MICA: a multi-omics method to predict gene regulatory networks in early human embryos.

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 M

SUBMITTER: Alanis-Lobato G 

PROVIDER: S-EPMC10599980 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature