Last updated: 2020-05-20
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Knit directory: Comparative_APA/analysis/
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Rmd | 3cd8e7c | brimittleman | 2020-05-20 | update figures for new levels |
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Rmd | 050e765 | brimittleman | 2020-05-18 | add translation analysis for dp not de |
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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'
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library(ggpubr)
Loading required package: magrittr
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library(tidyverse)
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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$gene)
upset(fromList(listInput), order.by = "freq", keep.order = T,empty.intersections = "on")
listInput <- list(IC=dIConly$gene, DE=DiffExp$gene, DP=Prot$gene)
upset(fromList(listInput), order.by = "freq", keep.order = T,empty.intersections = "on")
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$gene)
upset(fromList(listInput), order.by = "freq", keep.order = T,empty.intersections = "on")
76 in this set.
76/nrow(dAPAonly)
[1] 0.0607514
Smaller proportion. No sure how to test this.
Both:
Both=dAPAandDic %>%filter(Both=="Yes")
nrow(Both)
[1] 454
listInput <- list(BothAPA=Both$gene, DE=DiffExp$gene, DP=Prot$gene)
upset(fromList(listInput), order.by = "freq", keep.order = T,empty.intersections = "on")
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 |
---|---|---|
1a34e1b | brimittleman | 2020-05-17 |
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 \ngenes with site level differences") +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 with difference in both levles")+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 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 |
---|---|---|
1a34e1b | brimittleman | 2020-05-17 |
nrow(RiboAll)
[1] 9229
nrow(dAPAandDic_wP)
[1] 7028
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 |
---|---|---|
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 |
---|---|---|
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 108
2 Yes 34
teplots=plot_grid(numTE, propTe, nrow =1)
teplots
Version | Author | Date |
---|---|---|
cd50e4f | brimittleman | 2020-05-18 |
diff figures together:
fullplot=plot_grid(numberprop, teplots,interactionplots, nrow = 3)
fullplot
Version | Author | Date |
---|---|---|
cd50e4f | brimittleman | 2020-05-18 |
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