Chandrani Kumari | Searcharxiv

arXiv2026

scMTNI: Leveraging cellular trajectory and context to infer dynamic GRNs from single-cell multi-omics data

Transcriptional gene regulatory networks (GRNs) depict the directed relationships between regulators and target genes, determining gene expression patterns in a cell-type-specific manner. Single-cell multi-omics technologies, such as single-cell RNA sequencing (scRNA-seq) and single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), enable high-resolution measurement of cell-type-specific gene expression and regulation in an unprecedented way. However, tools for inferring cell-type-specific GRNs and modeling their dynamics remain scarce. To facilitate the inference and analysis of cell-type-specific GRNs in contexts such as cellular development or disease progression, where cell lineage structure and dynamics are important, we developed a multi-task learning framework, single-cell Multi-Task Network Inference (scMTNI). scMTNI and its associated network analyses tools offer a comprehensive package to define cell-type-specific GRNs and examine their dynamics. This book chapter describes the scMTNI tool and demonstrates its application to an existing cellular reprogramming single cell multi-modal dataset to infer cell-type-specific GRNs and identify key regulators of cellular fate transitions during cellular reprogramming.

q-bio.MN↗

arXiv2024

MMM and MMMSynth: Clustering of heterogeneous tabular data, and synthetic data generation

We provide new algorithms for two tasks relating to heterogeneous tabular datasets: clustering, and synthetic data generation. Tabular datasets typically consist of heterogeneous data types (numerical, ordinal, categorical) in columns, but may also have hidden cluster structure in their rows: for example, they may be drawn from heterogeneous (geographical, socioeconomic, methodological) sources, such that the outcome variable they describe (such as the presence of a disease) may depend not only on the other variables but on the cluster context. Moreover, sharing of biomedical data is often hindered by patient confidentiality laws, and there is current interest in algorithms to generate synthetic tabular data from real data, for example via deep learning. We demonstrate a novel EM-based clustering algorithm, MMM (``Madras Mixture Model''), that outperforms standard algorithms in determining clusters in synthetic heterogeneous data, and recovers structure in real data. Based on this, we demonstrate a synthetic tabular data generation algorithm, MMMsynth, that pre-clusters the input data, and generates cluster-wise synthetic data assuming cluster-specific data distributions for the input columns. We benchmark this algorithm by testing the performance of standard ML algorithms when they are trained on synthetic data and tested on real published datasets. Our synthetic data generation algorithm outperforms other literature tabular-data generators, and approaches the performance of training purely with real data.

cs.LG↗