Gene hunting with forests for multigroup time course data

Ariadni Papana, Hemant Ishwaran

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Gene hunting with forests is a new method for identifying differential gene expression profiles across experimental groups using time course data. Our approach utilizes a multi-dimensional filter that captures the functional nature of the data while adjusting for additional variables that may be part of the experimental design. The filter comprises one component measuring gene profile differences, and another component measuring estimation error. Interesting genes are those having substantial gene profile differences and low estimation error. We refer to this as our Gene Hunting Principle. We illustrate this methodology using a balanced design, involving the effects of muscle group-specific gene expressions on postnatal development. We also consider a more complex experimental design focusing on the effects of aging in the human kidney.

Original languageEnglish
Pages (from-to)1146-1154
Number of pages9
JournalStatistics and Probability Letters
Volume79
Issue number9
DOIs
StatePublished - May 1 2009
Externally publishedYes

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Gene
Estimation Error
Experimental design
Filter
Balanced Design
Gene Expression Profile
Differential Expression
Kidney
Muscle
Gene Expression
Hunting
Methodology
Profile
Gene expression
Estimation error

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Statistics and Probability

Cite this

Gene hunting with forests for multigroup time course data. / Papana, Ariadni; Ishwaran, Hemant.

In: Statistics and Probability Letters, Vol. 79, No. 9, 01.05.2009, p. 1146-1154.

Research output: Contribution to journalArticle

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