Last updated: 2020-03-16
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Knit directory: Comparative_APA/analysis/
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Rmd | a89d80b | brimittleman | 2019-12-17 | wflow_publish(“analysis/annotatePAS.Rmd”) |
html | f4bcae9 | brimittleman | 2019-10-15 | Build site. |
Rmd | 25a8b1e | brimittleman | 2019-10-15 | fix name bug add number PAS analysis |
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Rmd | 190a655 | brimittleman | 2019-10-09 | small changes 9.9 |
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Rmd | d6e07c1 | brimittleman | 2019-10-04 | subset 5 perc pas and pheno |
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Rmd | d7ac788 | brimittleman | 2019-10-04 | subset 5 perc pas and pheno |
html | fafaf61 | brimittleman | 2019-10-04 | Build site. |
Rmd | ca39a2a | brimittleman | 2019-10-04 | finish annoatation and quantification |
html | 4f7e30d | brimittleman | 2019-10-03 | Build site. |
Rmd | 8033ddb | brimittleman | 2019-10-03 | get to pheno to find prob |
html | e0ac227 | brimittleman | 2019-10-03 | Build site. |
Rmd | e3f0cdf | brimittleman | 2019-10-03 | add annotation analysis |
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 will annotate the PAS that passed the liftover. These PAS are in ../data/cleanPeaks_lifted
Map PAS to these annoations:
mkdir ../data/cleanPeaks_anno
bedtools map -a ../data/cleanPeaks_lifted/AllPAS_postLift.sort.bed -b /project2/gilad/briana/genome_anotation_data/hg38_refseq_anno/hg38_ncbiRefseq_Formatted_Allannotation.sort.bed -c 4 -S -o distinct > ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnno.bed
Chose annotation if PAS in multiple and create bed. I will have to lift this back to chimp then make saf files for both to do the feature count
python chooseAnno2Bed.py ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnno.bed ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnnoPARSED.bed
Lift this so I have it with chimp coordinates:
sbatch LiftOrthoPAS2chimp.sh
bed 2 SAF
python bed2SAF_gen.py ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnnoPARSED.bed ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnnoPARSED.SAF
python bed2SAF_gen.py ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnnoPARSED_chimpLoc.bed ../data/cleanPeaks_anno/AllPAS_postLift.sort_LocAnnoPARSED_chimpLoc.SAF
Use feature counts to quantify:
mkdir ../Human/data/CleanLiftedPeaks_FC/
mkdir ../Chimp/data/CleanLiftedPeaks_FC/
sbatch quantLiftedPAS.sh
Fix header:
python fixFChead_bothfrac.py ../Human/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human ../Human/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human_fixed.fc
python fixFChead_bothfrac.py ../Chimp/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Chimp ../Chimp/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Chimp_fixed.fc
#make file ID
python makeFileID.py ../Chimp/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Chimp ../Chimp/data/CleanLiftedPeaks_FC/ChimpFileID.txt
python makeFileID.py ../Human/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human ../Human/data/CleanLiftedPeaks_FC/HumanFileID.txt
Make phenotypes from these:
mkdir ../Human/data/phenotype/
mkdir ../Chimp/data/phenotype/
python makePheno.py ../Human/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human_fixed.fc ../Human/data/CleanLiftedPeaks_FC/HumanFileID.txt ../Human/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno.txt
python makePheno.py ../Chimp/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Chimp_fixed.fc ../Chimp/data/CleanLiftedPeaks_FC/ChimpFileID.txt ../Chimp/data/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno.txt
Convert these to numeric:
Rscript pheno2countonly.R -I ../Human/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno.txt -O ../Human/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnly.txt
Rscript pheno2countonly.R -I ../Chimp/data/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno.txt -O ../Chimp/data/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno_countOnly.txt
python convertNumeric.py ../Human/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnly.txt ../Human/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnlyNumeric.txt
python convertNumeric.py ../Chimp/data/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno_countOnly.txt ../Chimp/data/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno_countOnlyNumeric.txt
Plot usages to see if 5% is a good cutoff for this analysis as well.
HumanAnno=read.table("../Human/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno.txt", header = T, stringsAsFactors = F) %>% tidyr::separate(chrom, sep = ":", into = c("chr", "start", "end", "id")) %>% tidyr::separate(id, sep="_", into=c("gene", "strand", "peak")) %>% separate(peak,into=c("loc", "disc","PAS"), sep="-")
IndH=colnames(HumanAnno)[9:ncol(HumanAnno)]
HumanUsage=read.table("../Human/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno_countOnlyNumeric.txt", col.names = IndH)
HumanMean=as.data.frame(cbind(HumanAnno[,1:8], Human=rowMeans(HumanUsage)))
HumanUsage_anno=as.data.frame(cbind(HumanAnno[,1:8],HumanUsage ))
ChimpAnno=read.table("../Chimp/data/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno.txt", header = T, stringsAsFactors = F) %>% tidyr::separate(chrom, sep = ":", into = c("chr", "start", "end", "id")) %>% tidyr::separate(id, sep="_", into=c("gene", "strand", "peak")) %>% separate(peak,into=c("loc", "disc","PAS"), sep="-")
IndC=colnames(ChimpAnno)[9:ncol(ChimpAnno)]
ChimpUsage=read.table("../Chimp/data/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno_countOnlyNumeric.txt", col.names = IndC)
ChimpMean=as.data.frame(cbind(ChimpAnno[,1:8], Chimp=rowMeans(ChimpUsage)))
ChimpUsage_anno=as.data.frame(cbind(ChimpAnno[,1:8],ChimpUsage ))
Mean both:
BothMean=ChimpMean %>% full_join(HumanMean, by=c("chr","start","end","gene" ,"strand", "loc", "disc","PAS" ))
BothMeanM=melt(BothMean,id.vars =c("chr","start","end","gene" ,"strand", "loc", "disc","PAS" ),variable.name = "Species", value.name = "MeanUsage" ) %>% filter(loc !="008559")
Plot:
ggplot(BothMeanM, aes(x=loc, y=MeanUsage,by=Species,fill=Species)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2")
ggplot(BothMeanM, aes(x=MeanUsage,by=Species,col=Species)) + stat_ecdf(geom = "point", alpha=.25) + scale_color_brewer(palette = "Dark2") + labs(title="Cumulative Distribution plot for PAS Usage", x="Mean Usage- both fractions", y="F(Mean Usage)")
This is good. Globally the usages are similar across species.
