Last updated: 2019-02-26

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Knit directory: dc-bioc-limma/analysis/

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File Version Author Date Message
html 2372aa1 John Blischak 2019-01-09 Build site.
html f440a87 John Blischak 2018-08-20 Build site.
html f2c0198 John Blischak 2018-08-09 Build site.
Rmd 6893542 John Blischak 2018-08-09 Organize analysis of batch effects in TB data.

An example of diagnosing and correcting batch effects from one of my own studies on the response to infection with Mycobacterium tuberculosis (paper, code, data).

Setup

library(dplyr)
library(limma)
library(edgeR)
# Have to load Biobase after dplyr so that exprs function works
library(Biobase)

Download data.

file_url <- "https://bitbucket.org/jdblischak/tb-data/raw/bc0f64292eb8c42a372b3a2d50e3d871c70c202e/counts_per_sample.txt"
full <- read.delim(file_url, stringsAsFactors = FALSE)

Convert to ExpressionSet.

dim(full)
[1]   156 19419
full <- full[order(full$dir), ]
rownames(full) <- paste(full$ind, full$bact, full$time, sep = ".")

x <- t(full[, grep("ENSG", colnames(full))])
p <- full %>% select(ind, bact, time, extr, rin)
stopifnot(colnames(x) == rownames(p))

eset <- ExpressionSet(assayData = x,
                      phenoData = AnnotatedDataFrame(p))

Filter lowly expressed genes.

keep <- rowSums(cpm(exprs(eset)) > 1) > 6
sum(keep)
[1] 12728
eset <- eset[keep, ]
dim(eset)
Features  Samples 
   12728      156

Normalize with TMM.

norm_factors <- calcNormFactors(exprs(eset))
exprs(eset) <- cpm(exprs(eset), lib.size = colSums(exprs(eset)) * norm_factors,
                   log = TRUE)
plotDensities(eset, legend = FALSE)

Version Author Date
2372aa1 John Blischak 2019-01-09
f440a87 John Blischak 2018-08-20
f2c0198 John Blischak 2018-08-09

Clean up phenotype data frame to focus on early versus late timepoint for this example.

pData(eset)[, "infection"] <- ifelse(pData(eset)[, "bact"] == "none",
                                     "con", "inf")

pData(eset)[, "time"] <- ifelse(pData(eset)[, "time"] == 4,
                                "early", "late")

pData(eset)[, "batch"] <- sprintf("b%02d", pData(eset)[, "extr"])

table(pData(eset)[, c("time", "batch")])
       batch
time    b01 b02 b03 b04 b05 b06 b07 b08 b09 b10 b11 b12 b13
  early   4   4   4   4   4   4   4   4   4   4   4   4   6
  late    8   8   8   8   8   8   8   8   8   8   8   8   6

Remove batch effect

Visualize principal components 1 and 2 for the original data.

plotMDS(eset, labels = pData(eset)[, "time"], gene.selection = "common")

Version Author Date
2372aa1 John Blischak 2019-01-09
f440a87 John Blischak 2018-08-20
f2c0198 John Blischak 2018-08-09

Remove the effect of the technical variables: batch (discrete) and RIN (continuous; a measure of RNA quality).

exprs(eset) <- removeBatchEffect(eset, batch = pData(eset)[, "batch"],
                                 covariates = pData(eset)[, "rin"])

Visualize principal components 1 and 2 for the corrected data.

plotMDS(eset, labels = pData(eset)[, "time"], gene.selection = "common")

Version Author Date
2372aa1 John Blischak 2019-01-09
f440a87 John Blischak 2018-08-20
f2c0198 John Blischak 2018-08-09 


sessionInfo()
R version 3.5.2 (2018-12-20)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.2 LTS

Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/atlas/libblas.so.3.10.3
LAPACK: /usr/lib/x86_64-linux-gnu/atlas/liblapack.so.3.10.3

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] parallel  stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
[1] Biobase_2.42.0      BiocGenerics_0.28.0 edgeR_3.24.3       
[4] limma_3.38.3        dplyr_0.8.0.1      

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0           knitr_1.21           whisker_0.3-2       
 [4] magrittr_1.5         workflowr_1.2.0.9000 tidyselect_0.2.5    
 [7] lattice_0.20-38      R6_2.4.0             rlang_0.3.1         
[10] stringr_1.4.0        tools_3.5.2          grid_3.5.2          
[13] xfun_0.5             git2r_0.24.0         htmltools_0.3.6     
[16] yaml_2.2.0           rprojroot_1.2        digest_0.6.18       
[19] assertthat_0.2.0     tibble_2.0.1         crayon_1.3.4        
[22] purrr_0.3.0          fs_1.2.6             glue_1.3.0          
[25] evaluate_0.13        rmarkdown_1.11       stringi_1.3.1       
[28] compiler_3.5.2       pillar_1.3.1         backports_1.1.3     
[31] locfit_1.5-9.1       pkgconfig_2.0.2