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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
library(workflowr)
This is workflowr version 1.3.0
Run ?workflowr for help getting started
library(cowplot)
Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':
ggsave
In this analysis I will compute the PAS usage for the new phenotypes in the old data. To make the info comparable and I will rerun feature counts with the filtered phenotypes for both the old and the new data.
Convert the filtered data to an SAF
python finalPASbed2SAF.py ../data/peaks_5perc/APApeak_Peaks_GeneLocAnno.Nuclear.5perc.fc ../data/CompareOldandNew/APApeak_5perc_Nuclear.SAF
python finalPASbed2SAF.py ../data/peaks_5perc/APApeak_Peaks_GeneLocAnno.Total.5perc.fc ../data/CompareOldandNew/APApeak_5perc_Total.SAF
Run feature counts:
sbatch FC_newPeaks_olddata.sh
Convert to phenotypes:
fix headers on FC
python fixFChead.py ../data/CompareOldandNew/Total/New5percPeaks_Total_olddata.fc ../data/CompareOldandNew/Total/New5percPeaks_Total_olddata.fixed.fc
python fixFChead_bothfrac.py ../data/CompareOldandNew/Total/New5percPeaks_Total_newdata.fc ../data/CompareOldandNew/Total/New5percPeaks_Total_newdata.fixed.fc
python fixFChead.py ../data/CompareOldandNew/Nuclear/New5percPeaks_Nuclear_olddata.fc ../data/CompareOldandNew/Nuclear/New5percPeaks_Nuclear_olddata.fixed.fc
python fixFChead_bothfrac.py ../data/CompareOldandNew/Nuclear/New5percPeaks_Nuclear_newdata.fc ../data/CompareOldandNew/Nuclear/New5percPeaks_Nuclear_newdata.fixed.fc
python makePheno.py ../data/CompareOldandNew/Total/New5percPeaks_Total_olddata.fixed.fc ../data/peakCoverage/file_id_mapping_Total_Transcript.txt ../data/CompareOldandNew/Total/New5percPeaks_Total_olddata.fixed.pheno.fc
python makePheno.py ../data/CompareOldandNew/Total/New5percPeaks_Total_newdata.fixed.fc ../data/peakCoverage/file_id_mapping_Total_Transcript.txt ../data/CompareOldandNew/Total/New5percPeaks_Total_newdata.fixed.pheno.fc
python makePheno.py ../data/CompareOldandNew/Nuclear/New5percPeaks_Nuclear_olddata.fixed.fc ../data/peakCoverage/file_id_mapping_Nuclear_Transcript.txt ../data/CompareOldandNew/Nuclear/New5percPeaks_Nuclear_olddata.fixed.pheno.fc
python makePheno.py ../data/CompareOldandNew/Nuclear/New5percPeaks_Nuclear_newdata.fixed.fc ../data/peakCoverage/file_id_mapping_Nuclear_Transcript.txt ../data/CompareOldandNew/Nuclear/New5percPeaks_Nuclear_newdata.fixed.pheno.fc
COunts only:
Rscript pheno2countonly.R -I ../data/CompareOldandNew/Total/New5percPeaks_Total_olddata.fixed.pheno.fc -O ../data/CompareOldandNew/Total/New5percPeaks_Total_olddata.fixed.pheno.CountsOnly
Rscript pheno2countonly.R -I ../data/CompareOldandNew/Total/New5percPeaks_Total_newdata.fixed.pheno.fc -O ../data/CompareOldandNew/Total/New5percPeaks_Total_newdata.fixed.pheno.CountsOnly
Rscript pheno2countonly.R -I ../data/CompareOldandNew/Nuclear/New5percPeaks_Nuclear_olddata.fixed.pheno.fc -O ../data/CompareOldandNew/Nuclear/New5percPeaks_Nuclear_olddata.fixed.pheno.CountsOnly
Rscript pheno2countonly.R -I ../data/CompareOldandNew/Nuclear/New5percPeaks_Nuclear_newdata.fixed.pheno.fc -O ../data/CompareOldandNew/Nuclear/New5percPeaks_Nuclear_newdata.fixed.pheno.CountsOnly
Convert to numeric
python convertNumeric.py ../data/CompareOldandNew/Total/New5percPeaks_Total_olddata.fixed.pheno.CountsOnly ../data/CompareOldandNew/Total/New5percPeaks_Total_olddata.fixed.pheno.CountsOnlyNumeric
