Last updated: 2020-01-26

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
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Rmd b5ba82e brimittleman 2019-11-11 add diff expression and diff splicing 
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()
library(reshape2)

Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':

    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.

mkdir /project2/gilad/briana/Comparative_APA/Human/data/RNAseq/DiffSplice/
mkdir /project2/gilad/briana/Comparative_APA/Chimp/data/RNAseq/DiffSplice/
touch /project2/gilad/briana/Comparative_APA/Human/data/RNAseq/DiffSplice/human_juncfiles.txt
touch /project2/gilad/briana/Comparative_APA/Chimp/data/RNAseq/DiffSplice/chimp_juncfiles.txt

sbatch converBam2Junc.sh

Create a script that only keeps the number chromosomes (2A and 2B for chimp). This means I will not have any of the chimp contigs.

I should lift first then filter


mkdir ../data/DiffSplice_liftedJunc
sbatch liftJunctionFiles.sh

Assess the number that liftover: Resiprocal must map back to the same spot.

First I will look at how many pass the original lift.

#Original:
touch /project2/gilad/briana/Comparative_APA/data/DiffSplice_liftedJunc/JuncNums.txt
for i in $(ls /project2/gilad/briana/Comparative_APA/Human/data/RNAseq/sort/*.bam.junc)
do
describer=$(echo ${i} cut -d/ -f 9 | cut -d- -f 4)
number=$(wc -l < $i)
echo -e "\t" ${describer} ${number} "original" "Human" >>  /project2/gilad/briana/Comparative_APA/data/DiffSplice_liftedJunc/JuncNums.txt
done
#reverse
for i in $(ls /project2/gilad/briana/Comparative_APA/Human/data/RNAseq/sort/*.bam.junc.2Chimp)
do
describer=$(echo ${i} cut -d/ -f 9 | cut -d- -f 4)
number=$(wc -l < $i)
echo -e "\t" ${describer} ${number} "ForwardLift" "Human" >>  /project2/gilad/briana/Comparative_APA/data/DiffSplice_liftedJunc/JuncNums.txt
done
#chimp files
#original

for i in $(ls /project2/gilad/briana/Comparative_APA/Chimp/data/RNAseq/sort/*.bam.junc)
do
describer=$(echo ${i} cut -d/ -f 9 | cut -d- -f 4)
number=$(wc -l < $i)
echo -e "\t" ${describer} ${number} "original" "Chimp" >>  /project2/gilad/briana/Comparative_APA/data/DiffSplice_liftedJunc/JuncNums.txt
done
#reverse
for i in $(ls /project2/gilad/briana/Comparative_APA/Chimp/data/RNAseq/sort/*.bam.junc.2Human)
do
describer=$(echo ${i} cut -d/ -f 9 | cut -d- -f 4)
number=$(wc -l < $i)
echo -e "\t" ${describer} ${number} "ForwardLift" "Chimp" >>  /project2/gilad/briana/Comparative_APA/data/DiffSplice_liftedJunc/JuncNums.txt
done

Next I need to make sure everything lifts back to the same place.

I will write an R script that takes both files and inner joins to the same locations. I need the original and the final lift.

sbatch runCheckReverseLift.sh

Evaluate results:


for i in $(ls /project2/gilad/briana/Comparative_APA/Human/data/RNAseq/sort/*.SamePlace)
do
describer=$(echo ${i} cut -d/ -f 9 | cut -d- -f 4)
number=$(wc -l < $i)
echo -e "\t" ${describer} ${number} "SamePlace" "Human" >>  /project2/gilad/briana/Comparative_APA/data/DiffSplice_liftedJunc/JuncNums.txt
done

for i in $(ls /project2/gilad/briana/Comparative_APA/Chimp/data/RNAseq/sort/*.SamePlace)
do
describer=$(echo ${i} cut -d/ -f 9 | cut -d- -f 4)
number=$(wc -l < $i)
echo -e "\t" ${describer} ${number} "SamePlace" "Chimp" >>  /project2/gilad/briana/Comparative_APA/data/DiffSplice_liftedJunc/JuncNums.txt
done

