FastMix: A Versatile Multi-Omics Data Integration Pipeline for Cell Type-Specific Biomarker Inference

Abstract

AbstractWe developed a novel analytic pipeline - FastMix - to integrate flow cytometry, bulk transcriptomics, and clinical covariates for statistical inference of cell type-specific gene expression signatures. FastMix addresses the “large p, small n” problem via a carefully designed linear mixed effects model (LMER), which is applicable for both cross-sectional and longitudinal studies. With a novel moment-based estimator, FastMix runs and converges much faster than competing methods for big data analytics. The pipeline also includes a cutting-edge flow cytometry data analysis method for identifying cell population proportions. Simulation studies showed that FastMix produced smaller type I/II errors with more accurate parameter estimation than competing methods. When applied to real transcriptomics and flow cytometry data in two vaccine studies, FastMix-identified cell type-specific signatures were largely consistent with those obtained from the single cell RNA-seq data, with some unique interesting findings.