1.1-0
[58] tools_3.6.1 glue_1.3.1 hms_0.5.3
[61] parallel_3.6.1 yaml_2.2.0 colorspace_1.4-1
[64] rvest_0.3.5 knitr_1.23 haven_2.3.1