Last updated: 2020-05-01
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Knit directory: Comparative_APA/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | c78e612 | brimittleman | 2020-05-01 | add dic with others |
I used simpson to call differences in information content.
library(workflowr)This is workflowr version 1.6.0
Run ?workflowr for help getting startedlibrary(tidyverse)── Attaching packages ────────────────────────────────────────────────────── tidyverse 1.2.1 ──✔ ggplot2 3.1.1 ✔ purrr 0.3.2
✔ tibble 2.1.1 ✔ dplyr 0.8.0.1
✔ tidyr 0.8.3 ✔ stringr 1.3.1
✔ readr 1.3.1 ✔ forcats 0.3.0 ── Conflicts ───────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()I want to look at DE, dP, dt, dAPA, and dIC. I will do simple 2by2 tables at first.
For each set I will only consider the genes that I can test for those analylsis.
APA
Meta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) %>% dplyr::select(PAS, chr, start,end, loc)
DiffIso= read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T,stringsAsFactors = F) %>% inner_join(Meta, by=c("chr", 'start','end'))
#gene level:
SigGenesDI=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt",header = T,stringsAsFactors = F)
DiffIsoGene= DiffIso %>% select(gene) %>% unique() %>% mutate(dAPA=ifelse(gene %in% SigGenesDI$gene, "Yes", "No"))
DiffIsoGene %>% group_by(dAPA) %>% summarise(n())# A tibble: 2 x 2
dAPA `n()`
<chr> <int>
1 No 6717
2 Yes 1705DE
nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F) %>% dplyr::select(Gene_stable_ID, Gene.name)
DiffExp=read.table("../data/DiffExpression/DEtested_allres.txt",stringsAsFactors = F,header = F, col.names = c("Gene_stable_ID" ,"logFC" ,"AveExpr" , "t" , "P.Value" , "adj.P.Val", "B" )) %>% inner_join(nameID,by="Gene_stable_ID") %>% dplyr::rename('gene'=Gene.name) %>% dplyr::select(-Gene_stable_ID) %>% mutate(DE=ifelse(adj.P.Val<.05, "Yes", "No"))
DiffExp %>% group_by(DE) %>% summarise(n())# A tibble: 2 x 2
DE `n()`
<chr> <int>
1 No 6356
2 Yes 3794DiffExpSmall= DiffExp %>% select(gene,DE)DTE
Ribo=read.table("../data/Wang_ribo/Additionaltable5_translationComparisons.txt",header = T, stringsAsFactors = F) %>% rename("Gene_stable_ID"= ENSG) %>% inner_join(nameID,by="Gene_stable_ID") %>% dplyr::select(Gene.name, HvC.beta, HvC.pvalue, HvC.FDR) %>% rename("gene"=Gene.name) %>% mutate(dTE=ifelse(HvC.FDR <0.05, "Yes","No"))
Ribo %>% group_by(dTE) %>% summarise(n())# A tibble: 2 x 2
dTE `n()`
<chr> <int>
1 No 7236
2 Yes 1993RiboSmall= Ribo %>% select(gene,dTE)DP
(pval is adjusted already)
Prot= read.table("../data/Khan_prot/ProtData_effectSize.txt",header = T) %>% mutate(dP=ifelse(pval<0.05, "Yes", "No"))
Prot %>% group_by(dP) %>% summarise(n())# A tibble: 2 x 2
dP `n()`
<chr> <int>
1 No 2004
2 Yes 1266ProtSmall=Prot %>% select(gene, dP)Simpson Info Content
dICdata= read.table("../data/IndInfoContent/SimpsonMedianSignificance.txt", header = T, stringsAsFactors = F) %>% rename(dIC=sIC)
dICdata %>% group_by(dIC) %>% summarise(n())# A tibble: 2 x 2
dIC `n()`
<chr> <int>
1 No 7570
2 Yes 881dICSmall=dICdata %>% select(gene, dIC)I will start with dIC and dAPA, I expect a pretty high overlap for this.
dICanddAPA= dICSmall %>% inner_join(DiffIsoGene, by="gene")
dICanddAPA22=dICanddAPA %>% group_by(dIC, dAPA) %>% summarise(n=n()) %>% spread(dAPA, n) %>% column_to_rownames("dIC")
dICanddAPA22 No Yes
No 6291 1251
Yes 426 454#top is dIC, side is dAPADo this with proportion:
dICanddAPA %>% group_by(dIC, dAPA) %>% summarise(n=n()) %>% mutate(nG=nrow(dICanddAPA),Prop=n/nG) %>% select(dIC, dAPA, Prop) %>% spread(dAPA, Prop)# A tibble: 2 x 3
# Groups: dIC [2]
dIC No Yes
<chr> <dbl> <dbl>
1 No 0.747 0.149
2 Yes 0.0506 0.0539Enrichment:
x=nrow(dICanddAPA %>% filter(dIC=="Yes", dAPA=="Yes"))
m=nrow(dICanddAPA %>% filter(dAPA=="Yes"))
n=nrow(dICanddAPA %>% filter(dAPA=="No"))
k=nrow(dICanddAPA %>% filter(dIC=="Yes"))
N=nrow(dICanddAPA)
phyper(x-1,m,n,k,lower.tail=F)[1] 1.812359e-108(x/k)/(m/N)[1] 2.548379I will start with dIC and dAPA, I expect a pretty high overlap for this.
