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Evaluate the following code: # Compute ordinary t statistics library('genefilter') tt <- rowttests(exprs.eset, factor(eset$population)) names(tt) # Check that length of tt$statistic is 37744 length(tt$statistic)(Complete File)(Rout) |
Finally, what we have all been waiting for. Which genes are over-expressed.
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Evaluate the following code: # Compute ordinary t statistics library('genefilter') tt <- rowttests(exprs.eset, factor(eset$population)) names(tt) # Check that length of tt$statistic is 37744 length(tt$statistic)(Complete File)(Rout) |
Don't worry, it's not as dull as it seems.
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Evaluate the following code:
## Start the construction of the Volcano Plot ## Construct the lod scores... lod <- -log10(tt$p.value) o1 <- order(abs(Difference), decreasing = TRUE)[1:50] o2 <- order(abs(tt$statistic), decreasing = TRUE)[1:50] # Construct union and intersection of sets o <- union(o1, o2) i <- intersect(o1, o2) # Display i; note, we have 4 intersects i(Complete File)(Rout) and then this... # Construct simple Volcano plot to get an idea of the range # of the Difference values and lod values plot(Difference, lod) # Construct more sophisticated set of commands for a nicer Volcano plot par(mfrow = c(1,1)) plot(Difference[-o], lod[-o], cex = .25, xlim = c(-3,3), ylim = range(lod)) points(Difference[o1], lod[o1], pch = 18, col = 'blue', cex = 1.5) points(Difference[o2], lod[o2], pch = 1, col = 'red', cex = 2, lwd = 2) abline(h = 3) text(-2, 3.8, 'Red circles: 50 genes') text(-2, 3.6, 'with lowest p-values') text(-2, 3.4, '=====>>>') text(-2, 3.15, 'p < 0.001') title('Volcano Plot: lowest p values and largest fold differences')(Complete File)(Rout) |
Volcano plots are fun to look at, if somewhat hard to interpret.
They show a plot of the difference in expression levels between the two varieties and the log odds. In essence, we want a big variation of expression level for a gene BETWEEN strains, with a low level of variation of expression WITHIN a strain.
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