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NonLoss: a novel analytical method for differential biological module identification from single-cell transcriptome
BACKGROUND: The identification of disease-related biological modules plays an important role in our understanding of the process of diseases. Although single-cell RNA sequencing (scRNA-seq) provides high-resolution transcriptome data that can potentially characterize subtle gene expression changes w...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8756206/ https://www.ncbi.nlm.nih.gov/pubmed/35071482 http://dx.doi.org/10.21037/atm-21-6401 |
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author | Zhao, Hui Guo, Ying Ma, Yanan Chen, Yunping Sun, Haiming Sun, Donglin Wu, Nan Jin, Yan |
author_facet | Zhao, Hui Guo, Ying Ma, Yanan Chen, Yunping Sun, Haiming Sun, Donglin Wu, Nan Jin, Yan |
author_sort | Zhao, Hui |
collection | PubMed |
description | BACKGROUND: The identification of disease-related biological modules plays an important role in our understanding of the process of diseases. Although single-cell RNA sequencing (scRNA-seq) provides high-resolution transcriptome data that can potentially characterize subtle gene expression changes within cells, the susceptibility of the gene expression information to the influence of individual genes also makes it difficult to distinguish the biological module. METHODS: To quantify gene expression information for biological function modules, we adopted the method based on Shannon’s entropy and Spearman rank correlation analysis. The ingenious combination of these two methods enables the variation analysis of the former and the consistency analysis of the latter to make a more robust biological function analysis tool. RESULTS: We developed a computational analytical method and desktop application called NonLoss to analyze scRNA-seq data more robustly and to extract real biological differences between cell populations. The method derives its power by handling expression level data from all genes annotated to a specific function module, both for dimensionality reduction and reliability of function identification, avoiding random disturbance of individual genes. NonLoss can in principle be used to assess changes of function modules and identify vital functions simultaneously. Furthermore, specific genes contributing to important functions, even those with subtle expression changes, can be identified. The results demonstrated that NonLoss yields biologically significant insights into 3 different applications. CONCLUSIONS: NonLoss was developed with a user-friendly graphical user interface, and it could identify the module of biologically relevant expression changes at a single-cell resolution. |
format | Online Article Text |
id | pubmed-8756206 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-87562062022-01-21 NonLoss: a novel analytical method for differential biological module identification from single-cell transcriptome Zhao, Hui Guo, Ying Ma, Yanan Chen, Yunping Sun, Haiming Sun, Donglin Wu, Nan Jin, Yan Ann Transl Med Original Article BACKGROUND: The identification of disease-related biological modules plays an important role in our understanding of the process of diseases. Although single-cell RNA sequencing (scRNA-seq) provides high-resolution transcriptome data that can potentially characterize subtle gene expression changes within cells, the susceptibility of the gene expression information to the influence of individual genes also makes it difficult to distinguish the biological module. METHODS: To quantify gene expression information for biological function modules, we adopted the method based on Shannon’s entropy and Spearman rank correlation analysis. The ingenious combination of these two methods enables the variation analysis of the former and the consistency analysis of the latter to make a more robust biological function analysis tool. RESULTS: We developed a computational analytical method and desktop application called NonLoss to analyze scRNA-seq data more robustly and to extract real biological differences between cell populations. The method derives its power by handling expression level data from all genes annotated to a specific function module, both for dimensionality reduction and reliability of function identification, avoiding random disturbance of individual genes. NonLoss can in principle be used to assess changes of function modules and identify vital functions simultaneously. Furthermore, specific genes contributing to important functions, even those with subtle expression changes, can be identified. The results demonstrated that NonLoss yields biologically significant insights into 3 different applications. CONCLUSIONS: NonLoss was developed with a user-friendly graphical user interface, and it could identify the module of biologically relevant expression changes at a single-cell resolution. AME Publishing Company 2021-12 /pmc/articles/PMC8756206/ /pubmed/35071482 http://dx.doi.org/10.21037/atm-21-6401 Text en 2021 Annals of Translational Medicine. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Original Article Zhao, Hui Guo, Ying Ma, Yanan Chen, Yunping Sun, Haiming Sun, Donglin Wu, Nan Jin, Yan NonLoss: a novel analytical method for differential biological module identification from single-cell transcriptome |
title | NonLoss: a novel analytical method for differential biological module identification from single-cell transcriptome |
title_full | NonLoss: a novel analytical method for differential biological module identification from single-cell transcriptome |
title_fullStr | NonLoss: a novel analytical method for differential biological module identification from single-cell transcriptome |
title_full_unstemmed | NonLoss: a novel analytical method for differential biological module identification from single-cell transcriptome |
title_short | NonLoss: a novel analytical method for differential biological module identification from single-cell transcriptome |
title_sort | nonloss: a novel analytical method for differential biological module identification from single-cell transcriptome |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8756206/ https://www.ncbi.nlm.nih.gov/pubmed/35071482 http://dx.doi.org/10.21037/atm-21-6401 |
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