Last updated: 2020-03-09

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
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    Modified:   analysis/upsetter_DF.Rmd

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Rmd 6316829 brimittleman 2020-03-09 2 qqplots
html f1ec3e0 brimittleman 2020-03-09 Build site.
Rmd 552842c brimittleman 2020-03-09 add TvN 
library(workflowr)
This is workflowr version 1.6.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ───────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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── Conflicts ──────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

I am interested in testing for PAS that are differentially used between fractions in one species but not the other. I will condition this analysis on the PAS that pass both filters in the nuclear fraction. I need to filter the fc files for PAS in the final set.

Prep and run LC

fixed for leafcutter are in:

/project2/gilad/briana/Comparative_APA/Human/data/CleanLiftedPeaks4LC (filter with: /project2/gilad/briana/Comparative_APA/data/PAS_doubleFilter) /project2/gilad/briana/Comparative_APA/Chimp/data/CleanLiftedPeaks4LC

PAS_H=read.table("../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T, stringsAsFactors = F) %>% mutate(PASn=paste(chr,start,end,gene,sep=":") ) %>% dplyr::select(PASn, PAS) 



HumanAll=read.table("../Human/data/CleanLiftedPeaks4LC/ALLPAS_postLift_LocParsed_Human_fixed4LC.fc", header = T,stringsAsFactors = F) %>% rownames_to_column(var="PASn")  %>% inner_join(PAS_H,by="PASn")%>% dplyr::select(-PAS)

ChimpAll=read.table("../Chimp/data/CleanLiftedPeaks4LC/ALLPAS_postLift_LocParsed_Chimp_fixed4LC.fc", header = T,stringsAsFactors = F)  %>% rownames_to_column(var="PASn") %>% inner_join(PAS_H,by="PASn") %>% dplyr::select(-PAS)

I need to write these out without column names. I will then add the original headers back.

mkdir ../Human/data/CleanLiftedPeaks4LC_DF
head -n 1 ../Human/data/CleanLiftedPeaks4LC/ALLPAS_postLift_LocParsed_Human_fixed4LC.fc > ../Human/data/CleanLiftedPeaks4LC_DF/Header.txt
mkdir ../Chimp/data/CleanLiftedPeaks4LC_DF
head -n 1 ../Chimp/data/CleanLiftedPeaks4LC/ALLPAS_postLift_LocParsed_Chimp_fixed4LC.fc > ../Chimp/data/CleanLiftedPeaks4LC_DF/Header.txt
write.table(HumanAll, "../Human/data/CleanLiftedPeaks4LC_DF/ALLPAS_postLift_LocParsed_Human_fixed4LC.fc",quote = F, col.names = F, row.names = F)

write.table(ChimpAll, "../Chimp/data/CleanLiftedPeaks4LC_DF/ALLPAS_postLift_LocParsed_Chimp_fixed4LC.fc",quote = F, col.names = F, row.names = F)
cat ../Human/data/CleanLiftedPeaks4LC_DF/Header.txt ../Human/data/CleanLiftedPeaks4LC_DF/ALLPAS_postLift_LocParsed_Human_fixed4LC.fc > ../Human/data/CleanLiftedPeaks4LC_DF/ALLPAS_postLift_LocParsed_Human_fixed4LC_wInd.fc

cat ../Chimp/data/CleanLiftedPeaks4LC_DF/Header.txt ../Chimp/data/CleanLiftedPeaks4LC_DF/ALLPAS_postLift_LocParsed_Chimp_fixed4LC.fc > ../Chimp/data/CleanLiftedPeaks4LC_DF/ALLPAS_postLift_LocParsed_Chimp_fixed4LC_wInd.fc


