Last updated: 2020-01-25

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/speciesSpecific.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 0bdae14 brimittleman 2020-01-25 add pca 
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("scales")

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

    discard
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    col_factor
library("RColorBrewer")
library("gplots")

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    lowess
library(reshape2)

Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':

    smiths

Double filtered sites:

chr1:744266:744465:LOC107984841_+_utr3-Chimp-chimp242477

PASDF=read.table("../data/PAS_doubleFilter/PAS_10perc_either_HumanCoord_BothUsage_meta_doubleFilter.txt", header = T,stringsAsFactors = F) %>% mutate(strandPre=ifelse(strandFix=="+", "-","+"),geneLoc=paste(gene, strandPre, loc, sep="_"), genelocPAS=paste(geneLoc,disc, PAS, sep = "-"), chrom=paste(chr, start,end, genelocPAS,sep=":"))
humanPheno=read.table("../data/Pheno_5perc/ALLPAS_postLift_LocParsed_Human_Pheno_5perc.txt",stringsAsFactors = F, header = T)  %>% filter(chrom %in% PASDF$chrom)
chimpPheno=read.table("../data/Pheno_5perc/ALLPAS_postLift_LocParsed_Chimp_Pheno_5perc.txt",stringsAsFactors = F, header = T)%>% filter(chrom %in% PASDF$chrom)


allPhenoN=humanPheno %>% full_join(chimpPheno,by="chrom") %>% dplyr::select(-contains("_T"))
mkdir ../data/Pheno_5perc_DF_nuclear
write.table(allPhenoN, "../data/Pheno_5perc_DF_nuclear/ALLPAS_postLift_LocParsed_bothSpecies_pheno_DoubleFilter_Nuclear.txt", col.names = T, row.names = F, quote = F)

gzip ../data/Pheno_5perc_DF_nuclear/ALLPAS_postLift_LocParsed_bothSpecies_pheno_DoubleFilter_Nuclear.txt

#conda deactivate 
conda deactivate 
conda deactivate 
#python 2
source ~/activate_anaconda_python2.sh 
#go to directory ../data/Pheno_5perc_DF_nuclear/
python ../../code/prepare_phenotype_table.py ALLPAS_postLift_LocParsed_bothSpecies_pheno_DoubleFilter_Nuclear.txt.gz
#normalized 
cat ALLPAS_postLift_LocParsed_bothSpecies_pheno_DoubleFilter_Nuclear.txt.gz.qqnorm_chr* > ALLPAS_postLift_LocParsed_bothSpecies_pheno_DoubleFilter_Nuclear.txt.gz.qqnorm_AllChrom

Use these normalized phenotypes for the PCA

metaDataN=read.table("../data/metadata_HCpanel.txt", header = T, stringsAsFactors = F) %>% filter(Fraction=="Nuclear")
normPheno=read.table("../data/Pheno_5perc_DF_nuclear/ALLPAS_postLift_LocParsed_bothSpecies_pheno_DoubleFilter_Nuclear.txt.gz.qqnorm_AllChrom", col.names = c('Chr', 'start',    'end',  'ID',   '18498_N',      '18499_N',      '18502_N',      '18504_N',  '18510_N',  '18523_N',  '18358_N','3622_N',     '3659_N',   '4973_N',   'pt30_N',       'pt91_N'))

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

Run PCA:

pca_Pheno=prcomp(t(normPheno_matrix), center=T,scale=T)
scores = pca_Pheno$x
pca_df=as.data.frame(pca_Pheno$rotation) %>% rownames_to_column(var="ID")
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(metaDataN$Species,metaDataN$Line, 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(metaDataN$Species))], RowSideColors=pal[as.integer(as.factor(metaDataN$Line))+9], cexCol = 0.2 + 1/log10(15), cexRow = 0.2 + 1/log10(15))

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

x.pca <- pca_Pheno

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

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)

Proportion explained:

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

plot(proportion)


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

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