TL;DR
This chapter outlines a comprehensive protocol for joint embedding of chromatin accessibility and transcriptomic data using best practices developed in laboratories, along with detailed parameter tuning at each step to fully leveraging this pipeline.
This manuscript explores the current landscape of single-cell RNA sequencing (scRNA-seq), highlighting key studies that have leveraged this technology to advance biological understanding for clinical purposes through the construction of gene regulatory networks (GRNs) from single-cell transcriptomic data.
This chapter covers quality control, normalization, sparse data handling, and transcript quantification for both bulk and snRNA-seq, along with strategies to address challenges such as multi-mapped reads and batch effects, and examines how artificial intelligence and machine learning techniques can improve data process...
How spatially structured cellular ecosystems, rather than individual cell types, determine therapeutic response and resistance in lung cancer is organized around a single question - and observations that are reproducible across independent cohorts and platforms are explicitly separate from those that remain confined to...
This study established an integrative strategy enabling high confidence cell identity assignment in sparse epigenomic data, and revealed the roles of B cell subtypes in the regulation of tumor immunity.
A comprehensive ATAC-seq protocol that is utilized by laboratories, along with detailed troubleshooting steps at each point, is outlined in the hopes that it will aid other researchers in utilizing this tool to the fullest capability.
A structured multi-layer interpretation framework is proposed that links computational outputs across data-level processing, epigenomics-informed integrative regulatory modeling, and multi-omics-informed clinical interpretation, enabling traceable and mechanistically interpretable clinical inference.
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