Last updated: 2019-04-10
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Knit directory: dc-bioc-limma/analysis/
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| File | Version | Author | Date | Message |
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| html | 41b57ee | John Blischak | 2019-02-26 | Build site. |
| html | 2372aa1 | John Blischak | 2019-01-09 | Build site. |
| html | f440a87 | John Blischak | 2018-08-20 | Build site. |
| html | 5cf7b98 | John Blischak | 2018-08-08 | Build site. |
| 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:
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# 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 # 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# 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
Down 0 466 1635 1885 128
NotSig 11871 10915 7635 6989 11640
Up 0 490 2601 2997 103
sessionInfo()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/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] 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