Analytical approaches to RNA profiling data for the identification of genes enriched in specific cells

• Published in: Nucleic acids research Vol. 38; no. 13; pp. 4218 - 4230
• Main Authors: Dougherty, Joseph D, Schmidt, Eric F, Nakajima, Miho, Heintz, Nathaniel
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01-07-2010
• Subjects: Algorithms; Animals; Brain - cytology; Brain - metabolism; Computational Biology; Data Interpretation, Statistical; Gene Expression Profiling - methods; Mice; Oligonucleotide Array Sequence Analysis; RNA, Messenger - analysis; RNA, Messenger - isolation & purification; RNA, Messenger - metabolism
• ISSN: 0305-1048; 1362-4962
• DOI: 10.1093/nar/gkq130

We have recently developed a novel method for the affinity purification of the complete suite of translating mRNA from genetically labeled cell populations. This method permits comprehensive quantitative comparisons of the genes employed by each specific cell type. We provide a detailed description of tools for analysis of data generated with this and related methodologies. An essential question that arises from these data is how to identify those genes that are enriched in each cell type relative to all others. Genes relatively specifically employed by a cell type may contribute to the unique functions of that cell, and thus may become useful targets for development of pharmacological tools for cell-specific manipulations. We describe here a novel statistic, the specificity index, which can be used for comparative quantitative analysis to identify genes enriched in specific cell populations across a large number of profiles. This measure correctly predicts in situ hybridization patterns for many cell types. We apply this measure to a large survey of CNS cell-specific microarray data to identify those genes that are significantly enriched in each population Data and algorithms are available online (www.bactrap.org).