Last updated: 2020-05-17

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DeandNumPAS.Rmd
    Modified:   analysis/DiffTop2SecondDom.Rmd
    Modified:   analysis/DirSelectionKhan.Rmd
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/PTM_analysis.Rmd
    Modified:   analysis/TotalDomStructure.Rmd
    Modified:   analysis/TotalVNuclearBothSpecies.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/changeMisprimcut.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/dInforContent.Rmd
    Modified:   analysis/diffExpression.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/mRNADecay.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/phastCon.Rmd
    Modified:   analysis/pol2.Rmd
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Rmd 08fda9d brimittleman 2020-05-17 add agg plots
html b418f79 brimittleman 2020-05-07 Build site.
Rmd d13b414 brimittleman 2020-05-07 add dp not de 
library(workflowr)
This is workflowr version 1.6.0
Run ?workflowr for help getting started
library(UpSetR)
library(cowplot)
Loading required package: ggplot2

Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':

    ggsave
library(ggpubr)
Loading required package: magrittr

Attaching package: 'ggpubr'
The following object is masked from 'package:cowplot':

    get_legend
library(tidyverse)
── Attaching packages ──────────────────────────── tidyverse 1.2.1 ──
✔ tibble  2.1.1       ✔ purrr   0.3.2  
✔ tidyr   0.8.3       ✔ dplyr   0.8.0.1
✔ readr   1.3.1       ✔ stringr 1.3.1  
✔ tibble  2.1.1       ✔ forcats 0.3.0  
── Conflicts ─────────────────────────────── tidyverse_conflicts() ──
✖ tidyr::extract()   masks magrittr::extract()
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✖ cowplot::ggsave()  masks ggplot2::ggsave()
✖ dplyr::lag()       masks stats::lag()
✖ purrr::set_names() masks magrittr::set_names()

I saw that dIC genes are enriched for translation. I want to look at the dIC genes that are not dAPA, are any of these DP but not DE?

Meta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) 


Meta_genes= Meta %>% select(gene) %>% unique()

Meta_PAS=Meta %>%  select(PAS,gene)

dAPAGenes=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt", header = T, stringsAsFactors = F)
dAPAPAS=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header = T, stringsAsFactors = F) %>% inner_join(Meta, by=c("chr","start", "end","gene")) %>% select(PAS,gene,SigPAU2 ) 

dAPAPAS_genes= dAPAPAS %>% select(gene) %>% unique()

dAPATestedGenes= dAPAPAS  %>% select(gene) %>% unique() %>% mutate(dAPA=ifelse(gene %in% dAPAGenes$gene,"Yes", "No")) 

dICdata= read.table("../data/IndInfoContent/SimpsonMedianSignificance.txt", header = T, stringsAsFactors = F)%>% select(sIC,gene)

dAPAandDic= dICdata %>% inner_join(dAPATestedGenes,by="gene") %>% mutate(Both=ifelse(sIC=="Yes" & dAPA=="Yes", "Yes","No"),OnlyIC=ifelse(sIC=="Yes" & dAPA=="No", "Yes","No"),OnlyAPA=ifelse(sIC=="No" & dAPA=="Yes", "Yes","No"))

dIConly=dAPAandDic %>%filter(OnlyIC=="Yes")

How many of the dIConly genes are not DE?

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")) %>% select(gene,DE) %>% filter(DE=="Yes")

Prot= read.table("../data/Khan_prot/ProtData_effectSize.txt",header = T,stringsAsFactors = F)  %>% mutate(dP=ifelse(pval<0.05, "Yes", "No")) %>% filter(dP=="Yes")

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")) %>% filter(dTE=="Yes")
listInput <- list(IC=dIConly$gene, DE=DiffExp$gene, DT=Ribo$gene, DP=Prot$gene)

upset(fromList(listInput), order.by = "freq", keep.order = T,empty.intersections = "on")

Version Author Date
b418f79 brimittleman 2020-05-07 
listInput <- list(IC=dIConly$gene, DE=DiffExp$gene, DP=Prot$gene)

upset(fromList(listInput), order.by = "freq", keep.order = T,empty.intersections = "on")

Version Author Date
b418f79 brimittleman 2020-05-07

So 40 genes that are dP and dIC not DE.

Proportion of dIC genes:

40/nrow(dIConly)
[1] 0.09389671

Try only dAPA:

dAPAonly=dAPAandDic %>%filter(OnlyAPA=="Yes")
nrow(dAPAonly)
[1] 1251
listInput <- list(dAPA=dAPAonly$gene, DE=DiffExp$gene, DP=Prot$gene)

upset(fromList(listInput), order.by = "freq", keep.order = T,empty.intersections = "on")

Version Author Date
b418f79 brimittleman 2020-05-07

76 in this set.

76/nrow(dAPAonly)
[1] 0.0607514

Smaller proportion. No sure how to test this.

