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Zero-inflated quantile rank-score based test (ZIQRank) with application to scRNA-seq differential gene expression analysis. (English) Zbl 1498.62233

Summary: Differential gene expression analysis based on scRNA-seq data is challenging due to two unique characteristics of scRNA-seq data. First, multimodality and other heterogeneity of the gene expression among different cell conditions lead to divergences in the tail events or crossings of the expression distributions. Second, scRNA-seq data generally have a considerable fraction of dropout events, causing zero inflation in the expression. To account for the first characteristic, existing parametric approaches targeting the mean difference in gene expression are limited, while quantile regression that examines various locations in the distribution will improve the power. However, the second characteristic, zero inflation, makes the traditional quantile regression invalid and underpowered. We propose a quantile-based test that handles the two characteristics, multimodality and zero inflation, simultaneously. The proposed quantile rank-score based test for differential distribution detection (ZIQRank) is derived under a two-part quantile regression model for zero-inflated outcomes. It comprises a test in logistic modeling for the zero counts and a collection of rank-score tests adjusting for zero inflation at multiple prespecified quantiles of the positive part. The testing decision is based on an aggregate result by combining the marginal \(p\)-values by MinP or Cauchy procedure. The proposed test is asymptotically justified and evaluated with simulation studies. It shows a higher precision-recall AUC in detecting true differentially expressed genes (DEGs) than the existing methods. We apply the ZIQRank test to a TPM scRNA-seq data on human glioblastoma tumors and exclusively identify a group of DEGs between neoplastic and nonneoplastic cells, which are heterogeneous and have been proved to be associated with glioma. Application to a UMI count scRNA-seq data on cells from mouse intestinal organoids further demonstrates the capability of ZIQRank to improve and complement the existing approaches.

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis
62G10 Nonparametric hypothesis testing
62J05 Linear regression; mixed models
62G08 Nonparametric regression and quantile regression

Software:

DESeq2; MAST; KEGG
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References:

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