title: CellCODE: a robust latent variable approach to differential expression analysis for heterogeneous cell populations.
abstract: BACKGROUND
  Identifying alterations in gene expression associated with different clinical states is important for the study of human biology. However, clinical samples used in gene expression studies are often derived from heterogeneous mixtures with variable cell-type composition, complicating statistical analysis. Considerable effort has been devoted to modeling sample heterogeneity, and presently, there are many methods that can estimate cell proportions or pure cell-type expression from mixture data. However, there is no method that comprehensively addresses mixture analysis in the context of differential expression without relying on additional proportion information, which can be inaccurate and is frequently unavailable.
  
  
  RESULTS
  In this study, we consider a clinically relevant situation where neither accurate proportion estimates nor pure cell expression is of direct interest, but where we are rather interested in detecting and interpreting relevant differential expression in mixture samples. We develop a method, Cell-type COmputational Differential Estimation (CellCODE), that addresses the specific statistical question directly, without requiring a physical model for mixture components. Our approach is based on latent variable analysis and is computationally transparent; it requires no additional experimental data, yet outperforms existing methods that use independent proportion measurements. CellCODE has few parameters that are robust and easy to interpret. The method can be used to track changes in proportion, improve power to detect differential expression and assign the differentially expressed genes to the correct cell type.
authors.affiliation: Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15217, USA and Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
authors.forename: Maria
authors.initials: M
authors.lastname: Chikina
authors.affiliation: Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15217, USA and Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
authors.forename: Elena
authors.initials: E
authors.lastname: Zaslavsky
authors.affiliation: Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15217, USA and Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
authors.forename: Stuart C
authors.initials: SC
authors.lastname: Sealfon
mesh_terms: D001402:B-Lymphocytes / Q000187:drug effects / Q000378:metabolism / Q000821:virology; D019070:Cell Lineage / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D005786:Gene Expression Regulation; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D007252:Influenza Vaccines / Q000008:administration & dosage; D015233:Models, Statistical; D009504:Neutrophils / Q000166:cytology / Q000276:immunology / Q000378:metabolism; D017423:Sequence Analysis, RNA / Q000379:methods; D012984:Software; D013601:T-Lymphocytes / Q000187:drug effects / Q000378:metabolism / Q000821:virology; D013997:Time Factors
chemical_list: D007252:Influenza Vaccines