Last updated: 2020-03-07

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/ExploredAPA.Rmd
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File Version Author Date Message
Rmd c6a11f0 brimittleman 2020-03-07 add expression indep 
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
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── Conflicts ───────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(UpSetR)
library(ggpubr)
Loading required package: magrittr

Attaching package: 'magrittr'
The following object is masked from 'package:purrr':

    set_names
The following object is masked from 'package:tidyr':

    extract

Upload:

Protein=read.table("../data/Khan_prot/HC_SigProtein.txt", header = T, stringsAsFactors = F)%>% dplyr::rename("gene"=gene.symbol)
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) %>% dplyr::rename("gene"=Gene.name)
NucAPA=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt",header = T,stringsAsFactors = F)

I will do this first with these then I can start to look at it by significance.

214 dAPA and dp

APAandPnotE= NucAPA %>% inner_join(Protein, by="gene") %>% anti_join(DEgenes,by="gene")
listInput_nucOnly <- list(DE=DEgenes$gene, DAPA=NucAPA$gene, DP=Protein$gene)

#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")




upset(fromList(listInput_nucOnly), order.by = "freq", keep.order = T,empty.intersections = "on", queries = list(list(query=intersects, params=list("DAPA", "DP"), color="darkorchid4", active=T,query.name="APA, Protein")))

113 of these genes.

Learn about these genes.

Selection:

model.num.rna: : 1 = mRNA expression level pattern consistent with directional selection along human lineage, 2 = mRNA expression level pattern consistent with directional selection along chimpanzee lineage, 3 = undetermined pattern, 4 = patterns with no significant difference between mean expression levels; 5 = evidence for relaxation of constraint along human lineage, 6 = evidence of relaxation of constraint along chimpanzee lineage

model.num.protein: 1 = protein expression level pattern consistent with directional selection along human lineage, 2 = protein expression level pattern consistent with directional selection along chimpanzee lineage, 3 = undetermined pattern, 4 = patterns with no significant difference between mean expression levels; 5 = evidence for relaxation of constraint along human lineage, 6 = evidence of relaxation of constraint along chimpanzee lineage

KhanData=read.csv("../data/Khan_prot/Khan_TableS4.csv",stringsAsFactors = F)  %>% dplyr::select(gene.symbol,contains("model") ) %>% dplyr::rename("gene"=gene.symbol, "Protein"=model.num.protein, "RNA"=model.num.rna)


APAandPnotE_sel= APAandPnotE %>% inner_join(KhanData,by="gene")

Plot the information about the RNA and protein for these:

APAandPnotE_sel_g=APAandPnotE_sel %>% dplyr::select(gene, Protein, RNA) %>% gather("Set", "Model", -gene)


APAandPnotE_sel_g$Model= as.factor(APAandPnotE_sel_g$Model)
ggplot(APAandPnotE_sel_g,aes(x=Model, by=Set, fill=Set)) + geom_bar(stat="count", position="dodge") + scale_fill_brewer(palette = "Dark2")

Plot protein only:

APAandPnotE_sel_gOnlyP= APAandPnotE_sel_g %>% filter(Set=="Protein")

APAandPnotE_sel_gOnlyP$Model= as.factor(APAandPnotE_sel_gOnlyP$Model)

ggplot(APAandPnotE_sel_gOnlyP,aes(x=Model)) + geom_bar(stat="count", position="dodge", fill="darkorchid4") + labs(y="Number of Genes", x="Protein Selection Model", title="Protein and APA differences\n no difference in Expression") + scale_x_discrete( labels=c("Selection Human","Selection Chimp","Undetermined","No mean difference","Relaxation in Chimp"))+theme(axis.text.x=element_text(angle=90, hjust=0), text= element_text(size=16))

The genes in 1,2,5,6 are interesting.

APAandPnotE_selCalled= APAandPnotE_sel_g %>% filter(Set=="Protein", Model %in% c(1,2,5,6))

There are 27 of these genes:

APAandPnotE_selCalled
      gene     Set Model
1    MED17 Protein     1
2    PRIM1 Protein     2
3    SART3 Protein     1
4  ATP6V1D Protein     1
5    SEL1L Protein     1
6     DGKE Protein     2
7   GALNT2 Protein     2
8    GNAI3 Protein     2
9    PFDN2 Protein     1
10    PPIH Protein     1
11  SEC22B Protein     2
12   WDR77 Protein     2
13    KYNU Protein     2
14   PPIL3 Protein     1
15 ATP6V1A Protein     1
16    CPOX Protein     2
17   MANBA Protein     1
18   BNIP1 Protein     1
19    CCT5 Protein     2
20  CYFIP2 Protein     1
21    TARS Protein     1
22    MYO6 Protein     2
23   CCT6A Protein     2
24    CUL1 Protein     2
25   VPS41 Protein     1
26   PTBP3 Protein     6
27    STOM Protein     1

Where are the differential PAS in these genes:

#APAandPnotE_sel_gOnlyP
Meta=read.table("../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = T) %>% dplyr::rename("ChimpUsage"=Chimp, "HumanUsage"=Human)
NucAPAres=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T,stringsAsFactors = F) %>% inner_join(Meta, by=c("chr", "start","end", "gene"))
Warning: Column `chr` joining character vector and factor, coercing into
character vector
Warning: Column `gene` joining character vector and factor, coercing into
character vector
NucAPAres_DP= NucAPAres %>% filter(gene %in%APAandPnotE_sel_gOnlyP$gene ) %>% filter(SigPAU2=="Yes")


NucAPAresSig=NucAPAres %>% filter(SigPAU2=="Yes")

THere are 152 PAS in this set:

ggplot(NucAPAres_DP,aes(x=loc,fill=loc))+ geom_bar(stat="count") + scale_fill_brewer(palette = "Dark2")+theme(axis.text.x=element_text(angle=90, hjust=0), text= element_text(size=16), legend.position = "false") + labs(x="", y="Number of PAS", title="Expression independent PAS locations")

Enrichment for this:

Compare to all of the significant in that location.

NucAPAres_sig= NucAPAres %>% filter(SigPAU2=="Yes") %>% mutate(dPnotE=ifelse(PAS %in% NucAPAres_DP$PAS,"Yes", "No"))


enrich=c()
pval=c()

for (i in c("cds", "end", "intron", "utr3")){
  x=nrow(NucAPAres_sig %>% filter(dPnotE=="Yes", loc==i))
  m=nrow(NucAPAres_sig %>% filter( loc==i))
  n=nrow(NucAPAres_sig %>% filter(loc!=i))
  k=nrow(NucAPAres_sig %>% filter(dPnotE=="Yes"))
  N=nrow(NucAPAres_sig)
  pval=c(pval, phyper(x,m,n,k,lower.tail=F))
  enrichval=(x/k)/(m/N)
  enrich=c(enrich, enrichval)
}
enrich
[1] 1.4585111 0.4096527 0.7363810 1.1710288
pval
[1] 0.01963087 0.98702113 0.96568720 0.00735525
NucAPAres_DPLocEnrich=NucAPAres_DP %>% group_by(loc) %>% summarise(n=n()) %>% bind_cols(enrichment=enrich, pvalue=pval)


ggplot(NucAPAres_DPLocEnrich, aes(x=loc, y=n, fill=loc)) + geom_bar(stat="identity") + scale_fill_brewer(palette = "Dark2")+theme(axis.text.x=element_text(angle=90, hjust=0), text= element_text(size=16), legend.position = "false") + labs(x="", y="Number of PAS", title="Expression independent PAS locations")+ geom_text(aes(label=paste("Enrichment=",round(enrichment,2), "X", sep=""), vjust=0)) +geom_text(aes(label=paste("Pval=",round(pvalue,2), sep=""), vjust=2))

Interactions:

Are there differences in protien interactions for these.

