FitDevo: accurate inference of single-cell developmental potential using sample-specific gene weight.
In: Briefings in Bioinformatics, Jg. 23 (2022-09-01), Heft 5, S. 1-13
Online
academicJournal
Zugriff:
The quantification of developmental potential is critical for determining developmental stages and identifying essential molecular signatures in single-cell studies. Here, we present FitDevo, a novel method for inferring developmental potential using scRNA-seq data. The main idea of FitDevo is first to generate sample-specific gene weight (SSGW) and then infer developmental potential by calculating the correlation between SSGW and gene expression. SSGW is generated using a generalized linear model that combines sample-specific information and gene weight learned from a training dataset covering scRNA-seq data of 17 previously published datasets. We have rigorously validated FitDevo's effectiveness using a testing dataset with scRNA-seq data from 28 existing datasets and have also demonstrated its superiority over current methods. Furthermore, FitDevo's broad application scope has been illustrated using three practical scenarios: deconvolution analysis of epidermis, spatial transcriptomic data analysis of hearts and intestines, and developmental potential analysis of breast cancer. The source code and related data are available at https://github.com/jumphone/fitdevo. [ABSTRACT FROM AUTHOR]
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Titel: |
FitDevo: accurate inference of single-cell developmental potential using sample-specific gene weight.
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Autor/in / Beteiligte Person: | Zhang, Feng ; Yang, Chen ; Wang, Yihao ; Jiao, Huiyuan ; Wang, Zhiming ; Shen, Jianfeng ; Li, Lingjie |
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Zeitschrift: | Briefings in Bioinformatics, Jg. 23 (2022-09-01), Heft 5, S. 1-13 |
Veröffentlichung: | 2022 |
Medientyp: | academicJournal |
ISSN: | 1467-5463 (print) |
DOI: | 10.1093/bib/bbac293 |
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