Last updated: 2019-05-03

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File Version Author Date Message
Rmd 18604d4 brimittleman 2019-05-03 remove points in graphs
html 208916d brimittleman 2019-05-03 Build site.
Rmd 0c16f69 brimittleman 2019-05-03 add result plots
html 53eeea4 brimittleman 2019-05-02 Build site.
Rmd 2067946 brimittleman 2019-05-02 add old vs new data usage 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  
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── Conflicts ─────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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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_density2d() + labs(x="39 ind", y="15 ind", title="Total Usage Old data")
totalnew=ggplot(totalPeaknewM,aes(x=No,y=Yes)) + geom_density2d() + labs(x="39 ind", y="15 ind", title="Total Usage New data")
plot_grid(totalold,totalnew)

Version Author Date
208916d brimittleman 2019-05-03

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_density2d() + labs(x="39 ind", y="15 ind", title="Nuclear Usage Old data")
nuclearnew=ggplot(nuclearPeaknewM,aes(x=No,y=Yes)) +  geom_density2d() + labs(x="39 ind", y="15 ind", title="Nuclear Usage New data")
plot_grid(nuclearold,nuclearnew)

Version Author Date
208916d brimittleman 2019-05-03

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_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_density2d() + labs(x="39 ind", y="15 ind", title="Total Usage New data\n New Peaks ")

plot_grid(totaloldnewpeak,totalnewnewpeak)

Version Author Date
208916d brimittleman 2019-05-03

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_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_density2d() + labs(x="39 ind", y="15 ind", title="nuclear Usage New data\n New Peaks ")

plot_grid(nuclearoldnewpeak,nuclearnewnewpeak)

Version Author Date
208916d brimittleman 2019-05-03 

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