python convertNumeric.py ../data/CompareOldandNew/Total/New5percPeaks_Total_newdata.fixed.pheno.CountsOnly ../data/CompareOldandNew/Total/New5percPeaks_Total_newdata.fixed.pheno.CountsOnlyNumeric
python convertNumeric.py ../data/CompareOldandNew/Nuclear/New5percPeaks_Nuclear_olddata.fixed.pheno.CountsOnly ../data/CompareOldandNew/Nuclear/New5percPeaks_Nuclear_olddata.fixed.pheno.CountsOnlyNumeric
python convertNumeric.py ../data/CompareOldandNew/Nuclear/New5percPeaks_Nuclear_newdata.fixed.pheno.CountsOnly ../data/CompareOldandNew/Nuclear/New5percPeaks_Nuclear_newdata.fixed.pheno.CountsOnlyNumeric
Total New data
totalPeakUs_new=read.table("../data/CompareOldandNew/Total/New5percPeaks_Total_newdata.fixed.fc", header = T, stringsAsFactors = F) %>% select(-Chr,-Start,-End,-Strand,-Length)
ind=as.data.frame(colnames(totalPeakUs_new)[2:dim(totalPeakUs_new)[2]])
colnames(ind)=c("x")
ind=ind %>% separate(x,into=c("indiv", "fraction"), sep="_") %>%mutate(Individual=paste("NA",substring(indiv,2, 6), sep=""))
totalPeakUs_new_CountNum=read.table("../data/CompareOldandNew/Total/New5percPeaks_Total_newdata.fixed.pheno.CountsOnlyNumeric", col.names = ind$Individual)
#numeric with anno
totalPeakNew=as.data.frame(cbind(ID=totalPeakUs_new[,1], totalPeakUs_new_CountNum))
Total Old data
totalPeakUs_old=read.table("../data/CompareOldandNew/Total/New5percPeaks_Total_olddata.fixed.fc", header = T, stringsAsFactors = F) %>% select(-Chr,-Start,-End,-Strand,-Length)
totalPeakUs_old_CountNum=read.table("../data/CompareOldandNew/Total/New5percPeaks_Total_olddata.fixed.pheno.CountsOnlyNumeric", col.names = ind$Individual)
#numeric with anno
totalPeakold=as.data.frame(cbind(ID=totalPeakUs_old[,1], totalPeakUs_old_CountNum))
Seperate by batch
batch4=read.table("../data/MetaDataSequencing.txt", header=T,stringsAsFactors = F)%>% filter(fraction=="total") %>% select(line, batch) %>% filter(batch == 4)
newInd=batch4$line
totalPeakoldM=melt(totalPeakold, id.vars=c("ID"), value.name = "Usage", variable.name = "Individual") %>% mutate(New15=ifelse(Individual %in%newInd, "Yes", "No")) %>% group_by(New15,ID) %>% summarise(meanUsageOld=mean(Usage)) %>% spread(New15,meanUsageOld)
totalPeaknewM=melt(totalPeakNew, id.vars=c("ID"), value.name = "Usage", variable.name = "Individual") %>% mutate(New15=ifelse(Individual %in%newInd, "Yes", "No")) %>% group_by(New15,ID) %>% summarise(meanUsageNew=mean(Usage)) %>% spread(New15,meanUsageNew)
totalold=ggplot(totalPeakoldM,aes(x=No,y=Yes)) + geom_point() + geom_density2d() + labs(x="39 ind", y="15 ind", title="Total Usage Old data")
totalnew=ggplot(totalPeaknewM,aes(x=No,y=Yes)) + geom_point() + geom_density2d() + labs(x="39 ind", y="15 ind", title="Total Usage New data")
plot_grid(totalold,totalnew)
Nuclear New data
nuclearPeakUs_new=read.table("../data/CompareOldandNew/Nuclear/New5percPeaks_Nuclear_newdata.fixed.fc", header = T, stringsAsFactors = F) %>% select(-Chr,-Start,-End,-Strand,-Length)
nuclearPeakUs_new_CountNum=read.table("../data/CompareOldandNew/Nuclear/New5percPeaks_Nuclear_newdata.fixed.pheno.CountsOnlyNumeric", col.names = ind$Individual)
#numeric with anno
nuclearPeakNew=as.data.frame(cbind(ID=nuclearPeakUs_new[,1],nuclearPeakUs_new_CountNum))
Nuclear Old data
nuclearPeakUs_old=read.table("../data/CompareOldandNew/Nuclear/New5percPeaks_Nuclear_olddata.fixed.fc", header = T, stringsAsFactors = F) %>% select(-Chr,-Start,-End,-Strand,-Length)
nuclearPeakUs_old_CountNum=read.table("../data/CompareOldandNew/Nuclear/New5percPeaks_Nuclear_olddata.fixed.pheno.CountsOnlyNumeric", col.names = ind$Individual)
