Normalize Phenotypes Total

Last updated: 2019-10-09

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Knit directory: Comparative_APA/analysis/

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File	Version	Author	Date	Message
Rmd	1f06329	brimittleman	2019-10-09	add PCA by total and nuclear

library(ggpubr)

Loading required package: ggplot2

Loading required package: magrittr

library(tidyverse)

── Attaching packages ───────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

✔ tibble  2.1.1       ✔ purrr   0.3.2  
✔ tidyr   0.8.3       ✔ dplyr   0.8.0.1
✔ readr   1.3.1       ✔ stringr 1.3.1  
✔ tibble  2.1.1       ✔ forcats 0.3.0

── Conflicts ──────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ tidyr::extract()   masks magrittr::extract()
✖ dplyr::filter()    masks stats::filter()
✖ dplyr::lag()       masks stats::lag()
✖ purrr::set_names() masks magrittr::set_names()

library(reshape2)


Attaching package: 'reshape2'

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

    smiths

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)


allPhenoT=humanPheno %>% full_join(chimpPheno,by="chrom") %>% select(-contains("_N"))

mkdir ../data/Pheno_5perc_total

write.table(allPhenoT, "../data/Pheno_5perc_total/ALLPAS_postLift_LocParsed_bothSpecies_pheno_5perc_Total.txt", col.names = T, row.names = F, quote = F)


gzip ../data/Pheno_5perc_total/ALLPAS_postLift_LocParsed_bothSpecies_pheno_5perc_Total.txt

#conda deactivate 
conda deactivate 
conda deactivate 
#python 2
source ~/activate_anaconda_python2.sh 
#go to directory ../data/Pheno_5perc_total/
python ../../code/prepare_phenotype_table.py ALLPAS_postLift_LocParsed_bothSpecies_pheno_5perc_Total.txt.gz
cat ALLPAS_postLift_LocParsed_bothSpecies_pheno_5perc_Total.txt.gz.phen_chr* > ALLPAS_postLift_LocParsed_bothSpecies_pheno_5perc_Total.txt.gz.phen_AllChrom

Use these normalized phenotypes for the PCA

metaDataT=read.table("../data/metadata_HCpanel.txt", header = T, stringsAsFactors = F) %>% filter(Fraction=="Total")

normPheno=read.table("../data/Pheno_5perc_total/ALLPAS_postLift_LocParsed_bothSpecies_pheno_5perc_Total.txt.gz.phen_AllChrom", col.names = c('Chr', 'start',    'end',  'ID',   '18498_T',      '18499_T',      '18502_T',      '18504_T',  '18510_T',  '18523_T',  '18358_T','3622_T',     '3659_T',   '4973_T',   'pt30_T',       '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)

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


ggplot(top2PC,aes(x=PC1, y=PC2, col=Species)) + geom_point(size=3) + geom_text(aes(label=Line), position = position_nudge(y = 0.03) )

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= metaDataT %>% select(-ID,-Line,-FQlines,-Reads,-Mapped_wMP,-Mapped_Clean,-PerMap,-PerMapClean,-Fraction,-Cq, -Rerun, -Library_concentration)
covariate_pc_pve_heatmap(pca_df,metaData_sm, title="PCs")

ggplot(metaDataT, aes(x=Species, y=Concentration, fill=Species)) + geom_boxplot() + stat_compare_means(method = "t.test") + scale_fill_brewer(palette = "Dark2")

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] tidyverse_1.2.1 ggpubr_0.2      magrittr_1.5    ggplot2_3.1.1  

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5   haven_1.1.2        lattice_0.20-38   
 [4] colorspace_1.3-2   generics_0.0.2     htmltools_0.3.6   
 [7] yaml_2.2.0         rlang_0.4.0        pillar_1.3.1      
[10] glue_1.3.0         withr_2.1.2        RColorBrewer_1.1-2
[13] modelr_0.1.2       readxl_1.1.0       plyr_1.8.4        
[16] munsell_0.5.0      gtable_0.2.0       workflowr_1.4.0   
[19] cellranger_1.1.0   rvest_0.3.2        evaluate_0.12     
[22] labeling_0.3       knitr_1.20         broom_0.5.1       
[25] Rcpp_1.0.2         scales_1.0.0       backports_1.1.2   
[28] jsonlite_1.6       fs_1.3.1           hms_0.4.2         
[31] digest_0.6.18      stringi_1.2.4      grid_3.5.1        
[34] rprojroot_1.3-2    cli_1.1.0          tools_3.5.1       
[37] lazyeval_0.2.1     crayon_1.3.4       whisker_0.3-2     
[40] pkgconfig_2.0.2    xml2_1.2.0         lubridate_1.7.4   
[43] assertthat_0.2.0   rmarkdown_1.10     httr_1.3.1        
[46] rstudioapi_0.10    R6_2.3.0           nlme_3.1-137      
[49] git2r_0.25.2       compiler_3.5.1

Normalize Phenotypes Total

Briana Mittleman

10/9/2019