Last updated: 2019-11-21
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Knit directory: Comparative_APA/analysis/
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File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | 17d5d33 | brimittleman | 2019-11-21 | add diff splice without 2 samples |
In this analysis I will run the differential splicing pipeline I previously ran but without Chimp 4973 and Human 18498.
library(tidyverse)
── Attaching packages ────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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── Conflicts ───────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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library(reshape2)
Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':
smiths
I now have my RNA seq for each species. I can write a script that runs the junctions for each species.
mkdir ../Human/data/RNAseq/sort_removebad
mkdir ../Human/data/RNAseq/DiffSplice_removBad
mkdir ../Chimp/data/RNAseq/sort_removebad
mkdir ../Chimp/data/RNAseq/DiffSplice_removeBad
#move good files to this
sbatch converBam2Junc_removeBad.sh
sbatch quantJunc_removeBad.sh
Now I need to do reciprocal liftover with the clusters.
Chain files are in /data/chainFiles/ * panTro5ToHg38.over.chain
* hg38ToPanTro5.over.chain
I first need to make bedfiles with these clusters.
The clusters all have _NA I dont this this is correct.
gunzip ../Human/data/RNAseq/DiffSplice_removeBad/humanJunc_perind.counts.gz
gunzip ../Chimp/data/RNAseq/DiffSplice_removeBad/chimpJunc_perind.counts.gz
python cluster2bed.py ../Human/data/RNAseq/DiffSplice_removeBad/humanJunc_perind.counts ../Human/data/RNAseq/DiffSplice_removeBad/humanJunc.bed
python cluster2bed.py ../Chimp/data/RNAseq/DiffSplice_removeBad/chimpJunc_perind.counts ../Chimp/data/RNAseq/DiffSplice_removeBad/chimpJunc.bed
#I need to name the clusters before I can do the lift. (this is like the naming in apa 1-n(clusters))
python nameClusters.py ../Human/data/RNAseq/DiffSplice_removeBad/humanJunc.bed ../Human/data/RNAseq/DiffSplice_removeBad/humanJuncNamed.bed
python nameClusters.py ../Chimp/data/RNAseq/DiffSplice_removeBad/chimpJunc.bed ../Chimp/data/RNAseq/DiffSplice_removeBad/chimpJuncNamed.bed
sbatch clusterLiftprimary_removebad.sh
sbatch clusterLiftReverse_removebad.sh
Evaluate results:
(this code is from the lift for the PAS)
unliftedH=read.table("../Human/data/RNAseq/DiffSplice_removeBad/humanJunc_unlifted.bed",stringsAsFactors = F) %>% nrow()
unliftedC=read.table("../Chimp/data/RNAseq/DiffSplice_removeBad/chimpJunc_unlifted.bed",stringsAsFactors = F) %>% nrow()
liftedH=read.table("../Human/data/RNAseq/DiffSplice_removeBad/humanJunc_inChimp.bed",stringsAsFactors = F) %>% nrow()
liftedC=read.table("../Chimp/data/RNAseq/DiffSplice_removeBad/chimpJunc_inHuman.bed",stringsAsFactors = F) %>% nrow()
primaryUnC=c("Chimp","Unlifted", unliftedC)
primaryUnH=c("Human","Unlifted", unliftedH)
primaryLH=c("Human","Lifted", liftedH)
primaryLC=c("Chimp","Lifted", liftedC)
header=c("species", "liftStat", "PAS")
primaryDF= as.data.frame(rbind(primaryLH,primaryLC, primaryUnH,primaryUnC))
colnames(primaryDF)=header
primaryDF$PAS=as.numeric(as.character(primaryDF$PAS))
primaryDF= primaryDF %>% group_by(species) %>% mutate(nPAS=sum(PAS)) %>% ungroup() %>% mutate(proportion=PAS/nPAS)
ggplot(primaryDF,aes(x=species, y=PAS, fill=liftStat)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Primary Liftover Results", y="Isoforms")
ggplot(primaryDF,aes(x=species, y=proportion, fill=liftStat)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Primary Liftover Results")
Look at the lifted:
OriginalHuman=read.table("../Human/data/RNAseq/DiffSplice_removeBad/humanJunc.bed",stringsAsFactors = F)
liftedHuman=read.table("../Human/data/RNAseq/DiffSplice_removeBad/humanJunc_inChimp.bed",stringsAsFactors = F)
OriginalChimp=read.table("../Chimp/data/RNAseq/DiffSplice_removeBad/chimpJunc.bed",stringsAsFactors = F)
liftedChimp=read.table("../Chimp/data/RNAseq/DiffSplice_removeBad/chimpJunc_inHuman.bed",stringsAsFactors = F)
Reverse lift:
re_unliftedH=read.table("../Human/data/RNAseq/DiffSplice_removeBad/humanJunc_inChimp_B2Human_unlifted.bed",stringsAsFactors = F) %>% nrow()
