Last updated: 2020-04-29

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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/ExploredAPA.Rmd
    Modified:   analysis/ExploredAPA_DF.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/establishCutoffs.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/mRNADecay.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/pol2.Rmd
    Modified:   analysis/signalsites_doublefilter.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 19a855b brimittleman 2020-04-29 hist mark plots and info with vars
html fa67b41 brimittleman 2020-04-28 Build site.
Rmd e51455f brimittleman 2020-04-28 add h3 and info with other vars

In this analysis I will look at info content and some other measures I have calculated to learn more about the regulatory landscape. (constraint of RNA expression and APA)

For example: - variance in gene expression - number of tissues gene is expressed - dn/ds (conservation)

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() ──
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library(ggpubr)
Loading required package: magrittr

Attaching package: 'magrittr'
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    set_names
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    extract
library(cowplot)

Attaching package: 'cowplot'
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library(workflowr)
This is workflowr version 1.6.0
Run ?workflowr for help getting started
SimpHuman=read.table("../data/InfoContent/Human_SimpsonInfoContent.txt", header = T, stringsAsFactors = F) %>% rename(simpson_Human=simpson) %>% mutate(simpOpp_Human=1-simpson_Human)
SimpChimp=read.table("../data/InfoContent/Chimp_SimpsonInfoContent.txt", header = T, stringsAsFactors = F)%>% rename(simpson_Chimp=simpson)%>% mutate(simpOpp_Chimp=1-simpson_Chimp)

BothSimp= SimpHuman %>% inner_join(SimpChimp, by=c("gene", "numPAS")) %>% filter(numPAS > 1)
HumanResInfo= read.table("../data/InfoContent/Human_InfoContent.txt", header = T,stringsAsFactors = F) %>% rename(Human_Base2=base2, Human_basee= basee)
ChimpResInfo= read.table("../data/InfoContent/Chimp_InfoContent.txt", header = T,stringsAsFactors = F) %>% rename(Chimp_Base2=base2, Chimp_basee= basee)

BothResInfo= HumanResInfo %>% inner_join(ChimpResInfo, by=c("gene", "numPAS")) %>% filter(numPAS > 1)
BothResBothInfoDomEH=BothResInfo %>% mutate(human_EH=Human_Base2/log2(as.numeric(as.character(numPAS))), chimp_EH=Chimp_Base2/log2(as.numeric(as.character(numPAS)))) 


AllInfoValues=BothResBothInfoDomEH %>% inner_join(BothSimp, by=c("gene", "numPAS"))
#write out:  

write.table(AllInfoValues, "../data/InfoContent/AllInforContentMetrics.txt", col.names = T, row.names = F, quote = F)

Expression variance

nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F)
expressionPassing=read.table("../data/DiffExpression/NormalizedExpressionPassCutoff.txt", stringsAsFactors = F, header = T)%>% inner_join(nameID, by="Gene_stable_ID")  %>% select(-Source_of_gene_name, -Gene_stable_ID) %>% rename(gene=Gene.name)

expressionPassing_human= expressionPassing %>% select(-NA4973,-NAPT30, -NA3622,-NA3659, -NA18358,-NAPT91) %>% gather("ind", "count",-gene) %>% group_by(gene) %>% summarise(HumanMean=mean(count), HumanVar=var(count))
expressionPassing_chimp= expressionPassing %>% select(-NA18498,-NA18504, -NA18510,-NA18523, -NA18502,-NA18499) %>% gather("ind", "count",-gene) %>% group_by(gene) %>% summarise(ChimpMean=mean(count), ChimpVar=var(count))


ExpressionPassingBoth=expressionPassing_human %>% inner_join(expressionPassing_chimp, by="gene") %>% inner_join(AllInfoValues, by="gene")

Plot variance and the information content by species:

ggplot(ExpressionPassingBoth,aes(x=simpOpp_Human,y=log10(HumanVar))) + geom_point() + stat_cor() + geom_density2d(color="blue")

