Last updated: 2018-08-20

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    File Version Author Date Message
    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).


# Have to load Biobase after dplyr so that exprs function works

Download data.

file_url <- ""
full <- read.delim(file_url, stringsAsFactors = FALSE)

Convert to ExpressionSet.

[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
[1] 12728
eset <- eset[keep, ]
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)

Expand here to see past versions of normalize-1.png:
Version Author Date
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")])
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")

Expand here to see past versions of pre-1.png:
Version Author Date
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")

Expand here to see past versions of post-1.png:
Version Author Date
f2c0198 John Blischak 2018-08-09

Session information

R version 3.5.0 (2018-04-23)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 17134)

Matrix products: default

[1] LC_COLLATE=English_United States.1252 
[2] LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
[1] Biobase_2.40.0      BiocGenerics_0.26.0 edgeR_3.22.2       
[4] limma_3.36.1        dplyr_0.7.5        

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.17      knitr_1.20        bindr_0.1.1      
 [4] whisker_0.3-2     magrittr_1.5      workflowr_1.1.1  
 [7] tidyselect_0.2.4  lattice_0.20-35   R6_2.2.2         
[10] rlang_0.2.1       stringr_1.3.1     tools_3.5.0      
[13] grid_3.5.0        R.oo_1.22.0       git2r_0.21.0     
[16] htmltools_0.3.6   yaml_2.1.19       rprojroot_1.3-2  
[19] digest_0.6.15     assertthat_0.2.0  tibble_1.4.2     
[22] bindrcpp_0.2.2    purrr_0.2.5       R.utils_2.6.0    
[25] glue_1.2.0        evaluate_0.10.1   rmarkdown_1.10   
[28] stringi_1.2.3     pillar_1.2.3      compiler_3.5.0   
[31] backports_1.1.2   R.methodsS3_1.7.1 locfit_1.5-9.1   
[34] pkgconfig_2.0.1  

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