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
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File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | 3aee984 | brimittleman | 2020-01-10 | delta pau and sig |
html | 417783c | brimittleman | 2020-01-10 | Build site. |
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 | d6a1ed8 | brimittleman | 2019-12-18 | Build site. |
Rmd | d4e10f0 | brimittleman | 2019-12-18 | update pantro6 |
html | ecd8410 | brimittleman | 2019-10-16 | Build site. |
Rmd | 238e54c | brimittleman | 2019-10-16 | fix label |
html | aab50e4 | brimittleman | 2019-10-16 | Build site. |
Rmd | 4a1903c | brimittleman | 2019-10-16 | redo volcano plots |
html | 9d67688 | brimittleman | 2019-10-15 | Build site. |
Rmd | f8676d2 | brimittleman | 2019-10-15 | think about vol plot |
html | f4bcae9 | brimittleman | 2019-10-15 | Build site. |
Rmd | 25a8b1e | brimittleman | 2019-10-15 | fix name bug add number PAS analysis |
html | d0c98c2 | brimittleman | 2019-10-09 | Build site. |
Rmd | 14a3f66 | brimittleman | 2019-10-09 | add pca and human v chimp in nuc analysis |
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 nuclear 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/NuclearHvC
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")
AllNuclear=Allsamps %>% dplyr::select(chrom,contains("_N")) %>% column_to_rownames(var="chrom")
write.table(AllNuclear, "../data/NuclearHvC/ALLPAS_postLift_LocParsed_HvC_Nuclear_fixed4LC.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/sample_goups.txt",row.names = F, col.names = F, quote = F)
Split by chromosome.
mkdir ../data/DiffIso_Nuclear/
python subset_diffisopheno_Nuclear_HvC.py 1
python subset_diffisopheno_Nuclear_HvC.py 2
python subset_diffisopheno_Nuclear_HvC.py 3
python subset_diffisopheno_Nuclear_HvC.py 4
python subset_diffisopheno_Nuclear_HvC.py 5
python subset_diffisopheno_Nuclear_HvC.py 6
python subset_diffisopheno_Nuclear_HvC.py 7
python subset_diffisopheno_Nuclear_HvC.py 8
python subset_diffisopheno_Nuclear_HvC.py 9
python subset_diffisopheno_Nuclear_HvC.py 10
python subset_diffisopheno_Nuclear_HvC.py 11
python subset_diffisopheno_Nuclear_HvC.py 12
python subset_diffisopheno_Nuclear_HvC.py 13
python subset_diffisopheno_Nuclear_HvC.py 14
python subset_diffisopheno_Nuclear_HvC.py 16
python subset_diffisopheno_Nuclear_HvC.py 18
python subset_diffisopheno_Nuclear_HvC.py 19
python subset_diffisopheno_Nuclear_HvC.py 20
python subset_diffisopheno_Nuclear_HvC.py 21
python subset_diffisopheno_Nuclear_HvC.py 22
Run leafcutter:
sbatch runNuclearDifffIso.sh
Concatinate results:
awk '{if(NR>1)print}' ../data/DiffIso_Nuclear/TN_diff_isoform_chr*.txt_effect_sizes.txt > ../data/DiffIso_Nuclear/TN_diff_isoform_allChrom.txt_effect_sizes.txt
awk '{if(NR>1)print}' ../data/DiffIso_Nuclear/TN_diff_isoform_chr*.txt_cluster_significance.txt > ../data/DiffIso_Nuclear/TN_diff_isoform_allChrom.txt_significance.txt
Significant clusters:
sig=read.table("../data/DiffIso_Nuclear/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$p.adjust),ylab="-log10 Adjusted Leafcutter pvalue", xlab="-log 10 Uniform expectation", main="Leafcutter differencial isoform analysis between Species")
abline(0,1)
tested_genes=nrow(sig)
tested_genes
[1] 9678
sig_genes=sig %>% filter(p.adjust<.05)
number_sig_genes=nrow(sig_genes)
number_sig_genes
[1] 6972
Effect Sizes
effectsize=read.table("../data/DiffIso_Nuclear/TN_diff_isoform_allChrom.txt_effect_sizes.txt", stringsAsFactors = F, col.names=c('intron', 'logef' ,'Human', 'Chimp','deltaPAU')) %>% filter(intron != "intron")
effectsize$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/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()
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] 3616
#genes
effectsize_sep_pval_sig_genes=effectsize_sep_pval_sig %>% dplyr::select(gene) %>% unique()
nrow(effectsize_sep_pval_sig_genes)
[1] 2501
effectsize_wES_chimpOnly= effectsize_wES %>% filter(Species=="Chimp")
effectsize_wES_HumanOnly= effectsize_wES %>% filter(Species=="Human")
write.table(effectsize_wES_chimpOnly,"../data/DiffIso_Nuclear/SignifianceChimpPAS_2_Nuclear.txt",col.names =T, row.names = F,quote = F)
write.table(effectsize_wES_HumanOnly,"../data/DiffIso_Nuclear/SignifianceHumanPAS_2_Nuclear.txt",col.names =T, row.names = F,quote = F)
write.table(effectsize_sep_pval_sig,"../data/DiffIso_Nuclear/SignifianceEitherPAS_2_Nuclear.txt",col.names =T, row.names = F,quote = F)
write.table(effectsize_sep_pval_sig_genes,"../data/DiffIso_Nuclear/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