Version Author Date
fa67b41 brimittleman 2020-04-28
ggplot(ExpressionPassingBoth,aes(x=simpOpp_Chimp,y=log10(ChimpVar))) + geom_point() + stat_cor()+ geom_density2d(color="blue")

Version Author Date
fa67b41 brimittleman 2020-04-28

Difference in variance:

Chimp -human

dAPAGenes=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt", header=T,stringsAsFactors=F)
DiffIso=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt", header=T,stringsAsFactors = F) %>% select(gene) %>% unique() %>% mutate(dAPA=ifelse(gene %in% dAPAGenes$gene, "Yes", "No"))


ExpressionPassingBoth_diff= ExpressionPassingBoth %>% mutate(DiffVar=ChimpVar-HumanVar, DiffSimp=simpOpp_Chimp-simpOpp_Human)   %>% inner_join(DiffIso,by="gene")



ggplot(ExpressionPassingBoth_diff, aes(y=DiffVar, x=simpOpp_Human)) + geom_point() + geom_density2d()+ stat_cor()

Version Author Date
fa67b41 brimittleman 2020-04-28
ggplot(ExpressionPassingBoth_diff, aes(y=DiffVar, x=simpOpp_Chimp)) + geom_point() + geom_density2d()+ stat_cor()

Version Author Date
fa67b41 brimittleman 2020-04-28
bothdapa=ggplot(ExpressionPassingBoth_diff, aes(y=DiffVar, x=DiffSimp,col=dAPA)) + geom_point(alpha=.4) + geom_density2d() + stat_cor() + scale_color_brewer(palette = "Set1") + labs(x= "Chimp Simpson - Human Simpson", y="Chimp DE Variance - Human DE Variance")

Looks like there are dAPA gene examples that have pretty different info indicies but not different gene expression variance.

They go in different dimensions rather than in a correlation.

humanAPA=ggplot(ExpressionPassingBoth_diff, aes(y=DiffSimp, x=HumanVar,col=dAPA)) + geom_point(alpha=.2) + geom_density2d()+labs(y="Chimp Simpson - Human Simpson", x="Variance in Human DE")+ scale_color_brewer(palette = "Set1")


humanApasep=ggplot(ExpressionPassingBoth_diff, aes(y=DiffSimp, x=HumanVar,col=dAPA)) + geom_point(alpha=.2) + geom_density2d()+labs(y="Chimp Simpson - Human Simpson", x="Variance in Human DE")+ scale_color_brewer(palette = "Set1") + facet_grid(~dAPA)


chimpAPA=ggplot(ExpressionPassingBoth_diff, aes(y=DiffSimp, x=ChimpVar,col=dAPA)) + geom_point(alpha=.2) + geom_density2d()+labs(y="Chimp Simpson - Human Simpson", x="Variance in Chimp DE")+ scale_color_brewer(palette = "Set1")

chimpApasep=ggplot(ExpressionPassingBoth_diff, aes(y=DiffSimp, x=ChimpVar,col=dAPA)) + geom_point(alpha=.2) + geom_density2d()+labs(y="Chimp Simpson - Human Simpson", x="Variance in Chimp DE")+ scale_color_brewer(palette = "Set1") + facet_grid(~dAPA)

Color by DE:

DE= read.table("../data/DiffExpression/DEtested_allres.txt",header=F, stringsAsFactors = F,col.names = c('Gene_stable_ID', 'logFC' ,'AveExpr', 't', 'P.Value', 'adj.P.Val', 'B')) %>% inner_join(nameID, by="Gene_stable_ID") %>% dplyr::select(-Gene_stable_ID, -Source_of_gene_name) %>% rename("gene"=Gene.name) %>% mutate(DE=ifelse(adj.P.Val<=.05, "Yes","No")) %>% select(DE,gene)


