Modeling dynamic correlation in zero‐inflated bivariate count data with applications to single‐cell RNA sequencing data
| Autor: | Zhen Yang, Yen-Yi Ho |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
Computer science Negative binomial distribution Latent variable Bivariate analysis computer.software_genre 01 natural sciences General Biochemistry Genetics and Molecular Biology Correlation 010104 statistics & probability 03 medical and health sciences Exome Sequencing Computer Simulation 0101 mathematics Dropout (neural networks) 030304 developmental biology 0303 health sciences Models Statistical General Immunology and Microbiology Sequence Analysis RNA Applied Mathematics High-Throughput Nucleotide Sequencing RNA General Medicine Zero (linguistics) Data mining General Agricultural and Biological Sciences computer Count data |
| Zdroj: | Biometrics. 78:766-776 |
| ISSN: | 1541-0420 0006-341X |
| Popis: | Interactions between biological molecules in a cell are tightly coordinated and often highly dynamic. As a result of these varying signaling activities, changes in gene co-expression patterns could often be observed. The advancements in next-generation sequencing technologies bring new statistical challenges for studying these dynamic changes of gene co-expression. In recent years, methods have been developed to examine genomic information from individual cells. Single-cell RNA sequencing (scRNA-seq) data are count-based, and often exhibit characteristics such as over-dispersion and zero-inflation. To explore the dynamic dependence structure in scRNA-seq data and other zero-inflated count data, new approaches are needed. In this paper, we consider over-dispersion and zero-inflation in count outcomes and propose a ZEro-inflated Negative binomial dynamic COrrelation model (ZENCO). The observed count data are modeled as a mixture of two components: success amplifications and dropout events in ZENCO. A latent variable is incorporated into ZENCO in order to model the covariate-dependent correlation structure. We conduct simulation studies to evaluate the performance of our proposed method and to compare it with existing approaches. We also illustrate the implementation of our proposed approach using scRNA-seq data from a study of minimal residual disease in melanoma. |
| Databáze: | OpenAIRE |
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