Format

Send to:

Choose Destination
See comment in PubMed Commons below
Bioinformatics. 2013 Oct 1;29(19):2410-8. doi: 10.1093/bioinformatics/btt433. Epub 2013 Aug 11.

ROBNCA: robust network component analysis for recovering transcription factor activities.

Author information

  • 1Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA, Corporate Research and Development, Qualcomm Technologies Inc., San Diego, CA 92121, USA, Department of Chemical Engineering and Department of Electrical Engineering, Texas A&M University at Qatar, Doha Qatar.

Abstract

MOTIVATION:

Network component analysis (NCA) is an efficient method of reconstructing the transcription factor activity (TFA), which makes use of the gene expression data and prior information available about transcription factor (TF)-gene regulations. Most of the contemporary algorithms either exhibit the drawback of inconsistency and poor reliability, or suffer from prohibitive computational complexity. In addition, the existing algorithms do not possess the ability to counteract the presence of outliers in the microarray data. Hence, robust and computationally efficient algorithms are needed to enable practical applications.

RESULTS:

We propose ROBust Network Component Analysis (ROBNCA), a novel iterative algorithm that explicitly models the possible outliers in the microarray data. An attractive feature of the ROBNCA algorithm is the derivation of a closed form solution for estimating the connectivity matrix, which was not available in prior contributions. The ROBNCA algorithm is compared with FastNCA and the non-iterative NCA (NI-NCA). ROBNCA estimates the TF activity profiles as well as the TF-gene control strength matrix with a much higher degree of accuracy than FastNCA and NI-NCA, irrespective of varying noise, correlation and/or amount of outliers in case of synthetic data. The ROBNCA algorithm is also tested on Saccharomyces cerevisiae data and Escherichia coli data, and it is observed to outperform the existing algorithms. The run time of the ROBNCA algorithm is comparable with that of FastNCA, and is hundreds of times faster than NI-NCA.

AVAILABILITY:

The ROBNCA software is available at http://people.tamu.edu/∼amina/ROBNCA

PMID:
23940252
[PubMed - indexed for MEDLINE]
Free full text
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for HighWire
    Loading ...
    Write to the Help Desk