Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering

By Audrey P. Gasch, Michael B. Eisen. Genome Biology, 2002

531 citations recorded by the source corpus.

BackgroundOrganisms simplify the orchestration of gene expression by coregulating genes whose products function together in the cell. Many proteins serve different roles depending on the demands of the organism, and therefore the corresponding genes are often coexpressed with different groups of genes under different situations. This poses a challenge in analyzing whole-genome expression data, because many genes will be similarly expressed to multiple, distinct groups of genes. Because most commonly used analytical methods cannot appropriately represent these relationships, the connections between conditionally coregulated genes are often missed.ResultsWe used a heuristically modified version of fuzzy k-means clustering to identify overlapping clusters of yeast genes based on published gene-expression data following the response of yeast cells to environmental changes. We have validated the method by identifying groups of functionally related and coregulated genes, and in the process we have uncovered new correlations between yeast genes and between the experimental conditions based on similarities in gene-expression patterns. To investigate the regulation of gene expression, we correlated the clusters with known transcription factor binding sites present in the genes' promoters. These results give insights into the mechanism of the regulation of gene expression in yeast cells responding to environmental changes.ConclusionsFuzzy k-means clustering is a useful analytical tool for extracting biological insights from gene-expression data. Our analysis presented here suggests that a prevalent theme in the regulation of yeast gene expression is the condition-specific coregulation of overlapping sets of genes.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/386552731ef0e8e7c5587a47b9c16d2fafdf623d

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Citations in the corpus

Defining transcription modules using large-scale gene expression data (2004, methodology): “results when applied to relatively small datasets, their utility for the analysis of large datasets appears to be limited by several drawbacks (Bittner et al., 1999; Cheng and Church, 2000; Gasch and Eisen, 2002; Getz et al., 2000; Hastie et al., 2000; Tanay et al., 2002).”
Mining coherent dense subgraphs across massive biological networks for functional discovery (2005, background): “It is known that under different conditions, one gene may serve different roles and be involved in different functional groups (Gasch and Eisen, 2002), thus identifying overlapping clusters is important in biological applications.”
Sequencing and comparison of yeast species to identify genes and regulatory elements (2003, background): “…deﬁned with a common GO classiﬁcation in SGD 43 ; 106 sets of genes whose upstream region was identiﬁed as binding a given transcription factor in genome-wide chromatin immunoprecipitation (ChIP) experiments 44 ; and 92 sets of genes showing coordinate regulation in RNA expression studies 45 .”
Recovering the number of clusters in data sets with noise features using feature rescaling factors (2015, methodology): “Due to its popularity, K-Means has been applied in a wide range of fields including bioinformatics, data mining, natural language processing [32, 29, 21, 31, 19, 14, 26].”
Computational cluster validation in post-genomic data analysis (2005, methodology): “…traditional clustering techniques such as hierarchical clustering algorithms (Eisen, 1998), k -means (Tavazoie et al ., 1999), fuzzy c -means (Gasch and Eisen, 2002), ﬁnite mixture models (Yeung et al ., 2001b) and SOMs (Tamayo et al ., 1999) remain the predominant methods in post-genomics—a…”
FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data (2007, methodology): “…variants of FCM have been proposed in the past years, including a heuristic variant that incorporates principle component analysis (PCA) and hierarchical clustering [15], and Fuzzy J-Means, that applies variable neighborhood searching to avoid cluster solution being trapped in local minima [16].”
Noise-robust Soft Clustering of Gene Expression Time-course Data (2005, background): “Gasch and Eisen demonstrated for yeast microarray data that FCM identifies clusters previously unrecognised by hierarchical and k -means clustering.(19) They also found that genes were assigned to several clusters if they underlie multiple transcription mechanisms.”