ExpressionPassingBoth_diffDE= ExpressionPassingBoth_diff %>% inner_join(DE, by="gene")


humanDE=ggplot(ExpressionPassingBoth_diffDE, aes(y=DiffSimp, x=HumanVar,col=DE)) + geom_point(alpha=.2) + geom_density2d()+labs(y="Chimp Simpson - Human Simpson", x="Variance in Human DE")+ scale_color_brewer(palette = "Set1")


humanDEsep=ggplot(ExpressionPassingBoth_diffDE, aes(y=DiffSimp, x=HumanVar,col=DE)) + geom_point(alpha=.2) + geom_density2d()+labs(y="Chimp Simpson - Human Simpson", x="Variance in Human DE")+ scale_color_brewer(palette = "Set1") + facet_grid(~DE)



chimpDE=ggplot(ExpressionPassingBoth_diffDE, aes(y=DiffSimp, x=ChimpVar,col=DE)) + geom_point(alpha=.2) + geom_density2d()+labs(y="Chimp Simpson - Human Simpson", x="Variance in Chimp DE")+ scale_color_brewer(palette = "Set1")



chimpDEsep=ggplot(ExpressionPassingBoth_diffDE, aes(y=DiffSimp, x=ChimpVar,col=DE)) + geom_point(alpha=.2) + geom_density2d()+labs(y="Chimp Simpson - Human Simpson", x="Variance in Chimp DE")+ scale_color_brewer(palette = "Set1")+ facet_grid(~DE)

bothde=ggplot(ExpressionPassingBoth_diffDE, aes(y=DiffVar, x=DiffSimp,col=DE)) + geom_point(alpha=.4) + geom_density2d() + stat_cor() + scale_color_brewer(palette = "Set1") + labs(x= "Chimp Simpson - Human Simpson", y="Chimp DE Variance - Human DE Variance")
plot_grid(humanAPA,chimpAPA,humanDE,chimpDE)

Version Author Date
fa67b41 brimittleman 2020-04-28
plot_grid(humanApasep, chimpApasep)

Version Author Date
fa67b41 brimittleman 2020-04-28
plot_grid(humanDEsep, chimpDEsep)

Version Author Date
fa67b41 brimittleman 2020-04-28
plot_grid(bothdapa, bothde)

Version Author Date
fa67b41 brimittleman 2020-04-28
ggplot(ExpressionPassingBoth_diffDE, aes(y=DiffSimp, x=log10(HumanVar),col=DE)) + geom_point(alpha=.2) + geom_density2d()+labs(y="Chimp Simpson - Human Simpson", x="Variance in Human DE")+ scale_color_brewer(palette = "Set1") 

Version Author Date
fa67b41 brimittleman 2020-04-28
ggplot(ExpressionPassingBoth_diff, aes(y=DiffSimp, x=log10(HumanVar),col=dAPA)) + geom_point(alpha=.2) + geom_density2d()+labs(y="Chimp Simpson - Human Simpson", x="Variance in Human DE")+ scale_color_brewer(palette = "Set1") 

Version Author Date
fa67b41 brimittleman 2020-04-28

Tissue number and variacne with GTEX

I will use gtex data to look at how many tissues the genes are expressed in. I can then see if this corrleates with the info content.

At first I will use TPM >10 for expressed. I have the data for expression from the apaQTL revisions.

geneNames=read.table("../../genome_anotation_data/ensemble_to_genename.txt", sep="\t", col.names = c('gene_id', 'gene', 'source' ),stringsAsFactors = F, header = T)  %>% select(gene_id, gene)
GTEX=read.table("../../apaQTL/data/nPAS/GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_median_tpm.gct", header = T, skip=2, sep = '\t') %>% 
  separate(Name,into=c("gene_id","extra"), sep="\\.") %>% 
  inner_join(geneNames, by="gene_id") %>% 
  select(-gene_id,-Description,-extra) %>% 
  gather("tissue", "TPM",-gene) %>% 
  filter(TPM >= 10) %>%
  group_by(gene) %>% 
  summarise(nTissue=n()) %>% 
  filter(nTissue<=54)

nrow(GTEX)
[1] 19144
nrow(AllInfoValues)
[1] 8451
InfoandTissue=GTEX %>% inner_join(AllInfoValues,by="gene")

ggplot(InfoandTissue, aes(x=simpOpp_Human, y=nTissue)) + geom_point() +stat_cor(col="blue") + geom_smooth(method="lm")