re_unliftedC=read.table("../Chimp/data/RNAseq/DiffSplice_removeBad/chimpJunc_inHuman_B2Chimp_unlifted.bed",stringsAsFactors = F) %>% nrow()
re_liftedH=read.table("../Human/data/RNAseq/DiffSplice_removeBad/humanJunc_inChimp_B2Human.bed",stringsAsFactors = F) %>% nrow()
re_liftedC=read.table("../Chimp/data/RNAseq/DiffSplice_removeBad/chimpJunc_inHuman_B2Chimp.bed",stringsAsFactors = F) %>% nrow()
re_UnC=c("Chimp","Unlifted", re_unliftedC)
re_UnH=c("Human","Unlifted", re_unliftedH)
re_LH=c("Human","Lifted", re_liftedH)
re_LC=c("Chimp","Lifted", re_liftedC)
header=c("species", "liftStat", "PAS")
re_DF= as.data.frame(rbind(re_LH,re_LC, re_UnH,re_UnC))
colnames(re_DF)=header
re_DF$PAS=as.numeric(as.character(re_DF$PAS))
re_DF= re_DF %>% group_by(species) %>% mutate(nPAS=sum(PAS)) %>% ungroup() %>% mutate(proportion=PAS/nPAS)
ggplot(re_DF,aes(x=species, y=PAS, fill=liftStat)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(title="Reverse Liftover Results", y="Isoforms")
ggplot(re_DF,aes(x=species, y=proportion, fill=liftStat)) + geom_bar(stat="identity",position = "dodge") + scale_fill_brewer(palette = "Dark2")+ labs(title="Reverse Liftover Results")
How many lifted both ways?
#human
re_liftedH/nrow(OriginalHuman)
[1] 0.9311927
#chimp
re_liftedC/nrow(OriginalChimp)
[1] 0.9257284
The next step will be to find the corresponding clusters. This is important because I will need to get the quantifications for the same introns and clusters. To do this I will need to write code that looks for the intron location from the primary lift in the reverse lift.
For now I will only look at those introns identified in both species. I need to do this because I need junctions we have quantifications for in both species.
I can make files with the human and chimp coordintats for the clusters that lift both ways. I will have to number each cluster
Human cluser:
humanRevlift=read.table("../Human/data/RNAseq/DiffSplice_removeBad/humanJunc_inChimp_B2Human.bed",stringsAsFactors = F,col.names = c("Hchr","Hstart", "Hend", "cluster", "score", "strand")) %>% select(-strand)
#number clusters
humanRevlift %>% select(cluster) %>% unique() %>% nrow()
[1] 793
humanRevlift$score=as.character(humanRevlift$score)
humanRevlift= humanRevlift %>% mutate(Name=paste("Human", score, sep="_")) %>% select(-score)
humanInChimp=read.table("../Human/data/RNAseq/DiffSplice_removeBad/humanJunc_inChimp.bed",stringsAsFactors = F,col.names = c("Cchr","Cstart", "Cend", "cluster", "score", "strand"))%>% select(-strand)
humanInChimp$score=as.character(humanInChimp$score)
humanInChimp= humanInChimp %>% mutate(Name=paste("Human", score, sep="_")) %>% select(-score)
humanliftedBoth=humanRevlift %>% inner_join(humanInChimp, by=c("cluster", "Name"))
Chimp clusters:
chimpRevLift=read.table("../Chimp/data/RNAseq/DiffSplice_removeBad/chimpJunc_inHuman_B2Chimp.bed",stringsAsFactors = F,col.names = c("Cchr","Cstart", "Cend", "cluster", "score", "strand")) %>% select(-strand)
chimpRevLift$score=as.character(chimpRevLift$score)
chimpRevLift= chimpRevLift %>% mutate(Name=paste("Chimp", score, sep="_")) %>% select(-score)
chimpRevLift %>% select(cluster) %>% unique() %>% nrow()
[1] 2046
chimpInHuman=read.table("../Chimp/data/RNAseq/DiffSplice_removeBad/chimpJunc_inHuman.bed",stringsAsFactors = F,col.names = c("Hchr","Hstart", "Hend", "cluster", "score", "strand"))%>% select(-strand)
chimpInHuman$score=as.character(chimpInHuman$score)
chimpInHuman= chimpInHuman %>% mutate(Name=paste("Chimp", score, sep="_")) %>% select(-score)
chimpliftedBoth=chimpRevLift %>% inner_join(chimpInHuman, by=c("cluster", "Name"))
Try to join these by the human and chimp coordinates
AllClusters=chimpliftedBoth %>% inner_join(humanliftedBoth, by=c("Cchr", "Cstart","Cend", "Hchr", "Hstart", "Hend")) %>% mutate(ChimpName=paste(Cchr,Cstart,Cend, cluster.x, sep=":" ),HumanName=paste(Hchr,Hstart,Hend, cluster.y, sep=":" ) )
nrow(AllClusters)
[1] 1141
AllClusters %>% select(cluster.x) %>% unique() %>% nrow()
[1] 519
AllClusters %>% select(cluster.y) %>% unique() %>% nrow()
[1] 520
This means there are ~1k isoforms from about 500 genes.
