search for: dsohal

Hi all, Here's an interesting (for me, at least!) problem I came across: I have a correlation matrix, let's say with 6 variables, A to F, as column headings and the same 6 as row headings. The matrix is filled with correlation coefficients. Therefore, the diagonal is all 1's, and each of the two triangles formed by the diagonal has the same 15 correlation coefficients. I need to

Hi, I am new to R...a recent convert from SAS. I have a dataset that looks like this: SEQ A1 A2 A 532.5 554.5 B 25.5 35.5 C 265.2 522.2 D 245.55 521.56 E 546.52 141.52 F 243.25 32.56 G 452.55 635.56 H 15.14 16.54 I 543.4 646.56 J 54.4 654.5 K 646.5 64.54 L 645.4 614.46 M 646.54 634.46 I want to make a histogram

Hi, I'm new to R; this is my second email to this forum. I have a dataset that looks like this: Gene A B C D A 511 6116 1515 1515 B 164 115 1515 494 C 21646 D

Hi, I am making a dendrogram with 180 terminal values. Whether I keep it horizontal or vertical, it gives a 'squished' graph that refuses to be stretched beyond the window size and I cannot read the labels. (I'm using hclust and then plot to make the tree.) Is there a way to stretch the graph in R, or by exporting it somehow, so that I can read the 180 values and see what the results

Hi, I'm using R on Windows and upgraded the computer memory to 4GB, as R was telling me that it is out of memory (for making heatmaps). It still says that the maximum memory is 1024Mb, even if I increase it using memory.limit and memory.size. Is there a way to permanently increase R's memory quota to 4GB? Please help. Many thanks, -DS. [[alternative HTML version deleted]]

Hi all, I'm using Pelora (supclust) package for supervised clustering of a microarray dataset. The original package does not delete the genes that are present in the earlier clusters for further cluster identification. This leaves us with mostly redundant clusters. I played around with the source code but couldn't get around it. Is there a way to fix it so that the genes already identified

Hello all, I am doing SAM and the median of positive genes in the permutated sets is = false positives, and the "parent set" gives true positives. FDR = FP/TP * 100. My FDR comes to greater than 100. Is that possible? Please help! Thanks, -D. [[alternative HTML version deleted]]