Generation of Centered Log-Ratio Normalized Antibody-Derived Tag Counts from Large Single-Cell Sequencing Datasets

Benjamin Lacar

doi:10.1007/978-1-0716-1771-7_14

Generation of Centered Log-Ratio Normalized Antibody-Derived Tag Counts from Large Single-Cell Sequencing Datasets

Methods Mol Biol. 2022:2386:203-217. doi: 10.1007/978-1-0716-1771-7_14.

Author

Benjamin Lacar^{1

2}

Affiliations

¹ University of California, San Francisco, San Francisco, CA, USA. [email protected].
² University of California, Berkeley, Berkeley, CA, USA. [email protected].

PMID: 34766274
DOI: 10.1007/978-1-0716-1771-7_14

Abstract

Recent developments in single-cell analysis has provided the ability to assay >50 surface-level proteins by combining oligo-conjugated antibodies with sequencing technology. These methods, such as CITE-seq and REAP-seq, have added another modality to single-cell analysis, enhancing insight across many biological subdisciplines. While packages like Seurat have greatly facilitated analysis of single-cell protein expression, the practical steps to carry out the analysis with increasingly larger datasets have been fragmented. In addition, using data visualizations, I will highlight some details about the centered log-ratio (CLR) normalization of antibody-derived tag (ADT) counts that may be overlooked. In this method chapter, I provide detailed steps to generate CLR-normalized CITE-seq data using cloud computing from a large CITE-seq dataset.

Keywords: AWS; Seurat; Single-cell CITE-seq; Single-cell REAP-seq; Single-cell RNA and protein expression.

MeSH terms

Antibodies
Gene Expression Profiling
High-Throughput Nucleotide Sequencing
Sequence Analysis, RNA
Single-Cell Analysis*

Substances

Antibodies