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Abstract
A complex tissue contains a variety of cells with distinct molecular signatures. Single-cell RNA sequencing has characterized the transcriptomes of different cell types and enables researchers to discover the underlying mechanisms of cellular heterogeneity. A critical task in single-cell transcriptome studies is to uncover transcriptional differences among specific cell types. However, the intercellular transcriptional variation is usually confounded with high level of technical noise, which masks the important biological signals. Here, we propose a new computational method DiffGE for differential analysis, adopting network entropy to measure the expression dynamics of gene groups among different cell types and to identify the highly differential gene groups. To evaluate the effectiveness of our proposed method, DiffGE is applied to three independent single-cell RNA-seq datasets and to identify the highly dynamic gene groups that exhibit distinctive expression patterns in different cell types. We compare the results of our method with those of three widely applied algorithms. Further, the gene function analysis indicates that these detected differential gene groups are significantly related to cellular regulation processes. The results demonstrate the power of our method in evaluating the transcriptional dynamics and identifying highly differential gene groups among different cell types.
Highlights
A complex tissue contains a variety of heterogeneous cell types, each with its own distinct features and function (Guo et al, 2017)
Limma is based on linear modeling and has shown good performance in previous comparison studies (Ritchie et al, 2015)
We propose DiffGE, a new computational method for differential analysis from singlecell transcriptome
Summary
A complex tissue contains a variety of heterogeneous cell types, each with its own distinct features and function (Guo et al, 2017). Single-cell RNA sequencing (scRNA-seq) has allowed researchers to quantify gene expression at a cellular resolution (Jaitin et al, 2014; Wu et al, 2014). Given the scRNA-seq data of a population of cells, one of the fundamental data analysis tasks involves characterizing cellular heterogeneity and quantifying such substantial variability via differential analysis (Stegle et al, 2015). The objective of differential analysis is to searching for those genes exhibiting significant differences in abundance associated with different cell types (Jaakkola et al, 2017). This is a key step for downstream analysis, such as identifying
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