ArticleLife science alliance2024
MICA: a multi-omics method to predict gene regulatory networks in early human embryos.
Article in Life science alliance, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 22 citations in OpenAlex.
- Inferring gene-regulatory networks using epigenomic priors.iScience · 2026Article
- BARTsc identifies key transcriptional regulators from single-cell omics data.bioRxiv : the preprint server for biology · 2026Article
- Gene regulatory network inference with popInfer reveals the dynamic regulation of hematopoietic stem cell quiescence.iScience · 2025Article
- Suppression of ERK signalling promotes pluripotent epiblast in the human blastocyst.Nature communications · 2025Article
- Leveraging prior knowledge to infer gene regulatory networks from single-cell RNA-sequencing data.Molecular systems biology · 2025Review
- Advancements in single-cell RNA sequencing and spatial transcriptomics: transforming biomedical research.Acta biochimica Polonica · 2025Review
- Optimal variable identification for accurate detection of causal expression Quantitative Trait Loci with applications in heart-related diseases.Computational and structural biotechnology journal · 2024Article
- OneSC: a computational platform for recapitulating cell state transitions.Bioinformatics (Oxford, England) · 2024Article
- Integrating Signaling Pathways with Transcription Factor Networks-On the Trail of Sisyphus?Biomolecules · 2024Article
- Controlled noise: evidence of epigenetic regulation of single-cell expression variability.Bioinformatics (Oxford, England) · 2024Article
- OneSC: A computational platform for recapitulating cell state transitions.bioRxiv : the preprint server for biology · 2024Article
- Recent advances in exploring transcriptional regulatory landscape of crops.Frontiers in plant science · 2024Review
- Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data.NPJ systems biology and applications · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 5 institutions in 2 countries.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.