Journal
JOURNAL OF BIOMEDICAL INFORMATICS
Volume 130, Issue -, Pages -Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jbi.2022.104093
Keywords
scRNA-seq; Downsampling; Noise-reduction; Clustering; Data integration
Funding
- Fundamental Research Funds for the Chinese Central Universities [20720190101]
- National Natural Science Foundation of China [81802823]
Ask authors/readers for more resources
Research shows that the proposed MURPXMBD algorithm can reduce noise in single-cell RNA sequencing data, improve the quality and accuracy of clustering algorithms, help discover new cell types, and enhance the performance of dataset integration algorithms.
The random noises, sampling biases, and batch effects often confound true biological variations in single-cell RNA-sequencing (scRNA-seq) data. Adjusting such biases is key to the robust discoveries in downstream analyses, such as cell clustering, gene selection and data integration. Here we propose a model-based downsampling algorithm based on minimal unbiased representative points (MURPXMBD). MURPXMBD is designed to retrieve a set of representative points by reducing gene-wise random independent errors, while retaining the covariance structure of biological origin hence provide an unbiased representation of the cell population. Subsequent validation using benchmark datasets shows that MURPXMBD can improve the quality and accuracy of clustering algorithms, and thus facilitate the discovery of new cell types. Besides, MURPXMBD also improves the performance of dataset integration algorithms. In summary, MURPXMBD serves as a useful noise-reduction method for single-cell sequencing analysis in biomedical studies.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available