Question: Which package for gene expression correlation analysis
gravatar for Naomi Altman
9.4 years ago by
Naomi Altman6.0k
Naomi Altman6.0k wrote:
When you do thousands of correlations with only 4 samples, you can expect a lot of very high correlations just by chance. So, you should filter your genes by some criterion of "interestingness" before performing correlation analysis. --Naomi At 12:02 PM 6/28/2010, Steve Lianoglou wrote: >Hi, > >On Mon, Jun 28, 2010 at 10:53 AM, Yuan Hao <yuan.hao at=""""> 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: > >_______________________________________________ >Bioconductor mailing list >Bioconductor at > >Search the archives: > Naomi S. Altman 814-865-3791 (voice) Associate Professor Dept. of Statistics 814-863-7114 (fax) Penn State University 814-865-1348 (Statistics) University Park, PA 16802-2111
cancer affy • 1.2k views
ADD COMMENTlink written 9.4 years ago by Naomi Altman6.0k
Please log in to add an answer.


Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 16.09
Traffic: 337 users visited in the last hour