Last updated: 2019-05-09

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File Version Author Date Message
Rmd 1c60a3a brimittleman 2019-05-09 add metadata
html 144c00b brimittleman 2019-05-08 Build site.
Rmd 5e39f1c brimittleman 2019-05-08 choose pcs and start qtl rerun
html f5af9c6 brimittleman 2019-05-08 Build site.
Rmd 1ba7d2b brimittleman 2019-05-08 add pca 
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
totalqqnorm=read.table("../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm.allChrom", col.names = c('Chr',  'start',    'end',  'ID',   'NA18486',  'NA18498',  'NA18499',  'NA18501',  'NA18502',  'NA18504', 'NA18505',   'NA18508',  'NA18510',  'NA18511',  'NA18516',  'NA18517',  'NA18519',  'NA18520',  'NA18522',  'NA18852', 'NA18853',   'NA18855',  'NA18856',  'NA18858',  'NA18861',  'NA18862',  'NA18870',  'NA18907',  'NA18909',  'NA18912', 'NA18913',   'NA18916',  'NA19092',  'NA19093',  'NA19101',  'NA19119',  'NA19128',  'NA19130',  'NA19131',  'NA19137','NA19138',    'NA19140',  'NA19141',  'NA19144',  'NA19152',  'NA19153',  'NA19160',  'NA19171',  'NA19193',  'NA19200','NA19207',    'NA19209',  'NA19210',  'NA19223',  'NA19225',  'NA19238',  'NA19239',  'NA19257'))

totalqqnorm_matrix=as.matrix(totalqqnorm %>% select(-Chr, -start, -end, -ID))

RUn PCA:

pca_tot_peak=prcomp(totalqqnorm_matrix, center=T,scale=T)
pca_tot_df=as.data.frame(pca_tot_peak$rotation) %>% rownames_to_column(var="lib") %>% select(1:11)

pca_tot_df_fix=bind_cols(line=pca_tot_df[,dim(pca_tot_df)[[2]]],pca_tot_df[,3:dim(pca_tot_df)[[2]]-1])

Variance explained:

eigs_tot <- pca_tot_peak$sdev^2
proportion_tot = eigs_tot/sum(eigs_tot)

plot(proportion_tot)

Version Author Date
f5af9c6 brimittleman 2019-05-08 
nuclearqqnorm=read.table("../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm.allChrom", col.names = c('Chr',  'start',    'end',  'ID',   'NA18486',  'NA18498',  'NA18499',  'NA18501',  'NA18502',  'NA18504', 'NA18505',   'NA18508',  'NA18510',  'NA18511',  'NA18516',  'NA18517',  'NA18519',  'NA18520',  'NA18522',  'NA18852', 'NA18853',   'NA18855',  'NA18856',  'NA18858',  'NA18861',  'NA18862',  'NA18870',  'NA18907',  'NA18909',  'NA18912', 'NA18913',   'NA18916',  'NA19092',  'NA19093',  'NA19101',  'NA19119',  'NA19128',  'NA19130',  'NA19131',  'NA19137','NA19138',    'NA19140',  'NA19141',  'NA19144',  'NA19152',  'NA19153',  'NA19160',  'NA19171',  'NA19193',  'NA19200','NA19207',    'NA19209',  'NA19210',  'NA19223',  'NA19225',  'NA19238',  'NA19239',  'NA19257'))

nuclearqqnorm_matrix=as.matrix(nuclearqqnorm %>% select(-Chr, -start, -end, -ID))
pca_nuc_peak=prcomp(nuclearqqnorm_matrix, center=T,scale=T)
pca_nuc_df=as.data.frame(pca_nuc_peak$rotation) %>% rownames_to_column(var="lib") %>% select(1:11)

pca_nuc_df_fix=bind_cols(line=pca_nuc_df[,dim(pca_nuc_df)[[2]]],pca_nuc_df[,3:dim(pca_nuc_df)[[2]]-1])

Variance explained:

eigs_nuc <- pca_nuc_peak$sdev^2
proportion_nuc = eigs_nuc/sum(eigs_nuc)

plot(proportion_nuc)

Version Author Date
f5af9c6 brimittleman 2019-05-08

Plot together:

both_prop=as.data.frame(cbind(PCs=seq(1,54,1),Total=proportion_tot,Nuclear=proportion_nuc))

both_prop_melt=melt(both_prop, id.var=c("PCs"), variable.name="Fraction",value.name = "VariationExplained" )
ggplot(both_prop_melt, aes(x=PCs, y=VariationExplained,group=Fraction, color=Fraction)) + geom_line() + geom_vline(xintercept = 6, col="red") + annotate("text", label="6 PCs", x=10, y=.1) + labs(title="Proportion of variance explained \nin PCA on normalized APA usage")

Version Author Date
f5af9c6 brimittleman 2019-05-08 
both_prop_melt_filt=both_prop_melt %>% filter(PCs<10)

ggplot(both_prop_melt_filt, aes(x=PCs, y=VariationExplained,group=Fraction, color=Fraction)) + geom_line() + geom_vline(xintercept = 4, col="red") + annotate("text", label="4 PCs", x=5, y=.1) + labs(title="Proportion of variance explained \nin PCA on normalized APA usage")

Version Author Date
144c00b brimittleman 2019-05-08

WHich factors correlate with PCs:

metadata=read.table("../data/MetaDataSequencing.txt", stringsAsFactors = F, header = T)
metadata_tot=metadata %>% filter(fraction=="total") %>% select(batch,Sex, alive_avg, undiluted_avg, library_conc,ratio260_280)
metadata_nuc=metadata %>% filter(fraction=="nuclear") %>% select(batch,Sex, alive_avg, undiluted_avg, library_conc,ratio260_280)

Function from Ben

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

# 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)$adj.r.squared
    }
  }
  pve_map
  ord <- hclust( dist(scale(pve_map), method = "euclidean"), method = "ward.D" )$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)
}
covariate_pc_pve_heatmap(pca_tot_df,metadata_tot, title="Total PCs")

#covariate_pc_pve_heatmap(pca_nuc_df,metadata_nuc, title="Nuclear PCs")

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_1.0.0       cellranger_1.1.0 pillar_1.3.1     compiler_3.5.1  
 [5] git2r_0.23.0     plyr_1.8.4       workflowr_1.3.0  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_0.3.1      cli_1.0.1        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_1.2.6        
[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