I will have to go back and figure out how to call clusters for more genes.
I need to reformat these back into the counts format.
#chr1:17055:17233:clu_1
AllClustersNames=AllClusters %>% select(HumanName, ChimpName)
ChimpCluster=read.table("../Chimp/data/RNAseq/DiffSplice_removeBad/chimpJunc_perind_numers.counts.gz") %>% rownames_to_column(var="ChimpName")
FilteredChimpCluster= ChimpCluster %>% inner_join(AllClustersNames, by="ChimpName")
#map human onto these
HumanCluster=read.table("../Human/data/RNAseq/DiffSplice_removeBad/humanJunc_perind_numers.counts.gz") %>% rownames_to_column(var="HumanName")
FilteredClusterBoth=HumanCluster %>% inner_join(FilteredChimpCluster, by="HumanName") %>% select(-ChimpName)
FilteredClusterBothfixed=FilteredClusterBoth[!duplicated(FilteredClusterBoth$HumanName),]
#create group file- this should have the name of the bams and the group
Bams=as.data.frame(colnames(FilteredClusterBothfixed)) %>% mutate(Species=ifelse(grepl("H",colnames(FilteredClusterBothfixed)), "Human", "Chimp")) %>% slice(2:n())
# mkdir ../data/DiffSplice_removeBad/
write.table(Bams, "../data/DiffSplice_removeBad/groups_file.txt", col.names = F, row.names = F, quote = F, sep="\t" )
write.table(FilteredClusterBothfixed, "../data/DiffSplice_removeBad/BothSpec_perind.counts", col.names = T, row.names = F, quote = F, sep="\t" )
Remove the first name in header and zip the file:
(manually)
vi ../data/DiffSplice_removeBad/BothSpec_perind.counts
gzip ../data/DiffSplice_removeBad/BothSpec_perind.counts
Run leafcutter with python 2
sbatch DiffSplice_removebad.sh
Look at results:
sig=read.table("../data/DiffSplice_removeBad/_cluster_significance.txt",sep="\t" ,header =T,stringsAsFactors = F) %>% filter(status=="Success")
sig$p.adjust=as.numeric(as.character(sig$p.adjust))
qqplot(-log10(runif(nrow(sig))), -log10(sig$p.adjust),ylab="-log10 Total Adjusted Leafcutter pvalue", xlab="-log 10 Uniform expectation", main="Leafcutter Differential Splicing")
abline(0,1)
sig %>% filter(p.adjust < .05 ) %>% nrow()
[1] 80
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] reshape2_1.4.3 forcats_0.3.0 stringr_1.3.1 dplyr_0.8.0.1
[5] purrr_0.3.2 readr_1.3.1 tidyr_0.8.3 tibble_2.1.1
[9] ggplot2_3.1.1 tidyverse_1.2.1
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] workflowr_1.5.0 cellranger_1.1.0 rvest_0.3.2
[22] evaluate_0.12 labeling_0.3 knitr_1.20
[25] httpuv_1.4.5 broom_0.5.1 Rcpp_1.0.2
[28] promises_1.0.1 scales_1.0.0 backports_1.1.2
[31] jsonlite_1.6 fs_1.3.1 hms_0.4.2
[34] digest_0.6.18 stringi_1.2.4 grid_3.5.1
[37] rprojroot_1.3-2 cli_1.1.0 tools_3.5.1
[40] magrittr_1.5 lazyeval_0.2.1 crayon_1.3.4
[43] whisker_0.3-2 pkgconfig_2.0.2 xml2_1.2.0
[46] lubridate_1.7.4 assertthat_0.2.0 rmarkdown_1.10
[49] httr_1.3.1 rstudioapi_0.10 R6_2.3.0
[52] nlme_3.1-137 git2r_0.26.1 compiler_3.5.1