ROBNCA: Robust network component analysis for recovering transcription factor activities

Amina Noor, Aitzaz Ahmad, Erchin Serpedin, Mohamed Nounou, Hazem Nounou

Research output: Contribution to journalArticle

12 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)2410-2418
Number of pages9
JournalBioinformatics
Volume29
Issue number19
DOIs
Publication statusPublished - 2013

Fingerprint

Network components
Transcription factors
Transcription Factor
Transcription Factors
Algorithm Analysis
Outlier
Microarray Data
Microarrays
Gene expression
Gene Regulation
Saccharomyces Cerevisiae
Prior Information
Synthetic Data
Gene Expression Data
Closed-form Solution
Inconsistency
Escherichia Coli
Iterative Algorithm
Yeast
Escherichia coli

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Computational Mathematics
  • Statistics and Probability
  • Medicine(all)

Cite this

ROBNCA : Robust network component analysis for recovering transcription factor activities. / Noor, Amina; Ahmad, Aitzaz; Serpedin, Erchin; Nounou, Mohamed; Nounou, Hazem.

In: Bioinformatics, Vol. 29, No. 19, 2013, p. 2410-2418.

Research output: Contribution to journalArticle

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