liftStats=read.table("../data/DiffSplice_liftedJunc/JuncNums.txt", col.names = c("line", "Njunc","File", "Species"),stringsAsFactors = F)
#spread by line
liftStatsSpread= liftStats %>% spread(key="File", value="Njunc") %>% mutate(PercLift=ForwardLift/original, PercSame=SamePlace/original)


ggplot(liftStatsSpread, aes(x=line, fill=Species, y=PercLift)) + geom_bar(stat="identity")+geom_text(aes(label=round(PercLift,3)), vjust=1.6, color="black") + labs(title="Proportion of Junctions lifting first")

Version Author Date
9d57aba brimittleman 2019-12-18
5ac753a brimittleman 2019-12-11 
ggplot(liftStatsSpread, aes(x=line, fill=Species, y=PercSame)) + geom_bar(stat="identity")+geom_text(aes(label=round(PercSame,3)), vjust=1.6, color="black") + labs(title="Proportion of Junctions Passing reciprocal liftover")

Version Author Date
9d57aba brimittleman 2019-12-18

Lift the passing chimps to human:

sbatch LiftFinalChimpJunc2Human.sh

I need to write a script that fixes the lifted files. I need to make column 9 255,0,0 and remove the commas from the last 2 columns.

I can impliment the fix in the filter file.

sbatch runFilterNumChroms.sh

Make clusters:

juncfiles=read.table("../data/DiffSplice_liftedJunc/BothSpec_juncfiles.txt", header = F)
humanFiles=juncfiles %>% slice(1:6)
write.table(humanFiles, "../data/DiffSplice_liftedJunc/Human_juncfiles.txt", quote = F, col.names = F, row.names = F)
chimpFiles=juncfiles %>% slice(7:12)
write.table(chimpFiles, "../data/DiffSplice_liftedJunc/Chimp_juncfiles.txt", quote = F, col.names = F, row.names = F)
sbatch quantJunc.sh

Now I can merge all of the culsters with: /project2/yangili1/yangili/leafcutter_scripts/merge_leafcutter_clusters.py

sbatch MergeClusters.sh
sbatch QuantMergedClusters.sh
gunzip ../data/DiffSplice_liftedJunc/MergeCombined_perind_numers.counts.gz

Make the sample list:

combinedCounts=read.table("../data/DiffSplice_liftedJunc/MergeCombined_perind_numers.counts", header=T)
x=colnames(combinedCounts)
#YG-BM-S8-18499H-Total_S8_R1_001-sort.bam
indiv=as.data.frame(x)  %>%  separate(x, into=c("yg", "bm","lane", "sample", "total",  "sort", "bam"), sep="[.]") %>% mutate(sample=paste(yg, "-", bm, "-", lane, "-", sample, "-", total, "-", sort, ".", bam, sep="")) %>% dplyr::select(sample) %>%  mutate(Species=ifelse(grepl("H",sample), "Human", "Chimp"))


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

fix to -Total instead of .Total

counts=read.table('../data/DiffSplice_liftedJunc/MergeCombined_perind_numers.counts', header=T, check.names = F)
meta=read.table("../data/DiffSplice_liftedJunc/groups_file.txt", header=F, stringsAsFactors = F)
colnames(meta)[1:2]=c("sample","group")
counts=counts[,meta$sample]
#rownames(counts)
gzip ../data/DiffSplice_liftedJunc/MergeCombined_perind_numers.counts

I am going to write a python work around to change the cluster format. This will take the unziped version of the counts file. When I run it, I can unzip and zip the results in the bash script.


