Iterative signature algorithm for the analysis of large-scale gene expression data.

By Sven Bergmann, Jan Ihmels, Naama Barkai. Physical review. E, Statistical, nonlinear, and soft matter physics, 2002

482 citations recorded by the source corpus.

We present an approach for the analysis of genome-wide expression data. Our method is designed to overcome the limitations of traditional techniques, when applied to large-scale data. Rather than alloting each gene to a single cluster, we assign both genes and conditions to context-dependent and potentially overlapping transcription modules. We provide a rigorous definition of a transcription module as the object to be retrieved from the expression data. An efficient algorithm, which searches for the modules encoded in the data by iteratively refining sets of genes and conditions until they match this definition, is established. Each iteration involves a linear map, induced by the normalized expression matrix, followed by the application of a threshold function. We argue that our method is in fact a generalization of singular value decomposition, which corresponds to the special case where no threshold is applied. We show analytically that for noisy expression data our approach leads to better classification due to the implementation of the threshold. This result is confirmed by numerical analyses based on in silico expression data. We discuss briefly results obtained by applying our algorithm to expression data from the yeast Saccharomyces cerevisiae.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b09f8e37e05b1ec56e1eda03dda7fffbde0191c3

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Citations in the corpus

Defining transcription modules using large-scale gene expression data (2004, background): “In a complementary publication (Bergmann et al., 2003), we argue that the ISA scheme is in fact a generalization of SVD (Alter et al., 2000) and demonstrate analytically the central role of the threshold in distinguishing co-regulated genes in the presence of noise.”
Global analysis of protein localization in budding yeast (2003, methodology): “To assess the extent of this correlation, we made use of a study that identified 33 transcriptional ‘modules’ of genes with marked co-regulation based on analysis of over 1,000 microarray data sets reflecting the results of different mutant strain backgrounds or environmental perturbation”
Global analysis of protein expression in yeast (2003, background): “A recent analysis of mRNA expression profiles from ∼1,000 published microarray experiments allowed for the identification of 33 ‘modules’ of transcriptionally co-regulated gene”
On the Complexity of Nonnegative Matrix Factorization (2007, background): “Another class of heuristics is based on greedy rank-one downdating [1, 2, 3].”