MINI-EX version 2: cell-type-specific gene regulatory network inference using an integrative single-cell transcriptomics approach

Abstract

Understanding and predicting cell-type-specific gene regulatory networks (GRNs) is essential for unraveling the complex interactions between transcription factors (TFs) that modulate the expression of target genes and control diverse biological processes in multicellular organisms. MINI-EX (Motif-Informed Network Inference based on single-cell EXpression data) is an integrative tool tailored for identifying cell-type-specific GRNs in plants. Leveraging single-cell transcriptomics data, MINI-EX constructs expression-based networks and integrates TF motif information to produce GRNs with increased accuracy. Furthermore, it assigns regulatory modules to distinct cell types and prioritizes candidate regulators by employing a strategy that encompasses network centrality measures, functional annotations, and expression specificity. Taken together, MINI-EX offers a powerful approach to identify cell-type-specific transcriptional cascades and enhance our understanding of TF functions in plant biology. Here, we discuss recent advancements in the tools latest version and explain how single-cell GRNs can be identified for non-model species lacking TF motif information. Additionally, we provide a comprehensive guide to use MINI-EX, covering the entire pipeline from preparing input files starting from a single-cell experiment, over configuring parameters, to interpreting output data.