Gene regulatory network inference in the era of single-cell multi-omics
P Badia-i-Mompel, L Wessels, S Müller-Dott… - Nature Reviews …, 2023 - nature.com
The interplay between chromatin, transcription factors and genes generates complex
regulatory circuits that can be represented as gene regulatory networks (GRNs). The study …
regulatory circuits that can be represented as gene regulatory networks (GRNs). The study …
Principles and challenges of modeling temporal and spatial omics data
Studies with temporal or spatial resolution are crucial to understand the molecular dynamics
and spatial dependencies underlying a biological process or system. With advances in high …
and spatial dependencies underlying a biological process or system. With advances in high …
Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
We present a systematic evaluation of state-of-the-art algorithms for inferring gene
regulatory networks from single-cell transcriptional data. As the ground truth for assessing …
regulatory networks from single-cell transcriptional data. As the ground truth for assessing …
[HTML][HTML] Functional inference of gene regulation using single-cell multi-omics
Cells require coordinated control over gene expression when responding to environmental
stimuli. Here we apply scATAC-seq and single-cell RNA sequencing (scRNA-seq) in resting …
stimuli. Here we apply scATAC-seq and single-cell RNA sequencing (scRNA-seq) in resting …
A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data
Gene regulatory network is a complicated set of interactions between genetic materials,
which dictates how cells develop in living organisms and react to their surrounding …
which dictates how cells develop in living organisms and react to their surrounding …
Modeling gene regulatory networks using neural network architectures
Gene regulatory networks (GRNs) encode the complex molecular interactions that govern
cell identity. Here we propose DeepSEM, a deep generative model that can jointly infer …
cell identity. Here we propose DeepSEM, a deep generative model that can jointly infer …
[HTML][HTML] From bench to bedside: Single-cell analysis for cancer immunotherapy
EF Davis-Marcisak, A Deshpande, GL Stein-O'Brien… - Cancer cell, 2021 - cell.com
Single-cell technologies are emerging as powerful tools for cancer research. These
technologies characterize the molecular state of each cell within a tumor, enabling new …
technologies characterize the molecular state of each cell within a tumor, enabling new …
Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications
M Su, T Pan, QZ Chen, WW Zhou, Y Gong, G Xu… - Military Medical …, 2022 - Springer
The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has
advanced our understanding of the pathogenesis of disease and provided valuable insights …
advanced our understanding of the pathogenesis of disease and provided valuable insights …
Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application
Alzheimer's disease (AD) is the most common form of dementia, characterized by
progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic …
progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic …
Single-cell transcriptomics unveils gene regulatory network plasticity
Background Single-cell RNA sequencing (scRNA-seq) plays a pivotal role in our
understanding of cellular heterogeneity. Current analytical workflows are driven by …
understanding of cellular heterogeneity. Current analytical workflows are driven by …