Hi all, I have some illumina SNP arrays data from a prostate cancer study. Many of the tumor samples were contaminated with normal samples such that two types of data profiles occurred: 1. LRR are around 0 (2 copies) but BAF has 4 clusters (3 copies) 2. LRR are below 0 (loss) but BAF has 3 clusters (2 copies) In either case, cnvPartition doesn't estimate the copy numbers correctly. It called a gain (3 copies) in type 1, and didn't call any loss in type 2. My question is: Among all the algorithms available there, which one would be more suitable for this situation? Thanks! Yu Chuan

Hi all, I have some illumina SNP arrays data from a prostate cancer study. Many of the tumor samples were contaminated with normal samples such that two types of data profiles occurred: 1. LRR are around 0 (2 copies) but BAF has 4 clusters (3 copies) 2. LRR are below 0 (loss) but BAF has 3 clusters (2 copies) In either case, cnvPartition doesn't estimate the copy numbers correctly. It called a gain (3 copies) in type 1, and didn't call any loss in type 2. My question is: Among all the algorithms available there, which one would be more suitable for this situation? Thanks! Yu Chuan

Hi all, I have some illumina SNP arrays data from a prostate cancer study. Many of the tumor samples were contaminated with normal samples such that two types of data profiles occurred: 1. LRR are around 0 (2 copies) but BAF has 4 clusters (3 copies) 2. LRR are below 0 (loss) but BAF has 3 clusters (2 copies) In either case, cnvPartition doesn't estimate the copy numbers correctly. It called a gain (3 copies) in type 1, and didn't call any loss in type 2. My question is: Among all the algorithms available there, which one would be more suitable for this situation? Thanks! Yu Chuan