Last updated: 2019-02-15
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Rmd | c3b3bbb | Briana Mittleman | 2019-01-03 | start covariate correlation with pc analysis |
In this analysis I will look at which collected covariates help explain the variation in the peak data. I am using code from Ben Strobers github, available at https://github.com/BennyStrobes/ipsc_preprocess_pipeline. Specifcially I am looking at the covariate_pc_pve_heatmap function.
library(tidyverse)
── Attaching packages ───────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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Warning: package 'stringr' was built under R version 3.5.2
── Conflicts ──────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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library(workflowr)
This is workflowr version 1.2.0
Run ?workflowr for help getting started
library(cowplot)
Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':
ggsave
library(reshape2)
Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':
smiths
Load in coverage files:
total_Cov=read.table("../data/PeakCounts/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc", header=T, stringsAsFactors = F)[,7:45]
nuclear_Cov=read.table("../data/PeakCounts/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc", header=T, stringsAsFactors = F)[,7:45]
Perform PCA:
Total
pca_tot_peak=prcomp(total_Cov, center=T,scale=T)
pca_tot_df=as.data.frame(pca_tot_peak$rotation) %>% rownames_to_column(var="lib") %>% select(1:11) %>% mutate(line=substr(lib,2,6))
pca_tot_df_fix=bind_cols(line=pca_tot_df[,dim(pca_tot_df)[[2]]],pca_tot_df[,3:dim(pca_tot_df)[[2]]-1])
Nuclear
pca_nuc_peak=prcomp(nuclear_Cov, center=T,scale=T)
pca_nuc_df=as.data.frame(pca_nuc_peak$rotation) %>% rownames_to_column(var="lib") %>% select(1:11) %>% mutate(line=substr(lib,2,6))
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])
Get the line order as a vector
line_order=pca_nuc_df_fix[["line"]]
Load covariate File- filter out lines not yet sequenced and reorder.
covar=read.csv("../data/threePrimeSeqMetaData.csv")[1:78,]
Subset by fraction:
tot_covar=covar %>% filter(fraction=="total") %>% slice(match(line_order, line))
nuc_covar=covar %>% filter(fraction=="nuclear")%>% slice(match(line_order, line))
Subset only a few covariates to try first:
tot_covar_filt=tot_covar %>% select(batch,comb_mapped,Sex, alive_avg, undiluted_avg, cycles)
nuc_covar_filt=nuc_covar %>% select(batch,Sex, alive_avg, undiluted_avg, cycles)
Update Ben’s Function for my data:
covariate_pc_pve_heatmap <- function(pc_df, covariate_df, output_file, title) {
# Load in data
pcs <- pc_df
covs <- covariate_df
# 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
ggsave(heatmap, file=output_file,width = 19,height=13.5,units="cm")
}
Try it:
Total
covariate_pc_pve_heatmap(pca_tot_df_fix, tot_covar_filt, "../output/plots/TotalCovariatesagainstPCs.39ind.png", "Total Covariates")
Try it: Nuclear
covariate_pc_pve_heatmap(pca_nuc_df_fix, nuc_covar_filt, "../output/plots/NuclearCovariatesagainstPCs.39ind.png", "Nuclear Covariates")
sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.14.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] bindrcpp_0.2.2 reshape2_1.4.3 cowplot_0.9.3 workflowr_1.2.0
[5] forcats_0.3.0 stringr_1.4.0 dplyr_0.7.6 purrr_0.2.5
[9] readr_1.1.1 tidyr_0.8.1 tibble_1.4.2 ggplot2_3.0.0
[13] tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] tidyselect_0.2.4 haven_1.1.2 lattice_0.20-35 colorspace_1.3-2
[5] htmltools_0.3.6 yaml_2.2.0 rlang_0.2.2 pillar_1.3.0
[9] glue_1.3.0 withr_2.1.2 modelr_0.1.2 readxl_1.1.0
[13] bindr_0.1.1 plyr_1.8.4 munsell_0.5.0 gtable_0.2.0
[17] cellranger_1.1.0 rvest_0.3.2 evaluate_0.13 labeling_0.3
[21] knitr_1.20 broom_0.5.0 Rcpp_0.12.19 scales_1.0.0
[25] backports_1.1.2 jsonlite_1.6 fs_1.2.6 hms_0.4.2
[29] digest_0.6.17 stringi_1.2.4 grid_3.5.1 rprojroot_1.3-2
[33] cli_1.0.1 tools_3.5.1 magrittr_1.5 lazyeval_0.2.1
[37] crayon_1.3.4 whisker_0.3-2 pkgconfig_2.0.2 xml2_1.2.0
[41] lubridate_1.7.4 assertthat_0.2.0 rmarkdown_1.11 httr_1.3.1
[45] rstudioapi_0.9.0 R6_2.3.0 nlme_3.1-137 git2r_0.24.0
[49] compiler_3.5.1