Model-based Clustering of Zero-Inflated Single-Cell RNA Sequencing Data via the EM Algorithm
arxiv(2024)
Abstract
Biological cells can be distinguished by their phenotype or at the molecular
level, based on their genome, epigenome, and transcriptome. This paper focuses
on the transcriptome, which encompasses all the RNA transcripts in a given cell
population, indicating the genes being expressed at a given time. We consider
single-cell RNA sequencing data and develop a novel model-based clustering
method to group cells based on their transcriptome profiles. Our clustering
approach takes into account the presence of zero inflation in the data, which
can occur due to genuine biological zeros or technological noise. The proposed
model for clustering involves a mixture of zero-inflated Poisson or
zero-inflated negative binomial distributions, and parameter estimation is
carried out using the EM algorithm. We evaluate the performance of our proposed
methodology through simulation studies and analyses of publicly available
datasets.
MoreTranslated text
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined