: Single-cell studies are enabling our understanding of the molecular processes of normal cell development and the onset of several pathologies. For instance, single-cell RNA sequencing (scRNA-Seq) measures the transcriptome-wide gene expression at a single-cell resolution, allowing for studying the heterogeneity among the cells of the same population and revealing complex and rare cell populations. On the other hand, single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-Seq) can be used to define transcriptional and epigenetic changes by analyzing the chromatin accessibility at the single-cell level. However, the integration of multi-omics data still remains one of the most difficult tasks in bioinformatics. In this chapter, we focus on the combination of scRNA-Seq and scATACSeq data to perform an integrative analysis of the single-cell transcriptome and chromatin accessibility of human fetal progenitors.

Integration of scATAC-Seq with scRNA-Seq Data

Tangherloni, Andrea
2023

Abstract

: Single-cell studies are enabling our understanding of the molecular processes of normal cell development and the onset of several pathologies. For instance, single-cell RNA sequencing (scRNA-Seq) measures the transcriptome-wide gene expression at a single-cell resolution, allowing for studying the heterogeneity among the cells of the same population and revealing complex and rare cell populations. On the other hand, single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-Seq) can be used to define transcriptional and epigenetic changes by analyzing the chromatin accessibility at the single-cell level. However, the integration of multi-omics data still remains one of the most difficult tasks in bioinformatics. In this chapter, we focus on the combination of scRNA-Seq and scATACSeq data to perform an integrative analysis of the single-cell transcriptome and chromatin accessibility of human fetal progenitors.
2023
9781071627556
9781071627563
Calogero, Raffaele A.; Benes, Vladimir
Single cell transcriptomics : methods and protocols
Berest, Ivan; Tangherloni, Andrea
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11565/4058996
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