Last updated: 2020-01-21

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Knit directory: Comparative_APA/analysis/

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File Version Author Date Message
Rmd 46c489b brimittleman 2020-01-22 nuclear diff filtered 
library(reshape2)
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()

Compare nuclear fraction PAS between human and chimp. I need to fiter the phenotypes fixed for leafcutter further to have only the double filtered PAS.

I need a fc file with the human and chimp nuclear samples. I will make a group file with the identifier being human or chimp.

These were created with prepareCleanLiftedFC_5perc4LC.py (in human and chimp extra analysis TvN)

../Chimp/data/CleanLiftedPeaks4LC/ALLPAS_postLift_LocParsed_Chimp_fixed4LC.fc ../Human/data/CleanLiftedPeaks4LC/ALLPAS_postLift_LocParsed_Human_fixed4LC.fc

mkdir ../data/NuclearHvC_DF
PAS=read.table("../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) %>% mutate(chrom=paste(chr,start,end,gene, sep=":"))
human=read.table("../Human/data/CleanLiftedPeaks4LC/ALLPAS_postLift_LocParsed_Human_fixed4LC.fc", stringsAsFactors = F, header = T) %>% rownames_to_column(var="chrom") %>% filter(chrom %in% PAS$chrom)


chimp=read.table("../Chimp/data/CleanLiftedPeaks4LC/ALLPAS_postLift_LocParsed_Chimp_fixed4LC.fc", stringsAsFactors = F, header = T)%>% rownames_to_column(var="chrom")%>% filter(chrom %in% PAS$chrom)
Allsamps=human %>% full_join(chimp,by="chrom") 

AllNuclear=Allsamps %>% dplyr::select(chrom,contains("_N")) %>% column_to_rownames(var="chrom")

write.table(AllNuclear, "../data/NuclearHvC_DF/ALLPAS_postLift_LocParsed_HvC_Nuclear_fixed4LC_Doublefilt.fc",row.names = T, col.names = T, quote = F)

I will make the id file here.

Inds=colnames(AllNuclear) 
Species=c(rep("Human",6), rep("Chimp", 6))

idFileDF=as.data.frame(cbind(Inds,Species))

write.table(idFileDF, "../data/NuclearHvC_DF/sample_goups.txt",row.names = F, col.names = F, quote = F)

Split by chromosome.

mkdir ../data/DiffIso_Nuclear_DF/

python subset_diffisopheno_Nuclear_HvC_DF.py 1
python subset_diffisopheno_Nuclear_HvC_DF.py 2
python subset_diffisopheno_Nuclear_HvC_DF.py 3
python subset_diffisopheno_Nuclear_HvC_DF.py 4
python subset_diffisopheno_Nuclear_HvC_DF.py 5
python subset_diffisopheno_Nuclear_HvC_DF.py 6
python subset_diffisopheno_Nuclear_HvC_DF.py 7
python subset_diffisopheno_Nuclear_HvC_DF.py 8
python subset_diffisopheno_Nuclear_HvC_DF.py 9
python subset_diffisopheno_Nuclear_HvC_DF.py 10
python subset_diffisopheno_Nuclear_HvC_DF.py 11
python subset_diffisopheno_Nuclear_HvC_DF.py 12
python subset_diffisopheno_Nuclear_HvC_DF.py 13
python subset_diffisopheno_Nuclear_HvC_DF.py 14
python subset_diffisopheno_Nuclear_HvC_DF.py 16
python subset_diffisopheno_Nuclear_HvC_DF.py 18
python subset_diffisopheno_Nuclear_HvC_DF.py 19
python subset_diffisopheno_Nuclear_HvC_DF.py 20
python subset_diffisopheno_Nuclear_HvC_DF.py 21
python subset_diffisopheno_Nuclear_HvC_DF.py 22

