Last updated: 2020-04-06

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DiffUsedIntronic.Rmd
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/TotalVNuclearBothSpecies.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/changeMisprimcut.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/speciesSpecific.Rmd

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.

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File Version Author Date Message
Rmd 92f06e4 brimittleman 2020-04-06 update anno
html 0f00c12 brimittleman 2020-01-26 Build site.
Rmd 8fc483e brimittleman 2020-01-26 fix splice plots for CM
html 5910b06 brimittleman 2020-01-24 Build site.
html 35dd92e brimittleman 2020-01-23 Build site.
Rmd 3ac4dab brimittleman 2020-01-23 upset w/out splicing
html de29125 brimittleman 2020-01-23 Build site.
Rmd 95a319f brimittleman 2020-01-23 add hypergeo pvalues
html 8536528 brimittleman 2020-01-22 Build site.
Rmd 07fd459 brimittleman 2020-01-22 DF for upset and translation protein ven

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")

Version Author Date
8536528 brimittleman 2020-01-22

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")

Version Author Date
8536528 brimittleman 2020-01-22

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")

Version Author Date
35dd92e brimittleman 2020-01-23

Will use hypergeometric to test overlaps.

https://lauren-blake.github.io/Regulatory_Evol/analysis/Tissue_specific_overlap.html#human-chimpanzee-rhesus-macaque-tissue-specific-overlap

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] 702
#actual:
DeandAPA
[1] 764
#pval
phyper(DeandAPA, nrow(DEgenes), NotDe, DEtestedandAPA,lower.tail=F)
[1] 0.000465643

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] 340
#actual:
nrow(TeandAPA)
[1] 377
#pval
phyper(nrow(TeandAPA), nrow(Translation), nrow(TranslationNotTE), nrow(TranslationTestedandAPA),lower.tail=F)
[1] 0.006893096

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] 219
#actual:
nrow(PEandAPA)
[1] 219
#pval
phyper(nrow(PEandAPA), nrow(Protein), nrow(ProtNotPE), nrow(ProtTestedandAPA),lower.tail=F)
[1] 0.6134345

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] 66
#actual:
nrow(ApaProtTrans)
[1] 75
#pval


phyper(x,m,n,k,lower.tail=F)
[1] 0.1021648

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