mkdir ../Human/data/DiffIso_Human_DF/

python subset_diffisopheno_Huma_tvN_DF.py 1
python subset_diffisopheno_Huma_tvN_DF.py 2
python subset_diffisopheno_Huma_tvN_DF.py 3
python subset_diffisopheno_Huma_tvN_DF.py 4
python subset_diffisopheno_Huma_tvN_DF.py 5
python subset_diffisopheno_Huma_tvN_DF.py 6
python subset_diffisopheno_Huma_tvN_DF.py 7
python subset_diffisopheno_Huma_tvN_DF.py 8
python subset_diffisopheno_Huma_tvN_DF.py 9
python subset_diffisopheno_Huma_tvN_DF.py 10
python subset_diffisopheno_Huma_tvN_DF.py 11
python subset_diffisopheno_Huma_tvN_DF.py 12
python subset_diffisopheno_Huma_tvN_DF.py 13
python subset_diffisopheno_Huma_tvN_DF.py 14
python subset_diffisopheno_Huma_tvN_DF.py 15
python subset_diffisopheno_Huma_tvN_DF.py 16
python subset_diffisopheno_Huma_tvN_DF.py 18
python subset_diffisopheno_Huma_tvN_DF.py 19
python subset_diffisopheno_Huma_tvN_DF.py 20
python subset_diffisopheno_Huma_tvN_DF.py 21
python subset_diffisopheno_Huma_tvN_DF.py 22

mkdir ../Chimp/data/DiffIso_Chimp_DF/

#subset_diffisopheno_Chimp_tvN_DF.py

python subset_diffisopheno_Chimp_tvN_DF.py 1
python subset_diffisopheno_Chimp_tvN_DF.py 2
python subset_diffisopheno_Chimp_tvN_DF.py 3
python subset_diffisopheno_Chimp_tvN_DF.py 4
python subset_diffisopheno_Chimp_tvN_DF.py 5
python subset_diffisopheno_Chimp_tvN_DF.py 6
python subset_diffisopheno_Chimp_tvN_DF.py 7
python subset_diffisopheno_Chimp_tvN_DF.py 8
python subset_diffisopheno_Chimp_tvN_DF.py 9
python subset_diffisopheno_Chimp_tvN_DF.py 10
python subset_diffisopheno_Chimp_tvN_DF.py 11
python subset_diffisopheno_Chimp_tvN_DF.py 12
python subset_diffisopheno_Chimp_tvN_DF.py 13
python subset_diffisopheno_Chimp_tvN_DF.py 14
python subset_diffisopheno_Chimp_tvN_DF.py 15
python subset_diffisopheno_Chimp_tvN_DF.py 16
python subset_diffisopheno_Chimp_tvN_DF.py 18
python subset_diffisopheno_Chimp_tvN_DF.py 19
python subset_diffisopheno_Chimp_tvN_DF.py 20
python subset_diffisopheno_Chimp_tvN_DF.py 21
python subset_diffisopheno_Chimp_tvN_DF.py 22

#Samples groups:  
#../Human/data/DiffIso_Human/sample_groups.txt
#../Chimp/data/DiffIso_Chimp/sample_groups.txt 

sbatch runHumanDiffIsoDF.sh
sbatch runChimpDiffIsoDF.sh

Human Results:

Results are in ../Human/data/DiffIso_Human_DF/

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


awk '{if(NR>1)print}' ../Human/data/DiffIso_Human_DF/TN_diff_isoform_chr*.txt_cluster_significance.txt > ../Human/data/DiffIso_Human_DF/TN_diff_isoform_allChrom.txt_significance.txt
sigH=read.table("../Human/data/DiffIso_Human_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") 

sigH$p.adjust=as.numeric(as.character(sigH$p.adjust))
sigH_genes=sigH %>% filter(p.adjust<.05)
number_sigH_genes=nrow(sigH_genes)
number_sigH_genes
[1] 6133
effectsizeH=read.table("../Human/data/DiffIso_Human_DF/TN_diff_isoform_allChrom.txt_effect_sizes.txt", stringsAsFactors = F, col.names=c('intron',  'logef' ,'Nuclear', 'Total','deltaPAU')) %>% filter(intron != "intron")

effectsizeH$deltaPAU=as.numeric(as.character(effectsizeH$deltaPAU))
effectsizeH$logef=as.numeric(as.character(effectsizeH$logef))

effectsizeH_deltaPAU= effectsizeH %>% mutate(SigDPAU2=ifelse(abs(deltaPAU) > .2, "Yes", "No"))  %>% separate(intron, into=c('chr','start','end','gene'), sep=":") %>% mutate(cluster=paste(chr,gene, sep=":")) %>% inner_join(sigH_genes,by="cluster")


nrow(effectsizeH_deltaPAU %>% filter(SigDPAU2=="Yes"))
[1] 3508

3500 differentially used PAS.

plot(sort(effectsizeH$deltaPAU),main="Human Leafcutter delta PAU", ylab="Delta PAU", xlab="PAS Index")

