Last updated: 2018-08-28

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Introduction

The goal of this script is to identify the major drivers of gene expression level variation in the data.

library(DESeq2)
Loading required package: S4Vectors
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':

    anyDuplicated, append, as.data.frame, cbind, colMeans,
    colnames, colSums, do.call, duplicated, eval, evalq, Filter,
    Find, get, grep, grepl, intersect, is.unsorted, lapply,
    lengths, Map, mapply, match, mget, order, paste, pmax,
    pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce,
    rowMeans, rownames, rowSums, sapply, setdiff, sort, table,
    tapply, union, unique, unsplit, which, which.max, which.min

Attaching package: 'S4Vectors'
The following object is masked from 'package:base':

    expand.grid
Loading required package: IRanges
Loading required package: GenomicRanges
Loading required package: GenomeInfoDb
Loading required package: SummarizedExperiment
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.
Loading required package: DelayedArray
Loading required package: matrixStats

Attaching package: 'matrixStats'
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    anyMissing, rowMedians

Attaching package: 'DelayedArray'
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    colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges
The following object is masked from 'package:base':

    apply
library("pheatmap")
Warning: package 'pheatmap' was built under R version 3.4.4
library("gplots")

Attaching package: 'gplots'
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    space
The following object is masked from 'package:S4Vectors':

    space
The following object is masked from 'package:stats':

    lowess
library("RColorBrewer")
library("ggplot2")
Warning: package 'ggplot2' was built under R version 3.4.4
library("cowplot")
Warning: package 'cowplot' was built under R version 3.4.4

Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':

    ggsave
# Read in the filtered data (file made from the end of voom_limma.Rmd)

init_pc <- read.csv("../data/gene_expression_filtered_T1T5.csv")

dim(init_pc)
[1] 11504   157
init_pc <- init_pc[,2:157]

labels <- read.csv("../data/lm_covar_fixed_random.csv")
labels_123 <- as.data.frame(paste(labels$Individual, labels$Time, sep = "_"))
colnames(labels_123) <- c("ID_time")

PCA on all data (vst transformed from DESEq2)

vst <- readRDS("../data/vsd_values_hg38_gc.rds")

# Run PCA on the normalized data

pca_genes <- prcomp(t(vst), center = TRUE)

matrixpca <- pca_genes$x
PC1 <- matrixpca[,1]
PC2 <- matrixpca[,2]
pc3 <- matrixpca[,3]
pc4 <- matrixpca[,4]
pc5 <- matrixpca[,5]

matrixpca <- as.data.frame(matrixpca)

summary <- summary(pca_genes)

head(summary$importance[2,1:5])
    PC1     PC2     PC3     PC4     PC5 
0.12229 0.07022 0.05801 0.05184 0.03742 
norm_count <- ggplot(data=matrixpca, aes(x=PC1, y=PC2, color=as.factor(labels$Time))) + geom_point(aes(colour = as.factor(labels$Time))) + ggtitle("PCA of normalized counts") + scale_color_discrete(name = "Time")

plot_grid(norm_count)

PCA on filtered data

# Load gene expression data from all 156 samples

normalized_data <- read.csv("../data/gene_expression_filtered_T1T5.csv")

# Run PCA on the normalized data

pca_genes <- prcomp(t(normalized_data[,2:157]), scale = TRUE, center = TRUE)

matrixpca <- pca_genes$x
PC1 <- matrixpca[,1]
PC2 <- matrixpca[,2]
pc3 <- matrixpca[,3]
pc4 <- matrixpca[,4]
pc5 <- matrixpca[,5]

matrixpca <- as.data.frame(matrixpca)

summary <- summary(pca_genes)

head(summary$importance[2,1:5])
    PC1     PC2     PC3     PC4     PC5 
0.23496 0.13460 0.09950 0.05124 0.03724 
norm_count <- ggplot(data=matrixpca, aes(x=PC1, y=PC2, color=as.factor(labels$Time))) + geom_point(aes(colour = as.factor(labels$Time))) + ggtitle("PCA of normalized counts") + scale_color_discrete(name = "Time")

plot_grid(norm_count)

