Benchmarking of analytical combinations for COVID-19 outcome prediction using single-cell RNA sequencing data.
Brief Bioinform
; 24(3)2023 05 19.
Article
in English
| MEDLINE | ID: covidwho-2292897
ABSTRACT
The advances of single-cell transcriptomic technologies have led to increasing use of single-cell RNA sequencing (scRNA-seq) data in large-scale patient cohort studies. The resulting high-dimensional data can be summarized and incorporated into patient outcome prediction models in several ways; however, there is a pressing need to understand the impact of analytical decisions on such model quality. In this study, we evaluate the impact of analytical choices on model choices, ensemble learning strategies and integrate approaches on patient outcome prediction using five scRNA-seq COVID-19 datasets. First, we examine the difference in performance between using single-view feature space versus multi-view feature space. Next, we survey multiple learning platforms from classical machine learning to modern deep learning methods. Lastly, we compare different integration approaches when combining datasets is necessary. Through benchmarking such analytical combinations, our study highlights the power of ensemble learning, consistency among different learning methods and robustness to dataset normalization when using multiple datasets as the model input.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Benchmarking
/
COVID-19
Type of study:
Cohort study
/
Experimental Studies
/
Observational study
/
Prognostic study
Limits:
Humans
Language:
English
Journal subject:
Biology
/
Medical Informatics
Year:
2023
Document Type:
Article
Affiliation country:
Bib
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