Please use this identifier to cite or link to this item: http://dx.doi.org/10.25673/112994
Title: Scrutinised and compared : HVG identification methods in terms of common metrics
Author(s): Kasianchuk, Nadiia
Kukuruza, Yevhenii
Ostash, Vladyslav
Boshtova, Anastasiia
Tsvyk, Dmytro
Mykhailichenko, Matvii
Granting Institution: Hochschule Anhalt
Issue Date: 2023
Language: English
Subjects: Highly Variable Genes
Single-Cell RNA-Sequencing
Differential Expression
Abstract: Highly variable gene (HVG) identification plays a critical role in unravelling gene expression patterns and understanding cellular heterogeneity in single-cell RNA-sequencing (scRNA-seq) data. A plethora of software packages have been developed for this purpose; however, their comparative performance is yet to be explored. This study addresses this gap by independently evaluating 22 methods from 9 different packages to provide a comprehensive assessment of the HVG identification methods. For such purpose it was deemed necessary to employ a set of common metrics, namely overlap with highly and lowly expressed genes, runtime, and clustering indices (e.g., Calinski-Harabasz, Davies-Bouldin, and ROGUE). The results reveal substantial disparities not only between different methods but also in the performance of a single method across diverse datasets. That is to say, the dimensionality of the provided data, spike-ins, and background noise are some of the key factors influencing the results. These variations underscore the significant impact of dataset characteristics on analysis outcomes. Therefore, consistent consideration of data nature is imperative. The study emphasises the urgent need for a standardised, data-driven assessment framework to ensure reliable and effective scRNA-seq analyses. This work serves as a valuable resource for both scRNA-seq software developers and experimental researchers seeking optimal methods for their investigations.
URI: https://opendata.uni-halle.de//handle/1981185920/114951
http://dx.doi.org/10.25673/112994
http://dx.doi.org/10.25673/112994
Open Access: Open access publication
License: (CC BY-SA 4.0) Creative Commons Attribution ShareAlike 4.0(CC BY-SA 4.0) Creative Commons Attribution ShareAlike 4.0
Appears in Collections:International Conference on Applied Innovations in IT (ICAIIT)

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