PAS number and dominance in total

Last updated: 2020-04-10

Checks: 7 0

Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.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/multiMap.Rmd
    Modified:   analysis/pol2.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
html	4c131c9	brimittleman	2020-04-08	Build site.
Rmd	2f5d4d5	brimittleman	2020-04-08	change number and add 3; utr
html	e45850e	brimittleman	2020-04-08	Build site.
Rmd	856c506	brimittleman	2020-04-08	fix labels
html	5e58f03	brimittleman	2020-04-08	Build site.
Rmd	87bde36	brimittleman	2020-04-08	cons and dom in total

library(workflowr)

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

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() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(reshape2)


Attaching package: 'reshape2'

The following object is masked from 'package:tidyr':

    smiths

Recreate the conservation of PAS number analysis but with the means from the total fraction.

PASMeta=read.table("../data/TotalFractionPAS/TotalFraction_PASmeta.txt", header = T, stringsAsFactors = F) %>% rename(Human=HumanTot, Chimp=ChimpTot)
PASpregene=PASMeta %>% group_by(gene) %>% summarize(nPAS=n())
PASmore2=PASpregene %>% filter(nPAS>1)
DiffDom=read.table("../data/TotalFractionPAS/TotalFraction_DiffDominant.txt",header = T,stringsAsFactors = F) %>% filter(gene %in% PASmore2$gene)
SameDom=read.table("../data/TotalFractionPAS/TotalFraction_sameDominant.txt",header = T,stringsAsFactors = F) %>% mutate(DiffinDom=Chimp-Human) %>% filter(gene %in% PASmore2$gene)

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

Version	Author	Date
5e58f03	brimittleman	2020-04-08

summary(SameDom$DiffinDom)

    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-0.77667 -0.11600 -0.04433 -0.05220  0.01600  0.49833

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
5e58f03	brimittleman	2020-04-08

For saem 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
5e58f03	brimittleman	2020-04-08

Different dominant:

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
5e58f03	brimittleman	2020-04-08

Number of PAS in same and different

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 \n Total Fraction")

Version	Author	Date
5e58f03	brimittleman	2020-04-08

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
e45850e	brimittleman	2020-04-08
5e58f03	brimittleman	2020-04-08

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

Version	Author	Date
5e58f03	brimittleman	2020-04-08

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
e45850e	brimittleman	2020-04-08
5e58f03	brimittleman	2020-04-08

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

Version	Author	Date
5e58f03	brimittleman	2020-04-08

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 \n Total fraction") + 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
5e58f03	brimittleman	2020-04-08

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 \n Total fraction")

Version	Author	Date
5e58f03	brimittleman	2020-04-08

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 \n Total fraction")

Version	Author	Date
5e58f03	brimittleman	2020-04-08

Does what is the dist for number here:

PASMeta_sm=PASMeta %>% dplyr::select(gene, Chimp, Human)
PASMeta_m5= melt(PASMeta_sm, id.var="gene", variable.name="species", value.name="meanUsage") %>% filter(meanUsage >=0.05) %>% group_by(species, gene) %>% summarise(nPAS=n())
PASMeta_m5$nPAS=as.factor(PASMeta_m5$nPAS)
ggplot(PASMeta_m5,aes(x=nPAS, by=species, fill=species)) + geom_histogram(stat="count",position = "dodge") +  scale_fill_brewer(palette = "Dark2") + labs(title="Distribution for number of PAS at 5% Total", x="Number of PAS",y="Genes")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Version	Author	Date
4c131c9	brimittleman	2020-04-08

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] reshape2_1.4.3  forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1  
 [5] purrr_0.3.2     readr_1.3.1     tidyr_0.8.3     tibble_2.1.1   
 [9] ggplot2_3.1.1   tidyverse_1.2.1 workflowr_1.6.0

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5   haven_1.1.2        lattice_0.20-38   
 [4] colorspace_1.3-2   generics_0.0.2     htmltools_0.3.6   
 [7] yaml_2.2.0         rlang_0.4.0        later_0.7.5       
[10] pillar_1.3.1       glue_1.3.0         withr_2.1.2       
[13] RColorBrewer_1.1-2 modelr_0.1.2       readxl_1.1.0      
[16] plyr_1.8.4         munsell_0.5.0      gtable_0.2.0      
[19] cellranger_1.1.0   rvest_0.3.2        evaluate_0.12     
[22] labeling_0.3       knitr_1.20         httpuv_1.4.5      
[25] broom_0.5.1        Rcpp_1.0.2         promises_1.0.1    
[28] scales_1.0.0       backports_1.1.2    jsonlite_1.6      
[31] fs_1.3.1           hms_0.4.2          digest_0.6.18     
[34] stringi_1.2.4      grid_3.5.1         rprojroot_1.3-2   
[37] cli_1.1.0          tools_3.5.1        magrittr_1.5      
[40] lazyeval_0.2.1     crayon_1.3.4       whisker_0.3-2     
[43] pkgconfig_2.0.2    xml2_1.2.0         lubridate_1.7.4   
[46] assertthat_0.2.0   rmarkdown_1.10     httr_1.3.1        
[49] rstudioapi_0.10    R6_2.3.0           nlme_3.1-137      
[52] git2r_0.26.1       compiler_3.5.1

PAS number and dominance in total

Briana Mittleman

4/7/2020