Last updated: 2020-01-21
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Knit directory: Comparative_APA/analysis/
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File | Version | Author | Date | Message |
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Rmd | 414b47c | brimittleman | 2020-01-21 | update dominant PAS |
In this analysis I want to find the most dominant PAS for each gene in each species. I am interested in genes where the dominant PAS in human and chimp are intronic vs utr respectively. I will do this first in the nuclear fraction.
I will compare these genes with those identified in the differential APA analysis. This will be helpful to narrow down the genes I want to visualize.
library(workflowr)
This is workflowr version 1.5.0
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()
These are the PAS
allPAS= read.table("../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T)
ChimpPASwMean =allPAS %>% dplyr::select(-Human)
HumanPASwMean =allPAS %>% dplyr::select(-Chimp)
Find the dominant PAS per gene:
I will remove genes with ties for now
Chimp_Dom= ChimpPASwMean %>%
group_by(gene) %>%
top_n(1,Chimp) %>%
mutate(nPer=n()) %>%
filter(nPer==1) %>%
dplyr::select(gene,loc,PAS,Chimp) %>%
rename(ChimpLoc=loc, ChimpPAS=PAS)
Human_Dom= HumanPASwMean %>%
group_by(gene) %>%
top_n(1, Human) %>%
mutate(nPer=n()) %>%
filter(nPer==1) %>%
dplyr::select(gene,loc,PAS,Human) %>%
rename(HumanLoc=loc, HumanPAS=PAS)
#merge
BothDom= Chimp_Dom %>% inner_join(Human_Dom,by="gene")
Look at how many have the same dominat and where these are:
SameDom=BothDom %>% filter(ChimpPAS==HumanPAS,HumanLoc!="008559")
ggplot(SameDom, aes(x=HumanLoc))+ geom_histogram(stat="count") + labs(x="Location", y="Number of Genes", title="Dominant PAS for genes with matching by species")
Warning: Ignoring unknown parameters: binwidth, bins, pad
Plot this as boxplots as well.
SameDom_gather=SameDom %>% dplyr::select(gene, HumanLoc, Chimp,Human) %>% gather(species, value, -c(gene,HumanLoc))
ggplot(SameDom_gather,aes(x=HumanLoc, y=value,fill=species)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2") + labs(x="PAS Location", y="Mean Usage accross individuals", title="Mean usage for Genes with matching Dominant PAS")
Write out the intronic for location analysis.
mkdir ../data/DominantPAS_DF
SameDom_intron=SameDom %>% filter(ChimpLoc=="intron")
write.table(SameDom_intron, "../data/DominantPAS_DF/SameDominantIntronic.txt", col.names = T, row.names = F, quote = F)
Different PAS but in the same location:
DiffDom_sameLoc=BothDom %>% filter(ChimpPAS!=HumanPAS,HumanLoc!="008559", ChimpLoc==HumanLoc)
ggplot(DiffDom_sameLoc,aes(x=HumanLoc)) + geom_histogram(stat="count") + labs(x="PAS Location", y= "Number of Genes", title="Dominat PAS in same genic location but different PAS")
Warning: Ignoring unknown parameters: binwidth, bins, pad
Now I can look at those that are in different locations.
DiffDom_diffLoc=BothDom %>% filter(ChimpPAS!=HumanPAS,HumanLoc!="008559", ChimpLoc!=HumanLoc)
ggplot(DiffDom_diffLoc,aes(x=ChimpLoc))+ geom_histogram(stat="count")+ labs(x="Chimp Dominant Location", y="Number of Genes", title="Location of dominant PAS when they are differnet between species")
Warning: Ignoring unknown parameters: binwidth, bins, pad
ggplot(DiffDom_diffLoc,aes(x=HumanLoc))+ geom_histogram(stat="count")+labs(x="Human Dominant Location", y="Number of Genes", title="Location of dominant PAS when they are differnet between species")
Warning: Ignoring unknown parameters: binwidth, bins, pad
This is interesting but could be based on the annotations. I will look at the genes with human in intron and chimp in 3’ UTR.
DiffDom_diffLoc_humanIntronChimpUTR= DiffDom_diffLoc %>% filter(ChimpLoc=="utr3", HumanLoc=="intron")
nrow(DiffDom_diffLoc_humanIntronChimpUTR)
[1] 275
Opposite Direction
DiffDom_diffLoc_humanUTRChimpInton= DiffDom_diffLoc %>% filter(ChimpLoc=="intron", HumanLoc=="utr3")
nrow(DiffDom_diffLoc_humanUTRChimpInton)
[1] 84
prop.test(x=c(nrow(DiffDom_diffLoc_humanIntronChimpUTR),nrow(DiffDom_diffLoc_humanUTRChimpInton)), n=c(nrow(DiffDom_diffLoc), nrow(DiffDom_diffLoc)))
2-sample test for equality of proportions with continuity
correction
data: c(nrow(DiffDom_diffLoc_humanIntronChimpUTR), nrow(DiffDom_diffLoc_humanUTRChimpInton)) out of c(nrow(DiffDom_diffLoc), nrow(DiffDom_diffLoc))
X-squared = 120.94, df = 1, p-value < 2.2e-16
alternative hypothesis: two.sided
95 percent confidence interval:
0.1475340 0.2111515
sample estimates:
prop 1 prop 2
0.25821596 0.07887324
I will look to see if these genes are those I see with differential APA.
write.table(DiffDom_diffLoc_humanIntronChimpUTR, "../data/DominantPAS_DF/Nuclear_HumanIntronicChimpUTR.txt", col.names = T, row.names = F, quote = F)
write.table(DiffDom_diffLoc_humanUTRChimpInton, "../data/DominantPAS_DF/Nuclear_HumanUTRChimpIntronic.txt", col.names = T, row.names = F, quote = F)
write.table(SameDom, "../data/DominantPAS_DF/Nuclear_SameDom.txt", col.names = T, row.names = F, quote = F)
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_1.5.0
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