Remark: This is the reply to a question that Martin had sent to the bioconductor mailing list with CC to myself. Because of a large attachment (example data) however that message had not been distributed on the list. In the following, first the question, then my answer. - Wolfgang Martin Kerick wrote: > Dear Wolfgang, > > I have a question concerning your vsn package. I encounter the following > error when applying vsn on one array: > > vsn: 27648 x 2 matrix (1 stratum). Please wait for 10 dots: Error: L-BFGS-B > needs finite values of fn > > I have read the Bioconductor archives and found the following statements: > > >>does your data matrix contain Inf (infinity) or an excessive number of 0s > > (e.g. through "flooring" the negative values?). > >>If there are infinities in the data, this will probably also lead to an > > infinite likelihood, which could explain your error message. > >>If there are other singularities (e.g. if a whole column of the data matrix > > has the same value), this may also lead to infinite values in the likelihood > calculations. > > It seems to me, that none of the above applies to my data. I had some values > occurring multiple (2-6) times in one column and corrected for that, but the > error remained. Since I don't use any background correction I assume that > negative or "floored" values are probably not the problem. > I am using vsn 1.4.11 and arrayMagic 1.3.7 on R 1.9.0 > I attached the data file leading to the crash. > Any help would be greatly appreciated, > Kind regards, > Martin Hi Martin, this is indeed one of the rare cases where the iterative algorithm in vsn does not converge with default start parameters. By specifying different start parameters (see the code example below), it does converge, apparently to a reasonable result. In your data, the F635 and F532 intensities from the two color channels are quite unbalanced (both w.r.t. background and slope) - my impression is that you would do much better if you used background subtraction (see code example). Also, in that case vsn does indeed work with default settings. If you're worried about too much variability in the "background" intensities, some spatial smoothing might help. Running vsn involves involves the maximization of a likelihood function that is not parabolic, but usually concave. In rare cases, the numerical optimizer runs into nirwana before finding the optimum. In these cases, choosing a different start value may help. In the case of the example data that you provided, one might argue that (without background subtraction) it also involves a quality problem. Best regards Wolfgang ------------------------------------- Wolfgang Huber Division of Molecular Genome Analysis German Cancer Research Center Heidelberg, Germany Phone: +49 6221 424709 Fax: +49 6221 42524709 Http: www.dkfz.de/abt0840/whuber ------------------------------------- library(vsn) dat <- read.table("test1", header=TRUE, sep="\t") print(dim(dat)) par(mfrow=c(2,2)) maplot <- function(x, ...) { stopifnot(is.matrix(x), ncol(x)==2) plot(rowMeans(x), x[,2]-x[,1], pch=".", xlab="A", ylab="M", ...) abline(h=0, col="red") } y <- as.matrix(dat[, c("F532", "F635")]) ## Try this! ## y <- as.matrix(dat[, c("F532", "F635")]-dat[, c("B532", "B635")]) plot(y,pch=".",xlim=c(7,20),ylim=c(7,20)) abline(a=0,b=1,col="red") maplot(log(y)) pstart <- array(c(0, 0, 1, 1), dim=c(1,2,2)) ## ny <- vsn(y) ## will produce an error ny <- vsn(y, pstart=pstart) cols <- c("red", "black")[1+preproc(description(ny))$vsnTrimSelection] maplot(exprs(ny), col=cols)