Number of Conserved PAS

Last updated: 2020-04-10

Checks: 7 0

Knit directory: Comparative_APA/analysis/

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Unstaged changes:
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    Modified:   analysis/speciesSpecific.Rmd

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File	Version	Author	Date	Message
html	e45850e	brimittleman	2020-04-08	Build site.
Rmd	856c506	brimittleman	2020-04-08	fix labels
html	321219e	brimittleman	2020-04-07	Build site.
Rmd	d231c12	brimittleman	2020-04-07	diff in dom
html	d81b148	brimittleman	2020-04-07	Build site.
Rmd	a672b02	brimittleman	2020-04-07	numb dist for dom

library(workflowr)

This is workflowr version 1.6.0
Run ?workflowr for help getting started

library(tidyverse)

── Attaching packages ─────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

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── Conflicts ────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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I look at genes with the same dominant PAS but I want to look at the distribution of PAS in a gene. Are the number of PAS used for each gene conserved. I can use different usage filters then see if the distributions are the same or different when the gene has the same dominant site.

One question is whether we see examples of 1 dominant site in one species and the other has more than one site used at similar proportions.

I can start by looking at the genes with shared dominant PAS to see if the usages in each species are similar.

I will filter for genes with more than 1 PAS

PASMeta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F)
PASpregene=PASMeta %>% group_by(gene) %>% summarize(nPAS=n())
PASmore2=PASpregene %>% filter(nPAS>1)
DiffDom=read.table("../data/DominantPAS_DF/Nuclear_DiffDom.txt",header = T,stringsAsFactors = F) %>% filter(gene %in% PASmore2$gene)
SameDom=read.table("../data/DominantPAS_DF/Nuclear_SameDom.txt",header = T,stringsAsFactors = F) %>% mutate(DiffinDom=Chimp-Human) %>% filter(gene %in% PASmore2$gene)

Plot this: - is more in human:

ggplot(SameDom,aes(x=DiffinDom))+ geom_histogram(bins=100)

Version	Author	Date
d81b148	brimittleman	2020-04-07

A bit shifted toward chimp.

summary(SameDom$DiffinDom)

     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
-0.763000 -0.004833  0.061833  0.066015  0.138000  0.548833

I can look at examples at each extreme.

Fore both species, i want to get the number of PAS used at certain cutoffs, 0%-50%

Cutoff=seq(0,.5,.1)
cutoffCol=c()
nPAS=seq(1,5,1)
ChimpGenes=c()
HumanGenes=c()
nPAScol=c()
for (i in Cutoff){
  for (n in nPAS){
    human= PASMeta %>% filter(gene %in% PASmore2$gene, Human >= i) %>% group_by(gene) %>% summarise(nPASingene=n()) %>% filter(nPASingene==n) %>% nrow() 
    HumanGenes=c(HumanGenes,human)
    chimp= PASMeta %>% filter(gene %in% PASmore2$gene,Chimp >= i) %>% group_by(gene) %>% summarise(nPASingene=n()) %>% filter(nPASingene==n) %>% nrow() 
    ChimpGenes=c(ChimpGenes,chimp)
    nPAScol=c(nPAScol,n)
    cutoffCol=c(cutoffCol,i)
  }
}

DFdata=as.data.frame(cbind(nPAScol,cutoffCol,ChimpGenes, HumanGenes))
DFdata_gather=DFdata %>% gather("Species", "NGene", -nPAScol, -cutoffCol)
DFdata_gather$nPAScol=as.factor(DFdata_gather$nPAScol)
DFdata_gather$cutoffCol=as.factor(DFdata_gather$cutoffCol)

ggplot(DFdata_gather,aes(x=cutoffCol, by=nPAScol, y=NGene,fill=nPAScol))+ geom_bar(position = "dodge", stat="identity") +facet_grid(~Species) + scale_fill_brewer(palette="Dark2", name="Number of PAS") + labs(title="Number of PAS per gene by usage",y="Number of Genes", x="Usage is at least")

Version	Author	Date
d81b148	brimittleman	2020-04-07

Do the same thing for same dominant:

PASMetaSame= PASMeta %>% filter(gene %in% SameDom$gene)
cutoffCol_same=c()
ChimpGenes_same=c()
HumanGenes_same=c()
nPAScol_same=c()
for (i in Cutoff){
  for (n in nPAS){
    human= PASMetaSame %>% filter( Human >= i) %>% group_by(gene) %>% summarise(nPASingene=n()) %>% filter(nPASingene==n) %>% nrow() 
    HumanGenes_same=c(HumanGenes_same,human)
    chimp= PASMetaSame %>% filter(Chimp >= i) %>% group_by(gene) %>% summarise(nPASingene=n()) %>% filter(nPASingene==n) %>% nrow() 
    ChimpGenes_same=c(ChimpGenes_same,chimp)
    nPAScol_same=c(nPAScol_same,n)
    cutoffCol_same=c(cutoffCol_same,i)
  }
}

