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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Jan 5, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

Clustering methods for single-cell RNA-sequencing expression data: performance evaluation with varying sample sizes

¹Department of Biostatistics and Medical Informatics, Faculty of Medicine, Ege University, Bornova, İzmir, Turkey.

Statistical Applications in Genetics and Molecular Biology

|October 25, 2019

Summary

Clustering single-cell RNA sequencing (scRNA-seq) data performance heavily relies on dataset size and complexity. Larger sample sizes generally improve clustering accuracy, especially for detecting rare cell populations.

Keywords:

RNA sequencing clustering performance evaluation single cell

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Specialized clustering methods are crucial for analyzing single-cell RNA sequencing (scRNA-seq) expression data.
Existing studies evaluate clustering method performance but lack systematic analysis considering sample size and cell composition.

Purpose of the Study:

To comprehensively evaluate the performance of 11 selected scRNA-seq clustering methods.
To assess the impact of sample size, cell composition, and transcriptome complexity on clustering accuracy.

Main Methods:

Utilized synthetic scRNA-seq datasets with controlled sample sizes and subpopulation numbers.
Varied transcriptome complexity levels across datasets.
Performed a systematic performance evaluation of 11 distinct clustering algorithms.

Main Results:

Clustering method performance is significantly influenced by dataset sample size and complexity.
Increased sample size generally led to improved clustering performance across most methods.
Sample size is critical for the effective identification of rare cell subpopulations.

Conclusions:

The effectiveness of scRNA-seq clustering tools is strongly dependent on the characteristics of the input data, particularly sample size.
Researchers should carefully consider sample size when selecting clustering methods for scRNA-seq analysis to ensure accurate detection of cell types, including rare ones.