Alanis-Lobato, Gregorio;
Bartlett, Thomas E;
Huang, Qiulin;
Simon, Claire S;
McCarthy, Afshan;
Elder, Kay;
Snell, Phil;
... Niakan, Kathy K; + view all
(2024)
MICA: a multi-omics method to predict gene regulatory networks in early human embryos.
Life Science Alliance
, 7
(1)
, Article e202302415. 10.26508/lsa.202302415.
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Abstract
Recent advances in single-cell omics have transformed characterisation of cell types in challenging-to-study biological contexts. In contexts with limited single-cell samples, such as the early human embryo inference of transcription factor-gene regulatory network (GRN) interactions is especially difficult. Here, we assessed application of different linear or non-linear GRN predictions to single-cell simulated and human embryo transcriptome datasets. We also compared how expression normalisation impacts on GRN predictions, finding that transcripts per million reads outperformed alternative methods. GRN inferences were more reproducible using a non-linear method based on mutual information (MI) applied to single-cell transcriptome datasets refined with chromatin accessibility (CA) (called MICA), compared with alternative network prediction methods tested. MICA captures complex non-monotonic dependencies and feedback loops. Using MICA, we generated the first GRN inferences in early human development. MICA predicted co-localisation of the AP-1 transcription factor subunit proto-oncogene JUND and the TFAP2C transcription factor AP-2γ in early human embryos. Overall, our comparative analysis of GRN prediction methods defines a pipeline that can be applied to single-cell multi-omics datasets in especially challenging contexts to infer interactions between transcription factor expression and target gene regulation.
| Type: | Article |
|---|---|
| Title: | MICA: a multi-omics method to predict gene regulatory networks in early human embryos |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.26508/lsa.202302415 |
| Publisher version: | https://doi.org/10.26508/lsa.202302415 |
| Language: | English |
| Additional information: | Copyright © 2023 Alanis-Lobato et al. This article is available under a Creative Commons License (Attribution 4.0 International, as described at https://creativecommons.org/licenses/by/4.0/). |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10180268 |
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