Hi all,
I am having trouble viewing the full color spectrum (I only see "red") for
data that I have
clustered using the hopach method and displaying in either MapleTree or
TreeView.  When I use the golub or kidney data sets
("Bioconductor" examples) I have no problem seeing the different colors
(green - red).  The data set I am using is comprised of
normalized (RMA) microarray data from 6 normal vs 30 ovarian tumor tissue
samples that were orignially generated from
Affymetrix HG_133A chip experiments and deposited in TCGA.  After
normalizing the raw CEL files I select the top 200
with the highest variance before using this data for clustering.
Below is the code I am using.  Could anyone tell me where I might be going
wrong - i.e. why my
cluster files (e.g. OvarianFzy.cdt, OvarianFzy.fct, OvarianFzy.mb;
OvarianTree.atr, OvarianTree.cdt, OvarianTree.gtr)
when opened in MapleTree or TreeView show only a red map as opposed to the
green-red gradient ?

I apologize in advance if my question is not clear,
I am only a beginner at using the clustering methods.

Thank you,
Mark

# Take the 200 genes with highest variance across the arrays in my
normalized eset.r.m matrix.

vars <- apply(eset.r.m, 1, var)
selected <- vars > quantile(vars, (nrow(eset.r.m) - 200)/nrow(eset.r.m))
esetSub <- eset.r.m[selected, ]
dim(esetSub)

# create a table with only the probes selected and add coresponding gene
names to column 2 of table (I do this using Excel until I can figure out
how to do it in R)
# this is only so that I can view the genes associated with the clustered
data.
write.table(selected,
"C:\\temp\\JHU\\MicroArray_Analysis\\Bio_Rad\\Ovarian\\batch_9\\selected.txt")

# after adding gene symbols now read it back into R (this table has all of
the selected probe IDs in column 1 and all of the corresponding gene
symbols in column 2)
esetSub.probes.desc <-
read.table("C:\\temp\\JHU\\MicroArray_Analysis\\Bio_Rad\\Ovarian\\batch_9\\esetSub.probes.desc.txt",
sep = "\t")

# now change the data type to character and mode to matrix
esetSub.probes.desc <- as.character(esetSub.probes.desc)
esetSub.probes.desc <- as.matrix(esetSub.probes.desc)

library(hopach)

# compute distance matrix
gene.dist <- distancematrix(esetSub, d = "cosangle")
dim(gene.dist)

# run hopach to cluster genes
gene.hobj <- hopach(esetSub, dmat = gene.dist)
gene.hobj$clust$k

# plot gene distance
dplot(gene.dist, gene.hobj, ord = "final", main = "Ovarian:Gene Distance",
showclusters = FALSE)

# bootstrap resampling
bobj <- boothopach(esetSub, gene.hobj, B = 100)
bootplot(bobj, gene.hobj, ord = "bootp", main = "Ovarian cancer",
showclusters = FALSE)

# Clustering of arrays
array.hobj <- hopach(t(esetSub), d = "euclid")
array.hobj$clust$k

# gene clustering and bootstrap results table
makeoutput(esetSub, gene.hobj, bobj, file =
"C:\\temp\\JHU\\MicroArray_Analysis\\Bio_Rad\\Ovarian\\batch_9\\Ovarian.data\\ovarian.out",
gene.names = esetSub.probes.desc[,2])

# bootstrap fuzzy clustering
boot2fuzzy(esetSub, bobj, gene.hobj, array.hobj, file =
"C:\\temp\\JHU\\MicroArray_Analysis\\Bio_Rad\\Ovarian\\batch_9\\Ovarian.data\\OvarianFzy",
gene.names = esetSub.probes.desc[,2])

# HOPACH hierarchical clustering in MapleTree
hopach2tree(esetSub, file =
"C:\\temp\\JHU\\MicroArray_Analysis\\Bio_Rad\\Ovarian\\batch_9\\Ovarian.data\\data\\OvarianTree",
hopach.genes = gene.hobj, hopach.arrays = array.hobj, dist.genes =
gene.dist,

gene.names = esetSub.probes.desc[,2])



-- 
Mark Baumeister

http://sites.google.com/site/lfmmab/

	[[alternative HTML version deleted]]

