DIC not DE but DP

Last updated: 2020-05-31

Checks: 7 0

Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DeandNumPAS.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/incorporateQTLsAncestral.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/mRNADecay.Rmd
    Modified:   analysis/miRNAanalysis.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/phastCon.Rmd
    Modified:   analysis/pol2.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.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File	Version	Author	Date	Message
Rmd	2c88fe0	brimittleman	2020-05-31	fig 5 and 6
html	2d70737	brimittleman	2020-05-22	Build site.
Rmd	c5cb2cc	brimittleman	2020-05-22	add prot mod and effect sizes
html	afecfdb	brimittleman	2020-05-20	Build site.
Rmd	3cd8e7c	brimittleman	2020-05-20	update figures for new levels
html	cd50e4f	brimittleman	2020-05-18	Build site.
Rmd	050e765	brimittleman	2020-05-18	add translation analysis for dp not de
html	1a34e1b	brimittleman	2020-05-17	Build site.
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")


RiboAll=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"))

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
1a34e1b	brimittleman	2020-05-17
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
1a34e1b	brimittleman	2020-05-17
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
1a34e1b	brimittleman	2020-05-17
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
1a34e1b	brimittleman	2020-05-17
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="")+scale_x_discrete(labels=c(Both="Both", OnlyAPA="PAS\nLevel",OnlydIC= "Isoform\n Diversity"))

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")+scale_x_discrete(labels=c(Both="Both", OnlyAPA="PAS\nLevel",OnlydIC= "Isoform\n Diversity"))

numberprop=plot_grid(numberPlot,propPlot)
numberprop

Version	Author	Date
afecfdb	brimittleman	2020-05-20
1a34e1b	brimittleman	2020-05-17

Are there differences in number of protein for these sets?

normalize interactions by length of gene.

ProtInfo=read.table("../data/PTM/ProtLength.txt", sep = "\t",stringsAsFactors = F,header = T,col.names = c("entry","organism", "nAA", "gene")) %>% select(nAA, gene)

Interactions=read.table("../data/bioGRID/GeneswInteractions.txt",stringsAsFactors = F, header = T) %>% inner_join(ProtInfo, by="gene")%>% mutate(NormInter=nInt/nAA)

#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(Interactions, 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(NormInter),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(Normalized Interaction)", title="Protein Interactions \ngenes with site level differences") +geom_jitter(alpha=.1)


bothProt=ggplot(dAPAandDic_wP_both,aes(x=dPnotDE, y=log10(NormInter),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(Normalized Interaction)", title="Protein Interactions \n with difference in both levles")+geom_jitter(alpha=.1)

dicProt=ggplot(dAPAandDic_wP_IC,aes(x=dPnotDE, y=log10(NormInter),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(Normalized Interaction)", title="Protein Interactions \n genes with isoform diversity differences")+geom_jitter(alpha=.1)

interactionplots=plot_grid(bothProt,dapaProt,dicProt, nrow=1)

Plot full:

plot_grid(numberprop, interactionplots, nrow = 2)

Version	Author	Date
2d70737	brimittleman	2020-05-22
afecfdb	brimittleman	2020-05-20
1a34e1b	brimittleman	2020-05-17

are these diff in translation:

nrow(RiboAll)

[1] 9229

nrow(dAPAandDic_wP)

[1] 7228

dAPAandDic_wP_trans= dAPAandDic_wP %>% inner_join(RiboAll,by="gene")



dAPAandDic_wP_trans_dpnote= dAPAandDic_wP_trans %>% filter(dPnotDE=="Yes") %>% select(gene,Both, OnlyIC, OnlyAPA,dTE)


dAPAandDic_wP_trans_dpnoteOnlyAPA=dAPAandDic_wP_trans_dpnote  %>% filter(OnlyAPA=="Yes") %>%  group_by(dTE) %>% summarise(ndTE=n()) %>% mutate(set="OnlyAPA")

dAPAandDic_wP_trans_dpnoteBoth=dAPAandDic_wP_trans_dpnote  %>% filter(Both=="Yes") %>%  group_by(dTE) %>% summarise(ndTE=n()) %>% mutate(set="Both")

dAPAandDic_wP_trans_dpnoteIC=dAPAandDic_wP_trans_dpnote  %>% filter(OnlyIC=="Yes") %>%  group_by(dTE) %>% summarise(ndTE=n()) %>% mutate(set="OnlyIC")


