scRNA-seq for Microcephaly Research [III]: Computational Analysis of scRNA-seq Data

Benjamin Babcock; Daniel Malawsky

doi:10.1007/978-1-0716-2752-5_10

scRNA-seq for Microcephaly Research [III]: Computational Analysis of scRNA-seq Data

Methods Mol Biol. 2023:2583:105-121. doi: 10.1007/978-1-0716-2752-5_10.

Authors

Benjamin Babcock^{1

2}, Daniel Malawsky^{3

4}

Affiliations

¹ Department of Medicine, Division of Immunology, Lowance Center for Human Immunology, Emory University School of Medicine, Atlanta, GA, USA. ben.babcock@emory.edu.
² Department of Neurology, University of North Carolina Medical School, Chapel Hill, NC, USA. ben.babcock@emory.edu.
³ Department of Neurology, University of North Carolina Medical School, Chapel Hill, NC, USA.
⁴ Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.

PMID: 36418729
DOI: 10.1007/978-1-0716-2752-5_10

Abstract

Single-cell transcriptomic analysis (scRNA-seq) can enable researchers to explore the gene expression patterns of thousands of individual cells simultaneously. Processing the complex data generated by scRNA-seq requires specialized computational tools. This chapter focuses on the analytical aspect of scRNA-seq workflow, with a focus on resolving biological signals from large-scale scRNA-seq data produced by the Drop-Seq platform.

Keywords: Computational methods; Louvain clustering; PCA; scRNA-seq.

MeSH terms

Exome Sequencing
Humans
Microcephaly*
Research Personnel
Single-Cell Analysis
Transcriptome