ArticlePLoS computational biology2020
Inferring TF activation order in time series scRNA-Seq studies.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Establishment of single-cell transcriptional states during seed germination.Nature plants · 2024Article
- Temporal modelling using single-cell transcriptomics.Nature reviews. Genetics · 2022Review
- TraSig: inferring cell-cell interactions from pseudotime ordering of scRNA-Seq data.Genome biology · 2022Article
- Chromatin Velocity reveals epigenetic dynamics by single-cell profiling of heterochromatin and euchromatin.Nature biotechnology · 2022Article
- Computational tools for analyzing single-cell data in pluripotent cell differentiation studies.Cell reports methods · 2021Review
- Computational biology approaches for mapping transcriptional regulatory networks.Computational and structural biotechnology journal · 2021Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Methods for the analysis of time series single cell expression data (scRNA-Seq) either do not utilize information about transcription factors (TFs) and their targets or only study these as a post-processing step. Using such information can both, improve the accuracy of the reconstructed model and cell assignments, while at the same time provide information on how and when the process is regulated. We developed the Continuous-State Hidden Markov Models TF (CSHMM-TF) method which integrates probabilistic modeling of scRNA-Seq data with the ability to assign TFs to specific activation points in the model. TFs are assumed to influence the emission probabilities for cells assigned to later time points allowing us to identify not just the TFs controlling each path but also their order of activation. We tested CSHMM-TF on several mouse and human datasets. As we show, the method was able to identify known and novel TFs for all processes, assigned time of activation agrees with both expression information and prior knowledge and combinatorial predictions are supported by known interactions. We also show that CSHMM-TF improves upon prior methods that do not utilize TF-gene interaction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.