Last updated: 2020-04-10
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Knit directory: Comparative_APA/analysis/
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Unstaged changes:
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In this analysis I will use the UpSetR package to look at all of the differential gene regulation phenotype results in one plot. This should be easier to visualize than the venn diagrams.
library(UpSetR)
library(workflowr)
This is workflowr version 1.6.0
Run ?workflowr for help getting started
library(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()
Input the datasets:
#protein
Protein=read.table("../data/Khan_prot/HC_SigProtein.txt", header = T, stringsAsFactors = F)
#trans
Translation=read.table("../data/Wang_ribo/HC_SigTranslation.txt", header = T, stringsAsFactors = F)
#expression
nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F)
DEgenes=read.table("../data/DiffExpression/DE_genes.txt", header = F,col.names = c("Gene_stable_ID"),stringsAsFactors = F) %>% inner_join(nameID, by="Gene_stable_ID") %>% dplyr::select(Gene.name)
#nuclear apa
NucAPA=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt",header = T,stringsAsFactors = F)
DSgenes=read.table("../data/DiffSplice_liftedJunc/orderedGeneListFixed.txt",stringsAsFactors = F, col.names = "DS")
Create a named list object
listInput_nucOnly <- list(DE=DEgenes$Gene.name, DS=DSgenes$DS, DAPA=NucAPA$gene, DT=Translation$Gene, DP=Protein$gene.symbol)
upset(fromList(listInput_nucOnly), order.by = "freq", keep.order = T,empty.intersections = "on")
Add colors for certain queries:
#upset(movies, queries = list(list(query = intersects, params = list("Drama",
# "Comedy", "Action"), color = "orange", active = T), list(query = intersects,
# params = list("Drama"), color = "red", active = F), list(query = intersects,
# params = list("Action", "Drama"), active = T)))
upset(fromList(listInput_nucOnly), queries = list(list(query=intersects, params=list("DAPA", "DT", "DP"), color="red", active=T,query.name="APA,Ribo, Protein"),list(query=intersects, params=list("DE", "DT", "DP"), color="orange", active=T, query.name="Expression,Ribo, Protein"), list(query=intersects, params=list("DS", "DT", "DP"), color="green", active=T,query.name="Splicing ,Ribo, Protein"),list(query=intersects, params=list("DAPA", "DT"), color="blue", active=T, query.name="APA,Ribo") ,list(query=intersects, params=list("DAPA", "DP"), color="purple", active=T, query.name="APA, Protein")), order.by = "freq", query.legend = "bottom")
Remove splicing:
listInput_nosplice <- list(DE=DEgenes$Gene.name, DAPA=NucAPA$gene, DT=Translation$Gene, DP=Protein$gene.symbol)
upset(fromList(listInput_nosplice), queries = list(list(query=intersects, params=list("DAPA", "DT", "DP"), color="red", active=T,query.name="APA, Ribo, Protein"),list(query=intersects, params=list("DE", "DT", "DP"), color="orange", active=T, query.name="Expression,Ribo, Protein"),list(query=intersects, params=list("DAPA", "DT"), color="blue", active=T, query.name="APA,Ribo") ,list(query=intersects, params=list("DAPA", "DP"), color="purple", active=T, query.name="APA, Protein"),list(query=intersects, params=list("DAPA", "DE"), color="green", active=T, query.name="APA, Expression")), order.by = "freq", query.legend = "bottom")
Will use hypergeometric to test overlaps.
phyper(success in sample, sucesss in possible, failure possible, sample size)
Success is the overlap, de, not DE, sample size is the apagenes tested in DE
I need the full lists for this. Not just the significant ones.
I will start with expression and apa. I need all of the genes tested for both.
DEgenestested=read.table("../data/DiffExpression/DE_Testedgenes.txt", header = F,col.names = c("Gene_stable_ID"),stringsAsFactors = F) %>% inner_join(nameID, by="Gene_stable_ID") %>% dplyr::select(Gene.name)
apaTested=read.table("../data/DiffIso_Nuclear_DF/TN_diff_isoform_allChrom.txt_significance.txt",sep="\t" ,col.names = c('status','loglr','df','p','cluster','p.adjust'),stringsAsFactors = F) %>% filter(status=="Success") %>% separate(cluster, into=c("chr", "Gene.name"),sep=":")
DEtestedandAPA=NucAPA %>%rename("Gene.name"=gene) %>% inner_join(DEgenestested, by="Gene.name") %>% nrow()
DeandAPA=NucAPA %>%rename("Gene.name"=gene) %>% inner_join(DEgenes, by="Gene.name")%>% nrow()
NotDe= nrow(DEgenestested)-nrow(DEgenes)
x=DeandAPA
m=nrow(DEgenes)
n=NotDe
k=DEtestedandAPA
#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
[1] 546
#actual:
DeandAPA
[1] 610
#pval
phyper(DeandAPA, nrow(DEgenes), NotDe, DEtestedandAPA,lower.tail=F)
[1] 9.105728e-05
Translation:
Success is the overlap, T, not T, sample size is the apagenes tested in DE
