A review of computational strategies for denoising and imputation of single-cell transcriptomic data. Issue 4 (1st October 2020)

• Record Type: Journal Article
• Authors: Patruno, Lucrezia; Maspero, Davide; Craighero, Francesco; Angaroni, Fabrizio; Antoniotti, Marco; Graudenzi, Alex
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 4(2021)
• Publication Date: 2020-10-01
• Subjects: denoising -- imputation -- single-cell RNA-sequencing -- machine learning; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbaa222
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Motivation: The advancements of single-cell sequencing methods have paved the way for the characterization of cellular states at unprecedented resolution, revolutionizing the investigation on complex biological systems. Yet, single-cell sequencing experiments are hindered by several technical issues, which cause output data to be noisy, impacting the reliability of downstream analyses. Therefore, a growing number of data science methods has been proposed to recover lost or corrupted information from single-cell sequencing data. To date, however, no quantitative benchmarks have been proposed to evaluate such methods. Results: We present a comprehensive analysis of the state-of-the-art computational approaches for denoising and imputation of single-cell transcriptomic data, comparing their performance in different experimental scenarios. In detail, we compared 19 denoising and imputation methods, on both simulated and real-world datasets, with respect to several performance metrics related to imputation of dropout events, recovery of true expression profiles, characterization of cell similarity, identification of differentially expressed genes and computation time. The effectiveness and scalability of all methods were assessed with regard to distinct sequencing protocols, sample size and different levels of biological variability and technical noise. As a result, we identify a subset of versatile approaches exhibiting solid performances on most tests and show that certain algorithmic families prove effective on specific tasks but inefficient on others. Finally, most methods appear to benefit from the introduction of appropriate assumptions on noise distribution of biological processes.