Dear List, I would like to ask if there is such a bioconductor package available that can help to achieve the following purpose. Thank you very much in advance! I got 16 Affy chips corresponding to 4 samples: wild-type treated, wide-type untreated, knocked-down treated, and knocked-down untreated, i.e. 4 replicates for each sample. I want to look at the expression correlations between genes. Say, my gene of interest is gene X. I would like to find out other genes on the chip which have the similar expression profiles with gene X across samples. In other words, if expression levels of gene X increased from wild-type treated to knocked-out treated, I would like to find all the other genes have the same trend. Any comments would be appreciated! The best, Yuan

Hi, On Mon, Jun 28, 2010 at 10:53 AM, Yuan Hao <yuan.hao at="" cantab.net=""> wrote: > Dear List, > > I would like to ask if there is such a bioconductor package available that > can help to achieve the following purpose. Thank you very much in advance! > > I got 16 Affy chips corresponding to 4 samples: wild-type treated, > wide-type untreated, knocked-down treated, and knocked-down untreated, > i.e. 4 replicates for each sample. > > I want to look at the expression correlations between genes. Say, my gene > of interest is gene X. I would like to find out other genes on the chip > which have the similar expression profiles with gene X across samples. In > other words, if expression levels of gene X increased from wild-type > treated to knocked-out treated, I would like to find all the other genes > have the same trend. Given the size of the bioconductor universe, it's hard to say with any certainty that a certain function does NOT exist, but I'd be somehow surprised if this function actually is there, since it's relatively easy for you to implement yourself. You are essentially repeatedly performing a test against each row of your expression matrix, so think "loops" or some incantation of *apply methods. Here's an easy one. Let's assume: * `exprs` is a (gene x experiment) matrix with your expression value. * the value `x` holds the row index of the gene you are interested R> set.seed(123) R> exprs <- matrix(rnorm(100), 5) R> x <- 1 Now you want to test the correlation of the vector @ x with the rest. R> cors <- apply(exprs[-x,], 1, cor.test, exprs[x,]) This gives you a list of correlation tests that you can (i) get the statistic out of; and (ii) order R> cors.estimate <- sapply(cors, '[[', 'estimate') ## (i) R> alike <- order(cors.estimate, decreasing=TRUE) ## (ii) `alike` now has the indices of genes that are "most + correlated" to "most - correlated" to gene "x" +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ If you're a bit more familiar with R functions, you might have known the there is function named "cor" that creates a correlation matrix out of matrix. This function works column-wise, so you first have to transpose your matrix: R> all.cors <- cor(t(exprs)) R> cors.estimate cor cor cor cor -0.01971735 -0.26353249 0.03361119 -0.11578081 R> all.cors[1,] [1] 1.00000000 -0.01971735 -0.26353249 0.03361119 -0.11578081 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ The various cluster/heatmap fucntions do correlation based clustering by default (I believe), which will group your genes row-wise (and column wise) for you. Look at ?heatmap and check what that function returns to you in the "Value" section. -steve -- Steve Lianoglou Graduate Student: Computational Systems Biology | Memorial Sloan-Kettering Cancer Center | Weill Medical College of Cornell University Contact Info: http://cbio.mskcc.org/~lianos/contact

Dear Yuan you probably want to cluster the genes, e.g. using hierarchical clustering and heatmap display or k-means clustering. The "heatmap.2" function in the gplots package might be a good place to start, as is the book http://www.bioconductor.org/pub/biocases or a Google search on the terms "heatmap" and "microarray". It is important to think about what choice of distance metric between genes you want, and/or to try different ones. Best wishes Wolfgang On Jun/28/10 4:53 PM, Yuan Hao wrote: > Dear List, > > I would like to ask if there is such a bioconductor package available that > can help to achieve the following purpose. Thank you very much in advance! > > I got 16 Affy chips corresponding to 4 samples: wild-type treated, > wide-type untreated, knocked-down treated, and knocked-down untreated, > i.e. 4 replicates for each sample. > > I want to look at the expression correlations between genes. Say, my gene > of interest is gene X. I would like to find out other genes on the chip > which have the similar expression profiles with gene X across samples. In > other words, if expression levels of gene X increased from wild-type > treated to knocked-out treated, I would like to find all the other genes > have the same trend. > > Any comments would be appreciated! > > The best, > Yuan > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor -- Wolfgang Huber EMBL http://www.embl.de/research/units/genome_biology/huber