Last updated: 2020-01-10

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File Version Author Date Message
Rmd 521dc81 brimittleman 2020-01-10 update dPAS > 0.2
html fa86df3 brimittleman 2019-12-30 Build site.
Rmd 771d239 brimittleman 2019-12-30 add write out
html 2644747 brimittleman 2019-12-27 Build site.
Rmd 4c8973d brimittleman 2019-12-27 add total human vs chimp 
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 merge the 5% phenotypes from the human and chimp. I need a fc file with the human and chimp total samples. I will make a group file with the identifier being human or chimp.

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

mkdir ../data/TotalHvC
human=read.table("../Human/data/CleanLiftedPeaks4LC/ALLPAS_postLift_LocParsed_Human_fixed4LC.fc", stringsAsFactors = F, header = T) %>% rownames_to_column(var="chrom")
chimp=read.table("../Chimp/data/CleanLiftedPeaks4LC/ALLPAS_postLift_LocParsed_Chimp_fixed4LC.fc", stringsAsFactors = F, header = T)%>% rownames_to_column(var="chrom")
Allsamps=human %>% full_join(chimp,by="chrom") 

AllTotal=Allsamps %>% dplyr::select(chrom,contains("_T")) %>% column_to_rownames(var="chrom")

write.table(AllTotal, "../data/TotalHvC/ALLPAS_postLift_LocParsed_HvC_Total_fixed4LC.fc",row.names = T, col.names = T, quote = F)

I will make the id file here.

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

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

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

Split by chromosome.

mkdir ../data/DiffIso_Total/

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

Run leafcutter:


sbatch runTotalDiffIso.sh

Concatinate results:

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


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

Significant clusters:

sig=read.table("../data/DiffIso_Total/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 Total Fraction")
abline(0,1)

Version Author Date
2644747 brimittleman 2019-12-27 
tested_genes=nrow(sig)
tested_genes
[1] 9640
sig_genes=sig %>% filter(p.adjust<.05)
number_sig_genes=nrow(sig_genes)
number_sig_genes
[1] 6129

Effect Sizes

effectsize=read.table("../data/DiffIso_Total/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")

Version Author Date
2644747 brimittleman 2019-12-27

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

PASinfo=read.table("../data/Peaks_5perc/Peaks_5perc_either_bothUsage_noUnchr.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()

Version Author Date
2644747 brimittleman 2019-12-27

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))

Version Author Date
2644747 brimittleman 2019-12-27

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))

Version Author Date
2644747 brimittleman 2019-12-27 
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))

Version Author Date
2644747 brimittleman 2019-12-27

Write out the significant genes with >.2 difference.

effectsize_wES_chimpOnly= effectsize_wES %>% filter(Species=="Chimp")
effectsize_wES_HumanOnly= effectsize_wES %>% filter(Species=="Human")
effectsize_wES_either=effectsize_wES %>% filter(Species!="Neither")

write.table(effectsize_wES_chimpOnly,"../data/DiffIso_Total/SignifianceChimpPAS_2_Total.txt",col.names =T, row.names = F,quote = F)

write.table(effectsize_wES_HumanOnly,"../data/DiffIso_Total/SignifianceHumanPAS_2_Total.txt",col.names =T, row.names = F,quote = F)
write.table(effectsize_wES_HumanOnly,"../data/DiffIso_Total/SignifianceEitherPAS_2_Total.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