Identifying strengths and weaknesses of methods for computational network inference from single-cell RNA-seq data

Abstract Single-cell RNA-sequencing (scRNA-seq) offers unparalleled insight into the transcriptional programs of different cellular states by measuring the transcriptome of thousands of individual cells. An emerging problem in the analysis of scRNA-seq is the inference of transcriptional gene regula...

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Vydáno v:G3 : genes - genomes - genetics Ročník 13; číslo 3
Hlavní autoři: McCalla, Sunnie Grace, Fotuhi Siahpirani, Alireza, Li, Jiaxin, Pyne, Saptarshi, Stone, Matthew, Periyasamy, Viswesh, Shin, Junha, Roy, Sushmita
Médium: Journal Article
Jazyk:angličtina
Vydáno: US Oxford University Press 09.03.2023
Genetics Society of America
Témata:
Algorithms
Animals
BASIC BIOLOGICAL SCIENCES
benchmarking
Datasets
Gene Expression Profiling
Gene Expression Regulation
Gene Regulatory Networks
Genetics & Heredity
Humans
Investigation
Mice
network inference algorithms
Neurofibromin 2
prior knowledge
Saccharomyces cerevisiae
Sequence Analysis, RNA - methods
Single-Cell Analysis - methods
Single-Cell Gene Expression Analysis
single-cell RNA-seq
network inference algorithms
single-cell RNA-seq
gene regulatory networks
benchmarking
prior knowledge
ISSN:2160-1836, 2160-1836
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Shrnutí:Abstract Single-cell RNA-sequencing (scRNA-seq) offers unparalleled insight into the transcriptional programs of different cellular states by measuring the transcriptome of thousands of individual cells. An emerging problem in the analysis of scRNA-seq is the inference of transcriptional gene regulatory networks and a number of methods with different learning frameworks have been developed to address this problem. Here, we present an expanded benchmarking study of eleven recent network inference methods on seven published scRNA-seq datasets in human, mouse, and yeast considering different types of gold standard networks and evaluation metrics. We evaluate methods based on their computing requirements as well as on their ability to recover the network structure. We find that, while most methods have a modest recovery of experimentally derived interactions based on global metrics such as Area Under the Precision Recall curve, methods are able to capture targets of regulators that are relevant to the system under study. Among the top performing methods that use only expression were SCENIC, PIDC, MERLIN or Correlation. Addition of prior biological knowledge and the estimation of transcription factor activities resulted in the best overall performance with the Inferelator and MERLIN methods that use prior knowledge outperforming methods that use expression alone. We found that imputation for network inference did not improve network inference accuracy and could be detrimental. Comparisons of inferred networks for comparable bulk conditions showed that the networks inferred from scRNA-seq datasets are often better or at par with the networks inferred from bulk datasets. Our analysis should be beneficial in selecting methods for network inference. At the same time, this highlights the need for improved methods and better gold standards for regulatory network inference from scRNAseq datasets.
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USDOE Office of Science (SC)
National Science Foundation (NSF)
National Institutes of Health (NIH)
SC0021052; R01GM117339; U01EB029371
These authors contributed equally.
ISSN:2160-1836
2160-1836
DOI:10.1093/g3journal/jkad004