Last updated: 2020-04-13
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Knit directory: Comparative_APA/analysis/
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Rmd | c5fc23c | brimittleman | 2020-04-13 | add long short info |
library(workflowr)
This is workflowr version 1.6.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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library(ggpubr)
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extract
I will start by looking at the genes with 2 PAS in the orthologous 3’ UTRs. By understanding shortening and lengthening patterns, I may be able to learn more about mechanisms.
OverlapOrtho=read.table("../data/orthoUTR/FilteredPASOverlapOrthoUTR.text", header = T,stringsAsFactors = F)
Filter to genes with 2 PAS in the set.
TwoPAS=OverlapOrtho %>% group_by(gene) %>% summarise(nPAS=n()) %>% filter(nPAS==2)
OverlapOrtho2PAS= OverlapOrtho %>% filter(gene %in% TwoPAS$gene)
See if any of these are the only 2 PAS for the gene. That would be the most simple case. I can annotate these as long or short isoforms.
PASMeta=read.table("../data/PAS_doubleFilter/PAS_5perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt",header = T) %>% group_by(gene) %>% mutate(nPAS=n()) %>% filter(nPAS==2)
ChimpUsage= PASMeta %>% ungroup() %>% select(PAS,Chimp) %>% rename(chimpusage=Chimp)
OverlapOrtho2PASoverall= OverlapOrtho2PAS %>% filter(gene %in% PASMeta$gene) %>% inner_join(ChimpUsage, by="PAS")
Warning: Column `PAS` joining character vector and factor, coercing into
character vector
OverlapOrtho2PASoverallPos = OverlapOrtho2PASoverall %>% filter(strand=="+") %>% group_by(gene) %>% arrange(startpas) %>% mutate(id = 1:n()) %>% mutate(Isofrom=ifelse(id==1, "Short", "Long"))%>% select(-id)
OverlapOrtho2PASoverallNeg = OverlapOrtho2PASoverall %>% filter(strand=="-") %>% group_by(gene) %>% arrange(startpas) %>% mutate(id = 1:n()) %>% mutate(Isofrom=ifelse(id==1, "Long", "Short")) %>% select(-id)
OverlapOrtho2PASoverallBoth=OverlapOrtho2PASoverallPos %>% bind_rows(OverlapOrtho2PASoverallNeg)
Look at the PAS that are differentially used:
DiffUsed=read.table("../data/DiffIso_Nuclear_DF/AllPAS_withGeneSig.txt",header = T, stringsAsFactors = F) %>% filter(gene %in% OverlapOrtho2PASoverallBoth$gene) %>% select(chr, start, end, gene, deltaPAU, SigPAU2) %>% rename(chrpas=chr, startpas=start, endpas=end)
OverlapOrtho2PASoverallBothDiffUsed=OverlapOrtho2PASoverallBoth %>% inner_join(DiffUsed, by=c("chrpas", "startpas","endpas", "gene"))
OverlapOrtho2PASoverallBothDiffUsedSig= OverlapOrtho2PASoverallBothDiffUsed %>% filter(SigPAU2=="Yes")
nrow(OverlapOrtho2PASoverallBothDiffUsedSig)
[1] 36
nrow(OverlapOrtho2PASoverallBothDiffUsedSig %>% select(gene) %>% unique())
[1] 18
There are only 36 significant PAS in 18 genes.
Negative is
OverlapOrtho2PASoverallBothDiffUsedSig %>% select(PAS,gene, Isofrom, deltaPAU)
# A tibble: 36 x 4
# Groups: gene [18]
PAS gene Isofrom deltaPAU
<chr> <chr> <chr> <dbl>
1 human138532 HIC1 Short -0.305
2 human138539 HIC1 Long 0.305
3 chimp226080 MAN2B2 Short -0.292
4 human252668 MAN2B2 Long 0.292
5 human145205 EIF1 Short 0.440
6 human145206 EIF1 Long -0.440
7 human212400 BCAS4 Short 0.215
8 human212411 BCAS4 Long -0.215
9 human134514 NOL3 Short 0.254
10 human134515 NOL3 Long -0.254
# … with 26 more rows
Does long and short segregate with pos negative?
test= OverlapOrtho2PASoverallBothDiffUsedSig %>% select(PAS,gene, Isofrom, deltaPAU)
ggplot(test, aes(x=Isofrom, y=deltaPAU)) + geom_boxplot() + stat_compare_means()
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:
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