Info and other measures

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  
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library(ggpubr)

Loading required package: magrittr


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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

Info and other measures

Briana Mittleman

4/28/2020

Expression variance

Tissue number and variacne with GTEX

DN/DS

General Reg cascade differences