Optimal deconvolution of transcriptional profiling data using quadratic programming with application to complex clinical blood samples.

ABSTRACT: Large-scale molecular profiling technologies have assisted the identification of disease biomarkers and facilitated the basic understanding of cellular processes. However, samples collected from human subjects in clinical trials possess a level of complexity, arising from multiple cell types, that can obfuscate the analysis of data derived from them. Failure to identify, quantify, and incorporate sources of heterogeneity into an analysis can have widespread and detrimental effects on subsequent statistical studies.We describe an approach that builds upon a linear latent variable model, in which expression levels from mixed cell populations are modeled as the weighted average of expression from different cell types. We solve these equations using quadratic programming, which efficiently ide

SUBMITTER: Gong T 

PROVIDER: S-EPMC3217948 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature