Biology subjects

El Kazwini, N.

Publications and source records attributed to El Kazwini, N..

3 recordsLinked to original sources

bioRxiv2024

CRAK-Velo: Chromatin Accessibility Kinetics integration improves RNA Velocity estimation

RNA velocity has recently emerged as a key tool in the analysis of single-cell transcriptomic data, yet connecting RNA velocity analyses to underlying regulatory processes has proved challenging. Here we propose CRAK-Velo, a semi-mechanistic model which integrates chromatin accessibility data in the estimation of RNA velocities. CRAK-Velo provides biologically consistent estimates of developmental flows and enables accurate cell-type deconvolution, while additionally shining light on regulatory processes at the level of interactions between genes and chromatin regions.

bioinformatics↗

bioRxiv2023

NeuroVelo: interpretable learning of cellular dynamics from single-cell transcriptomic data

Reconstructing temporal cellular dynamics from static single-cell transcriptomics remains a major challenge. Methods based on RNA velocity are useful, but interpreting their results to learn new biology remains difficult, and their predictive power is limited. Here we propose NeuroVelo, a method that couples learning of an optimal linear projection with non-linear Neural Ordinary Differential Equations. Unlike current methods, it uses dynamical systems theory to model biological processes over time, hence NeuroVelo can identify gene interactions that drive the observed temporal dynamics of gene expression. We benchmark NeuroVelo against several state-of-the-art methods using single-cell datasets, demonstrating that NeuroVelo simultaneously reconstructs correct cell-type transitions and identifies gene regulatory networks that drive cell fate directly from the data.

bioinformatics↗

bioRxiv2023

SHARE-Topic: Bayesian Inerpretable Modelling of Single-Cell Multi-Omic Data

Single-cell sequencing technologies are providing unprecedented insights into the molecular biology of individual cells. More recently, multi-omic technologies have emerged which can simultaneously measure gene expression and the epigenomic state of the same cell, holding the promise to unlock our understanding of the epigenetic mechanisms of gene regulation. However, the sparsity and noisy nature of the data pose fundamental statistical challenges which hinder our ability to extract biological knowledge from these complex data sets. Here we propose SHARE-Topic, a Bayesian generative model of multi-omic single cell data which addresses these challenges from the point of view of topic models. SHARE-Topic identifies common patterns of co-variation between different omic layers, providing interpretable explanations for the complexity of the data. Tested on joint ATAC and expression data, SHARE-Topic was able to provide low dimensional representations that recapitulate known biology, and to define in a principled way associations between genes and distal regulators in individual cells. We illustrate SHARE-Topic in a case study of B-cell lymphoma, studying the usage of alternative promoters in the regulation of the FOXP1 transcription factors.

bioinformatics↗