[BioC] GO graph plotting with some in color
engrav at u.washington.edu
Sat Dec 1 20:17:29 CET 2007
I think my initial foray into Bioconductor and GO plotting is finished.
Thank you all for the help. Was fun.
> rows2 <- read.delim(file="339namesColorsRowsBBtab.txt", sep="\t",
check.names = FALSE)
> colors <- as.matrix(rows2)[1,]
> nAttrs = list()
> nAttrs$fillcolor <- colors
> nAttrs$color <- colors
> bpCutLeaves <- scan(file="339namesRowBB.txt", what = "character")
Read 339 items
> bpCutLeavestree <- GOGraph(bpCutLeaves, GOBPPARENTS)
> postscript ("bpCutLeavestree100_40_4.ps", width=100, height = 40,
paper="special"); plot (bpCutLeavestree, nodeAttrs=nAttrs); dev.off()
Then "fixed" it in Illustrator
"Final" pdf graph is up at <http://homepage.mac.com/engrav/Menu9.html> and
hit File Sharing arrow. Red refers to over expression, blue to under
expression, and green when the two events collapse into one GO term.
We have 1019 differentially expressed genes, loaded them up into Bio,
postscripted out the induced BP graph, and touched it up in Illustrator.
R and Bio are not easy. The other long thread on can documentation be
improved warrants further discussion, IMO.
The postscript that emerges is useful but not elegant. For example, the GO
terms become the labels in "text" objects. But some are one text object and
some are two making the file more complex than need be and tough to
manipulate. But then maybe again I do not know how to do postscript in R.
But the large question remains, at least to me
If one has 1019 differentially expressed genes (some over expressed and some
under) and plots the induced biological process graph with no consideration
of enrichment, etc, just plots the induced graph...
This visualizes where in the GO tree the over and under genes are located in
the tree. But does this "simple" plot "prove" anything?
Dunno, but it is interesting that there are three bands on the right, red
blue red. It will be fun to see what processes are involved in these bands.
Thank you again for the help. I will now try the same thing with MF and
then hierarchical clustering.
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