Version Author Date
f1ec3e0 brimittleman 2020-03-09 
effectsizeH_deltaPAU_col= effectsizeH_deltaPAU %>% arrange(deltaPAU) %>%  mutate(diffuse=ifelse(abs(deltaPAU)>=.2,"Yes", "No"))
effectsizeH_deltaPAU_col$color <- ifelse(effectsizeH_deltaPAU_col$diffuse=="Yes", "red", "black")

plot(sort(effectsizeH_deltaPAU_col$deltaPAU),main="Leafcutter delta PAU Human", ylab="Delta PAU", xlab="PAS Index",col=alpha(effectsizeH_deltaPAU_col$color, 0.6))

Version Author Date
f1ec3e0 brimittleman 2020-03-09

Chimp results

Results are in ../Chimp/data/DiffIso_Chimp_DF/

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


awk '{if(NR>1)print}' ../Chimp/data/DiffIso_Chimp_DF/TN_diff_isoform_chr*.txt_cluster_significance.txt > ../Chimp/data/DiffIso_Chimp_DF/TN_diff_isoform_allChrom.txt_significance.txt
sigC=read.table("../Chimp/data/DiffIso_Chimp_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") 

sigC$p.adjust=as.numeric(as.character(sigC$p.adjust))
sigC_genes=sigC %>% filter(p.adjust<.05)
number_sigC_genes=nrow(sigC_genes)
number_sigC_genes
[1] 4414
effectsizeC=read.table("../Chimp/data/DiffIso_Chimp_DF/TN_diff_isoform_allChrom.txt_effect_sizes.txt", stringsAsFactors = F, col.names=c('intron',  'logef' ,'Nuclear', 'Total','deltaPAU')) %>% filter(intron != "intron")

effectsizeC$deltaPAU=as.numeric(as.character(effectsizeC$deltaPAU))
effectsizeC$logef=as.numeric(as.character(effectsizeC$logef))

effectsizeC_deltaPAU= effectsizeC %>% mutate(SigDPAU2=ifelse(abs(deltaPAU) > .2, "Yes", "No"))  %>% separate(intron, into=c('chr','start','end','gene'), sep=":") %>% mutate(cluster=paste(chr,gene, sep=":")) %>% inner_join(sigC_genes,by="cluster")


nrow(effectsizeC_deltaPAU %>% filter(SigDPAU2=="Yes"))
[1] 1291
plot(sort(effectsizeC$deltaPAU),main="Chimp Leafcutter delta PAU", ylab="Delta PAU", xlab="PAS Index")

Version Author Date
f1ec3e0 brimittleman 2020-03-09 
effectsizeC_deltaPAU_col= effectsizeC_deltaPAU %>% arrange(deltaPAU) %>%  mutate(diffuse=ifelse(abs(deltaPAU)>=.2,"Yes", "No"))
effectsizeC_deltaPAU_col$color <- ifelse(effectsizeC_deltaPAU_col$diffuse=="Yes", "red", "black")

plot(sort(effectsizeC_deltaPAU_col$deltaPAU),main="Leafcutter delta PAU Chimp", ylab="Delta PAU", xlab="PAS Index",col=alpha(effectsizeC_deltaPAU_col$color, 0.6))

Version Author Date
f1ec3e0 brimittleman 2020-03-09

1291 differentially used

This doesnt seem right. Let me check it.

plot(sort(effectsizeH_deltaPAU_col$deltaPAU),main="Leafcutter delta PAU Human", ylab="Delta PAU", xlab="PAS Index",col=alpha(effectsizeH_deltaPAU_col$color, 0.6))

Version Author Date
f1ec3e0 brimittleman 2020-03-09 
plot(sort(effectsizeC_deltaPAU_col$deltaPAU),main="Leafcutter delta PAU Chimp", ylab="Delta PAU", xlab="PAS Index",col=alpha(effectsizeC_deltaPAU_col$color, 0.6))

Version Author Date
f1ec3e0 brimittleman 2020-03-09

Explore dPAU

I will graph the number of significant PAS by the dPAU in each species.

