Last updated: 2020-04-28

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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
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    Modified:   analysis/correlationPhenos.Rmd
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    Modified:   analysis/mRNADecay.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/pol2.Rmd
    Modified:   analysis/signalsites_doublefilter.Rmd
    Modified:   analysis/speciesSpecific.Rmd

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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 ──
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── Conflicts ─────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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library(ggpubr)
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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")

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

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()

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

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)

plot_grid(humanApasep, chimpApasep)

plot_grid(humanDEsep, chimpDEsep)

plot_grid(bothdapa, bothde)

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") 

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")

###Tissue number

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")

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

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

Think about better way to plot.

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.


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