Last updated: 2019-12-11

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/CorrbetweenInd.Rmd
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    Modified:   analysis/liftoverPAS.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/verifyBAM.Rmd

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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.

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=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")

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

There are either not enough samples or min coverage problems. Let me compare these to the numbers.

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))

results %>% filter(p.adjust < .05 ) %>% nrow()
[1] 2221
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
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")

Use the leafcutter tool to visualize

sbatch DiffSplicePlots.sh

prepare genes:

genes=results_sig %>% arrange(p.adjust) %>% dplyr::select(genes) %>% unique()
nrow(genes)
[1] 1774
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 prepareeafvizAnno.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,

Convert juntions to bed to look at in IGV:

sbatch ConvertJunc2Bed.sh

Lift chimp bams to look at in IGV as well.

Pantro5- 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