A1316
Title: Generalized estimating equation for cell-cell correlation in single-cell RNA seq data
Authors: Xinlian Zhang - University of California, San Diego (United States) [presenting]
Toni Gui - University of Florida (United States)
Tuo Lin - University of Florida (United States)
Xin Tu - University of California San Diego (United States)
Abstract: For analyzing the single-cell RNA sequencing data, it is believed that cells from the same individual share common genetic and environmental backgrounds and are not statistically independent. Many popular methods, such as the default Wilcox test in the FindMarker function in the Seurat package, do not address this issue, leading to potentially biased inference. There are more recent works arguing for the generalized linear mixed models with a random intercept for the individual, to properly account for the correlation among measures from cells within an individual. However, the traditional mixed effect model has strong assumptions requiring the same and strictly positive correlation across all cells in one individual. It is demonstrated that this can be rather restrictive for real data seen, given the strong heterogeneous nature of all cells. In the case of a violated positive correlation assumption, the classical random effects model demonstrates consistent biased inference. The aim is to propose the usage of the generalized estimating equation-based semi-parametric approach for this issue and demonstrate its robust and efficient performance in both simulation and real data that focuses on revealing common and unique gene expression signatures in primary CD4+ T cells latently infected with HIV under different conditions.
