ArticleMethods in molecular biology (Clifton, N.J.)2022
Generation of Centered Log-Ratio Normalized Antibody-Derived Tag Counts from Large Single-Cell Sequencing Datasets.
Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed, 2 citations in OpenAlex.
- Capturing the multifaceted function of adipose tissue macrophages.Frontiers in immunology · 2023Review
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1 author at 1 institution in 1 country.
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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.
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