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基于单细胞数据的免疫分析的统计和机器学习方法。

Statistical and machine learning methods for immunoprofiling based on single-cell data.

机构信息

Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.

Department of Statistics, Pennsylvania State University, University Park, PA, USA.

出版信息

Hum Vaccin Immunother. 2023 Aug 1;19(2):2234792. doi: 10.1080/21645515.2023.2234792. Epub 2023 Jul 24.

DOI:10.1080/21645515.2023.2234792
PMID:37485833
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10373621/
Abstract

Immunoprofiling has become a crucial tool for understanding the complex interactions between the immune system and diseases or interventions, such as therapies and vaccinations. Immune response biomarkers are critical for understanding those relationships and potentially developing personalized intervention strategies. Single-cell data have emerged as a promising source for identifying immune response biomarkers. In this review, we discuss the current state-of-the-art methods for immunoprofiling, including those for reducing the dimensionality of high-dimensional single-cell data and methods for clustering, classification, and prediction. We also draw attention to recent developments in data integration.

摘要

免疫分析已成为理解免疫系统与疾病或干预措施（如治疗和疫苗接种）之间复杂相互作用的重要工具。免疫反应生物标志物对于理解这些关系并可能开发个性化干预策略至关重要。单细胞数据已成为识别免疫反应生物标志物的有前途的来源。在这篇综述中，我们讨论了免疫分析的最新方法，包括用于降低高维单细胞数据维度的方法以及聚类、分类和预测方法。我们还提请注意数据集成的最新发展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e207/10373621/dd765d7744b3/KHVI_A_2234792_F0011_OC.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e207/10373621/5cf6f571033a/KHVI_A_2234792_F0005_OC.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e207/10373621/dd765d7744b3/KHVI_A_2234792_F0011_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e207/10373621/914b69fdc4d2/KHVI_A_2234792_F0001_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e207/10373621/6288e3a99d4c/KHVI_A_2234792_F0002_B.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e207/10373621/e5b91329ade2/KHVI_A_2234792_F0003_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e207/10373621/9026b8d67f12/KHVI_A_2234792_F0004_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e207/10373621/5cf6f571033a/KHVI_A_2234792_F0005_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e207/10373621/6912f7e8483a/KHVI_A_2234792_F0006_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e207/10373621/dfc2b2981ed6/KHVI_A_2234792_F0007_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e207/10373621/91b6f66d22d0/KHVI_A_2234792_F0008_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e207/10373621/60d71f162889/KHVI_A_2234792_F0009_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e207/10373621/a2bb61c3565f/KHVI_A_2234792_F0010_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e207/10373621/dd765d7744b3/KHVI_A_2234792_F0011_OC.jpg

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Clustering single-cell multi-omics data with MoClust.使用 MoClust 对单细胞多组学数据进行聚类。
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Neoadjuvant atezolizumab for resectable non-small cell lung cancer: an open-label, single-arm phase II trial.
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