Normalize Usage Phenotype

Last updated: 2019-12-17

Checks: 7 0

Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/CorrbetweenInd.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/PASnumperSpecies.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/diffExpression.Rmd
    Modified:   analysis/index.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/verifyBAM.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	8842225	brimittleman	2019-12-17	update with new pca code and pantro6
html	4689510	brimittleman	2019-10-10	Build site.
Rmd	9855436	brimittleman	2019-10-10	fix code for pve
html	d0c98c2	brimittleman	2019-10-09	Build site.
Rmd	14a3f66	brimittleman	2019-10-09	add pca and human v chimp in nuc analysis

I want to normalize the phenotypes with the leafcutter scripts. This can be used to perform a PCA and assess the data quality. I will include, total nuclear human and chimp.

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(ggpubr)

Loading required package: magrittr


Attaching package: 'magrittr'

The following object is masked from 'package:purrr':

    set_names

The following object is masked from 'package:tidyr':

    extract

library("scales")


Attaching package: 'scales'

The following object is masked from 'package:purrr':

    discard

The following object is masked from 'package:readr':

    col_factor

library("gplots")


Attaching package: 'gplots'

The following object is masked from 'package:stats':

    lowess

library("RColorBrewer")

These are the inclusive phenotypes. I will need to subset of the 5% pas.
../Human/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno.txt ../Chimp/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno.txt

The 5% pas are in ../data/Peaks_5perc/Peaks_5perc_either_bothUsage_noUnchr.txt

I will make a python script that will do this. I

python filter5percPAS.py ../Human/data/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno.txt  ../data/Pheno_5perc/ALLPAS_postLift_LocParsed_Human_Pheno_5perc.txt

python filter5percPAS.py ../Chimp/data/phenotype/ALLPAS_postLift_LocParsed_Chimp_Pheno.txt  ../data/Pheno_5perc/ALLPAS_postLift_LocParsed_Chimp_Pheno_5perc.txt

Join these to normalize the phenotypes together:

humanPheno=read.table("../data/Pheno_5perc/ALLPAS_postLift_LocParsed_Human_Pheno_5perc.txt",stringsAsFactors = F, header = T)
chimpPheno=read.table("../data/Pheno_5perc/ALLPAS_postLift_LocParsed_Chimp_Pheno_5perc.txt",stringsAsFactors = F, header = T)


allPheno=humanPheno %>% full_join(chimpPheno,by="chrom")


write.table(allPheno, "../data/Pheno_5perc/ALLPAS_postLift_LocParsed_bothSpecies_pheno_5perc.txt", col.names = T, row.names = F, quote = F)

gzip ../data/Pheno_5perc/ALLPAS_postLift_LocParsed_bothSpecies_pheno_5perc.txt

#conda deactivate 
conda deactivate 
conda deactivate 
#python 2
source ~/activate_anaconda_python2.sh 
#go to directory ../data/Pheno_5perc/
python ../../code/prepare_phenotype_table.py ALLPAS_postLift_LocParsed_bothSpecies_pheno_5perc.txt.gz

cat ALLPAS_postLift_LocParsed_bothSpecies_pheno_5perc.txt.gz.phen_chr* > ALLPAS_postLift_LocParsed_bothSpecies_pheno_5perc.txt.gz.phen_AllChrom

Use these normalized phenotypes for the PCA

metaData=read.table("../data/metadata_HCpanel.txt", header = T, stringsAsFactors = F)

normPheno=read.table("../data/Pheno_5perc/ALLPAS_postLift_LocParsed_bothSpecies_pheno_5perc.txt.gz.phen_AllChrom", col.names = c('Chr', 'start',    'end',  'ID',   '18498_N',  '18498_T',  '18499_N',  '18499_T',  '18502_N',  '18502_T',  '18504_N',  '18504_T',  '18510_N',  '18510_T',  '18523_N',  '18523_T',  '18358_N',  '18358_T',  '3622_N',   '3622_T',   '3659_N',   '3659_T',   '4973_N',   '4973_T',   'pt30_N',   'pt30_T',   'pt91_N',   'pt91_T'))

normPheno_matrix=as.matrix(normPheno %>% dplyr::select(-Chr, -start, -end, -ID))

