Author

Thalia E. Chan

Imperial College London

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Systems Biology· 3 Molecular Biology· 1

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Research Focus:gene regulatory network inference· 4 Approach:statistical model· 4 Organism:mouse· 3 Assay:scRNA-seq· 3 Research Focus:cell type annotation· 2 Research Focus:differential expression· 2 Approach:Bayesian inference· 1 Assay:bulk RNA-seq· 1

Frequent Co-Authors

Michael P. H. StumpfiD· 4 shared Ann C. BabtieiD· 3 shared Kirsten R. McEweniD· 2 shared Alessandro SardiniiD· 1 shared Ananth V. Pallaseni· 1 shared Andreas SchuppertiD· 1 shared Ben D. MacArthuriD· 1 shared Colin P. PleaseiD· 1 shared

Activity by Year

2017-2018 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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4 shared papersSystems Biology

Michael P. H. StumpfORCID

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4 papers

Recent·Most cited

Signalling pathways drive heterogeneity of ground state pluripotency

Kirsten R. McEwen, Sarah Linnett, Harry G. Leitch, Prashant Kumar Srivastava, Lara Al Zouabi, Tien-Chi Huang, Maxime Rotival, Alessandro Sardini, Thalia E. Chan, Sarah L. Filippi, Michael P. H. Stumpf, Enrico G. Petretto, Petra Hájková

This study shows that 2i conditions increase transcriptional heterogeneity in pluripotent stem cells, driven by signaling pathways and negative feedback loss.

bioRxiv · Aug 16, 2018 · PreprintMolecular Biology

Empirical Bayes Meets Information Theoretical Network Reconstruction from Single Cell Data

Thalia E. Chan, Ananth V. Pallaseni, Ann C. Babtie, Kirsten R. McEwen, Michael P.H. Stumpf

The paper introduces a method combining empirical Bayes and information theory to reconstruct gene regulatory networks from single-cell data, improving accuracy with prior information.

bioRxiv · Feb 13, 2018 · PreprintSystems Biology

Gene regulatory network inference from single-cell data using multivariate information measures

Thalia E. Chan, Michael P.H. Stumpf, Ann C. Babtie

This paper introduces PIDC, a method using multivariate information theory to infer gene regulatory networks from single-cell data, showing improved performance over pairwise methods in simulated data.

Cell Systems · Sep 1, 2017Peer-reviewedSystems Biology

Stem cell differentiation is a stochastic process with memory

Patrick S. Stumpf, Rosanna C. G. Smith, Michael Lenz, Andreas Schuppert, Franz-Josef Müller, Ann C. Babtie, Thalia E. Chan, Michael P.H. Stumpf, Colin P. Please, Sam Howison, Fumio Arai, Ben D. MacArthur

This study shows that stem cell differentiation involves stochastic transitions through hidden microstates, revealing epigenetic memory and a statistical mechanics framework for cellular identity.

Cell Systems · Sep 1, 2017Peer-reviewedSystems Biology