Version Author Date
fa67b41 brimittleman 2020-04-28
ggplot(InfoandTissue, aes(x=simpOpp_Chimp, y=nTissue)) + geom_point()+stat_cor(col="blue") + geom_smooth(method="lm") 

Version Author Date
fa67b41 brimittleman 2020-04-28
cor.test(InfoandTissue$nTissue, InfoandTissue$simpOpp_Chimp)$estimate
       cor 
-0.2694089 
cor.test(InfoandTissue$nTissue, InfoandTissue$simpOpp_Chimp)$p.value
[1] 4.180922e-125

Small but significant negative correlation, this means less dominance and fewer tissues. More dominance and more tissues.

Think about better way to plot.

I should get the correlation based on different cutoffs.

GTEXin=read.table("../../apaQTL/data/nPAS/GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_median_tpm.gct", header = T, skip=2, sep = '\t') %>%  separate(Name,into=c("gene_id","extra"), sep="\\.") %>% 
    inner_join(geneNames, by="gene_id") %>% 
    select(-gene_id,-Description,-extra) %>% 
    gather("tissue", "TPM",-gene)
CorHuman=c()
pHuman=c()
CorChimp=c()
pChimp=c()
Exp=seq(10,100,10)
for (i in Exp){
    tissueEx=GTEXin %>% 
    filter(TPM >= i) %>%
    group_by(gene) %>% 
    summarise(nTissue=n()) %>% 
    filter(nTissue<=54) %>% 
    inner_join(AllInfoValues,by="gene")
    chimpCor=cor.test(tissueEx$nTissue, tissueEx$simpOpp_Chimp)
    CorChimp=c(CorChimp,chimpCor$estimate )
    pChimp=c(pChimp, chimpCor$p.value)
    humanCor=cor.test(tissueEx$nTissue, tissueEx$simpOpp_Human)
    CorHuman=c(CorHuman, humanCor$estimate )
    pHuman=c(pHuman, humanCor$p.value)
}

TissueDF=as.data.frame(cbind(Exp, CorChimp, pChimp, CorHuman,pHuman))

Plot the correlations:

TissueDFg= TissueDF %>% select(Exp, CorChimp, CorHuman) %>% gather("Species", "Corr", -Exp) 
TissueDFg$Exp=as.factor(TissueDFg$Exp)
ggplot(TissueDFg,aes(x=Exp,y=Corr, by=Species, fill=Species)) + geom_bar(stat="identity", position="dodge") + labs(x="Expression Cutoff (TPM)", y="Correlation", title="Correlation between Simpson Index and Number of Tissues") + scale_fill_brewer(labels=c("Chimp", "Human"),palette = "Dark2")

More tissues have lower scores ( more dominance), fewer tissues have higher simpson scores ( less dominance.)

Do this with expression variance:

#ExpressionPassingBoth  
CorVarHuman=c()
pVarHuman=c()
CorVarChimp=c()
pVarChimp=c()

for (i in Exp){
    tissueEx=GTEXin %>% 
    filter(TPM >= i) %>%
    group_by(gene) %>% 
    summarise(nTissue=n()) %>% 
    filter(nTissue<=54) %>% 
    inner_join(ExpressionPassingBoth,by="gene")
    chimpCor=cor.test(tissueEx$nTissue, tissueEx$ChimpVar)
    CorVarChimp=c(CorVarChimp,chimpCor$estimate )
    pVarChimp=c(pVarChimp, chimpCor$p.value)
    humanCor=cor.test(tissueEx$nTissue, tissueEx$HumanVar)
    CorVarHuman=c(CorVarHuman, humanCor$estimate )
    pVarHuman=c(pVarHuman, humanCor$p.value)
}