Interactions=read.table("../data/bioGRID/GeneswInteractions.txt",stringsAsFactors = F, header = T)

InteractionsAPA=Interactions %>%filter(gene %in% NucAPAresSig$gene) %>% mutate(dPnotE=ifelse(gene %in% NucAPAres_DP$gene, "Yes", "No"))


ggplot(InteractionsAPA,aes(x=dPnotE, y=log10(nInt+1),fill=dPnotE)) + geom_boxplot() + stat_compare_means() + scale_fill_brewer(palette = "Dark2")+theme(axis.text.x=element_text(angle=90, hjust=0), text= element_text(size=16), legend.position = "false") + labs(x="Gene in Expression independent set", y="log10(Number of Protein Interactions)", title="Protein Interactions for Expression \nindependent dAPA genes")

More likly to have one:

InteractionsAPA %>% mutate(HasInteraction=ifelse(nInt>0, "Yes", "No")) %>% group_by(dPnotE, HasInteraction) %>% summarise(nWithSet=n())
# A tibble: 2 x 3
# Groups:   dPnotE [2]
  dPnotE HasInteraction nWithSet
  <chr>  <chr>             <int>
1 No     Yes                1756
2 Yes    Yes                 113

Set should be the interaction set dAPA, de, and dP.

Alldiff=Protein %>% inner_join(DEgenes,by="gene") %>% inner_join(NucAPA, by="gene") %>% dplyr::select(gene)
#This is 101 genes.  
geneAPAPnotEG=APAandPnotE %>% dplyr::select(gene)

GenesMatter= Alldiff %>% bind_rows(geneAPAPnotEG) %>% mutate(Ex=ifelse(gene %in% geneAPAPnotEG$gene, "No", "Yes")) %>% inner_join(Interactions, by="gene")
ggplot(GenesMatter, aes(x=Ex, y=nInt, fill=Ex))+ geom_boxplot() + stat_compare_means() + scale_fill_brewer(palette = "Dark2")+theme(axis.text.x=element_text(angle=90, hjust=0), text= element_text(size=16), legend.position = "false") + labs(x="DE gene", y="Number of protein protein interactions", title="dAPA, DP, and DE")


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] ggpubr_0.2      magrittr_1.5    UpSetR_1.3.3    forcats_0.3.0  
 [5] stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1    
 [9] tidyr_0.8.3     tibble_2.1.1    ggplot2_3.1.1   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5   haven_1.1.2        lattice_0.20-38   
 [4] colorspace_1.3-2   generics_0.0.2     htmltools_0.3.6   
 [7] yaml_2.2.0         utf8_1.1.4         rlang_0.4.0       
[10] later_0.7.5        pillar_1.3.1       glue_1.3.0        
[13] withr_2.1.2        RColorBrewer_1.1-2 modelr_0.1.2      
[16] readxl_1.1.0       plyr_1.8.4         munsell_0.5.0     
[19] gtable_0.2.0       workflowr_1.6.0    cellranger_1.1.0  
[22] rvest_0.3.2        evaluate_0.12      labeling_0.3      
[25] knitr_1.20         httpuv_1.4.5       fansi_0.4.0       
[28] broom_0.5.1        Rcpp_1.0.2         promises_1.0.1    
[31] scales_1.0.0       backports_1.1.2    jsonlite_1.6      
[34] fs_1.3.1           gridExtra_2.3      hms_0.4.2         
[37] digest_0.6.18      stringi_1.2.4      grid_3.5.1        
[40] rprojroot_1.3-2    cli_1.1.0          tools_3.5.1       
[43] lazyeval_0.2.1     crayon_1.3.4       whisker_0.3-2     
[46] pkgconfig_2.0.2    xml2_1.2.0         lubridate_1.7.4   
[49] assertthat_0.2.0   rmarkdown_1.10     httr_1.3.1        
[52] rstudioapi_0.10    R6_2.3.0           nlme_3.1-137      
[55] git2r_0.26.1       compiler_3.5.1