Species Specific PAS Double Filter

Last updated: 2020-01-21

Checks: 7 0

Knit directory: Comparative_APA/analysis/

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Unstaged changes:
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    Modified:   analysis/establishCutoffs.Rmd
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    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/speciesSpecific.Rmd

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File	Version	Author	Date	Message
Rmd	010c249	brimittleman	2020-01-21	DF species specific

In this analysis I want to look at the PAS that are identified at at least 10% in one species but are not identified in the other species. I will work with avergage nuclear. I can then run the differential apa analysis with only the PAS identified in both.

library(tidyverse)

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

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

Loading required package: magrittr


Attaching package: 'magrittr'

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

    set_names

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

    extract

PAS=read.table("../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F)

HumanSpec= PAS %>% filter(Chimp==0)
nrow(HumanSpec)

[1] 233

ChimpSpec= PAS %>% filter(Human==0)
nrow(ChimpSpec)

[1] 226

This is a lot better. This is down from 2500 and 4000 respecively.

Look at the distribution of these accross the gene.

ggplot(HumanSpec,aes(x=loc,fill=loc)) + geom_bar(stat="count")  + labs(x="Genic location", y="Number of PAS", title="Location of Human Specific PAS")

ggplot(ChimpSpec,aes(x=loc,fill=loc)) + geom_bar(stat="count")  + labs(x="Genic location", y="Number of PAS", title="Location of Chimp Specific PAS")

Next I will see if these are due to low expression. I will pull in the average normalized expression and rerun the filter.

nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F) %>% dplyr::select(Gene_stable_ID,Gene.name)
expr=read.table("../data/DiffExpression/NoramalizedExpression.txt",header = T,stringsAsFactors = F) %>% rename('Gene_stable_ID'=genes) %>% inner_join(nameID, by="Gene_stable_ID") %>% dplyr::select(Gene.name,Chimp, Human) %>% rename("ChimpExp"=Chimp, "HumanExp"=Human, "gene"=Gene.name)

PAS_exp=PAS %>% inner_join(expr,by="gene")
nrow(PAS_exp)

[1] 36028

nrow(PAS_exp)/nrow(PAS)

[1] 0.8513635

HumanSpecExp= PAS_exp %>% filter(Chimp==0)
nrow(HumanSpecExp)

[1] 153

nrow(HumanSpecExp)/nrow(HumanSpec)

[1] 0.6566524

ChimpSpecExp= PAS_exp %>% filter(Human==0)
nrow(ChimpSpecExp)

[1] 143

nrow(ChimpSpecExp)/nrow(ChimpSpec)

[1] 0.6327434

We dont lose as many this way. This is evident the filter worked.

PAS_exp_spe=PAS_exp %>% mutate(HumanSpec=ifelse(gene %in%HumanSpecExp$gene, "yes", "no"), ChimpSpec=ifelse(gene %in% ChimpSpecExp$gene, "yes","no"))

ggplot(PAS_exp_spe,aes(x=HumanSpec,y=HumanExp)) + geom_boxplot() + stat_compare_means(method = "t.test") + labs(x="Presence of Human Specific PAS", y="Average Normalized Expression", title="Expression in Genes with Human Specific PAS")

ggplot(PAS_exp_spe,aes(x=ChimpSpec,y=ChimpExp)) + geom_boxplot() + stat_compare_means(method = "t.test") + labs(x="Presence of Chimp Specific PAS", y="Average Normalized Expression", title="Expression in Genes with Chimp Specific PAS")

Still a difference but not as strong.

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

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attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
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[33] digest_0.6.18    stringi_1.2.4    grid_3.5.1       rprojroot_1.3-2 
[37] cli_1.1.0        tools_3.5.1      lazyeval_0.2.1   crayon_1.3.4    
[41] whisker_0.3-2    pkgconfig_2.0.2  xml2_1.2.0       lubridate_1.7.4 
[45] assertthat_0.2.0 rmarkdown_1.10   httr_1.3.1       rstudioapi_0.10 
[49] R6_2.3.0         nlme_3.1-137     git2r_0.26.1     compiler_3.5.1

Species Specific PAS Double Filter

Briana Mittleman

1/21/2020