Question: clustering methods for gene expression

0

Donna Toleno •

**90**wrote:Hello list,
I have a question about using the Bioconductor tools to reproduce a
certain illustration I have seen in publications. I took some sample
data and used hcluster on my microarray arrays as in the following
example. I subset on the genes and then cluster the arrays based on
the genes of interest. I use Euclidean distance and average linking.
Is this the same as using PHYLIP to make a UPGMA tree with distances
calculated based on pairwise "mean of the squared difference in
expression intensities"? If I am not mistaken, the Euclidean distance
would be the square root of the sum of the squared differences. It
seems it is a similar distance measure. But what if we took the mean
probe signal intensities for each of two previously defined groups and
then took a mean squared difference between the group means? Then
within one of the groups the expression variation is defined in the
literature as "mean probe signal intensity variation". These seem like
unusual ways to measure the with
in and between group variation. Wouldn't it be more acceptable to use
a linear model in limma to quantify the variance components within and
between groups?Also, is there any reason to use PHYLIP to display the
relationships of expression profiles instead of using hcluster or some
other clustering algorithm? Any feedback at all would be appreciated.
Feel free to criticize my R code appearing below. I am somewhat new to
microarray data analysis and Bioconductor and I recently pieced
together this code to carry out clustering.
mydata <- ReadAffy()
eset <- rma(mydata)
#transpose the expression set so that the rows are the genes
data_frame_expression <- t(as.data.frame(eset))
vars <- apply(data_frame_expression,1, var)
means <- apply(data_frame_expression,1, mean)
stdevs <- apply(data_frame_expression,1, sd)
CV <- stdevs/means
#subset the data on the rows (genes)
# an example:subset <- vars >
quantile(vars,(nrow(data_frame_expression
-200)/nrow(data_frame_expression))
subset <- CV > 0.15
expression_subset <- data_frame_expression[subset, ]
#cluster on the columns (samples)
hc <- hcluster(t(expression_subset), link = "ave")
plot (hc)
write(hc2Newick(hc),file='hclust_subset.newick')
subset_distances <- as.matrix(dist(t(expression_subset), method =
"euclidean", diag = FALSE, upper = FALSE))
write (subset_distances, file='subset_of_genes_dist_file') # the
problem here is that the distance matrix didn't print to a file
properly.
Thank you for reading!