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Resource CellBiAge: Improved single-cell age classification using data binarization
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DOI：10.1016/j.celrep.2023.113500.png)
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Abstract
En 中文
Aging is a major risk factor for many diseases. Accurate methods for predicting age in specific cell types are essential to understand the heterogeneity of aging and to assess rejuvenation strategies. However, classi-fying organismal age at single-cell resolution using transcriptomics is challenging due to sparsity and noise. Here, we developed CellBiAge, a robust and easy-to-implement machine learning pipeline, to classify the age of single cells in the mouse brain using single-cell transcriptomics. We show that binarization of gene expres-sion values for the top highly variable genes significantly improved test performance across different models, techniques, sexes, and brain regions, with potential age-related genes identified for model prediction. Addi-tionally, we demonstrate CellBiAge's ability to capture exercise-induced rejuvenation in neural stem cells. This study provides a broadly applicable approach for robust classification of organismal age of single cells in the mouse brain, which may aid in understanding the aging process and evaluating rejuvenation methods.
Keywords:
LIFE-SPAN
NEUROGENESIS
IDENTIFICATION
EXPRESSION
LONGEVITY
PROFILES
DYNAMICS
MICE
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