Computational Methods for Single-Cell Proteomics.

Autor: Guldberg SM; Department of Otolaryngology-Head and Neck Surgery and Department of Microbiology and Immunology, University of California, San Francisco, California, USA; email: matthew.spitzer@ucsf.edu.; Biomedical Sciences Graduate Program, University of California, San Francisco, California, USA.; Gladstone-UCSF Institute for Genomic Immunology, San Francisco, California, USA., Okholm TLH; Department of Otolaryngology-Head and Neck Surgery and Department of Microbiology and Immunology, University of California, San Francisco, California, USA; email: matthew.spitzer@ucsf.edu.; Gladstone-UCSF Institute for Genomic Immunology, San Francisco, California, USA.; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California, USA., McCarthy EE; Department of Otolaryngology-Head and Neck Surgery and Department of Microbiology and Immunology, University of California, San Francisco, California, USA; email: matthew.spitzer@ucsf.edu.; Biomedical Sciences Graduate Program, University of California, San Francisco, California, USA.; Institute for Human Genetics; Division of Rheumatology, Department of Medicine; Medical Scientist Training Program; and Biological and Medical Informatics Graduate Program, University of California, San Francisco, California, USA., Spitzer MH; Department of Otolaryngology-Head and Neck Surgery and Department of Microbiology and Immunology, University of California, San Francisco, California, USA; email: matthew.spitzer@ucsf.edu.; Gladstone-UCSF Institute for Genomic Immunology, San Francisco, California, USA.; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California, USA.; Parker Institute for Cancer Immunotherapy, San Francisco, California, USA.; Chan Zuckerberg Biohub, San Francisco, California, USA.
Jazyk: angličtina
Zdroj: Annual review of biomedical data science [Annu Rev Biomed Data Sci] 2023 Aug 10; Vol. 6, pp. 47-71. Date of Electronic Publication: 2023 Apr 11.
DOI: 10.1146/annurev-biodatasci-020422-050255
Abstrakt: Advances in single-cell proteomics technologies have resulted in high-dimensional datasets comprising millions of cells that are capable of answering key questions about biology and disease. The advent of these technologies has prompted the development of computational tools to process and visualize the complex data. In this review, we outline the steps of single-cell and spatial proteomics analysis pipelines. In addition to describing available methods, we highlight benchmarking studies that have identified advantages and pitfalls of the currently available computational toolkits. As these technologies continue to advance, robust analysis tools should be developed in tandem to take full advantage of the potential biological insights provided by these data.
Databáze: MEDLINE