PCA on scaled data

# Add Bioinformatics step of scaling each gene

# centering with 'scale()'
center_scale <- function(x) {
    scale(x, scale = TRUE)
}

# apply it
centered_init_pc <- center_scale(init_pc)


check <- cor(init_pc)
cx <- sweep(check, 2, colMeans(check), "-")

pca_genes <- prcomp(check, center = TRUE, scale = FALSE)

matrixpca <- pca_genes$x
PC1 <- matrixpca[,1]
PC2 <- matrixpca[,2]
pc3 <- matrixpca[,3]
pc4 <- matrixpca[,4]
pc5 <- matrixpca[,5]

matrixpca <- as.data.frame(matrixpca)

summary <- summary(pca_genes)

head(summary$importance[2,1:5])
    PC1     PC2     PC3     PC4     PC5 
0.65175 0.18426 0.07038 0.03644 0.01884 
norm_count <- ggplot(data=matrixpca, aes(x=PC1, y=PC2, color=as.factor(labels$Time))) + geom_point(aes(colour = as.factor(labels$Time))) + ggtitle("PCA of normalized counts") + scale_color_discrete(name = "Time")

plot_grid(norm_count)

PCA on normalized, filtered data- use loadings only

pca_genes <- prcomp(init_pc, center = TRUE, scale = FALSE)

pca_rot <- as.data.frame(pca_genes$rotation)
pca_rot[,1] <- as.numeric(pca_rot[,1])
pca_rot[,2] <- as.numeric(pca_rot[,2])


norm_count <- ggplot(data=pca_rot, aes(x=PC1, y=PC2, color=as.factor(labels$Time))) + geom_point(aes(colour = as.factor(labels$Time))) + ggtitle("PCA of normalized counts") + scale_color_discrete(name = "Time")

plot_grid(norm_count)

SVD on filtered data

# Run PCA

X = t(scale(t(init_pc),center=TRUE,scale=FALSE))
sv = svd(t(X))
U = sv$u
V = sv$v
D = sv$d
## in R calculate the rank of a matrix is by
#qr(t(X))$rank

plot(U[,1],U[,2],xlab="PC1",ylab="PC2")

U <- as.data.frame(U)
norm_count <- ggplot(data=U, aes(x=U[,1], y=U[,2], color=labels$Time)) + geom_point(aes(colour = as.factor(labels$Time))) + ggtitle("PCA of normalized counts") + scale_color_discrete(name = "Time") + xlab("PC1") + ylab("PC2")

plot_grid(norm_count)

#save_plot("/Users/laurenblake/Dropbox/Lauren Blake/Figures/PCA_time_August10.png", norm_count,
#          base_aspect_ratio = 1)



# Variance explained 

varex = 0
cumvar = 0
denom = sum(D^2)
for(i in 1:64){
  varex[i] = D[i]^2/denom
  cumvar[i] = sum(D[1:i]^2)/denom
}

## variance explained by each PC cumulatively
varex
 [1] 0.191251884 0.101480941 0.076890551 0.056966384 0.047740308
 [6] 0.028534255 0.024038043 0.022025028 0.021309608 0.017922737
[11] 0.016249126 0.015280302 0.013255328 0.012700219 0.011990477
[16] 0.010997071 0.010762881 0.009782479 0.009333403 0.008706081
[21] 0.008313230 0.008213857 0.007578975 0.007354793 0.007018724
[26] 0.006604362 0.006492543 0.006201593 0.005842621 0.005607058
[31] 0.005434460 0.005379485 0.005218961 0.004967081 0.004903652
[36] 0.004583266 0.004531455 0.004453111 0.004248581 0.004233394
[41] 0.004162922 0.003957800 0.003902488 0.003785255 0.003691630
[46] 0.003594334 0.003489787 0.003408952 0.003371267 0.003297509
[51] 0.003219381 0.003093576 0.003056514 0.002994842 0.002942849
[56] 0.002890365 0.002825417 0.002793333 0.002717778 0.002548626
[61] 0.002523417 0.002504802 0.002406615 0.002389410
cumvar
 [1] 0.1912519 0.2927328 0.3696234 0.4265898 0.4743301 0.5028643 0.5269024
 [8] 0.5489274 0.5702370 0.5881597 0.6044089 0.6196892 0.6329445 0.6456447
[15] 0.6576352 0.6686323 0.6793951 0.6891776 0.6985110 0.7072171 0.7155303
[22] 0.7237442 0.7313232 0.7386780 0.7456967 0.7523010 0.7587936 0.7649952
[29] 0.7708378 0.7764449 0.7818793 0.7872588 0.7924778 0.7974449 0.8023485
[36] 0.8069318 0.8114632 0.8159163 0.8201649 0.8243983 0.8285612 0.8325190
[43] 0.8364215 0.8402068 0.8438984 0.8474927 0.8509825 0.8543915 0.8577627
[50] 0.8610603 0.8642796 0.8673732 0.8704297 0.8734246 0.8763674 0.8792578
[57] 0.8820832 0.8848765 0.8875943 0.8901429 0.8926664 0.8951712 0.8975778
[64] 0.8999672