TranslationTested=read.table("../data/Wang_ribo/HC_AllTestedTranslation.txt",header = T,stringsAsFactors = F) %>% rename("gene"=Gene)
TranslationNotTE= TranslationTested %>% filter(HvC.FDR>=.05)
#actual overlap
TeandAPA=Translation %>% rename("gene"=Gene) %>% inner_join(NucAPA,by="gene")
TranslationTestedandAPA= TranslationTested %>% inner_join(NucAPA,by="gene")
x=nrow(TeandAPA)
m= nrow(Translation)
n=nrow(TranslationNotTE)
k=nrow(TranslationTestedandAPA)
#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
[1] 261
#actual:
nrow(TeandAPA)
[1] 301
#pval
phyper(nrow(TeandAPA), nrow(Translation), nrow(TranslationNotTE), nrow(TranslationTestedandAPA),lower.tail=F)
[1] 0.001317099
Protein
Success is the overlap, dp, not no dp, sample size is the apagenes tested in dp
ProtTested=read.table("../data/Khan_prot/HC_AlltestedProtein.txt",header = T,stringsAsFactors = F) %>% rename("gene"=gene.symbol)
ProtNotPE= ProtTested %>% filter(HC.qvalues.protein>=.05)
#actual overlap
PEandAPA=Protein %>% rename("gene"=gene.symbol) %>% inner_join(NucAPA,by="gene")
ProtTestedandAPA= ProtTested %>% inner_join(NucAPA,by="gene")
x=nrow(PEandAPA)
m= nrow(Protein)
n=nrow(ProtNotPE)
k=nrow(ProtTestedandAPA)
#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
[1] 171
#actual:
nrow(PEandAPA)
[1] 181
#pval
phyper(nrow(PEandAPA), nrow(Protein), nrow(ProtNotPE), nrow(ProtTestedandAPA),lower.tail=F)
[1] 0.1350306
Use lauren’s code for the 3 set
Human-chimpanzee-rhesus macaque tissue specific overlap:
m is the overlap of human+chimpanzee tissue-specific genes n is the Total genes - (overlap of human+chimpanzee tissue-specific genes) x/q is the overlap between human, chimpanzee, rhesus macaque tissue-specific genes k is the total rhesus tissue-specific genes
phyper(x=overlap all pheno, m=overlap 2 (not apa) , n= total - overlap of 2 (not apa), k=total for apa)
ApaProtTrans=PEandAPA %>% inner_join(TeandAPA, by="gene")
PeandTE=Translation %>% inner_join(Protein) %>% rename("gene"=Gene)
Joining, by = "ENSG"
#not dp and dt is all tested in both - pe and te set
PeandTEtested= ProtTested %>% full_join(TranslationTested, by="gene") %>% anti_join(PeandTE,by="gene")
apaTestedinboth= ProtTested %>% full_join(TranslationTested, by="gene") %>% inner_join(NucAPA, by="gene")
x=nrow(ApaProtTrans)
m= nrow(PeandTE)
n=nrow(PeandTEtested)
k=nrow(apaTestedinboth)
#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
[1] 51
#actual:
nrow(ApaProtTrans)
[1] 67
#pval
phyper(x,m,n,k,lower.tail=F)
[1] 0.007006879
Expression te and pe
Success is the overlap, dp and dt, not dp and dt, sample size is the egenes tested in both dp and dt
Protein_g = Protein %>% rename("gene"=gene.symbol)
translation_g= Translation%>% rename("gene"=Gene)
DEgenes_g= DEgenes %>% rename("gene"=Gene.name)
EProtTrans=DEgenes %>% rename("gene"=Gene.name) %>% inner_join(Protein_g, by="gene") %>% inner_join(translation_g, by="gene")
PeandTE=Translation %>% inner_join(Protein, by = "ENSG") %>% rename("gene"=Gene)
#not dp and dt is all tested in both - pe and te set
PeandTEtested= ProtTested %>% full_join(TranslationTested, by="gene") %>% anti_join(PeandTE,by="gene")
expressioninboth= ProtTested %>% full_join(TranslationTested, by="gene") %>% inner_join(DEgenes_g, by="gene")
x=nrow(EProtTrans)
m= nrow(PeandTE)
n=nrow(PeandTEtested)
k=nrow(expressioninboth)
#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
[1] 125
#actual:
nrow(EProtTrans)
[1] 232
#pval
phyper(x,m,n,k,lower.tail=F)
[1] 3.771448e-30
I can make a table for this- I’ll have the set, the actual, the expected, the pvalue. I can also add these numbers onto the figure above.
Splicing results:
listInput_Splice <- list(DE=DEgenes$Gene.name, DS=DSgenes$DS, DT=Translation$Gene, DP=Protein$gene.symbol)
upset(fromList(listInput_Splice), order.by = "freq", keep.order = T,empty.intersections = "on")
Version | Author | Date |
---|---|---|
0f00c12 | brimittleman | 2020-01-26 |
DeandAPAList=NucAPA %>%rename("Gene.name"=gene) %>% inner_join(DEgenes, by="Gene.name") %>% rename("gene"=Gene.name)
write.table(DeandAPAList,"../data/DiffIso_Nuclear_DF/GeneswithDEanddAPA.txt", col.names = T,row.names = F,quote = F)
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 UpSetR_1.3.3
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 rlang_0.4.0
[9] later_0.7.5 pillar_1.3.1 withr_2.1.2 glue_1.3.0
[13] modelr_0.1.2 readxl_1.1.0 plyr_1.8.4 munsell_0.5.0
[17] gtable_0.2.0 cellranger_1.1.0 rvest_0.3.2 evaluate_0.12
[21] labeling_0.3 knitr_1.20 httpuv_1.4.5 broom_0.5.1
[25] Rcpp_1.0.2 promises_1.0.1 scales_1.0.0 backports_1.1.2
[29] jsonlite_1.6 fs_1.3.1 gridExtra_2.3 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