Hi Thomas, Thank you very much. I will check this out. Best, Alex On 6/19/07, Thomas Girke wrote: > > Alex, > > I guess Martin answered your question. > > A similar result, but with slower computation, can obtained by applying > the IQR function like this: > > apply(iris[,1:3], 1, IQR) > > Thomas > > On Tue 06/19/07 21:10, Martin Morgan wrote: > > Alex, > > > > > library(Biobase) > > [snip] > > > args(rowQ) > > function (imat, which) > > NULL > > > showMethods("rowQ") > > Function: rowQ (package Biobase) > > imat="ExpressionSet", which="numeric" > > imat="exprSet", which="numeric" > > imat="matrix", which="numeric" > > > > so it looks like x should be a matrix rather than a data frame. > > > > Martin > > > > "ssls sddd" writes: > > > > > Hi Thomas, > > > > > > Thanks! Sorry for getting back to it late because I was out > > > of town for a couple of days. > > > > > > I like the idea of 'removing all rows with low variability across > > > samples'. I searched around and found an online tutorial > > > > http://www.economia.unimi.it/projects/marray/2006/material/Lab3/MachineLearning/ML-lab.pdfis > > > doing very similar thing which teaches how to filter some > > > undifferentially > > > expressed genes. > > > > > > It takes the simplistic approach of using the 75th percentile of the > > > interquartile range > > > (IQR) as the cut-off point and computes quantiles using rowQ. > > > > > > I followed their method and my code is: > > > > > > library("Biobase") > > > lowQ = rowQ(x, floor(0.25 * 49))#49 for 49 samples > > > upQ = rowQ(x, ceiling(0.75 * 49)) > > > iqrs = upQ - lowQ > > > giqr = iqrs > quantile(iqrs, probs = 0.75) > > > sum(giqr) > > > xsub = x[giqr, ] > > > dim(xsub) > > > > > > But the error message is like: > > > > > > function (classes, fdef, mtable) : > > > unable to find an inherited method for function "rowQ", for > > > signature "data.frame", "numeric" > > > > > > Perhaps you can any experience in using 'rowQ'? If I want to use IQR > > > function, how should I approach this? > > > > > > I really appreciate your help! > > > > > > Thank you very much! > > > > > > Sincerely, > > > > > > Alex > > > > > > > > > > > > On 6/13/07, Thomas Girke wrote: > > >> > > >> Dear Alex, > > >> > > >> In addition, to Sean's advice, I would like to point out that the > > >> sample you are giving below indicates that you are trying to pass on > > >> to the heatmap function a column dendrogram plus a row dendrogram. > With > > >> your > > >> matrix of 238,000 rows by 49 columns you should have only a column > > >> dendrogram, because the row dendrogram would take more than 200 GB of > > >> memory to > > >> calculate. You can still use the heatmap or heatmap.2 functions by > turning > > >> off the row > > >> sorting by setting the Rowv argument to NA. In addition to this, I > would > > >> consider to filter your rows in a meaningful manner to a much smaller > > >> number, perhaps by using R's IQR function to remove all rows with > very > > >> low variability. I am suggesting this because, you won't see any > > >> patterns in the heatmap when you have so many rows. If the row > filtering > > >> works then you could generate a dendrogram for the row dimension as > well. > > >> Remember: hclust will require ~4 GB of memory to cluster ~30,000 > items > > >> and < 1 GB for 10,000 items, and pvclust that uses hclust internally > will > > >> need even much more than this. > > >> > > >> As a more general advice, when working with large data sets in R > always > > >> subset > > >> your data to something very small to test out your strategy first, > because > > >> this > > >> will save you a lot of time. > > >> In your case, this could by done by selecting just the first 100 rows > of > > >> your > > >> matrix like this: > > >> my_matrix <- my_matrix[1:100, ] > > >> > > >> Once you have tested things out then just remove in your > script/protocol > > >> the '[1:100,]' part. > > >> > > >> Best, > > >> > > >> Thomas > > >> > > >> > > >> On Wed 06/13/07 06:02, Sean Davis wrote: > > >> > ssls sddd wrote: > > >> > > Dear Dr.Thomas Girke, > > >> > > > > >> > > I have one more question for you. I tried pvclust in the session > of > > >> > > 'Obtain significant clusters by pvclust bootstrap analysis' for > my > > >> data, x. > > >> > > > > >> > > But I have a problem with: > > >> > > > > >> > > heatmap(x, Rowv=dend_colored, Colv=as.dendrogram(hc), col= > my.colorFct > > >> (), > > >> > > scale="row", RowSideColors=mycolhc) > > >> > > > > >> > > the error was: > > >> > > > > >> > > error in heatmap(x, Rowv = dend_colored, Colv = as.dendrogram(hc), > col > > >> = > > >> > > my.colorFct(), : > > >> > > 'x' must be a numeric matrix > > >> > > > > >> > > I ran 'x[1:3,1:3]' and it produced the following: > > >> > > > > >> > > AIRNS_A09 AIRNS_A11 AIRNS_A12 > > >> > > SNP_A-1780271 1.85642 1.50956 1.73154 > > >> > > SNP_A-1780274 1.72140 1.83712 1.85948 > > >> > > SNP_A-1780277 2.04241 1.53458 1.65270 > > >> > > > > >> > > I think the x is a numeric matrix. Do you think where I may get > wrong? > > >> > > > >> > Try coercing the x into a matrix directly: > > >> > > > >> > heatmap(as.matrix(x), Rowv=dend_colored, Colv=as.dendrogram(hc), > > >> > col=my.colorFct(), scale="row", RowSideColors=mycolhc) > > >> > > > >> > Does this fix the problem? You can always check the class of an > object > > >> > by doing something like: > > >> > > > >> > class(x) > > >> > > > >> > which should report: > > >> > > > >> > [1] "matrix" > > >> > > > >> > Hope that helps. > > >> > > > >> > Sean > > >> > > > >> > > >> -- > > >> Dr. Thomas Girke > > >> Assistant Professor of Bioinformatics > > >> Director, IIGB Bioinformatic Facility > > >> Center for Plant Cell Biology (CEPCEB) > > >> Institute for Integrative Genome Biology (IIGB) > > >> Department of Botany and Plant Sciences > > >> 1008 Noel T. Keen Hall > > >> University of California > > >> Riverside, CA 92521 > > >> > > >> E-mail: thomas.girke@ucr.edu > > >> Website: http://faculty.ucr.edu/~tgirke < > http://faculty.ucr.edu/%7Etgirke> > > >> Ph: 951-827-2469 > > >> Fax: 951-827-4437 > > >> > > > > > > [[alternative HTML version deleted]] > > > > > > _______________________________________________ > > > Bioconductor mailing list > > > Bioconductor@stat.math.ethz.ch > > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > > > > -- > > Martin Morgan > > Bioconductor / Computational Biology > > http://bioconductor.org > > > > _______________________________________________ > > Bioconductor mailing list > > Bioconductor@stat.math.ethz.ch > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > > > > -- > Thomas Girke > Assistant Professor of Bioinformatics > Director, IIGB Bioinformatic Facility > Center for Plant Cell Biology (CEPCEB) > Institute for Integrative Genome Biology (IIGB) > Department of Botany and Plant Sciences > 1008 Noel T. Keen Hall > University of California > Riverside, CA 92521 > > E-mail: thomas.girke@ucr.edu > Website: http://faculty.ucr.edu/~tgirke > Ph: 951-827-2469 > Fax: 951-827-4437 > [[alternative HTML version deleted]]