Last updated: 2019-11-18

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Rmd 3c2512b brimittleman 2019-11-18 wflow_publish(“analysis/diffSplicing.Rmd”)
html 7770d55 brimittleman 2019-11-18 Build site.
Rmd fa2c075 brimittleman 2019-11-18 code for diff splice
html 106f3c1 brimittleman 2019-11-15 Build site.
Rmd 4ad7bce brimittleman 2019-11-15 look at results of cluster lift
html dc91b0a brimittleman 2019-11-11 Build site.
Rmd b5ba82e brimittleman 2019-11-11 add diff expression and diff splicing 
library(tidyverse)
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library(reshape2)

Attaching package: 'reshape2'
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    smiths

I want to use the RNA seq I collected to also perform a differential splicing analysis with leafcutter. I will follow the pipeline found at http://davidaknowles.github.io/leafcutter/articles/Usage.html. For a first pass I will use the bam files from the snakemake and differential expression analysis pipeline.

I will get clusters in both species then perform reciprocal liftover. I can use a liftover pipeline similar to the one I used for the differnetial PAS analysis.

Pipeline from example on leafcutter github.



for bamfile in `ls run/geuvadis/*chr1.bam`; do
    echo Converting $bamfile to $bamfile.junc
    samtools index $bamfile
    regtools junctions extract -a 8 -m 50 -M 500000 $bamfile -o $bamfile.junc
    echo $bamfile.junc >> test_juncfiles.txt
done

python ../clustering/leafcutter_cluster_regtools.py -j test_juncfiles.txt -m 50 -o testYRIvsEU -l 500000

At this point I will be able to liftover the junctions. I can use the human corrdinates for the differential splicing step.

../scripts/leafcutter_ds.R --num_threads 4 ../example_data/testYRIvsEU_perind_numers.counts.gz example_geuvadis/groups_file.txt

I now have my RNA seq for each species. I can write a script that runs the junctions for each species.

sbatch converBam2Junc.sh
sbatch quantJunc.sh

Now I need to do reciprocal liftover with the clusters.

  • /project2/gilad/briana/Comparative_APA/Human/data/RNAseq/DiffSplice/
  • /project2/gilad/briana/Comparative_APA/Chimp/data/RNAseq/DiffSplice/

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/humanJunc_perind.counts.gz

gunzip ../Chimp/data/RNAseq/DiffSplice/chimpJunc_perind.counts.gz


python cluster2bed.py ../Human/data/RNAseq/DiffSplice/humanJunc_perind.counts ../Human/data/RNAseq/DiffSplice/humanJunc.bed

python cluster2bed.py ../Chimp/data/RNAseq/DiffSplice/chimpJunc_perind.counts ../Chimp/data/RNAseq/DiffSplice/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/humanJunc.bed ../Human/data/RNAseq/DiffSplice/humanJuncNamed.bed

python nameClusters.py ../Chimp/data/RNAseq/DiffSplice/chimpJunc.bed ../Chimp/data/RNAseq/DiffSplice/chimpJuncNamed.bed

sbatch clusterLiftprimary.sh

sbatch clusterLiftReverse.sh

Evaluate results:

(this code is from the lift for the PAS)

unliftedH=read.table("../Human/data/RNAseq/DiffSplice/humanJunc_unlifted.bed",stringsAsFactors = F) %>% nrow()
unliftedC=read.table("../Chimp/data/RNAseq/DiffSplice/chimpJunc_unlifted.bed",stringsAsFactors = F) %>% nrow()

liftedH=read.table("../Human/data/RNAseq/DiffSplice/humanJunc_inChimp.bed",stringsAsFactors = F) %>% nrow()
liftedC=read.table("../Chimp/data/RNAseq/DiffSplice/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")

Version Author Date
106f3c1 brimittleman 2019-11-15 
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")

Version Author Date
106f3c1 brimittleman 2019-11-15

Look at the lifted:

OriginalHuman=read.table("../Human/data/RNAseq/DiffSplice/humanJunc.bed",stringsAsFactors = F) 
liftedHuman=read.table("../Human/data/RNAseq/DiffSplice/humanJunc_inChimp.bed",stringsAsFactors = F) 

OriginalChimp=read.table("../Chimp/data/RNAseq/DiffSplice/chimpJunc.bed",stringsAsFactors = F)
liftedChimp=read.table("../Chimp/data/RNAseq/DiffSplice/chimpJunc_inHuman.bed",stringsAsFactors = F)

Reverse lift:

re_unliftedH=read.table("../Human/data/RNAseq/DiffSplice/humanJunc_inChimp_B2Human_unlifted.bed",stringsAsFactors = F) %>% nrow()
re_unliftedC=read.table("../Chimp/data/RNAseq/DiffSplice/chimpJunc_inHuman_B2Chimp_unlifted.bed",stringsAsFactors = F) %>% nrow()

re_liftedH=read.table("../Human/data/RNAseq/DiffSplice/humanJunc_inChimp_B2Human.bed",stringsAsFactors = F) %>% nrow()
re_liftedC=read.table("../Chimp/data/RNAseq/DiffSplice/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")

Version Author Date
106f3c1 brimittleman 2019-11-15 
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")

Version Author Date
106f3c1 brimittleman 2019-11-15

How many lifted both ways?

#human
re_liftedH/nrow(OriginalHuman)
[1] 0.9200983
#chimp
re_liftedC/nrow(OriginalChimp)
[1] 0.9193779

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/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] 5085
humanRevlift$score=as.character(humanRevlift$score)
humanRevlift= humanRevlift %>%  mutate(Name=paste("Human", score, sep="_")) %>% select(-score)


humanInChimp=read.table("../Human/data/RNAseq/DiffSplice/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/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] 4886
chimpInHuman=read.table("../Chimp/data/RNAseq/DiffSplice/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] 7080
AllClusters %>% select(cluster.x) %>% unique() %>% nrow()
[1] 2967
AllClusters %>% select(cluster.y) %>% unique() %>% nrow()
[1] 2968

This means there are ~7k isoforms from about 3k genes. This is from the ~5k clusters I had before. I can move on with these using the human names.

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/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/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 
write.table(Bams, "../data/DiffSplice/groups_file.txt", col.names = F, row.names = F, quote = F, sep="\t" )


write.table(FilteredClusterBothfixed, "../data/DiffSplice/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/BothSpec_perind.counts

gzip ../data/DiffSplice/BothSpec_perind.counts

I will run the differential splicing analysis with the human exon file for now. Download the NCBI refseq exons from the table browser. I also downloaded the names with the common names so I can fix the exon file.

python processhg38exons.py
gzip ../data/DiffSplice/hg38_ncbiRefseq_exonsfixed

Run leafcutter with python 2

sbatch DiffSplice.sh

Look at results:

sig=read.table("../data/DiffSplice/leafcutter_ds_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)

Use the leafcutter tool to visualize

sbatch DiffSplicePlots.sh

try with gencode exons:

sbatch DiffSplice_gencode.sh
sbatch DiffSplicePlots_gencode.sh
sig_gencode=read.table("../data/DiffSplice/Gencode__cluster_significance.txt",sep="\t" ,header =T,stringsAsFactors = F) %>% filter(status=="Success") 

sig_gencode$p.adjust=as.numeric(as.character(sig_gencode$p.adjust))


qqplot(-log10(runif(nrow(sig_gencode))), -log10(sig_gencode$p.adjust),ylab="-log10 Total Adjusted Leafcutter pvalue", xlab="-log 10 Uniform expectation", main="Leafcutter Differential Splicing Gencode exons")
abline(0,1)


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