TY - JOUR
T1 - Statistical and machine learning methods for immunoprofiling based on single-cell data
AU - Zhang, Jingxuan
AU - Li, Jia
AU - Lin, Lin
N1 - Publisher Copyright:
© 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
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U2 - 10.1080/21645515.2023.2234792
DO - 10.1080/21645515.2023.2234792
M3 - Review article
C2 - 37485833
AN - SCOPUS:85165611321
SN - 2164-5515
VL - 19
JO - Human Vaccines and Immunotherapeutics
JF - Human Vaccines and Immunotherapeutics
IS - 2
M1 - 2234792
ER -