dICanddE= dICSmall %>% inner_join(DiffExpSmall, by="gene")
dICanddE22=dICanddE %>% group_by(dIC, DE) %>% summarise(n=n()) %>% spread(DE, n) %>% column_to_rownames("dIC")
dICanddE22 No Yes
No 4233 2464
Yes 466 317#top is dIC, side is DEDo this with proportion:
dICanddE %>% group_by(dIC, DE) %>% summarise(n=n()) %>% mutate(nG=nrow(dICanddE),Prop=n/nG) %>% select(dIC, DE, Prop) %>% spread(DE, Prop)# A tibble: 2 x 3
# Groups: dIC [2]
dIC No Yes
<chr> <dbl> <dbl>
1 No 0.566 0.329
2 Yes 0.0623 0.0424x=nrow(dICanddE %>% filter(dIC=="Yes", DE=="Yes"))
m=nrow(dICanddE %>% filter(DE=="Yes"))
n=nrow(dICanddE %>% filter(DE=="No"))
k=nrow(dICanddE %>% filter(dIC=="Yes"))
N=nrow(dICanddE)
phyper(x-1,m,n,k,lower.tail=F)[1] 0.02402776(x/k)/(m/N)[1] 1.088925I will start with dIC and dAPA, I expect a pretty high overlap for this.
dICanddT= dICSmall %>% inner_join(RiboSmall, by="gene")
dICanddT22=dICanddT %>% group_by(dIC, dTE) %>% summarise(n=n()) %>% spread(dTE, n) %>% column_to_rownames("dIC")
dICanddT22 No Yes
No 4557 1241
Yes 504 185#top is dIC, side is dTEDo this with proportion:
dICanddT %>% group_by(dIC, dTE) %>% summarise(n=n()) %>% mutate(nG=nrow(dICanddT),Prop=n/nG) %>% select(dIC, dTE, Prop) %>% spread(dTE, Prop)# A tibble: 2 x 3
# Groups: dIC [2]
dIC No Yes
<chr> <dbl> <dbl>
1 No 0.702 0.191
2 Yes 0.0777 0.0285x=nrow(dICanddT %>% filter(dIC=="Yes", dTE=="Yes"))
m=nrow(dICanddT %>% filter(dTE=="Yes"))
n=nrow(dICanddT %>% filter(dTE=="No"))
k=nrow(dICanddT %>% filter(dIC=="Yes"))
N=nrow(dICanddT)
phyper(x-1,m,n,k,lower.tail=F)[1] 0.0008020285(x/k)/(m/N)[1] 1.221453I will start with dIC and dAPA, I expect a pretty high overlap for this.
dICanddP= dICSmall %>% inner_join(ProtSmall, by="gene") Warning: Column `gene` joining character vector and factor, coercing into
character vectordICanddP22=dICanddP %>% group_by(dIC, dP) %>% summarise(n=n()) %>% spread(dP, n) %>% column_to_rownames("dIC")
dICanddP22 No Yes
No 1386 909
Yes 224 124#top is dIC, side is DPDo this with proportion:
dICanddP %>% group_by(dIC, dP) %>% summarise(n=n()) %>% mutate(nG=nrow(dICanddP),Prop=n/nG) %>% select(dIC, dP, Prop) %>% spread(dP, Prop)# A tibble: 2 x 3
# Groups: dIC [2]
dIC No Yes
<chr> <dbl> <dbl>
1 No 0.524 0.344
2 Yes 0.0848 0.0469x=nrow(dICanddP %>% filter(dIC=="Yes", dP=="Yes"))
m=nrow(dICanddP %>% filter(dP=="Yes"))
n=nrow(dICanddP %>% filter(dP=="No"))
k=nrow(dICanddP %>% filter(dIC=="Yes"))
N=nrow(dICanddP)
phyper(x-1,m,n,k,lower.tail=F)[1] 0.9304879(x/k)/(m/N)[1] 0.9116734Not enough power for this one.
sessionInfo()R version 3.5.1 (2018-07-02)
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] forcats_0.3.0 stringr_1.3.1 dplyr_0.8.0.1 purrr_0.3.2
[5] readr_1.3.1 tidyr_0.8.3 tibble_2.1.1 ggplot2_3.1.1
[9] tidyverse_1.2.1 workflowr_1.6.0
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 haven_1.1.2 lattice_0.20-38 colorspace_1.3-2
[5] generics_0.0.2 htmltools_0.3.6 yaml_2.2.0 utf8_1.1.4
[9] rlang_0.4.0 later_0.7.5 pillar_1.3.1 glue_1.3.0
[13] withr_2.1.2 modelr_0.1.2 readxl_1.1.0 plyr_1.8.4
[17] munsell_0.5.0 gtable_0.2.0 cellranger_1.1.0 rvest_0.3.2
[21] evaluate_0.12 knitr_1.20 httpuv_1.4.5 fansi_0.4.0
[25] broom_0.5.1 Rcpp_1.0.4.6 promises_1.0.1 scales_1.0.0
[29] backports_1.1.2 jsonlite_1.6 fs_1.3.1 hms_0.4.2
[33] digest_0.6.18 stringi_1.2.4 grid_3.5.1 rprojroot_1.3-2
[37] cli_1.1.0 tools_3.5.1 magrittr_1.5 lazyeval_0.2.1
[41] crayon_1.3.4 whisker_0.3-2 pkgconfig_2.0.2 xml2_1.2.0
[45] lubridate_1.7.4 assertthat_0.2.0 rmarkdown_1.10 httr_1.3.1
[49] rstudioapi_0.10 R6_2.3.0 nlme_3.1-137 git2r_0.26.1
[53] compiler_3.5.1