2022
DOI: 10.1093/nar/gkac452
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Detecting critical transition signals from single-cell transcriptomes to infer lineage-determining transcription factors

Abstract: Analyzing single-cell transcriptomes promises to decipher the plasticity, heterogeneity, and rapid switches in developmental cellular state transitions. Such analyses require the identification of gene markers for semi-stable transition states. However, there are nontrivial challenges such as unexplainable stochasticity, variable population sizes, and alternative trajectory constructions. By advancing current tipping-point theory-based models with feature selection, network decomposition, accurate estimation o… Show more

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Cited by 14 publications
(12 citation statements)
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“…Cell differentiation often involves transient bifurcation between stable cell states (57), which may be analyzed using the concept of critical transition (CT) (Table 1). Previously computational methods have been developed to predict and quantify the critical transition and infer the transcription factors that regulate this transition through concepts such as critical transition signals (CTSs) (58-60). To investigate the capability of exFINDER in identifying CTS and its connections with external signals, we first used BioTIP (60) to predict CT using the index of criticality (Ic) and infer CTS from single-cell transcriptomes data.…”
Section: Resultsmentioning
confidence: 99%
“…Cell differentiation often involves transient bifurcation between stable cell states (57), which may be analyzed using the concept of critical transition (CT) (Table 1). Previously computational methods have been developed to predict and quantify the critical transition and infer the transcription factors that regulate this transition through concepts such as critical transition signals (CTSs) (58-60). To investigate the capability of exFINDER in identifying CTS and its connections with external signals, we first used BioTIP (60) to predict CT using the index of criticality (Ic) and infer CTS from single-cell transcriptomes data.…”
Section: Resultsmentioning
confidence: 99%
“… 42 Importantly, DICE displayed a clear transition-like behavior, unlike the individual TF variances which increased but in a mostly asynchronous manner ( Figure 6 E). We also compared DICE to BioTIP, 19 a tool designed to detect cell-fate transitions using the concept of a dynamical network biomarker (DNB), 17 , 18 which also relies on covariation patterns, albeit not just of TFs but of specific subsets of all genes. In line with this, BioTIP’s criticality index also displayed a clear transition, although at an earlier timepoint compared to DICE, whilst also displaying larger fluctuations during the timecourse ( Figures S12 A and S12B).…”
Section: Resultsmentioning
confidence: 99%
“… 40 https://bioconductor.org/packages/release/bioc/html/destiny.html DICE v0.9.1 This paper https://github.com/aet21/DICE BioTIP v1.11.0 Yang et al. 19 https://bioconductor.org/packages/release/bioc/vignettes/BioTIP/inst/doc/BioTIP.html Scira v1.0.3 Teschendorff et al. 22 https://github.com/aet21/scira GillespieSSA v0.6.2 Pineda-Krch 53 https://github.com/rcannood/GillespieSSA deSolve v1.34 Soetaert 54 https://cran.r-project.org/web/packages/deSolve/ Signac v1.4.0 Stuart T et al.…”
Section: Methodsmentioning
confidence: 99%
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