Mining the Gene Expression Matrix: Inferring Gene Relationships from Large Scale Gene Expression Data

Abstract

In order to infer the logical principles underlying biological development and phenotypic change, it is necessary to determine large-scale temporal gene expression patters. To quote Eric Lander, “The mRNA levels sensitively reflect the state of the cell, perhaps uniquely defining cell types, stages, and responses. To decipher the logic of gene regulation, we should aim to be able to monitor the expression level of all genes simultaneously…” (Lander, 1996). One method for accomplishing this involves the use of reverse transcription polymerase chain reaction (RT-PCR) to assay the expression levels of large numbers of genes in a tissue at different time points during development, with a standard protocol. The relative amounts of mRNA produced at these time points provide a gene expression time series for each gene.