Run leafcutter:


sbatch runNuclearDiffIso_DF.sh

Concatinate results:

awk '{if(NR>1)print}' ../data/DiffIso_Nuclear_DF/TN_diff_isoform_chr*.txt_effect_sizes.txt > ../data/DiffIso_Nuclear_DF/TN_diff_isoform_allChrom.txt_effect_sizes.txt


awk '{if(NR>1)print}' ../data/DiffIso_Nuclear_DF/TN_diff_isoform_chr*.txt_cluster_significance.txt > ../data/DiffIso_Nuclear_DF/TN_diff_isoform_allChrom.txt_significance.txt

Significant clusters:

sig=read.table("../data/DiffIso_Nuclear_DF/TN_diff_isoform_allChrom.txt_significance.txt",sep="\t" ,col.names = c('status','loglr','df','p','cluster','p.adjust'),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 Adjusted Leafcutter pvalue", xlab="-log 10 Uniform expectation", main="Leafcutter differencial isoform analysis between Species")
abline(0,1)

tested_genes=nrow(sig)
tested_genes
[1] 8544
sig_genes=sig %>% filter(p.adjust<.05)
number_sig_genes=nrow(sig_genes)
number_sig_genes
[1] 6372

Effect Sizes

effectsize=read.table("../data/DiffIso_Nuclear_DF/TN_diff_isoform_allChrom.txt_effect_sizes.txt", stringsAsFactors = F, col.names=c('intron',  'logef' ,'Human', 'Chimp','deltaPAU')) %>% filter(intron != "intron")

effectsize$deltaPAU=as.numeric(as.character(effectsize$deltaPAU))
effectsize$logef=as.numeric(as.character(effectsize$logef))
plot(sort(effectsize$deltaPAU),main="Leafcutter delta PAU", ylab="Delta PAU", xlab="PAS Index")

Are those discovered used more in chimp those discovered in chimp?

PASinfo=read.table("../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt",header = T, stringsAsFactors = F)

Join this with the effect sizes.

effectsize_sep=effectsize %>% separate(intron, into=c("chr", "start", "end", "gene"),sep=":")
effectsize_sep$start=as.integer(effectsize_sep$start)
effectsize_sep$end=as.integer(effectsize_sep$end)
effectsize_anno=effectsize_sep %>% inner_join(PASinfo, by=c("chr", "start", "end","gene"))
ggplot(effectsize_anno, aes(x=disc, y=deltaPAU)) + geom_boxplot()

Volcano plot:

I need the effect sizes and the significance. I need to plot only the top PAS per cluster.

sig_geneP=sig %>% separate(cluster,into = c("chr", "gene"), sep=":") %>% dplyr::select(gene, p.adjust)

effectsizeTop=effectsize_sep %>% group_by(gene) %>% summarise(Min=min(deltaPAU), Max=max(deltaPAU)) %>% mutate(TopdPAU=ifelse(abs(Min)>Max, Min, Max))

#exclude when the max=min 
effectsizeTopFilt=effectsizeTop %>% filter(abs(Min) != Max)

effectsize_wES=effectsizeTopFilt %>% inner_join(sig_geneP, by="gene") %>% mutate(Species=ifelse(TopdPAU > 0.2 & p.adjust<.05, "Chimp", ifelse(TopdPAU < -0.2 & p.adjust< .05, "Human", "Neither")))

This is the significance for the gene.

ggplot(effectsize_wES,aes(x=TopdPAU, y=-log10(p.adjust))) +geom_point(aes(col=Species),alpha=.5) + labs(title="Top PAS per gene \nExclude 2 PAS genes")+ geom_text(data=subset(effectsize_wES, -log10(p.adjust) >20 & abs(TopdPAU)>.2 ), aes(x=TopdPAU,y=-log10(p.adjust) +2,label=gene))

Not the best way to visualize this because every PAS per gene is assigned the same pvalue.

