Normalize Usage Phenotype

Last updated: 2020-04-10

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/TotalVNuclearBothSpecies.Rmd
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    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/pol2.Rmd
    Modified:   analysis/speciesSpecific.Rmd

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File	Version	Author	Date	Message
Rmd	de30709	brimittleman	2020-04-10	anno remove 18499
html	065ffdc	brimittleman	2020-04-03	Build site.
html	47fbf09	brimittleman	2019-12-17	Build site.
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'

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library(ggpubr)

Loading required package: magrittr


Attaching package: 'magrittr'

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library("scales")


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library("gplots")


Attaching package: 'gplots'

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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',  '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
065ffdc	brimittleman	2020-04-03
47fbf09	brimittleman	2019-12-17
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
065ffdc	brimittleman	2020-04-03
47fbf09	brimittleman	2019-12-17
4689510	brimittleman	2019-10-10
d0c98c2	brimittleman	2019-10-09

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

plot(proportion)

Version	Author	Date
065ffdc	brimittleman	2020-04-03
47fbf09	brimittleman	2019-12-17

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)

Version	Author	Date
065ffdc	brimittleman	2020-04-03
47fbf09	brimittleman	2019-12-17

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)

Version	Author	Date
065ffdc	brimittleman	2020-04-03
47fbf09	brimittleman	2019-12-17

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_1.6.0    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