ggplot(BothMeanM, aes(x=log10(MeanUsage + .00001),by=Species,fill=Species)) + geom_histogram(alpha=.5, bins=30,position="dodge") + scale_fill_brewer(palette = "Dark2") + geom_vline(xintercept = log10(0.05))
Looks like 5% in either species is a good set.
Filter to PAS with 5% usage
BothMean mean in human or chimp > 0.5
BothMean_5= BothMean %>% filter(Chimp >=0.05 | Human >= 0.05)
BothMean_5M=melt(BothMean_5,id.vars =c("chr","start","end","gene" ,"strand", "loc", "disc","PAS" ),variable.name = "Species", value.name = "MeanUsage" ) %>% filter(loc !="008559")
ggplot(BothMean_5M, aes(x=loc, y=MeanUsage,by=Species,fill=Species)) + geom_boxplot() + scale_fill_brewer(palette = "Dark2")
ggplot(BothMean_5M, aes(x=MeanUsage,by=Species,col=Species)) + stat_ecdf(geom = "point", alpha=.25) + scale_color_brewer(palette = "Dark2") + labs(title="Cumulative Distribution plot for PAS Usage at 5%", x="Mean Usage- both fractions", y="F(Mean Usage)")
ggplot(BothMean_5M, aes(x=MeanUsage,by=Species,fill=Species)) + geom_histogram(alpha=.5, bins=30, position = "dodge") + scale_fill_brewer(palette = "Dark2")
Write this out this way and as a bed files with human and chimp scores:
mkdir ../data/Peaks_5perc
mkdir ../data/Pheno_5perc
BothMean_5_out=BothMean_5 %>% dplyr::select(PAS,disc, gene, loc,chr, start, end,Chimp, Human)
write.table(BothMean_5_out, "../data/Peaks_5perc/Peaks_5perc_either_bothUsage.txt", row.names = F, col.names = T, quote = F)
BothMean_5_out_noUN=BothMean_5 %>% dplyr::select(PAS,disc, gene, loc,chr, start, end,Chimp, Human) %>% filter(!grepl("Un",chr))
write.table(BothMean_5_out_noUN, "../data/Peaks_5perc/Peaks_5perc_either_bothUsage_noUnchr.txt", row.names = F, col.names = T, quote = F)
#write bed with human coord for igv
BothMean_5_bed=BothMean_5 %>% dplyr::select(chr, start, end, PAS, Human, strand)
write.table(BothMean_5_bed, "../data/Peaks_5perc/Peaks_5perc_either_HumanCoordHummanUsage.bed", row.names = F, col.names = T, quote = F)
I can filter the phenotypes and PAS with this list.
ggplot(BothMean_5_out, aes(x=disc, fill=disc))+ geom_bar(aes(y = (..count..)/sum(..count..)))+ scale_fill_brewer(palette = "Dark2")
BothMean_5_outmean= BothMean_5_out %>% mutate(meanUsage=(Human+Chimp)/2)
ggplot(BothMean_5M, aes(x=disc, by= Species, fill=Species, y=MeanUsage)) + geom_boxplot() + scale_y_log10()+ scale_fill_brewer(palette = "Dark2")
Warning: Transformation introduced infinite values in continuous y-axis
Warning: Removed 6678 rows containing non-finite values (stat_boxplot).
ChimpUsage_anno_5perc= ChimpUsage_anno %>% filter(PAS %in% BothMean_5$PAS)
write.table(ChimpUsage_anno_5perc, "../data/Pheno_5perc/Chimp_Pheno_5perc.txt", row.names = F, col.names = T, quote = F)
HumaUsage_anno_5perc= HumanUsage_anno %>% filter(PAS %in% BothMean_5$PAS)
write.table(HumaUsage_anno_5perc, "../data/Pheno_5perc/Human_Pheno_5perc.txt", row.names = F, col.names = T, 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] 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] 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] workflowr_1.6.0 cellranger_1.1.0 rvest_0.3.2
[22] evaluate_0.12 labeling_0.3 knitr_1.20
[25] httpuv_1.4.5 broom_0.5.1 Rcpp_1.0.2
[28] promises_1.0.1 scales_1.0.0 backports_1.1.2
[31] jsonlite_1.6 fs_1.3.1 hms_0.4.2
[34] digest_0.6.18 stringi_1.2.4 grid_3.5.1
[37] rprojroot_1.3-2 cli_1.1.0 tools_3.5.1
[40] magrittr_1.5 lazyeval_0.2.1 crayon_1.3.4
[43] whisker_0.3-2 pkgconfig_2.0.2 xml2_1.2.0
[46] lubridate_1.7.4 assertthat_0.2.0 rmarkdown_1.10
[49] httr_1.3.1 rstudioapi_0.10 R6_2.3.0
[52] nlme_3.1-137 git2r_0.26.1 compiler_3.5.1