Try this including the matching one. I will make 2 plots. One with human dominant, one with chimp dominant.

effectsizeTopHuman=effectsize_sep %>% group_by(gene) %>% summarise(Min=min(deltaPAU), Max=max(deltaPAU)) %>% mutate(TopdPAU=ifelse(abs(Min) > Max, Min, ifelse(abs(Min)==Max, Min, Max)),TwoPAS=ifelse(abs(Min)==Max, T, F))

effectsize_wES_human=effectsizeTopHuman %>% inner_join(sig_geneP, by="gene") %>% mutate(Species=ifelse(TopdPAU > 0.2 & p.adjust<.05, "Chimp", ifelse(TopdPAU < -0.2 & p.adjust< .05, "Human", "Neither")))

effectsizeTopChimp=effectsize_sep %>% group_by(gene) %>% summarise(Min=min(deltaPAU), Max=max(deltaPAU)) %>% mutate(TopdPAU=ifelse(abs(Max)>=abs(Min), Max, Min), TwoPAS=ifelse(abs(Min)==Max, T, F))

effectsize_wES_chimp=effectsizeTopChimp %>% inner_join(sig_geneP, by="gene") %>% mutate(Species=ifelse(TopdPAU > 0.2 & p.adjust<.05, "Chimp", ifelse(TopdPAU < -0.2 & p.adjust< .05, "Human", "Neither")))
ggplot(effectsize_wES_human,aes(x=TopdPAU, y=-log10(p.adjust))) + geom_point(aes(col=Species, shape=TwoPAS),alpha=.5) + labs(title="Top PAS per gene \nHuman dominant for 2 PAS")+ geom_text(data=subset(effectsize_wES_human,-log10(p.adjust) >20 & abs(TopdPAU)>.2 ), aes(x=TopdPAU,y=-log10(p.adjust) +2,label=gene))

ggplot(effectsize_wES_chimp,aes(x=TopdPAU, y=-log10(p.adjust))) + geom_point(aes(col=Species, shape=TwoPAS),alpha=.5)  + labs(title="Top PAS per gene \nChimp dominant for 2 PAS")+ geom_text(data=subset(effectsize_wES_chimp, -log10(p.adjust) >20 & abs(TopdPAU)>.2), aes(x=TopdPAU,y=-log10(p.adjust) +2,label=gene))

Write out the significant genes with >.2 difference.

effectsize_sep_pval=effectsize_sep %>% full_join(sig_geneP, by="gene")
#significant > .2
effectsize_sep_pval_sig= effectsize_sep_pval %>% filter(p.adjust <= .05,abs(deltaPAU) >=0.2)
nrow(effectsize_sep_pval_sig)
[1] 3076
#genes
effectsize_sep_pval_sig_genes=effectsize_sep_pval_sig %>% dplyr::select(gene) %>% unique()
nrow(effectsize_sep_pval_sig_genes)
[1] 2149
effectsize_wES_chimpOnly= effectsize_wES %>% filter(Species=="Chimp")
effectsize_wES_HumanOnly= effectsize_wES %>% filter(Species=="Human")



  
write.table(effectsize_wES_chimpOnly,"../data/DiffIso_Nuclear_DF/SignifianceChimpPAS_2_Nuclear.txt",col.names =T, row.names = F,quote = F)

write.table(effectsize_wES_HumanOnly,"../data/DiffIso_Nuclear_DF/SignifianceHumanPAS_2_Nuclear.txt",col.names =T, row.names = F,quote = F)

write.table(effectsize_sep_pval_sig,"../data/DiffIso_Nuclear_DF/SignifianceEitherPAS_2_Nuclear.txt",col.names =T, row.names = F,quote = F)

write.table(effectsize_sep_pval_sig_genes,"../data/DiffIso_Nuclear_DF/SignifianceEitherGENES_Nuclear.txt",col.names =T, row.names = F,quote = F)

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] forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2    
 [5] readr_1.3.1     tidyr_0.8.3     tibble_2.1.1    ggplot2_3.1.1  
 [9] tidyverse_1.2.1 reshape2_1.4.3 

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2       cellranger_1.1.0 compiler_3.5.1   pillar_1.3.1    
 [5] later_0.7.5      git2r_0.26.1     plyr_1.8.4       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    scales_1.0.0    
[41] backports_1.1.2  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     labeling_0.3    
[49] stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1     
[53] crayon_1.3.4