title: A self-directed method for cell-type identification and separation of gene expression microarrays.
abstract: Gene expression analysis is generally performed on heterogeneous tissue samples consisting of multiple cell types. Current methods developed to separate heterogeneous gene expression rely on prior knowledge of the cell-type composition and/or signatures--these are not available in most public datasets. We present a novel method to identify the cell-type composition, signatures and proportions per sample without need for a-priori information. The method was successfully tested on controlled and semi-controlled datasets and performed as accurately as current methods that do require additional information. As such, this method enables the analysis of cell-type specific gene expression using existing large pools of publically available microarray datasets.
authors.affiliation: Department of Cancer Immunotherapeutics and Tumor Immunology, City of Hope and Beckman Research Institute, Duarte, California, United States of America.
authors.forename: Neta S
authors.initials: NS
authors.lastname: Zuckerman
authors.forename: Yair
authors.initials: Y
authors.lastname: Noam
authors.forename: Andrea J
authors.initials: AJ
authors.lastname: Goldsmith
authors.forename: Peter P
authors.initials: PP
authors.lastname: Lee
mesh_terms: D000465:Algorithms; D000818:Animals; D001921:Brain / Q000378:metabolism; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008099:Liver / Q000378:metabolism; D009000:Monocytes / Q000378:metabolism; D009206:Myocardium / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D009928:Organ Specificity / Q000502:physiology*; D051381:Rats