An estimate of strain increments in a naturally deformed carbonate rock

Clicks: 6
ID: 294163
1973
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Abstract

Abstract

Single-cell RNA-sequencing (scRNA-seq) offers unparalleled insight into the transcriptional programs of different cellular states by measuring the transcriptome of thousands 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. Here we present an expanded benchmarking study of eleven recent network inference methods on six published single-cell RNA-sequencing 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 AUPR, methods are able to capture targets of regulators that are relevant to the system under study. Based on overall performance we grouped the methods into three main categories and found a combination of information-theoretic and regression-based methods to have a generally high performance. We also evaluate the utility of imputation for gene regulatory network inference and find that a small number of methods benefit from imputation, which further depends upon the dataset. Finally, comparisons to inferred networks for comparable bulk conditions showed that networks inferred from scRNA-seq datasets are often better or at par to those from bulk suggesting that scRNA-seq datasets can be a cost-effective way for gene regulatory network inference. Our analysis should be beneficial in selecting algorithms for performing network inference but also argues for improved methods and better gold standards for accurate assessment of regulatory network inference methods for mammalian systems.
Reference Key
openalex_W2334764729 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors John Wickham
Journal american journal of science
Year 1973
DOI
10.2475/ajs.273.1.23
URL
https://doi.org/10.2475/ajs.273.1.23
Keywords Keywords not found

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