DFdata_same=as.data.frame(cbind(nPAScol_same,cutoffCol_same,ChimpGenes_same, HumanGenes_same))
DFdata_gather_same=DFdata_same %>% gather("Species", "NGene", -nPAScol_same, -cutoffCol_same)
DFdata_gather_same$nPAScol_same=as.factor(DFdata_gather_same$nPAScol_same)
DFdata_gather_same$cutoffCol_same=as.factor(DFdata_gather_same$cutoffCol_same)

ggplot(DFdata_gather_same,aes(x=cutoffCol_same, by=nPAScol_same, y=NGene,fill=nPAScol_same))+ geom_bar(position = "dodge", stat="identity") +facet_grid(~Species) + scale_fill_brewer(palette="Dark2", name="Number of PAS") + labs(title="Number of PAS per gene by usage \n same domiant",y="Number of Genes", x="Usage is at least")

Version	Author	Date
d81b148	brimittleman	2020-04-07

Different dominannt:

PASMetaDiff= PASMeta %>% filter(gene %in% DiffDom$gene)
cutoffCol_Diff=c()
ChimpGenes_Diff=c()
HumanGenes_Diff=c()
nPAScol_Diff=c()
for (i in Cutoff){
  for (n in nPAS){
    human= PASMetaDiff %>% filter( Human >= i) %>% group_by(gene) %>% summarise(nPASingene=n()) %>% filter(nPASingene==n) %>% nrow() 
    HumanGenes_Diff=c(HumanGenes_Diff,human)
    chimp= PASMetaDiff %>% filter(Chimp >= i) %>% group_by(gene) %>% summarise(nPASingene=n()) %>% filter(nPASingene==n) %>% nrow() 
    ChimpGenes_Diff=c(ChimpGenes_Diff,chimp)
    nPAScol_Diff=c(nPAScol_Diff,n)
    cutoffCol_Diff=c(cutoffCol_Diff,i)
  }
}

DFdata_Diff=as.data.frame(cbind(nPAScol_Diff,cutoffCol_Diff,ChimpGenes_Diff, HumanGenes_Diff))
DFdata_gather_Diff=DFdata_Diff %>% gather("Species", "NGene", -nPAScol_Diff, -cutoffCol_Diff)
DFdata_gather_Diff$nPAScol_Diff=as.factor(DFdata_gather_Diff$nPAScol_Diff)
DFdata_gather_Diff$cutoffCol_Diff=as.factor(DFdata_gather_Diff$cutoffCol_Diff)

ggplot(DFdata_gather_Diff,aes(x=cutoffCol_Diff, by=nPAScol_Diff, y=NGene,fill=nPAScol_Diff))+ geom_bar(position = "dodge", stat="identity") +facet_grid(~Species) + scale_fill_brewer(palette="Dark2", name="Number of PAS") + labs(title="Number of PAS per gene by usage \n different domiant",y="Number of Genes", x="Usage is at least")

Version	Author	Date
d81b148	brimittleman	2020-04-07

Number of PAS in same vs diffferent:

NpasSame=PASMetaSame%>% group_by(gene) %>% summarise(nPAS=n()) %>% group_by(nPAS) %>% summarise(Same=n(),PropSame=Same/nrow(PASMetaSame)) %>% select(-Same)
NpasDiff=PASMetaDiff %>% group_by(gene) %>% summarise(nPAS=n()) %>% group_by(nPAS) %>% summarise(Different=n(), PropDiff=Different/nrow(PASMetaDiff)) %>% select(-Different)

NpasBoth= NpasSame %>% inner_join(NpasDiff) %>% gather("GeneSet", "Prop", -nPAS)

Joining, by = "nPAS"

NpasBoth$nPAS=as.factor(NpasBoth$nPAS)

ggplot(NpasBoth,aes(fill=GeneSet, y=Prop, x=nPAS))+ geom_bar(position = "dodge", stat="identity") + scale_fill_brewer(palette="Dark2", labels=c("Different Dominant", "Same Dominant"),name="") + labs(y="Proportion of Genes", x="Number of PAS", title="Number of PAS distribution by Dominance structure")

Version	Author	Date
d81b148	brimittleman	2020-04-07

When the dominant are diff:

PASMeta_humanDom_diff=PASMeta %>% filter(PAS%in%DiffDom$HumanPAS) %>% mutate(Diff=Human-Chimp)

ggplot(PASMeta_humanDom_diff,aes(x=Human, y=Chimp))+geom_point() + geom_abline(slope=1, intercept = 0,col="red") +labs(title="PAS usage for different dominant, condition on Human")

Version	Author	Date
321219e	brimittleman	2020-04-07

ggplot(PASMeta_humanDom_diff,aes(x=Diff))+geom_histogram(bins=100) + labs(title="Difference in Human - Chimp for Human Dominant")

Version	Author	Date
321219e	brimittleman	2020-04-07

PASMeta_ChimpDom_diff=PASMeta %>% filter(PAS%in%DiffDom$ChimpPAS) %>% mutate(Diff=Human-Chimp)

ggplot(PASMeta_ChimpDom_diff,aes(x=Human, y=Chimp))+geom_point() + geom_abline(slope=1, intercept = 0,col="red") +labs(title="PAS usage for different dominant, condition on Chimp")

