Last updated: 2019-12-18
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Knit directory: Comparative_APA/analysis/
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Unstaged changes:
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File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | ac06540 | brimittleman | 2019-12-18 | update pantro6 |
html | 5ac753a | brimittleman | 2019-12-11 | Build site. |
Rmd | 76142de | brimittleman | 2019-12-11 | update reciprocal lift |
html | d7f04d9 | brimittleman | 2019-12-11 | Build site. |
Rmd | 6b8b23c | brimittleman | 2019-12-11 | fix redundancy in splicing genes |
html | 8ae44d5 | brimittleman | 2019-12-10 | Build site. |
Rmd | 28c77be | brimittleman | 2019-12-10 | add examples and anno code |
html | 174fba1 | brimittleman | 2019-12-07 | Build site. |
Rmd | 81cbc5f | brimittleman | 2019-12-07 | add res |
html | e9cfec8 | brimittleman | 2019-12-07 | Build site. |
Rmd | 1d92e9f | brimittleman | 2019-12-07 | add res |
html | a288a29 | brimittleman | 2019-12-04 | Build site. |
Rmd | 558b39f | brimittleman | 2019-12-04 | add current error for splice and write out DE genes |
html | 2fbf2d4 | brimittleman | 2019-12-02 | Build site. |
Rmd | b5df813 | brimittleman | 2019-12-02 | initial results no overlap |
html | 12a7fcb | brimittleman | 2019-11-20 | Build site. |
Rmd | 6d6e7d5 | brimittleman | 2019-11-20 | fix label |
html | 9b73cff | brimittleman | 2019-11-18 | Build site. |
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)
── 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 |
---|---|---|
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")
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$p.adjust),ylab="-log10 Total Adjusted Leafcutter pvalue", xlab="-log 10 Uniform expectation", main="Human Chimp differential splicing")
abline(0,1)
effectsize=read.table("../data/DiffSplice_liftedJunc/MergedRes_effect_sizes.txt", stringsAsFactors = F,header=T)
plot(sort(effectsize$deltapsi), ,main="Human vs Chimp Effect sizes", ylab="Delta PSI", xlab="Cluster Index")
Version | Author | Date |
---|---|---|
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