Sai Moturu

Introduction Current approaches to microarray data analysis –Analysis of experimental data followed by a posterior process where biological information is incorporated to make inferences Integrative analysis technique in this paper –Integrate gene annotation with expression data to discover intrinsic associations among both data sources based on co-occurrence patterns

Methods and Data –Association Rules Discovery –Gene expression data –Gene annotation: Gene ontology categories, metabolic pathways and transcriptional regulators –Applied to two previously studied experiments

Association Rules Discovery –Antecedent -> Consequent X -> Y –Measures of Quality Support: P(XυY) Confidence: P(Y|X) = P(XυY)/P(Y) Improvement: Confidence/Consequent = P(XυY)/(P(X)*P(Y))

Association Rules Discovery –Itemsets Genes and the set of experiments in which gene is over or underexpressed Gene characteristics –Constraint Antecedent needs to be gene annotation –Expression Thresholds Genes with log expression values >1 are overexpressed and <-1 are underexpressed (two fold)

Mining Association Rules –The association rules that we are interested in have low support values and high confidence values –A variant of the apriori algorithm is used that has helped previously with mining low support-high confidence biologically significant patterns

Filtering –Major drawback with association rules is the number of rules generated is huge –Also there is redundancy –This is taken care of with two filters Redundant filter Single antecedent filter

Diauxic shift dataset –Gene expression accompanying the metabolic shift from fermentation to respiration that occurs when fermenting yeast cells –Expression levels recorded at 7 time points –External information Metabolic pathways Transcriptional regulators

Results –Association rules among metabolic pathways and expression patterns 1126 out of over 6000 genes were annotated with at least one pathway Association rules with minimum support of 5, minimum confidence of 40% and minimum improvement of 1 Redundant and single antecedent filters applied 21 association rules

Results –Association rules among transcriptional regulators and expression patterns 3490 genes were annotated with at least one regulator Association rules with minimum support of 5, minimum confidence of 80% and minimum improvement of 1 Redundant filter applied 28 association rules

Results –Association rules among transcriptional regulators, metabolic pathways and expression patterns 3882 genes Association rules with minimum support of 5, minimum confidence of 80% and minimum improvement of 1 Redundant filter applied 37 association rules

Results

Serum stimulation dataset –Gene expression program of human fibroblast after serum exposure –External information Gene ontology terms

Results –Association rules among biological process annotation and expression patterns 4092 genes of over 8000 Support of 4, min confidence of 10% and min improvement of 1 Single antecedent and redundant filters applied 12 associations

Results –Association rules among terms from all GO categories 4630 genes of over 8000 Support of 4, min confidence of 10% and min improvement of 1 Redundant filter applied 31 associations

Results

Conclusions –Some of the biological implications matched the ones found experimentally –The others could be explored further –Integrative data analysis is very useful for meaningful discoveries using gene expression data