Cell Subset Prediction for Blood Genomic Studies

Background: Genome-wide transcriptional profiling of patient blood samples offers a powerful tool to investigate underlying disease mechanisms and personalized treatment decisions. Most studies are based on analysis of total peripheral blood mononuclear cells (PBMCs), a mixed population. In this case, accuracy is inherently limited since cell subset-specific differential expression of gene signatures will be diluted by RNA from other cells. While using specific PBMC subsets for transcriptional profiling would improve our ability to extract knowledge from these data, it is rarely obvious which cell subset(s) will be the most informative. Results: We have developed a computational method (Subset Prediction from Enrichment Correlation, SPEC) to predict the cellular source for a pre-defined list of genes (i.e. a gene signature) using only data from total PBMCs. SPEC does not rely on the occurrence of cell subset-specific genes in the signature, but rather takes advantage of correlations w...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Christopher R. Bolen, Mohamed Uduman, Steven H. Kleinstein
• Keywords: Biometrics, BMCBI 2011, Gene Expression Data, Gene Expression Profiles, Peripheral Blood Mononuclear Cells