Last updated: 2019-12-11

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/CorrbetweenInd.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/PASnumperSpecies.Rmd
    Modified:   analysis/annotatePAS.Rmd
    Modified:   analysis/annotationInfo.Rmd
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    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/liftoverPAS.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/verifyBAM.Rmd

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Rmd 6b8b23c brimittleman 2019-12-11 fix redundancy in splicing genes
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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

liftover changes the junction files..

For the humans I can simply filter the original junctions that pass the reciprocal liftover. For the chimps I need to figure out why the junctions change with liftover.

Junction file format from regtools: bed12 format: - chrom - start - end - junction name - score- number of reads supporing junction - strand - thick start (same as chrom start) - thick end (same as chrom end) - itemRgb - default 255,0,0 - block cound- number blocks, defauld 2 - block size - comma- separated list of block sizes, number of items in list corresponds to blockCount - block start- comma separated list of block starts, all block star positions should be calculated relative to chromStrat and the number of items in the list shoudl correspond to blockCount

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="")) %>% 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] 2322
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
e9cfec8 brimittleman 2019-12-07 
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
174fba1 brimittleman 2019-12-07
e9cfec8 brimittleman 2019-12-07

Use the leafcutter tool to visualize

sbatch DiffSplicePlots.sh

prepare genes:

genes=results_sig %>% arrange(p.adjust) %>% dplyr::select(genes) %>% unique()
nrow(genes)
[1] 1839
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


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] Rcpp_1.0.2       cellranger_1.1.0 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.1     later_0.7.5      git2r_0.26.1     workflowr_1.5.0 
 [9] tools_3.5.1      digest_0.6.18    lubridate_1.7.4  jsonlite_1.6    
[13] evaluate_0.12    nlme_3.1-137     gtable_0.2.0     lattice_0.20-38 
[17] pkgconfig_2.0.2  rlang_0.4.0      cli_1.1.0        rstudioapi_0.10 
[21] yaml_2.2.0       haven_1.1.2      withr_2.1.2      xml2_1.2.0      
[25] httr_1.3.1       knitr_1.20       hms_0.4.2        generics_0.0.2  
[29] fs_1.3.1         rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5
[33] glue_1.3.0       R6_2.3.0         readxl_1.1.0     rmarkdown_1.10  
[37] modelr_0.1.2     magrittr_1.5     whisker_0.3-2    backports_1.1.2 
[41] scales_1.0.0     promises_1.0.1   htmltools_0.3.6  rvest_0.3.2     
[45] assertthat_0.2.0 colorspace_1.3-2 httpuv_1.4.5     stringi_1.2.4   
[49] lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1      crayon_1.3.4