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...
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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 networkinference 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 networkinference did not improve networkinference 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 networkinference. At the same time, this highlights the need for improved methods and better gold standards for regulatory networkinference from scRNAseq datasets.
Plethora of software tools are available for inference and visualization of Gene Regulatory networks (GRN) with their relative strengths and weaknesses. System and computational biologists quite often find it difficul...
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ISBN:
(纸本)9781479917341
Plethora of software tools are available for inference and visualization of Gene Regulatory networks (GRN) with their relative strengths and weaknesses. System and computational biologists quite often find it difficult to select a candidate tool for their experimentation. In this paper we present a comprehensive study on some of the promising and influential software tools developed so far for in-silico reconstruction and visualization of GRN. We discuss the features of each tool, the underlying technology used along with their relative merits and limitations. Researchers normally use synthetic gene expression data for evaluation and validation of GRN methods. We also discuss various synthetic data generators and tools that support benchmarking against gold standards like DREAM challenge data. Finally, we suggest few important issues that may be helpful for the development of effective inference and visualization softwares.
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