Last updated: 2019-04-10

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Knit directory: dc-bioc-limma/analysis/

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Rmd 457f59b John Blischak 2018-08-08 Organize analysis of Arabidopsis used in slides to demo 2x2 factorial study.

2x2 design to study effect of low temperature in plants:

  • 2 types of Arabidopsis thaliana: col, vte2
  • 2 temperatures: normal, low
  • Maeda et al. 2010


rds <- "../data/arabidopsis-eset.rds"
if (!file.exists(rds)) {
  gset <- getGEO("GSE53990", GSEMatrix = TRUE, getGPL = FALSE)
  if (length(gset) > 1) idx <- grep("GPL198", attr(gset, "names")) else idx <- 1
  gset <- gset[[idx]]
  eset <- gset
  plotDensities(eset, legend = FALSE)
  # RMA normalization already applied
  # > Raw chip data were analyzed with R/Bioconductor. Only perfect match (PM)
  # > intensities were used. RMA function as implemented in the affy package was
  # > used for background adjustment, normalization and summarization.
  sum(rowMeans(exprs(eset)) > 5)
  plotDensities(eset[rowMeans(exprs(eset)) > 5, ], legend = FALSE)
  eset <- eset[rowMeans(exprs(eset)) > 5, ]
  pData(eset) <- pData(eset)[, c("title", "genotype:ch1", "lt treatment time:ch1")]
  colnames(pData(eset)) <- c("title", "type", "temp")
  # Remove 48h sample. More noticeable effect at 120h (authors note that 48 hour
  # timepoint is more interesting to them since it is more likely to give insight
  # into mechanism since by 120h lots of downstream singaling has started.
  # However, the effect is much more minimal, and thus not as useful for my
  # pedagological needs)
  eset <- eset[, pData(eset)[, "temp"] != "48h"]
  # Clean up names
  pData(eset)[, "type"] <- tolower(pData(eset)[, "type"])
  pData(eset)[, "temp"] <- ifelse(pData(eset)[, "temp"] == "0h", "normal", "low")
  pData(eset)[, "rep"] <- sprintf("r%d",
                                  as.integer(str_sub(pData(eset)[, "title"], -1, -1)))
  pData(eset)[, "title"] <- NULL
  colnames(eset) <- paste(pData(eset)[, "type"],
                          pData(eset)[, "temp"],
                          pData(eset)[, "rep"], sep = "_")
  saveRDS(eset, file = "../data/arabidopsis-eset.rds")
} else {
  eset <- readRDS(rds)

Features  Samples 
   11871       12 
table(pData(eset)[, c("type", "temp")])
type   low normal
  col    3      3
  vte2   3      3

Design matrix

# Create single variable
group <- with(pData(eset), paste(type, temp, sep = "."))
group <- factor(group)

# Create design matrix with no intercept
design <- model.matrix(~0 + group)
colnames(design) <- levels(group)
head(design, 3)
  col.low col.normal vte2.low vte2.normal
1       0          1        0           0
2       0          1        0           0
3       0          1        0           0
# Count the number of samples modeled by each coefficient
    col.low  col.normal    vte2.low vte2.normal 
          3           3           3           3 

Contrasts matrix

# Create a contrasts matrix
cm <- makeContrasts(type_normal = vte2.normal - col.normal,
                    type_low = vte2.low - col.low,
                    temp_vte2 = vte2.low - vte2.normal,
                    temp_col = col.low - col.normal,
                    interaction = (vte2.low - vte2.normal) - (col.low - col.normal),
                    levels = design)

# View the contrasts matrix
Levels        type_normal type_low temp_vte2 temp_col interaction
  col.low               0       -1         0        1          -1
  col.normal           -1        0         0       -1           1
  vte2.low              0        1         1        0           1
  vte2.normal           1        0        -1        0          -1

Differential expression

# Fit the model
fit <- lmFit(eset, design)

# Fit the contrasts
fit2 <-, contrasts = cm)

# Calculate the t-statistics for the contrasts
fit2 <- eBayes(fit2)

# Summarize results
results <- decideTests(fit2)
       type_normal type_low temp_vte2 temp_col interaction
Down             0      466      1635     1885         128
NotSig       11871    10915      7635     6989       11640
Up               0      490      2601     2997         103

R version 3.5.3 (2019-03-11)
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/
LAPACK: /usr/lib/x86_64-linux-gnu/atlas/

 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            

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

other attached packages:
[1] stringr_1.4.0       limma_3.38.3        GEOquery_2.50.5    
[4] Biobase_2.42.0      BiocGenerics_0.28.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.1           xml2_1.2.0           knitr_1.22.6        
 [4] whisker_0.3-2        magrittr_1.5         workflowr_1.2.0.9000
 [7] hms_0.4.2            tidyselect_0.2.5     R6_2.4.0            
[10] rlang_0.3.3          dplyr_0.8.0.1        tools_3.5.3         
[13] xfun_0.6             git2r_0.25.1         htmltools_0.3.6     
[16] yaml_2.2.0           rprojroot_1.2        digest_0.6.18       
[19] assertthat_0.2.1     tibble_2.1.1         crayon_1.3.4        
[22] tidyr_0.8.3          readr_1.3.1          purrr_0.3.2         
[25] fs_1.2.7             glue_1.3.1           evaluate_0.13       
[28] rmarkdown_1.12       stringi_1.4.3        pillar_1.3.1        
[31] compiler_3.5.3       backports_1.1.3      pkgconfig_2.0.2