title: Spearman correlation identifies statistically significant gene expression clusters in spinal cord development and injury.
abstract: An important problem in the analysis of large-scale gene expression data is the validation of gene expression clusters. By examining the temporal expression patterns of 74 genes expressed in rat spinal cord under three different experimental conditions, we have found evidence that some genes cluster together under multiple conditions. Using RT-PCR data from spinal cord development and two sets of microarray data from spinal injury, we applied Spearman correlation to identify clusters and to assign P values to pairs of genes with highly similar temporal expression patterns. We found that 15% of genes occurred in statistically significant pairs in all three experimental conditions, providing both statistical and experimental support for the idea that genes that cluster together are co-regulated. In addition, we demonstrated that DNA microarray and RT-PCR data are comparable, and can be combined to confirm gene expression relationships.
authors.affiliation: Molecular Mining Corporation, Kingston, Ontario, Canada.
authors.forename: Max
authors.initials: M
authors.lastname: Kotlyar
authors.forename: Stefanie
authors.initials: S
authors.lastname: Fuhrman
authors.forename: Alan
authors.initials: A
authors.lastname: Ableson
authors.forename: Roland
authors.initials: R
authors.lastname: Somogyi
mesh_terms: D000375:Aging / Q000502:physiology*; D000818:Animals; D000831:Animals, Newborn / Q000502:physiology*; D005314:Embryonic and Fetal Development / Q000502:physiology; D005786:Gene Expression Regulation; D018507:Gene Expression Regulation, Developmental; D020411:Oligonucleotide Array Sequence Analysis; D051381:Rats; D020133:Reverse Transcriptase Polymerase Chain Reaction; D013116:Spinal Cord / Q000196:embryology* / Q000254:growth & development*; D013119:Spinal Cord Injuries / Q000235:genetics*