Last updated: 2019-10-09

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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

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/phenotype/ALLPAS_postLift_LocParsed_Human_Pheno.txt  ../data/Pheno_5perc/ALLPAS_postLift_LocParsed_Human_Pheno_5perc.txt

python filter5percPAS.py ../Chimp/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 %>% select(-Chr, -start, -end, -ID))

Run PCA:

pca_Pheno=prcomp(normPheno_matrix, center=T,scale=T)
pca_df=as.data.frame(pca_Pheno$rotation) %>% rownames_to_column(var="ID")
eigs <- pca_Pheno$sdev^2
proportion = eigs/sum(eigs)

plot(proportion)

Plot color as species and shape as total v nuclear

top2PC=pca_df %>% select(ID, PC1, PC2) %>% inner_join(metaData, by="ID")


ggplot(top2PC,aes(x=PC1, y=PC2, col=Species, shape=Fraction)) + geom_point(size=3)

covariate_pc_pve_heatmap <- function(pc_df, covariate_df, title) {
  # Load in data
  #pcs <- pc_df
  pcs=pca_df
  
  #covs <- covariate_df
  covs= metaData_sm


  # Remove unimportant columns
  pcs <- as.matrix(pcs[,2:dim(pcs)[[2]]])
  covs <- data.frame(as.matrix(covs[,1:dim(covs)[[2]]]))

  # Initialize PVE heatmap
  pve_map <- matrix(0, dim(covs)[2], dim(pcs)[2])
  colnames(pve_map) <- colnames(pcs)
  rownames(pve_map) <- colnames(covs)

  # Loop through each PC, COV Pair and take correlation
  num_pcs <- dim(pcs)[2]
  num_covs <- dim(covs)[2]
  for (num_pc in 1:num_pcs) {
    for (num_cov in 1:num_covs) {
      pc_vec <- pcs[,num_pc]
      cov_vec <- covs[,num_cov]
      lin_model <- lm(pc_vec ~ cov_vec)
      pve_map[num_cov, num_pc] <- summary(lin_model)$adj.r.squared
      if (pve_map[num_cov, num_pc] <0){pve_map[num_cov, num_pc]=0}
    }
  }
  pve_map
  ord <- hclust( dist(scale(pve_map), method = "euclidean"), method = "ward.D" )$order

  melted_mat <- melt(pve_map)
  colnames(melted_mat) <- c("Covariate", "PC","PVE")

  #  Use factors to represent covariate and pc name
  melted_mat$Covariate <- factor(melted_mat$Covariate, levels = rownames(pve_map)[ord])
  melted_mat$PC <- factor(melted_mat$PC)
  if (dim(pcs)[2] == 10) {
    levels(melted_mat$PC) <- c(levels(melted_mat$PC)[1],levels(melted_mat$PC)[3:10],levels(melted_mat$PC)[2])
  }
  if (dim(pcs)[2] == 21) {
    levels(melted_mat$PC) <- c(levels(melted_mat$PC)[1],levels(melted_mat$PC)[12],levels(melted_mat$PC)[15:21],levels(melted_mat$PC)[2:11], levels(melted_mat$PC)[13:14])
  }

  #  PLOT!
  heatmap <- ggplot(data=melted_mat, aes(x=Covariate, y=PC)) + geom_tile(aes(fill=PVE)) + scale_fill_gradient2(midpoint=-.05, guide="colorbar")
  heatmap <- heatmap + theme(text = element_text(size=14), panel.background = element_blank(), axis.text.x = element_text(angle = 90, vjust=.5))
  heatmap <- heatmap + labs(y="latent factor", title=title)

  # Save File
  return(heatmap)
}
metaData_sm= metaData %>% select(-ID,-FQlines,-Reads,-Mapped_wMP,-Mapped_Clean,-PerMap,-PerMapClean)
covariate_pc_pve_heatmap(pca_df,metaData_sm, title="PCs")

I need to seperate this by nuclear and total before running this.


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] Rcpp_1.0.2       cellranger_1.1.0 pillar_1.3.1     compiler_3.5.1  
 [5] git2r_0.25.2     plyr_1.8.4       workflowr_1.4.0  tools_3.5.1     
 [9] digest_0.6.18    lubridate_1.7.4  jsonlite_1.6     evaluate_0.12   
[13] nlme_3.1-137     gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2 
[17] rlang_0.4.0      cli_1.1.0        rstudioapi_0.10  yaml_2.2.0      
[21] haven_1.1.2      withr_2.1.2      xml2_1.2.0       httr_1.3.1      
[25] knitr_1.20       hms_0.4.2        generics_0.0.2   fs_1.3.1        
[29] rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5 glue_1.3.0      
[33] R6_2.3.0         readxl_1.1.0     rmarkdown_1.10   modelr_0.1.2    
[37] magrittr_1.5     whisker_0.3-2    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