Correlation analysis reveals the emergence of coherence in the gene expression dynamics following system perturbation

Abstract

Time course gene expression experiments are a popular means to infer co-expression. Many methods have been proposed to cluster genes or to build networks based on similarity measures of their expression dynamics. In this paper we apply a correlation based approach to network reconstruction to three datasets of time series gene expression following system perturbation: 1) Conditional, Tamoxifen dependent, activation of the cMyc proto-oncogene in rat fibroblast; 2) Genomic response to nutrition changes in D. melanogaster; 3) Patterns of gene activity as a consequence of ageing occurring over a life-span time series (25y–90y) sampled from T-cells of human donors.We show that the three datasets undergo similar transitions from an "uncorrelated" regime to a positively or negatively correlated one that is symptomatic of a shift from a "ground" or "basal" state to a "polarized" state.In addition, we show that a similar transition is conserved at the pathway level, and that this information can be used for the construction of "meta-networks" where it is possible to assess new relations among functionally distant sets of molecular functions.

Exon expression profiling reveals stimulus-mediated exon use in neural cells

“…In order to characterize the transcripts that are regulated by KCl, we identified groups of transcripts that exhibit similar patterns of expression between stimuli and across time points. Using hierarchical clustering with a correlation analysis on the expression of constituents of the KCl transcript dataset [ 35 ], we identified specific clusters of genes that respond to KCl and TPG in similar manners (Figure 3a ). Each plot in Figure 3a represents the averaged expression behavior of the transcripts in the indicated cluster.…”

“…We applied a correlation analysis [ 35 ] to each list in order to separate both transcripts and exons into groups with similar expression profiles. Hierarchical clustering with a correlation-based distance was used to identify the six top classes of expression profiles.…”

Identification of modules in Aspergillus niger by gene co-expression network analysis

“…Previous gene co-expression network studies used a subset of genes already known (Bergmann et al, 2004;Herrgård et al, 2003) or suspected (Neretti et al, 2007) to be involved in specific biological processes, or discussed network characteristics without zooming into biological details (Jordan et al, 2008;van Noort et al, 2004). In this study, however, an approach similar to the approach of Daub and Sonnhammer (2008) was followed.…”

“…While this approach provides an understanding of the network at a higher abstraction level, such knowledge cannot be converted easily into understanding the actual underlying biological processes. The second strategy is to investigate only a subset of genes that is relevant for a certain research interest, e.g., see Bergmann et al (2004), Lee et al (2004), Neretti et al (2007). For example, Bergmann and coworkers selected genes participating in eight well-defined S. cerevisiae biological processes, and examined co-expression of their orthologous genes in five other organisms (Bergmann et al, 2004).…”

Joint analysis of transcriptional and post- transcriptional brain tumor data: searching for emergent properties of cellular systems

“…Only recently both types of variables were analyzed simultaneously [ 6 - 8 ], while, typically, both types of data are analyzed in search for (i) molecules sharing similarity, using simply the expression available at the time ( unsupervised approaches, [ 9 ]) e.g. clustering [ 10 , 11 ] and association networks [ 12 - 14 ] or (ii) similarity with -or dependency from- other types of traits, providing for example clinical classes or other non-molecular information on the samples ( supervised approaches, [ 9 ]) i.e. Significant Analysis of Microarray (SAM [ 15 ]), Gene Set Enrichment Analysis (GSEA [ 16 ]).…”