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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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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

Benchmark and Parameter Sensitivity Analysis of Single-Cell RNA Sequencing Clustering Methods.

Monika Krzak¹, Yordan Raykov², Alexis Boukouvalas³

¹Institute for Applied Mathematics "Mauro Picone", Naples, Italy.

Frontiers in Genetics

|January 11, 2020

Summary

User choices significantly impact single-cell RNA sequencing (scRNAseq) clustering results. This study evaluates various methods and parameter settings to guide users in selecting optimal approaches for cell population identification.

Keywords:

benchmark clustering methods high-dimensional data analysis parameter sensitivity analysis single-cell RNA-seq

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Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

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Last Updated: Dec 31, 2025

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Published on: January 10, 2019

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Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Published on: July 18, 2019

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNAseq) reveals cellular heterogeneity.
Clustering methods are crucial for identifying distinct cell populations in scRNAseq data.
Existing methods often require complex preprocessing and parameter tuning, posing challenges for users.

Purpose of the Study:

To provide insights into the advantages and drawbacks of scRNAseq clustering methods.
To evaluate the impact of user-specific parameter settings on clustering performance.
To guide users in selecting appropriate methods and parameters for their datasets.

Main Methods:

Extensive evaluation of multiple scRNAseq clustering methods.
Application of methods to real and simulated datasets with varying dimensionality, cell population numbers, and noise levels.
Systematic analysis of different usage modes and parameter settings.

Main Results:

Clustering method performance exhibits high variability, strongly influenced by user-selected parameter settings.
Identified tendencies in performance related to usage modes and dataset types.
Determined which methods are most affected by data dimensionality in terms of computational time.

Conclusions:

User parameter choices are critical for successful scRNAseq data clustering.
Understanding method-specific sensitivities to parameters and data characteristics is essential.
Open challenges remain, particularly in automating the determination of the optimal number of clusters.