GEVM – Detection of high variability in gene expression from single-cell RNA-seq profiling

The advancement of the next-generation sequencing technology enables mapping gene expression at the single-cell level, capable of tracking cell heterogeneity and determination of cell subpopulations using single-cell RNA sequencing (scRNA-seq). Unlike the objectives of conventional RNA-seq where differential expression analysis is the integral component, the most important goal of scRNA-seq is to identify highly variable genes across a population of cells, to account for the discrete nature of single-cell gene expression and uniqueness of sequencing library preparation protocol for single-cell sequencing. However, there is lack of generic expression variation model for different scRNA-seq data sets. Hence, the objective of this study is to develop a gene expression variation model (GEVM), utilizing the relationship between coefficient of variation (CV) and average expression level to address the over-dispersion of single-cell data, and its corresponding statistical significance to quantify the variably expressed genes (VEGs).

Researchers from the University of Texas Health Science Center at San Antonio have built a simulation framework that generated scRNA-seq data with different number of cells, model parameters, and variation levels. They  implemented their GEVM and demonstrated the robustness by using a set of simulated scRNA-seq data under different conditions. They evaluated the regression robustness using root-mean-square error (RMSE) and assessed the parameter estimation process by varying initial model parameters that deviated from homogeneous cell population. They also applied the GEVM on real scRNA-seq data to test the performance under distinct cases.


Workflow of identifying significantly variably expressed genes and the following analyses for single-cell RNA-seq data

Availability – The R scripts of the algorithm and the UMI data for GSE65525 will be available from GitHub,

Chen HH, Jin Y, Huang Y, Chen Y. (2016) Detection of high variability in gene expression from single-cell RNA-seq profiling. BMC Genomics 17(Suppl 7):508. [article]

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