Format

Send to

Choose Destination
Methods Mol Biol. 2014;1101:87-112. doi: 10.1007/978-1-62703-721-1_6.

Pattern identification in time-course gene expression data with the CoGAPS matrix factorization.

Author information

1
Oncology Biostatistics and Bioinformatics, Johns Hopkins University, Baltimore, MD, USA.

Abstract

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.

PMID:
24233779
DOI:
10.1007/978-1-62703-721-1_6
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Springer
Loading ...
Support Center