SubPatCNV (Subspace Pattern-ming of Copy Number Variations) is a tool for mining CNV subspace patterns, which is able to identify all aberrant CNV regions specific to arbitrary patient subsets larger than a support threshold. SubPatCNV is an approximate association pattern mining algorithm under a spatial constraint on the positional CNV probe features. In the experiments on a large-scale bladder cancer dataset, SubPatCNV discovered many large aberrant CNV events in patient subgroups and also reported CNV regions highly specific to clinical variables such as tumor grade or stage and enriched with more known oncogenes compared with other existing CNV discovery methods.
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BMC Bioinformatics. 2015 Jan 16;16(1):16. [Epub ahead of print]
SubPatCNV: approximate subspace pattern mining for mapping copy-number variations.
Johnson N, Zhang H, Fang G, Kumar V, Kuang R.