Last updated: 2018-08-08

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    File Version Author Date Message
    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

Setup

library(Biobase)
library(GEOquery)
library(limma)
library(stringr)
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
  dim(eset)
  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 = "_")
  head(pData(eset))
  
  saveRDS(eset, file = "../data/arabidopsis-eset.rds")
} else {
  eset <- readRDS(rds)
}

dim(eset)
Features  Samples 
   11871       12 
table(pData(eset)[, c("type", "temp")])
      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
colSums(design)
    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
cm
             Contrasts
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.fit(fit, contrasts = cm)

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

# Summarize results
results <- decideTests(fit2)
summary(results)
   type_normal type_low temp_vte2 temp_col interaction
-1           0      466      1635     1885         128
0        11871    10915      7635     6989       11640
1            0      490      2601     2997         103

Session information

sessionInfo()
R version 3.4.4 (2018-03-15)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.1 LTS

Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1

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] stringr_1.3.1       limma_3.32.2        GEOquery_2.42.0    
[4] Biobase_2.36.2      BiocGenerics_0.22.1

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.18         knitr_1.20           whisker_0.3-2       
 [4] magrittr_1.5         workflowr_1.1.1.9000 R6_2.2.2            
 [7] httr_1.3.1           tools_3.4.4          R.oo_1.22.0         
[10] git2r_0.23.0         htmltools_0.3.6      yaml_2.2.0          
[13] rprojroot_1.3-2      digest_0.6.15        R.utils_2.6.0       
[16] bitops_1.0-6         RCurl_1.95-4.11      evaluate_0.11       
[19] rmarkdown_1.10       stringi_1.2.4        compiler_3.4.4      
[22] backports_1.1.2-9000 R.methodsS3_1.7.1    XML_3.98-1.11

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