Session information

sessionInfo()
R version 3.4.3 (2017-11-30)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: OS X El Capitan 10.11.6

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/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] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] cowplot_0.9.3              ggplot2_3.0.0             
 [3] RColorBrewer_1.1-2         gplots_3.0.1              
 [5] pheatmap_1.0.10            DESeq2_1.18.1             
 [7] SummarizedExperiment_1.8.1 DelayedArray_0.4.1        
 [9] matrixStats_0.54.0         Biobase_2.38.0            
[11] GenomicRanges_1.30.3       GenomeInfoDb_1.14.0       
[13] IRanges_2.12.0             S4Vectors_0.16.0          
[15] BiocGenerics_0.24.0       

loaded via a namespace (and not attached):
 [1] bitops_1.0-6           bit64_0.9-7            rprojroot_1.3-2       
 [4] tools_3.4.3            backports_1.1.2        R6_2.2.2              
 [7] rpart_4.1-13           KernSmooth_2.23-15     Hmisc_4.1-1           
[10] DBI_1.0.0              lazyeval_0.2.1         colorspace_1.3-2      
[13] nnet_7.3-12            withr_2.1.2            tidyselect_0.2.4      
[16] gridExtra_2.3          bit_1.1-14             compiler_3.4.3        
[19] git2r_0.23.0           htmlTable_1.12         labeling_0.3          
[22] caTools_1.17.1.1       scales_1.0.0           checkmate_1.8.5       
[25] genefilter_1.60.0      stringr_1.3.1          digest_0.6.16         
[28] foreign_0.8-71         rmarkdown_1.10         R.utils_2.6.0         
[31] XVector_0.18.0         base64enc_0.1-3        pkgconfig_2.0.2       
[34] htmltools_0.3.6        htmlwidgets_1.2        rlang_0.2.2           
[37] rstudioapi_0.7         RSQLite_2.1.1          bindr_0.1.1           
[40] BiocParallel_1.12.0    gtools_3.8.1           acepack_1.4.1         
[43] dplyr_0.7.6            R.oo_1.22.0            RCurl_1.95-4.11       
[46] magrittr_1.5           GenomeInfoDbData_1.0.0 Formula_1.2-3         
[49] Matrix_1.2-14          Rcpp_0.12.18           munsell_0.5.0         
[52] R.methodsS3_1.7.1      stringi_1.2.4          whisker_0.3-2         
[55] yaml_2.2.0             zlibbioc_1.24.0        plyr_1.8.4            
[58] grid_3.4.3             blob_1.1.1             gdata_2.18.0          
[61] crayon_1.3.4           lattice_0.20-35        splines_3.4.3         
[64] annotate_1.56.2        locfit_1.5-9.1         knitr_1.20            
[67] pillar_1.3.0           geneplotter_1.56.0     XML_3.98-1.16         
[70] glue_1.3.0             evaluate_0.11          latticeExtra_0.6-28   
[73] data.table_1.11.4      gtable_0.2.0           purrr_0.2.5           
[76] assertthat_0.2.0       xtable_1.8-2           survival_2.42-6       
[79] tibble_1.4.2           AnnotationDbi_1.40.0   memoise_1.1.0         
[82] workflowr_1.1.1        bindrcpp_0.2.2         cluster_2.0.7-1

This reproducible R Markdown analysis was created with workflowr 1.1.1