Last updated: 2020-01-26

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Knit directory: Comparative_APA/analysis/

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File Version Author Date Message
Rmd 6658151 brimittleman 2020-01-26 add density plots
html 838da9b brimittleman 2020-01-23 Build site.
Rmd a018c33 brimittleman 2020-01-23 add diff usgage and ss

In this analysis, I will ask if presence or absense of a signal site in either species can explain the diffentially used PAS. I can do overlaps and look at correlations.

library(workflowr)
This is workflowr version 1.5.0
Run ?workflowr for help getting started
library(RColorBrewer)
library(ggpubr)
Loading required package: ggplot2
Loading required package: magrittr
library(tidyverse)
── Attaching packages ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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── Conflicts ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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MetaPAS=read.table("../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter_withSSTop2.txt", header = T, stringsAsFactors = F)

Pull in the differentiall used PAS:

DiffUsed=read.table("../data/DiffIso_Nuclear_DF/SignifianceEitherPAS_2_Nuclear.txt", header = T, stringsAsFactors = F) %>% rename("Human_NormUse"=Human, "Chimp_NormUse"=Chimp)

Join this by chr, start,end, gene

DiffUsed_anno=DiffUsed %>% inner_join(MetaPAS,by=c("chr","start", "end","gene"))

Ask how many of the effect size either way are those with SS in one species

  • upreg in chimp
  • upred in human
DiffUsed_anno_humanup=DiffUsed_anno %>% filter(deltaPAU<0)
nrow(DiffUsed_anno_humanup)
[1] 1612
nrow(DiffUsed_anno_humanup %>% filter(HumanTopSS=="Yes", ChimpTopSS=="No"))
[1] 6
DiffUsed_anno_chimpup=DiffUsed_anno %>% filter(deltaPAU>0)
nrow(DiffUsed_anno_chimpup)
[1] 1464
nrow(DiffUsed_anno_chimpup %>% filter(HumanTopSS=="No", ChimpTopSS=="Yes"))
[1] 13

This means that of the 3076 only 19 are in PAS where signal site could account for it.

Significance:

humanOnly=nrow(MetaPAS %>%  filter(HumanTopSS=="Yes", ChimpTopSS=="No"))

chimpOnly=nrow(MetaPAS %>%  filter(HumanTopSS=="No", ChimpTopSS=="Yes"))

humanOnly+chimpOnly
[1] 682

Of the 42318 we see that 638 have the pattern of interest. We choose 3076 of them and 19 come out. Look to see if it is more than expected by change

phyper(success in sample, sucesss in possible, failure possible, sample size)

x= 19
m= 638
n=41680
k=3076


#expected
which(grepl(max(dhyper(1:x, m, n, k)), dhyper(1:x, m, n, k)))
[1] 19
phyper(19,638, 41680 , 3076,lower.tail=F)
[1] 0.9999979

No significant enrichment for the pattern.

Plot dPAU by presence and absense:

ggplot(DiffUsed_anno_humanup,aes(y=abs(deltaPAU), x=HumanTopSS))+ geom_boxplot()+ stat_compare_means() + labs(title="Human upregualted PAS by presence of Signal")

Version Author Date
838da9b brimittleman 2020-01-23 
ggplot(DiffUsed_anno_humanup,aes(x=abs(deltaPAU), by=HumanTopSS, fill=HumanTopSS))+ geom_density(alpha=.5)+  labs(title="Human upregualted PAS by presence of Signal") + scale_fill_discrete(name="Human Signal Site Detected") + annotate("text",label="Wilcoxon, p=0.73",x=.8,y=6)

Test with wilcoxan test:

There is a shift.

ggplot(DiffUsed_anno_chimpup,aes(x=ChimpTopSS, y=abs(deltaPAU))) + geom_boxplot() + stat_compare_means() + labs(title="Chimp upregualted PAS by presence of Signal")

Version Author Date
838da9b brimittleman 2020-01-23 
ggplot(DiffUsed_anno_chimpup,aes(x=abs(deltaPAU), by=ChimpTopSS, fill=ChimpTopSS))+ geom_density(alpha=.5)+  labs(title="Chimp upregualted PAS by presence of Signal") + scale_fill_discrete(name="Chimp Signal Site Detected") + annotate("text",label="Wilcoxon, p=0.025",x=.8,y=7.5)

It does not look like presence of a signal within the upregulated matters.

Overall it does not look like presense of a signal site or not can explain the differences.


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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 [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     
 [4] purrr_0.3.2        readr_1.3.1        tidyr_0.8.3       
 [7] tibble_2.1.1       tidyverse_1.2.1    ggpubr_0.2        
[10] magrittr_1.5       ggplot2_3.1.1      RColorBrewer_1.1-2
[13] workflowr_1.5.0   

loaded via a namespace (and not attached):
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 [5] generics_0.0.2   htmltools_0.3.6  yaml_2.2.0       rlang_0.4.0     
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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     cellranger_1.1.0 rvest_0.3.2      evaluate_0.12   
[21] labeling_0.3     knitr_1.20       httpuv_1.4.5     broom_0.5.1     
[25] Rcpp_1.0.2       promises_1.0.1   scales_1.0.0     backports_1.1.2 
[29] jsonlite_1.6     fs_1.3.1         hms_0.4.2        digest_0.6.18   
[33] stringi_1.2.4    grid_3.5.1       rprojroot_1.3-2  cli_1.1.0       
[37] tools_3.5.1      lazyeval_0.2.1   crayon_1.3.4     whisker_0.3-2   
[41] pkgconfig_2.0.2  xml2_1.2.0       lubridate_1.7.4  assertthat_0.2.0
[45] rmarkdown_1.10   httr_1.3.1       rstudioapi_0.10  R6_2.3.0        
[49] nlme_3.1-137     git2r_0.26.1     compiler_3.5.1