TissueVarDF=as.data.frame(cbind(Exp, CorVarChimp, pVarChimp, CorVarHuman,pVarHuman))
TissueVarDFg= TissueVarDF %>% select(Exp, CorVarChimp, CorVarHuman) %>% gather("Species", "Corr", -Exp) 
TissueVarDFg$Exp=as.factor(TissueDFg$Exp)
ggplot(TissueVarDFg,aes(x=Exp,y=Corr, by=Species, fill=Species)) + geom_bar(stat="identity", position="dodge") + labs(x="Expression Cutoff (TPM)", y="Correlation", title="Correlation between Expression Variance and Number of Tissues") + scale_fill_brewer(labels=c("Chimp", "Human"),palette = "Dark2")

Look at this by tissue expression variance like i did for the revisions.

GTEXvar=read.table("../../apaQTL//data/nPAS/GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_median_tpm.gct", header = T, skip=2, sep = '\t',stringsAsFactors = F) %>% 
  separate(Name,into=c("gene_id","extra"), sep="\\.") %>% 
  inner_join(geneNames, by="gene_id") %>% 
  select(-gene_id, -extra, -Description) %>% 
  gather("Tissue", "TPM", -gene) %>%
  group_by(gene) %>% 
  summarise(TissueVar=var(TPM)) %>% 
  inner_join(AllInfoValues)
Joining, by = "gene"
ggplot(GTEXvar, aes(x=numPAS, y=log10(TissueVar+1))) + geom_point() + stat_cor() + geom_smooth(method="lm")

GTEXvarG= GTEXvar %>% select(gene, TissueVar, simpOpp_Chimp, simpOpp_Human) %>% gather("Species","simpson", -gene, -TissueVar)
ggplot(GTEXvarG, aes(x=simpson, y=log10(TissueVar+1),col=Species)) + geom_point(alpha=.3) + stat_cor() + geom_smooth(method="lm")+ scale_color_brewer(labels=c("Chimp", "Human"),palette = "Dark2") + labs(title='Negative correlation between variance \n across GTEX tissues and Simpson scores in both species')

  • ubiquitously expressed were found to be more likely to harbor multiple PAS. (lower variance likely indicate ubiquitous expression)

This follow what I saw previously. Ubiquitously expressed genes also have higher simpson index and are less likely to have 1 dominant PAS.

DN/DS

I will see if info content is correlated with DN/DS as a measure of conservation at the seq level.
I will remove 0s in this

DNDS= read.csv("../data/DNDS/HumanChimp_DNDS.csv", header = T,stringsAsFactors = F) %>% drop_na() %>% group_by(Gene.name) %>% slice(1) %>% ungroup() %>% filter(dS.with.Chimpanzee>0, dN.with.Chimpanzee>0)%>% mutate(DNDSratio= dN.with.Chimpanzee/dS.with.Chimpanzee) %>% dplyr::select(Gene.name, dN.with.Chimpanzee,dS.with.Chimpanzee,DNDSratio) %>% rename("gene"=Gene.name) %>% select(gene, DNDSratio)  

InfoandDNDS=DNDS %>% inner_join(AllInfoValues,by="gene")
ggplot(InfoandDNDS, aes(y=log10(DNDSratio), x=simpOpp_Human)) + geom_point() + stat_cor()

No relationship.