Run PCA:

# Load colors 

colors <- colorRampPalette(c(brewer.pal(9, "Blues")[1],brewer.pal(9, "Blues")[9]))(100)

pal <- c(brewer.pal(9, "Set1"), brewer.pal(8, "Set2"), brewer.pal(12, "Set3"))
labels <- paste(metaData$Species,metaData$Line,metaData$Fraction, sep=" ")

cors <- cor(normPheno_matrix, method="spearman", use="pairwise.complete.obs")


heatmap.2( cors, scale="none", col = colors, margins = c(12, 12), trace='none', denscol="white", labCol=labels, ColSideColors=pal[as.integer(as.factor(metaData$Species))], RowSideColors=pal[as.integer(as.factor(metaData$Fraction))+9], cexCol = 0.2 + 1/log10(15), cexRow = 0.2 + 1/log10(15))

Version	Author	Date
d0c98c2	brimittleman	2019-10-09

pca_Pheno=prcomp(t(normPheno_matrix), scale=F)
scores = pca_Pheno$x

Scores code

#PCA function (original code from Julien Roux)
#Load in the plot_scores function
plot_scores <- function(pca, scores, n, m, cols, points=F, pchs =20, legend=T){
  xmin <- min(scores[,n]) - (max(scores[,n]) - min(scores[,n]))*0.05
  if (legend == T){ ## let some room (35%) for a legend                                                                                                                                                 
    xmax <- max(scores[,n]) + (max(scores[,n]) - min(scores[,n]))*0.50
  }
  else {
    xmax <- max(scores[,n]) + (max(scores[,n]) - min(scores[,n]))*0.05
  }
  ymin <- min(scores[,m]) - (max(scores[,m]) - min(scores[,m]))*0.05
  ymax <- max(scores[,m]) + (max(scores[,m]) - min(scores[,m]))*0.05
  plot(scores[,n], scores[,m], xlab=paste("PC", n, ": ", round(summary(pca)$importance[2,n],3)*100, "% variance explained", sep=""), ylab=paste("PC", m, ": ", round(summary(pca)$importance[2,m],3)*100, "% variance explained", sep=""), xlim=c(xmin, xmax), ylim=c(ymin, ymax), type="n")
  if (points == F){
    text(scores[,n],scores[,m], rownames(scores), col=cols, cex=1)
  }
  else {
    points(scores[,n],scores[,m], col=cols, pch=pchs, cex=1.3)
  }
}

metaData$Species=as.factor(metaData$Species)
for (n in 1:1){
  col.v <- pal[as.integer(metaData$Species)]
  plot_scores(pca_Pheno, scores, n, n+1, col.v)
}

Version	Author	Date
4689510	brimittleman	2019-10-10
d0c98c2	brimittleman	2019-10-09

eigs <- pca_Pheno$sdev^2
proportion = eigs/sum(eigs)

plot(proportion)

x.pca <- pca_Pheno

tech_factors <- metaData
tech_factors_sum <- tech_factors[,c(2:15)] %>% dplyr::select(-Library,-Line)

p_comps <- 1:6
pc_cov_cor <- matrix(nrow = ncol(tech_factors_sum), ncol = length(p_comps),
                     dimnames = list(colnames(tech_factors_sum), colnames(x.pca$x)[p_comps]))
for (pc in p_comps) {
  for (covariate in 1:ncol(tech_factors_sum)) {
    lm_result <- lm(x.pca$x[, pc] ~ tech_factors_sum[, covariate])
    r2 <- summary(lm_result)$r.squared
    pc_cov_cor[covariate, pc] <- r2
  }
}

pc_cov_pval <- matrix(nrow = ncol(tech_factors_sum), ncol = length(p_comps),
                      dimnames = list(colnames(tech_factors_sum), colnames(x.pca$x)[p_comps]))

for (pc in p_comps) {
  for (covariate_2 in 1:ncol(tech_factors_sum)) {
    lm_result_2 <- lm(x.pca$x[, pc] ~ tech_factors_sum[, covariate_2])
    pval <- anova(lm_result_2)$'Pr(>F)'[1]
    pc_cov_pval[covariate_2, pc] <- pval
  }
}

