title: Improving sensitivity of linear regression-based cell type-specific differential expression deconvolution with per-gene vs. global significance threshold.
abstract: BACKGROUND
  The goal of many human disease-oriented studies is to detect molecular mechanisms different between healthy controls and patients. Yet, commonly used gene expression measurements from blood samples suffer from variability of cell composition. This variability hinders the detection of differentially expressed genes and is often ignored. Combined with cell counts, heterogeneous gene expression may provide deeper insights into the gene expression differences on the cell type-specific level. Published computational methods use linear regression to estimate cell type-specific differential expression, and a global cutoff to judge significance, such as False Discovery Rate (FDR). Yet, they do not consider many artifacts hidden in high-dimensional gene expression data that may negatively affect linear regression. In this paper we quantify the parameter space affecting the performance of linear regression (sensitivity of cell type-specific differential expression detection) on a per-gene basis.
  
  
  RESULTS
  We evaluated the effect of sample sizes, cell type-specific proportion variability, and mean squared error on sensitivity of cell type-specific differential expression detection using linear regression. Each parameter affected variability of cell type-specific expression estimates and, subsequently, the sensitivity of differential expression detection. We provide the R package, LRCDE, which performs linear regression-based cell type-specific differential expression (deconvolution) detection on a gene-by-gene basis. Accounting for variability around cell type-specific gene expression estimates, it computes per-gene t-statistics of differential detection, p-values, t-statistic-based sensitivity, group-specific mean squared error, and several gene-specific diagnostic metrics.
  
  
  CONCLUSIONS
  The sensitivity of linear regression-based cell type-specific differential expression detection differed for each gene as a function of mean squared error, per group sample sizes, and variability of the proportions of target cell (cell type being analyzed). We demonstrate that LRCDE, which uses Welch's t-test to compare per-gene cell type-specific gene expression estimates, is more sensitive in detecting cell type-specific differential expression at α < 0.05 missed by the global false discovery rate threshold FDR < 0.3.
authors: Department of Biostatistics, Virginia Commonwealth University, School of Medicine, PO Box 980032, Richmond, VA, 23298, USA.
authors: Edmund R
authors: ER
authors: Glass
authors: Department of Biostatistics, Virginia Commonwealth University, School of Medicine, PO Box 980032, Richmond, VA, 23298, USA. mikhail.dozmorov@vcuhealth.org.
authors: Mikhail G
authors: MG
authors: Dozmorov
mesh_terms: D000465:Algorithms; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D016014:Linear Models; D008957:Models, Genetic; D018401:Sample Size; D012680:Sensitivity and Specificity
keywords: Cell type-specific; Deconvolution; Differential expression; Linear regression; Sensitivity analysis