General Reg cascade differences

ProteinSig=read.table("../data/Khan_prot/HC_SigProtein.txt", header = T, stringsAsFactors = F)%>% dplyr::rename("gene"=gene.symbol)
ProteinAll=read.table("../data/Khan_prot/HC_AlltestedProtein.txt", header = T, stringsAsFactors = F) %>%  rename(gene=gene.symbol) %>% inner_join(AllInfoValues,by="gene") %>% mutate(SigP=ifelse(gene %in% ProteinSig$gene, "Yes", "No"))

ggplot(ProteinAll, aes(x=SigP, y=simpOpp_Human)) + geom_boxplot()+stat_compare_means()

ggplot(ProteinAll, aes(x=SigP, y=simpOpp_Chimp)) + geom_boxplot() +stat_compare_means()

ProteinAll_g=ProteinAll %>% select(gene, SigP, simpOpp_Human,simpOpp_Chimp) %>% rename(Human=simpOpp_Human, Chimp=simpOpp_Chimp)%>% gather("Species", "Simpson", -gene, -SigP)

protboth=ggplot(ProteinAll_g, aes(x=Species, by=SigP,fill=SigP, y=Simpson)) + geom_boxplot() +stat_compare_means() + scale_fill_brewer(palette = "Set1", name="Differential Protein") + labs(title="No differences for Simpson\n index in dP")+ theme(legend.position = "bottom")
#ribo
Ribo=read.table("../data/Wang_ribo/HC_SigTranslation.txt",header = T, stringsAsFactors = F) 

RiboAll=read.table("../data/Wang_ribo/HC_AllTestedTranslation.txt",header = T, stringsAsFactors = F)%>% rename(gene=Gene) %>% inner_join(AllInfoValues,by="gene") %>% mutate(SigR=ifelse(gene %in% Ribo$Gene, "Yes", "No"))

ggplot(RiboAll, aes(x=SigR, y=simpOpp_Human)) + geom_boxplot()+stat_compare_means()

ggplot(RiboAll, aes(x=SigR, y=simpOpp_Chimp)) + geom_boxplot() +stat_compare_means()

RiboAlll_g=RiboAll %>% select(gene, SigR, simpOpp_Human,simpOpp_Chimp) %>% rename(Human=simpOpp_Human, Chimp=simpOpp_Chimp)%>% gather("Species", "Simpson", -gene, -SigR)

riboboth=ggplot(RiboAlll_g, aes(x=Species, by=SigR,fill=SigR, y=Simpson)) + geom_boxplot() +stat_compare_means() + scale_fill_brewer(palette = "Set1", name="Differential Translation") + labs(title="No differences for Simpson\n index in dRibo")+ theme(legend.position = "bottom")
nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F)
DE= read.table("../data/DiffExpression/DEtested_allres.txt",header=F, stringsAsFactors = F,col.names = c('Gene_stable_ID', 'logFC' ,'AveExpr', 't', 'P.Value', 'adj.P.Val', 'B')) %>% inner_join(nameID, by="Gene_stable_ID") %>% dplyr::select(-Gene_stable_ID, -Source_of_gene_name) %>% rename("gene"=Gene.name) %>% mutate(DE=ifelse(adj.P.Val<=.05, "Yes","No"))   %>% inner_join(AllInfoValues,by="gene")

ggplot(DE, aes(x=DE, y=simpOpp_Human)) + geom_boxplot()+stat_compare_means()

ggplot(DE, aes(x=DE, y=simpOpp_Chimp)) + geom_boxplot() +stat_compare_means()

DE_g=DE %>% select(gene, DE, simpOpp_Human,simpOpp_Chimp) %>% rename(Human=simpOpp_Human, Chimp=simpOpp_Chimp)%>% gather("Species", "Simpson", -gene, -DE)

DEboth=ggplot(DE_g, aes(x=Species, by=DE,fill=DE, y=Simpson)) + geom_boxplot() +stat_compare_means() + scale_fill_brewer(palette = "Set1", name="Differential Expression") + labs(title="No differences for Simpson\n index in DE") + theme(legend.position = "bottom")

DEboth

plot_grid(DEboth, riboboth, protboth, nrow=1 )