dPAUVals=seq(0,0.6,.05)
SigPASHuman=c()
SigPASHumanProp=c()
SigPASChimp=c()
SigPASChimpProp=c()
for (i in dPAUVals){
  sigatiH= nrow(effectsizeH_deltaPAU %>% filter(abs(deltaPAU)>=i))
  SigPASHuman=c(SigPASHuman, sigatiH)
  propH=sigatiH/nrow(effectsizeH_deltaPAU)
  SigPASHumanProp=c(SigPASHumanProp,propH )
  sigatiC= nrow(effectsizeC_deltaPAU %>% filter(abs(deltaPAU)>=i))
  propC=sigatiC/nrow(effectsizeC_deltaPAU)
  SigPASChimpProp=c(SigPASChimpProp,propC )
  SigPASChimp=c(SigPASChimp,sigatiC)
}

AllPASdf=as.data.frame(cbind(dPAU=dPAUVals, Human=SigPASHuman,HumanProp=SigPASHumanProp,Chimp=SigPASChimp, ChimpProp=SigPASChimpProp))
AllPASdf_g= AllPASdf %>% dplyr::select(-Human, -Chimp) %>% gather("Species", "Value", -dPAU)
AllPASdf_g$Value= as.numeric(AllPASdf_g$Value)
ggplot(AllPASdf_g,aes(x=dPAU, by=Species, fill=Species, y=Value))+ geom_bar(stat="identity", position = "dodge") + scale_fill_brewer(palette = "Dark2")+ labs(y="Proportion of PAS")

AllPASdf_gN= AllPASdf %>% dplyr::select(-HumanProp, -ChimpProp) %>% gather("Species", "Value", -dPAU)
AllPASdf_gN$Value= as.numeric(AllPASdf_gN$Value)
ggplot(AllPASdf_gN,aes(x=dPAU, by=Species, fill=Species, y=Value))+ geom_bar(stat="identity", position = "dodge") + scale_fill_brewer(palette = "Dark2") + labs(y="Nubmer of significant PAS")

Looks like the number of signifiacant genes are different.

#human
sigH_genes %>% nrow()
[1] 6133
#chimp
sigC_genes %>% nrow()
[1] 4414

QQ plots:

qqplot(-log10(runif(nrow(sigH))), -log10(sigH$p.adjust),ylab="-log10 Total v Nuclear Adjusted Leafcutter pvalue", xlab="-log 10 Uniform expectation", main="Leafcutter differencial isoform analysis between fractions")
points(sort(-log10(runif(nrow(sigC)))),sort(-log10(sigC$p.adjust)),col=alpha("blue"))
abline(0,1)
legend("topleft", legend=c("Human", "Chimp"),col=c("black","blue"), pch=16,bty = 'n')

qqplot(-log10(runif(nrow(sigC))), -log10(sigC$p.adjust),ylab="-log10 Total v Nuclear Adjusted Leafcutter pvalue", xlab="-log 10 Uniform expectation", main="Leafcutter differencial isoform analysis between fractions")
points(sort(-log10(runif(nrow(sigH)))),sort(-log10(sigH$p.adjust)),col=alpha("blue"))
abline(0,1)
legend("topleft", legend=c("Chimp","Human"),col=c("black","blue"), pch=16,bty = 'n')


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 workflowr_1.6.0

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5   haven_1.1.2        lattice_0.20-38   
 [4] colorspace_1.3-2   generics_0.0.2     htmltools_0.3.6   
 [7] yaml_2.2.0         rlang_0.4.0        later_0.7.5       
[10] pillar_1.3.1       glue_1.3.0         withr_2.1.2       
[13] RColorBrewer_1.1-2 modelr_0.1.2       readxl_1.1.0      
[16] plyr_1.8.4         munsell_0.5.0      gtable_0.2.0      
[19] cellranger_1.1.0   rvest_0.3.2        evaluate_0.12     
[22] labeling_0.3       knitr_1.20         httpuv_1.4.5      
[25] broom_0.5.1        Rcpp_1.0.2         promises_1.0.1    
[28] scales_1.0.0       backports_1.1.2    jsonlite_1.6      
[31] fs_1.3.1           hms_0.4.2          digest_0.6.18     
[34] stringi_1.2.4      grid_3.5.1         rprojroot_1.3-2   
[37] cli_1.1.0          tools_3.5.1        magrittr_1.5      
[40] lazyeval_0.2.1     crayon_1.3.4       whisker_0.3-2     
[43] pkgconfig_2.0.2    xml2_1.2.0         lubridate_1.7.4   
[46] assertthat_0.2.0   rmarkdown_1.10     httr_1.3.1        
[49] rstudioapi_0.10    R6_2.3.0           nlme_3.1-137      
[52] git2r_0.26.1       compiler_3.5.1