Both:

Both=dAPAandDic %>%filter(Both=="Yes")
nrow(Both)
[1] 454
listInput <- list(BothAPA=Both$gene, DE=DiffExp$gene, DP=Prot$gene)

upset(fromList(listInput), order.by = "freq", keep.order = T,empty.intersections = "on")

Version Author Date
b418f79 brimittleman 2020-05-07

33 here:

33/nrow(Both)
[1] 0.07268722

Plot number in set and proprotion:

Set=c("OnlydIC", "Both", "OnlyAPA")
Number=c(40, 33,76)
SetSize=c(nrow(dIConly),nrow(Both),nrow(dAPAonly) )

useCOl <- c("#d73027", "#4575b4","#fee090")
DFres=data.frame(cbind(Set,Number,SetSize)) 
DFres$Number=as.numeric(as.character(DFres$Number))
DFres$SetSize=as.numeric(as.character(DFres$SetSize))

DFres_prop=DFres %>% mutate(Prop=Number/SetSize)

numberPlot=ggplot(DFres_prop,aes(x=Set, fill=Set, y=Number))+ geom_bar(stat="identity")+ scale_fill_manual(values=useCOl)+geom_text(aes(label=Number), position=position_dodge(width=0.9), vjust=2)+ theme(legend.position = "none") + labs(title="Number of dP not DE genes", y="Number of Genes",x="")

propPlot=ggplot(DFres_prop,aes(x=Set, fill=Set, y=Prop))+ geom_bar(stat="identity")+ scale_fill_manual(values=useCOl) + labs(title="Proportion of APA set that are dP not DE", y="Proportion of APA set",x="")+geom_text(aes(label=round(Prop,3)), position=position_dodge(width=0.9), vjust=2) + theme(legend.position = "none")
numberprop=plot_grid(numberPlot,propPlot)
numberprop

Are there differences in number of protein for these sets?

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

OrthoUTR=read.table("../data/orthoUTR/HumanDistal3UTR.sort.bed", col.names = c("chr",'start','end','gene','score','strand'),stringsAsFactors = F) %>% mutate(length=end-start) %>% select(gene, length)

UTRInt=OrthoUTR %>% inner_join(Interactions, by="gene") %>% mutate(Density=nInt/length)

#DiffExp$gene, DT=Ribo$gene, DP=Prot$gene

dAPAandDic_wP=dAPAandDic %>% mutate(dE=ifelse(gene %in%DiffExp$gene, "Yes", "No" ), dP=ifelse(gene %in%Prot$gene,"Yes","No" ), dPnotDE=ifelse(dE=="No"&dP=="Yes", "Yes","No")) %>% inner_join(UTRInt, by="gene")

dAPAandDic_wP_dAPA= dAPAandDic_wP %>% filter(dAPA=="Yes")

dAPAandDic_wP_both= dAPAandDic_wP %>% filter(Both=="Yes")


dAPAandDic_wP_IC= dAPAandDic_wP %>% filter(OnlyIC=="Yes")

Plot for all 3 sets:

dapaProt=ggplot(dAPAandDic_wP_dAPA,aes(x=dPnotDE, y=log10(Density),fill=dPnotDE)) + geom_boxplot(notch = T) + stat_compare_means() + scale_fill_manual(values = c("grey", "#4575b4"))+theme(axis.text.x=element_text(angle=90, hjust=0), text= element_text(size=10), legend.position = "false") + labs(x="Expression independent set", y="log10(Interaction Density)", title="Protein Interactions \ndAPA only genes") +geom_jitter(alpha=.1)


bothProt=ggplot(dAPAandDic_wP_both,aes(x=dPnotDE, y=log10(Density),fill=dPnotDE)) + geom_boxplot(notch = T) + stat_compare_means() + scale_fill_manual(values = c("grey", "#d73027"))+ theme(axis.text.x=element_text(angle=90, hjust=0), text= element_text(size=10), legend.position = "false") + labs(x="Expression independent set", y="log10(Interaction Density)", title="Protein Interactions \n dAPA and dIC genes")+geom_jitter(alpha=.1)

dicProt=ggplot(dAPAandDic_wP_IC,aes(x=dPnotDE, y=log10(Density),fill=dPnotDE)) + geom_boxplot(notch = T) + stat_compare_means() + scale_fill_manual(values = c("grey", "#fee090"))+theme(axis.text.x=element_text(angle=90, hjust=0), text= element_text(size=10), legend.position = "false") + labs(x="Expression independent set", y="log10(Interaction Density)", title="Protein Interactions \n dIC only genes")+geom_jitter(alpha=.1)
interactionplots=plot_grid(bothProt,dapaProt,dicProt, nrow=1)

Plot full:

plot_grid(numberprop, interactionplots, nrow = 2)


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    tidyverse_1.2.1
 [9] ggpubr_0.2      magrittr_1.5    cowplot_0.9.4   ggplot2_3.1.1  
[13] UpSetR_1.3.3    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       rlang_0.4.0     
 [9] later_0.7.5      pillar_1.3.1     glue_1.3.0       withr_2.1.2     
[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_1.0.4.6     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      lazyeval_0.2.1   crayon_1.3.4    
[41] whisker_0.3-2    pkgconfig_2.0.2  xml2_1.2.0       lubridate_1.7.4 
[45] rstudioapi_0.10  assertthat_0.2.0 rmarkdown_1.10   httr_1.3.1      
[49] R6_2.3.0         nlme_3.1-137     git2r_0.26.1     compiler_3.5.1