Variance in protein.

copy human LCL data to ../data/HumanMolPheno

ProteinPheno=read.table("../data/HumanMolPheno/fastqtl_qqnorm_prot.fixed.noChr.txt", header = T, stringsAsFactors = F) %>% 
  rename(Gene_stable_ID= ID) %>% 
  inner_join(nameID, by = "Gene_stable_ID") %>%
  select(-Source_of_gene_name,-Gene_stable_ID, -start, -end, -Chr ) %>%
  rename(gene=Gene.name) %>% 
  gather("Ind", "level", -gene) %>% 
  group_by(gene) %>% 
  drop_na() %>% 
  summarise(MeanProt=mean(level), VarProt=var(level)) %>% 
  inner_join(AllInfoValues, by="gene") %>% 
  select(gene, VarProt, MeanProt, simpOpp_Human,simpOpp_Chimp) %>% 
  rename(Human=simpOpp_Human,Chimp=simpOpp_Chimp ) %>% 
  gather("species", "simpson", -gene, -VarProt, -MeanProt)


ggplot(ProteinPheno, aes(x=simpson, y=VarProt, col=species)) + geom_point() + stat_cor()

Variance in ribo:

RiboPheno=read.table("../data/HumanMolPheno/fastqtl_qqnorm_ribo_phase2.fixed.noChr.txt", header = T, stringsAsFactors = F) %>% 
  separate(ID, into=c("Gene_stable_ID", "Extra"), sep="\\.")  %>% 
  inner_join(nameID, by = "Gene_stable_ID") %>%
  select(-Source_of_gene_name,-Gene_stable_ID, -start, -end, -Chr, -Extra ) %>%
  rename(gene=Gene.name) %>% 
  gather("Ind", "level", -gene) %>% 
  group_by(gene)%>% 
  drop_na() %>% 
  summarise(MeanRibo=mean(level), VarRibo=var(level))%>% 
  inner_join(AllInfoValues, by="gene") %>% 
  select(gene, VarRibo, MeanRibo, simpOpp_Human,simpOpp_Chimp) %>% 
  rename(Human=simpOpp_Human,Chimp=simpOpp_Chimp ) %>% 
  gather("species", "simpson", -gene, -VarRibo, -MeanRibo)


ggplot(RiboPheno, aes(x=simpson, y=VarRibo, col=species)) + geom_point() + stat_cor()

No relationship gere.


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] workflowr_1.6.0 cowplot_0.9.4   ggpubr_0.2      magrittr_1.5   
 [5] forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2    
 [9] readr_1.3.1     tidyr_0.8.3     tibble_2.1.1    ggplot2_3.1.1  
[13] tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5   reshape2_1.4.3     haven_1.1.2       
 [4] lattice_0.20-38    colorspace_1.3-2   generics_0.0.2    
 [7] htmltools_0.3.6    yaml_2.2.0         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       cellranger_1.1.0   rvest_0.3.2       
[22] evaluate_0.12      labeling_0.3       knitr_1.20        
[25] httpuv_1.4.5       broom_0.5.1        Rcpp_1.0.4.6      
[28] promises_1.0.1     scales_1.0.0       backports_1.1.2   
[31] jsonlite_1.6       fs_1.3.1           hms_0.4.2         
[34] digest_0.6.18      stringi_1.2.4      grid_3.5.1        
[37] rprojroot_1.3-2    cli_1.1.0          tools_3.5.1       
[40] lazyeval_0.2.1     crayon_1.3.4       whisker_0.3-2     
[43] pkgconfig_2.0.2    MASS_7.3-51.1      xml2_1.2.0        
[46] lubridate_1.7.4    assertthat_0.2.0   rmarkdown_1.10    
[49] httr_1.3.1         rstudioapi_0.10    R6_2.3.0          
[52] nlme_3.1-137       git2r_0.26.1       compiler_3.5.1