Last updated: 2020-01-14

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Knit directory: Comparative_APA/analysis/

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Rmd e1c6d25 brimittleman 2020-01-14 add analysis on species spec PAS

In this analysis I want to look at the PAS that are identified at at least 5% 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 ──
✔ 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()
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library(ggpubr)
Loading required package: magrittr

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

    extract
PAS=read.table("../data/PAS/PAS_5perc_either_HumanCoord_BothUsage_meta.txt", header = T, stringsAsFactors = F)

HumanSpec= PAS %>% filter(Chimp==0)
nrow(HumanSpec)
[1] 2588
ChimpSpec= PAS %>% filter(Human==0)
nrow(ChimpSpec)
[1] 4090

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] 40778
nrow(PAS_exp)/nrow(PAS)
[1] 0.5578538
HumanSpecExp= PAS_exp %>% filter(Chimp==0)
nrow(HumanSpecExp)
[1] 275
nrow(HumanSpecExp)/nrow(HumanSpec)
[1] 0.1062597
ChimpSpecExp= PAS_exp %>% filter(Human==0)
nrow(ChimpSpecExp)/nrow(ChimpSpec)
[1] 0.06919315

Looks like the filter on gene expresion removes almost half of the PAS and leaves a lot fewer specfic PAS. I can compare expression levels for the remaining specific PAS

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")

This may be evidence for filtering the PAS the same way we filter expression before DE.


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:
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 [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] ggpubr_0.2      magrittr_1.5    forcats_0.3.0   stringr_1.3.1  
 [5] dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1     tidyr_0.8.3    
 [9] tibble_2.1.1    ggplot2_3.1.1   tidyverse_1.2.1

loaded via a namespace (and not attached):
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[13] modelr_0.1.2     readxl_1.1.0     plyr_1.8.4       munsell_0.5.0   
[17] gtable_0.2.0     workflowr_1.5.0  cellranger_1.1.0 rvest_0.3.2     
[21] evaluate_0.12    labeling_0.3     knitr_1.20       httpuv_1.4.5    
[25] broom_0.5.1      Rcpp_1.0.2       promises_1.0.1   scales_1.0.0    
[29] backports_1.1.2  jsonlite_1.6     fs_1.3.1         hms_0.4.2       
[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