sbatch RunFixLeafCluster.sh

sbatch DiffSplice.sh

results=read.table("../data/DiffSplice_liftedJunc/MergedRes_cluster_significance.txt",stringsAsFactors = F, header = T, sep="\t") %>% separate(cluster, into=c("chrom", "clus"),sep=":") %>% filter(status=="Success") 
results$p.adjust=as.numeric(as.character(results$p.adjust))


nrow(results)
[1] 6014
results %>% filter(p.adjust < .05 ) %>% nrow()
[1] 2274
results_sig= results %>% filter(p.adjust < .05 )
qqplot(-log10(runif(nrow(results))), -log10(results$p.adjust),ylab="-log10 Total Adjusted Leafcutter pvalue", xlab="-log 10 Uniform expectation", main="Human Chimp differential splicing")
abline(0,1)

Version Author Date
9d57aba brimittleman 2019-12-18
5ac753a brimittleman 2019-12-11
2fbf2d4 brimittleman 2019-12-02
9b73cff brimittleman 2019-11-18 
effectsize=read.table("../data/DiffSplice_liftedJunc/MergedRes_effect_sizes.txt", stringsAsFactors = F,header=T)
plot(sort(effectsize$deltapsi), ,main="Human vs Chimp Effect sizes", ylab="Delta PSI", xlab="Cluster Index")

Version Author Date
9d57aba brimittleman 2019-12-18
5ac753a brimittleman 2019-12-11

Use the leafcutter tool to visualize

sbatch DiffSplicePlots.sh

prepare genes:

genes=results_sig %>% arrange(p.adjust) %>% dplyr::select(genes) %>% unique()
nrow(genes)
[1] 1813
write.table(genes, "../data/DiffSplice_liftedJunc/orderedGenelist.txt", col.names = F, row.names = F, quote = F)

Fixlist:

tr , '\n' < ../data/DiffSplice_liftedJunc/orderedGenelist.txt > ../data/DiffSplice_liftedJunc/orderedGeneListFixed.txt

Build leafviz

sbatch prepareLeafvizAnno.sh 
sbatch buildLeafviz.sh
#with leafcutter annotation (https://github.com/davidaknowles/leafcutter/blob/master/leafviz/download_human_annotation_codes.sh)
sbatch buildLeafviz_leadAnno.sh
#classify clusters 
sbatch ClassifyLeafviz.sh

Run this from leafviz on my own computer

Lift chimp bams to look at in IGV as well.

Pantro6- hg38.

mkdir ../Chimp/data/RNAseq/Sort_hg38
sbatch CrossMapChimpRNA.sh

Interesting spots: - WSH3p= alt start -RPL22 (NM_000983.3) -RPL38 - chr3:129171277-129171446, NM_001127192.1, CNBP -chr20:46394503-46406548 - ELMO2, NM_001318253.1 - chimp extra exon chr10:79814716-79826198


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  colorspace_1.3-2
 [5] generics_0.0.2   htmltools_0.3.6  yaml_2.2.0       rlang_0.4.0     
 [9] later_0.7.5      pillar_1.3.1     glue_1.3.0       withr_2.1.2     
[13] modelr_0.1.2     readxl_1.1.0     plyr_1.8.4       munsell_0.5.0   
[17] gtable_0.2.0     workflowr_1.5.0  cellranger_1.1.0 rvest_0.3.2     
[21] evaluate_0.12    labeling_0.3     knitr_1.20       httpuv_1.4.5    
[25] broom_0.5.1      Rcpp_1.0.2       promises_1.0.1   scales_1.0.0    
[29] backports_1.1.2  jsonlite_1.6     fs_1.3.1         hms_0.4.2       
[33] digest_0.6.18    stringi_1.2.4    grid_3.5.1       rprojroot_1.3-2 
[37] cli_1.1.0        tools_3.5.1      magrittr_1.5     lazyeval_0.2.1  
[41] crayon_1.3.4     whisker_0.3-2    pkgconfig_2.0.2  xml2_1.2.0      
[45] lubridate_1.7.4  assertthat_0.2.0 rmarkdown_1.10   httr_1.3.1      
[49] rstudioapi_0.10  R6_2.3.0         nlme_3.1-137     git2r_0.26.1    
[53] compiler_3.5.1