Clarifications, and better color palettes for correlations
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hyginn
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RPR-GEO2R.R
114
RPR-GEO2R.R
@ -1,11 +1,5 @@
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# tocID <- "RPR_GEO2R.R"
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#
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# ---------------------------------------------------------------------------- #
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# PATIENCE ... #
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# Do not yet work wih this code. Updates in progress. Thank you. #
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# boris.steipe@utoronto.ca #
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# ---------------------------------------------------------------------------- #
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#
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# Purpose: A Bioinformatics Course:
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# R code accompanying the RPR_GEO2R unit.
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#
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@ -33,6 +27,8 @@
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# answer, or ask your instructor. Don't continue if you don't understand what's
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# going on. That's not how it works ...
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#
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# TO SUBMIT FOR CREDIT....
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#
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# Note: to submit tasks for credit for this unit, report on the sections
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# that have "Task ..." section headers, and report on the lines that are
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# identified with #TASK> comments.
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@ -68,6 +64,7 @@
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if (! requireNamespace("BiocManager", quietly = TRUE)) {
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install.packages("BiocManager")
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}
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if (! requireNamespace("Biobase", quietly = TRUE)) {
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BiocManager::install("Biobase")
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}
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@ -152,47 +149,59 @@ Biobase::sampleNames(GSE3635)[1:10] # Columns. What are these columns?
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# Access data
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Biobase::exprs(tmp) # exprs() gives us the actual expression values.
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# == 3.1 Task - understanding the data ==================================
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#TASK> What are the data:
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#TASK> What are the data values:
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#TASK> ... in each cell?
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#TASK> ... in each column?
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#TASK> ... in each row?
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# = 3 Column wise analysis - time points ==================================
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# Each column represents one experiment.
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#TASK> What are these experiments?
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# == 3.1 Task - Comparison of experiments ==================================
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# Get an overview of the distribution of data values in individual columns
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# Get an overview of the distribution of values in individual columns
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summary(Biobase::exprs(GSE3635)[ , 1])
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summary(Biobase::exprs(GSE3635)[ , 4])
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summary(Biobase::exprs(GSE3635)[ , 7])
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# as a boxplot
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cyclicPalette <- colorRampPalette(c("#00AAFF",
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"#DDDD00",
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"#FFAA00",
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"#00AAFF",
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"#DDDD00",
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"#FFAA00",
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"#00AAFF"))
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# This allows us to com pare the columns, comment on the quality of the data,
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# and get a sense for the distribution. We need to know how exactly these
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# numbers were produced: obviously, if we don't know how those numbers were
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# created in the first place, we would produce a major sin of Cargo Cult
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# bioinformatics if we would analyze them.
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# compare them in a a boxplot
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cyclicPalette <- colorRampPalette(c("#14b4c9",
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"#d2d1e6",
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"#e66594",
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"#d2d1e6",
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"#14b4c9",
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"#d2d1e6",
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"#e66594",
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"#d2d1e6",
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"#14b4c9"))
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tCols <- cyclicPalette(13)
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boxplot(Biobase::exprs(GSE3635), col = tCols)
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# == 3.1 Task - Comparison of experiments ==================================
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#TASK> Study this boxplot. What's going on? Are these expression values?
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#TASK> What do the numbers that exprs() returns from the dataset mean?
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#TASK> What do the numbers mean? (Summarize the process and computation
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#TASK> that has gone i to the preprocessing. You need to understand why
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#TASK> these columns all have the same mean and range.) Given what common
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#TASK> sense tells you about the variability of experiments, do you
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#TASK> believe your understanding is complete?
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# Lets plot the distributions of values in a more fine-grained manner:
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hT0 <- hist(Biobase::exprs(GSE3635)[ , 1], breaks = 100)
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hT3 <- hist(Biobase::exprs(GSE3635)[ , 4], breaks = 100)
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hT6 <- hist(Biobase::exprs(GSE3635)[ , 7], breaks = 100)
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hT9 <- hist(Biobase::exprs(GSE3635)[ , 10], breaks = 100)
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hT12 <- hist(Biobase::exprs(GSE3635)[ , 13], breaks = 100)
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hT0 <- hist(Biobase::exprs(GSE3635)[ , 1], breaks = 100, col = tCols[1])
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hT3 <- hist(Biobase::exprs(GSE3635)[ , 4], breaks = 100, col = tCols[4])
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hT6 <- hist(Biobase::exprs(GSE3635)[ , 7], breaks = 100, col = tCols[7])
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hT9 <- hist(Biobase::exprs(GSE3635)[ , 10], breaks = 100, col = tCols[10])
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hT12 <- hist(Biobase::exprs(GSE3635)[ , 13], breaks = 100, col = tCols[13])
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plot( hT0$mids, hT0$counts, type = "l", col = tCols[1], xlim = c(-0.5, 0.5))
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@ -289,7 +298,7 @@ readLines("./data/SGD_features.tab", n = 5)
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#
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# - read "./data/SGD_features.tab" into a data frame
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# called "SGD_features"
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# - remove unneeded columns - keep the following information:
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# - remove unneeded columns - keep the following data columns:
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# - Primary SGDID
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# - Feature type
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# - Feature qualifier
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@ -312,10 +321,12 @@ readLines("./data/SGD_features.tab", n = 5)
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# - confirm: are all rows of the expression data set represented in
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# the feature table? Hint: use setdiff() to print all that
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# are not.
