Transcriptome network component analysis with limited microarray data

Transcriptome network component analysis with limited microarray data

UNLABELLED Network component analysis (NCA) is a method to deduce transcription factor (TF) activities and TF-gene regulation control strengths from gene expression data and a TF-gene binding connectivity network. Previously, this method could analyze a maximum number of regulators equal to the total sample size because of the identifiability limit in data decomposition. As such, the total numb...

Fast network component analysis (FastNCA) for gene regulatory network reconstruction from microarray data

MOTIVATION Recently developed network component analysis (NCA) approach is promising for gene regulatory network reconstruction from microarray data. The existing NCA algorithm is an iterative method which has two potential limitations: computational instability and multiple local solutions. The subsequently developed NCA-r algorithm with Tikhonov regularization can help solve the first issue b...

cDNA Microarray Data Normalization

Robust Processing of Microarray Data by Independent Component Analysis

Microarray Data Processing is becoming a field of important activity for Signal Processing and Pattern Recognition areas, as the extraction and mining of meaningful data from large groupings of microarray patterns is of vital importance in Medicine, Genomics, Proteomics, Pharmacology, etc. In this paper emphasis is placed on studying and cataloging the nature of possible sources of corruption o...

Discovering biological processes from microarray data using independent component analysis

We propose a hypothesis-free methodology for discovering genome-wide expression patterns specific to underlying biological processes from DNA microarray expression data. We apply linear and nonlinear independent component analysis (ICA) as a tool for decomposing microarray data into statistically independent components. Each component represents a gene expression pattern of a putative underlyin...