Pattern identification in time-course gene expression data with the CoGAPS matrix factorization.
Pattern identification in time-course gene expression data with the CoGAPS matrix factorization.
复制标题
使用 CoGAPS 矩阵分解对时程基因表达数据进行模式识别。
DOI：
10.1007/978-1-62703-721-1_6
复制
发表时间：
2014
期刊：
影响因子：
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通讯作者：
Colantuoni,Carlo
中科院分区：
文献类型：
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作者：
Fertig,ElanaJ;Stein-O'Brien,Genevieve;Jaffe,Andrew;Colantuoni,Carlo
Patterns in time-course gene expression data can represent the biological processes that are active over the measured time period. However, the orthogonality constraint in standard pattern-finding algorithms, including notably principal components analysis (PCA), confounds expression changes resulting from simultaneous, non-orthogonal biological processes. Previously, we have shown that Markov chain Monte Carlo nonnegative matrix factorization algorithms are particularly adept at distinguishing such concurrent patterns. One such matrix factorization is implemented in the software package CoGAPS. We describe the application of this software and several technical considerations for identification of age-related patterns in a public, prefrontal cortex gene expression dataset.
