2020
DOI: 10.1101/2020.04.03.023002
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A single-cell gene regulatory network inference method for identifying complex regulatory dynamics across cell phenotypes

Abstract: Motivation: With the use of single-cell RNA sequencing (scRNA-Seq) technologies, it is now possible to acquire gene expression data for each individual cell in samples containing up to millions of cells. These cells can be further grouped into different states along an inferred cell differentiation path, which are potentially characterized by similar, but distinct enough, gene regulatory networks (GRNs). Hence, it would be desirable for scRNA-Seq GRN inference methods to capture the GRN dynamics across cell st… Show more

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Cited by 5 publications
(5 citation statements)
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“…To elucidate the molecular regulation of CAR High T cells, we applied SimiC ( 36 ), a novel GRN inference algorithm for scRNA-seq data that imposes a similarity constraint when jointly inferring the GRNs for each specific cell state. On the basis of this analysis, we observed regulons [a transcription factor (TF) and its associated target genes] that were similarly activated between CAR High T and the rest of the CAR T cells (fig.…”
Section: Resultsmentioning
confidence: 99%
“…To elucidate the molecular regulation of CAR High T cells, we applied SimiC ( 36 ), a novel GRN inference algorithm for scRNA-seq data that imposes a similarity constraint when jointly inferring the GRNs for each specific cell state. On the basis of this analysis, we observed regulons [a transcription factor (TF) and its associated target genes] that were similarly activated between CAR High T and the rest of the CAR T cells (fig.…”
Section: Resultsmentioning
confidence: 99%
“…To infer single-cell gene regulatory networks (GRNs), we used SimiC, which has been previously shown to perform well with honey bee brain scRNA-Seq data and is better optimized for comparing GRNs across behavioral states than other established methods, to which it has been benchmarked ( 48 ). SimiC imputes scRNA-Seq expression data to infer the relationship between a transcription factor (TF) and its predicted TGs.…”
Section: Methodsmentioning
confidence: 99%
“…To elucidate the molecular regulation of CAR High T cells we applied SimiC (34), a novel GRN inference algorithm for scRNA-seq data that imposes a similarity constraint when jointly inferring the GRNs for each specific cell state. Based on this analysis, we observed regulons (a TF and its associated target genes) that were similarly activated between CAR High and the rest of the CAR T cells (Fig.…”
Section: Car Density Is Associated With Differential Activation Of Regulatory Networkmentioning
confidence: 99%