WSEAS Transactions on Biology and Biomedicine
Print ISSN: 1109-9518, E-ISSN: 2224-2902
Volume 22, 2025
A Probabilistic Mixture Model Framework for scRNA-seq Read Simulation
Authors: , , , , ,
Abstract: Single-cell RNA sequencing (scRNA-seq) technologies have provided unprecedented insights into gene expression at the cellular level. Drop-seq is one of the most widely used scRNA-seq protocols, and the rapid development of analytical tools for Drop-seq data has followed. These methods are typically evaluated using spike-in experiments or simulated datasets, as the real-world differential gene expression is often unknown. However, spike-in experiments can be both costly and time-consuming, making simulated datasets a more practical alternative. Despite this, most existing RNA-seq simulators are designed for bulk RNA sequencing, highlighting the need for a specialized scRNA-seq simulating method tailored to Drop-seq technology. In this paper, we present Ds-Sim (Drop-seq reads Simulator), a mixture model-based RNA read simulator that generates sequencing reads mimicking those produced in Drop-seq experiments. Our proposed approach is capable of simulating large-scale Drop-seq reads based on user-defined experimental settings, and the generated data closely approximates real Drop-seq results.
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Keywords: Single-cell RNA sequencing (scRNA-seq), RNA-seq data simulator, Drop-seq data, Transcript expression, Positional bias modeling, Read alignment
Pages: 282-293
DOI: 10.37394/23208.2025.22.27