PCs <- c("PC1", "PC2", "PC3", "PC4", "PC5", "PC6")
Tech_fac <- colnames(tech_factors_sum)
#Tech_fac <- c("Species",   "Individual", "O2.",  "Condition" , "Sex", "RIN" , "CO2", "Purity_high", "Purity_med" ,
              #"Expt_Batch", "RNA_Batch", "Library_Batch", "Seq_pool", "Episomal_integration" )

heatmap.2(as.matrix(pc_cov_cor[Tech_fac,PCs]),col=brewer.pal(4, "Greens"), trace="none",
          Rowv=FALSE, Colv=FALSE, key=T, main="Cor. PCs & tech factors", dendrogram="none",
          key.title=NA, cexRow=0.9, cexCol=0.9)

log10_pc_cov_pval <- -log(pc_cov_pval)
heatmap.2(as.matrix(log10_pc_cov_pval[Tech_fac,PCs]), col=brewer.pal(9, "Greens"), trace="none",
          Rowv=FALSE, Colv=FALSE, key=T, main="-log10 pval of cor. PCs & tech factors", dendrogram="none",
          key.title=NA, cexRow=0.9, cexCol=0.9)

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] RColorBrewer_1.1-2 gplots_3.0.1       scales_1.0.0      
 [4] ggpubr_0.2         magrittr_1.5       reshape2_1.4.3    
 [7] forcats_0.3.0      stringr_1.3.1      dplyr_0.8.0.1     
[10] purrr_0.3.2        readr_1.3.1        tidyr_0.8.3       
[13] tibble_2.1.1       ggplot2_3.1.1      tidyverse_1.2.1   

loaded via a namespace (and not attached):
 [1] gtools_3.8.1       tidyselect_0.2.5   haven_1.1.2       
 [4] lattice_0.20-38    colorspace_1.3-2   generics_0.0.2    
 [7] htmltools_0.3.6    yaml_2.2.0         rlang_0.4.0       
[10] later_0.7.5        pillar_1.3.1       glue_1.3.0        
[13] withr_2.1.2        modelr_0.1.2       readxl_1.1.0      
[16] plyr_1.8.4         munsell_0.5.0      gtable_0.2.0      
[19] workflowr_1.5.0    cellranger_1.1.0   rvest_0.3.2       
[22] caTools_1.17.1.1   evaluate_0.12      knitr_1.20        
[25] httpuv_1.4.5       broom_0.5.1        Rcpp_1.0.2        
[28] KernSmooth_2.23-15 promises_1.0.1     backports_1.1.2   
[31] gdata_2.18.0       jsonlite_1.6       fs_1.3.1          
[34] hms_0.4.2          digest_0.6.18      stringi_1.2.4     
[37] grid_3.5.1         rprojroot_1.3-2    bitops_1.0-6      
[40] cli_1.1.0          tools_3.5.1        lazyeval_0.2.1    
[43] crayon_1.3.4       whisker_0.3-2      pkgconfig_2.0.2   
[46] xml2_1.2.0         lubridate_1.7.4    assertthat_0.2.0  
[49] rmarkdown_1.10     httr_1.3.1         rstudioapi_0.10   
[52] R6_2.3.0           nlme_3.1-137       git2r_0.26.1      
[55] compiler_3.5.1

Normalize Usage Phenotype

Briana Mittleman

10/9/2019