#numeric with anno
nuclearPeakold=as.data.frame(cbind(ID=nuclearPeakUs_old[,1], nuclearPeakUs_old_CountNum))
Seperate by batch
nuclearPeakoldM=melt(nuclearPeakold, id.vars=c("ID"), value.name = "Usage", variable.name = "Individual") %>% mutate(New15=ifelse(Individual %in%newInd, "Yes", "No")) %>% group_by(New15,ID) %>% summarise(meanUsageOld=mean(Usage)) %>% spread(New15,meanUsageOld)
nuclearPeaknewM=melt(nuclearPeakNew, id.vars=c("ID"), value.name = "Usage", variable.name = "Individual") %>% mutate(New15=ifelse(Individual %in%newInd, "Yes", "No")) %>% group_by(New15,ID) %>% summarise(meanUsageNew=mean(Usage)) %>% spread(New15,meanUsageNew)
nuclearold=ggplot(nuclearPeakoldM,aes(x=No,y=Yes)) + geom_point() + geom_density2d() + labs(x="39 ind", y="15 ind", title="Nuclear Usage Old data")
nuclearnew=ggplot(nuclearPeaknewM,aes(x=No,y=Yes)) + geom_point() + geom_density2d() + labs(x="39 ind", y="15 ind", title="Nuclear Usage New data")
plot_grid(nuclearold,nuclearnew)
Subset to new peaks:
NewPeak=read.table( file="../data/peaks_5perc/NewVOldPeaks.txt", header = T) %>% filter(New=="new")
Subset total:
totalPeakoldM_new=totalPeakoldM %>% separate(ID, into = c("peak", "chr", "Start", "end","strand","id"),sep=":") %>% filter(peak %in% NewPeak$peak)
totalPeaknewM_new=totalPeaknewM %>% separate(ID, into = c("peak", "chr", "Start", "end","strand","id"),sep=":") %>% filter(peak %in% NewPeak$peak)
Plot:
totaloldnewpeak=ggplot(totalPeakoldM_new,aes(x=No,y=Yes)) + geom_point() + geom_density2d() + labs(x="39 ind", y="15 ind", title="Total Usage Old data \n New Peaks ")
totalnewnewpeak=ggplot(totalPeaknewM_new,aes(x=No,y=Yes)) + geom_point() + geom_density2d() + labs(x="39 ind", y="15 ind", title="Total Usage New data\n New Peaks ")
plot_grid(totaloldnewpeak,totalnewnewpeak)
Subset total:
nuclearPeakoldM_new=nuclearPeakoldM %>% separate(ID, into = c("peak", "chr", "Start", "end","strand","id"),sep=":") %>% filter(peak %in% NewPeak$peak)
nuclearPeaknewM_new=nuclearPeaknewM %>% separate(ID, into = c("peak", "chr", "Start", "end","strand","id"),sep=":") %>% filter(peak %in% NewPeak$peak)
Plot:
nuclearoldnewpeak=ggplot(nuclearPeakoldM_new,aes(x=No,y=Yes)) + geom_point() + geom_density2d() + labs(x="39 ind", y="15 ind", title="nuclear Usage Old data \n New Peaks ")
nuclearnewnewpeak=ggplot(nuclearPeaknewM_new,aes(x=No,y=Yes)) + geom_point() + geom_density2d() + labs(x="39 ind", y="15 ind", title="nuclear Usage New data\n New Peaks ")
plot_grid(nuclearoldnewpeak,nuclearnewnewpeak)
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] cowplot_0.9.4 workflowr_1.3.0 reshape2_1.4.3 forcats_0.3.0
[5] stringr_1.3.1 dplyr_0.8.0.1 purrr_0.3.2 readr_1.3.1
[9] tidyr_0.8.3 tibble_2.1.1 ggplot2_3.1.1 tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.0 cellranger_1.1.0 pillar_1.3.1 compiler_3.5.1
[5] git2r_0.23.0 plyr_1.8.4 tools_3.5.1 digest_0.6.18
[9] lubridate_1.7.4 jsonlite_1.6 evaluate_0.12 nlme_3.1-137
[13] gtable_0.2.0 lattice_0.20-38 pkgconfig_2.0.2 rlang_0.3.1
[17] cli_1.0.1 rstudioapi_0.10 yaml_2.2.0 haven_1.1.2
[21] withr_2.1.2 xml2_1.2.0 httr_1.3.1 knitr_1.20
[25] hms_0.4.2 generics_0.0.2 fs_1.2.6 rprojroot_1.3-2
[29] grid_3.5.1 tidyselect_0.2.5 glue_1.3.0 R6_2.3.0
[33] readxl_1.1.0 rmarkdown_1.10 modelr_0.1.2 magrittr_1.5
[37] whisker_0.3-2 MASS_7.3-51.1 backports_1.1.2 scales_1.0.0
[41] htmltools_0.3.6 rvest_0.3.2 assertthat_0.2.0 colorspace_1.3-2
[45] labeling_0.3 stringi_1.2.4 lazyeval_0.2.1 munsell_0.5.0
[49] broom_0.5.1 crayon_1.3.4