Version	Author	Date
321219e	brimittleman	2020-04-07

ggplot(PASMeta_ChimpDom_diff,aes(x=Diff))+geom_histogram(bins=100) + labs(title="Difference in Human - Chimp for Chimp Dominant")

Version	Author	Date
321219e	brimittleman	2020-04-07

ggplot(PASMeta_humanDom_diff,aes(x=Diff))+geom_histogram(bins=100, fill="#D95F02",alpha=.5) + labs(title="Human Usage - Chimp Usage \n Colored by dominant") + geom_histogram(data=PASMeta_ChimpDom_diff,aes(x=Diff), bins = 100, fill="#1B9E77", alpha=.5) + geom_vline(xintercept = mean(PASMeta_ChimpDom_diff$Diff), col="#1B9E77")+ geom_vline(xintercept = mean(PASMeta_humanDom_diff$Diff), col="#D95F02") + geom_histogram(bins=100, data=SameDom, aes(x=DiffinDom), alpha=.3)+ geom_vline(xintercept = mean(SameDom$DiffinDom))

Version	Author	Date
e45850e	brimittleman	2020-04-08
321219e	brimittleman	2020-04-07

mean(PASMeta_humanDom_diff$Diff)

[1] 0.1023134

mean(PASMeta_ChimpDom_diff$Diff)

[1] -0.1567008

Do this same analysis but by number of PAS in the gene.

PASMeta_humanDom_diffsmall= PASMeta_humanDom_diff %>% select(gene, Diff) %>% mutate(Species="Human")%>% full_join(PASpregene, by="gene")

PASMeta_ChimpDom_diffsmall= PASMeta_ChimpDom_diff %>% select(gene, Diff) %>% mutate(Species="Chimp")%>% full_join(PASpregene, by="gene")

SameDomSmall=SameDom %>% rename(Diff=DiffinDom) %>% select(gene, Diff) %>% mutate(Species="Both")%>% full_join(PASpregene, by="gene")



Alldiff=PASMeta_humanDom_diffsmall %>% bind_rows(PASMeta_ChimpDom_diffsmall) %>% bind_rows(SameDomSmall) %>% full_join(PASpregene, by="gene")



ggplot(PASMeta_humanDom_diffsmall,aes(x=Diff)) +geom_histogram(bins=100, fill="#D95F02",alpha=.5) + geom_histogram(data=PASMeta_ChimpDom_diffsmall,aes(x=Diff), bins = 100, fill="#1B9E77", alpha=.5) + geom_histogram(bins=100, data=SameDomSmall, aes(x=Diff), alpha=.3)+ facet_grid(~nPAS)

Warning: Removed 6897 rows containing non-finite values (stat_bin).

Warning: Removed 6897 rows containing non-finite values (stat_bin).

Warning: Removed 3946 rows containing non-finite values (stat_bin).

Version	Author	Date
321219e	brimittleman	2020-04-07

I want something a bit different dominant PAS in human - human value for dominant PAS in chimp

DiffDomfromH= DiffDom %>% select(Human, ChimpPAS) %>% rename(PAS=ChimpPAS, humanDom=Human) %>% inner_join(PASMeta, by="PAS")%>% mutate(Diff=humanDom-Human,Dom="Human") %>% select(gene,Dom, Diff) %>% inner_join(PASpregene, by="gene")%>% filter(nPAS<6)

DiffDomfromC= DiffDom %>% select(Chimp, HumanPAS) %>% rename(PAS=HumanPAS, ChimpDom=Chimp) %>% inner_join(PASMeta, by="PAS")%>% mutate(Diff=ChimpDom-Chimp,Dom="Chimp")%>% select(gene,Dom, Diff) %>% inner_join(PASpregene, by="gene") %>% filter(nPAS<6)


ggplot(DiffDomfromH,aes(x=Diff))+ geom_histogram(bins=50, fill="#D95F02",alpha=.5) + geom_histogram(data=DiffDomfromC, bins=50,fill="#1B9E77", alpha=.5 ) + facet_grid(~nPAS) + labs(x="Difference in Mean usage", title="Dominant PAS in species - same species value for \nthe 'dominant' in other species")

Version	Author	Date
321219e	brimittleman	2020-04-07

ggplot(DiffDomfromH,aes(x=Diff))+ geom_histogram(bins=50, fill="#D95F02",alpha=.5) + geom_histogram(data=DiffDomfromC, bins=50,fill="#1B9E77", alpha=.5 ) + labs(x="Difference in Mean usage", title="Dominant PAS in species - same species value for \nthe 'dominant' in other species")

Version	Author	Date
321219e	brimittleman	2020-04-07

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    ggplot2_3.1.1  
 [9] tidyverse_1.2.1 workflowr_1.6.0

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_1.0.2        
[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] magrittr_1.5       lazyeval_0.2.1     crayon_1.3.4      
[43] whisker_0.3-2      pkgconfig_2.0.2    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

Number of Conserved PAS

Briana Mittleman

4/7/2020