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# Example: A <- c("duck", "crow", "gull", "tern")
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# B <- c("gull", "rook", "tern", "kite", "myna")
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# setdiff(A, B)
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# setdiff(B, A)
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# Example usage of setdiff():
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# A <- c("duck", "crow", "gull", "tern")
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# B <- c("gull", "rook", "tern", "kite", "myna")
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#
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# setdiff(A, B) # [1] "duck" "crow"
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# setdiff(B, A) # [1] "rook" "kite" "myna"
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# If some of the features in the expression set are not listed in the
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# systematic names, you have to be aware of that, when you try to get
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@ -331,6 +342,8 @@ readLines("./data/SGD_features.tab", n = 5)
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# == 4.2 Selected Expression profiles ======================================
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# The code below assumes that you have read ./data/SGD_features.tab and assigned
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# the resulting data frame to SGD_features, with columns as specified above.
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# Here is an expression profile for Mbp1.
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@ -484,7 +497,7 @@ for (i in 1:10) {
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points(seq(0, 120, by = 10), Biobase::exprs(GSE3635)[thisID, ], type = "b")
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}
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# Our guess that we might discover interesting genes be selecting groups A and B
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# Our guess that we might discover interesting genes by selecting groups A and B
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# like we did was not bad. But limma knows nothing about the biology and though
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# the expression profiles look good, there is no guarantee that these are the
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# most biologically relevant genes. Significantly different in expression
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@ -512,8 +525,8 @@ for (name in toupper(myControls)) {
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# == 5.1 Final task: Gene descriptions =====================================
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# Print the descriptions of the top ten differentially expressed genes
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# and comment on what they have in common (or not).
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#TASK> Print the descriptions of the top ten differentially expressed genes
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#TASK> and comment on what they have in common (or not).
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# = 6 Improving on Discovery by Differential Expression ===================
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@ -535,9 +548,12 @@ plot(seq(0, 120, by = 10),
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xlab = "time (min)",
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ylab = "expression",
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type = "b",
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col= "maroon")
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abline(h = 0, col = "#00000055")
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abline(v = 60, col = "#00000055")
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pch = 16,
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cex = 1.5,
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lwd = 2,
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col= "#40b886")
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abline(h = 0, col = "#0000FF55")
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abline(v = 60, col = "#0000FF55")
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# Set up a vector of correlation values
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@ -548,7 +564,8 @@ for (i in 1:length(myCorrelations)) {
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myCorrelations[i] <- cor(Cln2Profile, Biobase::exprs(GSE3635)[i, ])
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}
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myTopC <- order(myCorrelations, decreasing = TRUE)[1:10] # top ten
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nTOP <- 20
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myTopC <- order(myCorrelations, decreasing = TRUE)[1:nTOP]
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# Number 1
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(ID <- Biobase::featureNames(GSE3635)[myTopC[1]])
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@ -559,12 +576,15 @@ SGD_features[which(SGD_features$sysName == ID), ]
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# control for the experiment.
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# Let's plot the rest
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for (i in 2:length(myTopC)) {
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myPal <- colorRampPalette(c("#82f58d", "#E0F2E2", "#f6f6f6"))
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for (i in 2:nTOP) {
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ID <- Biobase::featureNames(GSE3635)[myTopC[i]]
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points(seq(0, 120, by = 10),
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Biobase::exprs(GSE3635)[ID, ],
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type = "b",
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col= "chartreuse")
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cex = 0.8,
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col= myPal(nTOP)[i])
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print(SGD_features[which(SGD_features$sysName == ID),
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c("name", "description")])
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}
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@ -576,13 +596,17 @@ for (i in 2:length(myTopC)) {
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# mean small biological effects? Certainly not!
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# And we haven't even looked at the anticorrelated genes yet...
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myBottomC <- order(myCorrelations, decreasing = FALSE)[1:10] # bottom ten
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for (i in 1:length(myBottomC)) {
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nBOT <- nTOP
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myBottomC <- order(myCorrelations, decreasing = FALSE)[1:nBOT] # bottom ten
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myPal <- colorRampPalette(c("#ba112a", "#E3C8CC", "#ebebeb"))
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for (i in 1:nBOT) {
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ID <- Biobase::featureNames(GSE3635)[myBottomC[i]]
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points(seq(0, 120, by = 10),
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Biobase::exprs(GSE3635)[ID, ],
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type = "b",
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col= "coral")
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cex = 0.8,
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col= myPal(nBOT)[i])
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print(SGD_features[which(SGD_features$sysName == ID),
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c("name", "description")])
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}
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