AllTenum= dAPAandDic_wP_trans_dpnoteOnlyAPA %>% bind_rows(dAPAandDic_wP_trans_dpnoteBoth) %>% bind_rows(dAPAandDic_wP_trans_dpnoteIC)

numTE=ggplot(AllTenum, aes(x=set,by=dTE, y=ndTE,fill=set, alpha=dTE)) +geom_bar(stat="identity", position = "dodge") + labs(title="Most dP not dE genes are not dTE", y="Number of Genes",x="") + scale_fill_manual(values = useCOl ) + scale_alpha_manual(values=c(.6, 1)) + theme(legend.position = "bottom") + geom_text(aes(label=ndTE), position=position_dodge(width=0.9), vjust=1)+guides(fill = FALSE)+scale_x_discrete(labels=c(Both="Both", OnlyAPA="PAS\nLevel",OnlyIC= "Isoform\n Diversity"))

numTE

Version	Author	Date
2d70737	brimittleman	2020-05-22
afecfdb	brimittleman	2020-05-20
cd50e4f	brimittleman	2020-05-18

propTe=ggplot(AllTenum, aes(x=set,by=dTE, y=ndTE,fill=set, alpha=dTE)) +geom_bar(stat="identity", position = "fill") + labs(title="Most dP not dE genes are not dTE", y="Proportion",x="") + scale_fill_manual(values = useCOl ) + scale_alpha_manual(values=c(.4, 1)) + theme(legend.position = "bottom")+guides(fill = FALSE)+scale_x_discrete(labels=c(Both="Both", OnlyAPA="PAS\nLevel",OnlyIC= "Isoform\n Diversity"))
propTe

Version	Author	Date
2d70737	brimittleman	2020-05-22
afecfdb	brimittleman	2020-05-20
cd50e4f	brimittleman	2020-05-18

Of 6477 we have data for each of the 3 phenotypes

from 149 to 142

dAPAandDic_wP_trans %>% filter(dPnotDE=="Yes") %>% filter(Both=="Yes" | OnlyIC=="Yes" | OnlyAPA=="Yes") %>% group_by(dTE) %>% summarise(n())

# A tibble: 2 x 2
  dTE   `n()`
  <chr> <int>
1 No      111
2 Yes      35

teplots=plot_grid(numTE, propTe, nrow =1)
teplots

Version	Author	Date
2d70737	brimittleman	2020-05-22
afecfdb	brimittleman	2020-05-20
cd50e4f	brimittleman	2020-05-18

diff figures together:

fullplot=plot_grid(numberprop, teplots,interactionplots, nrow = 3)
fullplot

Version	Author	Date
2d70737	brimittleman	2020-05-22
afecfdb	brimittleman	2020-05-20
cd50e4f	brimittleman	2020-05-18

write data for main figures:

write.table(DFres_prop, "../output/FigureDF/DPnotDE.txt", col.names = T, row.names = F, quote = F)

write.table(AllTenum, "../output/FigureDF/DPnotDE_trans.txt", col.names = T, row.names = F, quote = F)

write.table(dAPAandDic_wP_dAPA, "../output/FigureDF/PinteractiondAPA.txt", col.names = T, row.names = F, quote = F)
write.table(dAPAandDic_wP_both, "../output/FigureDF/Pinteractionboth.txt", col.names = T, row.names = F, quote = F)
write.table(dAPAandDic_wP_IC, "../output/FigureDF/PinteractiondIC.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    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       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    labeling_0.3     knitr_1.20       httpuv_1.4.5    
[25] fansi_0.4.0      broom_0.5.1      Rcpp_1.0.4.6     promises_1.0.1  
[29] scales_1.0.0     backports_1.1.2  jsonlite_1.6     fs_1.3.1        
[33] gridExtra_2.3    hms_0.4.2        digest_0.6.18    stringi_1.2.4   
[37] grid_3.5.1       rprojroot_1.3-2  cli_1.1.0        tools_3.5.1     
[41] lazyeval_0.2.1   crayon_1.3.4     whisker_0.3-2    pkgconfig_2.0.2 
[45] xml2_1.2.0       lubridate_1.7.4  rstudioapi_0.10  assertthat_0.2.0
[49] rmarkdown_1.10   httr_1.3.1       R6_2.3.0         nlme_3.1-137    
[53] git2r_0.26.1     compiler_3.5.1

DIC not DE but DP

Briana Mittleman

5/7/2020

are these diff in translation: