Data for the ccAFv2 training and testing

Single-cell transcriptomics has unveiled a vast landscape of cellular heterogeneity in which the cell cycle is a significant component. We trained a high-resolution cell cycle classifier (ccAFv2) using single cell RNA-seq (scRNA-seq) characterized human neural stem cells. The features of this classifier are that it classifies six cell cycle states (G1, Late G1, S, S/G2, G2/M, and M/Early G1) and a quiescent-like G0 state, and it incorporates a tunable parameter to filter out less certain classifications. The ccAFv2 classifier performed better than or equivalent to other state-of-the-art methods even while classifying more cell cycle states, including G0. We showcased the versatility of ccAFv2 by successfully applying it to classify cells, nuclei, and spatial transcriptomics data in humans and mice, using various normalization methods and gene identifiers. We provide methods to regress the cell cycle expression patterns out of single cell or nuclei data to uncover underlying biological signals. The classifier can be used either as an R package integrated with Seurat (https://github.com/plaisier-lab/ccafv2_R) or a PyPI package integrated with scanpy (https://pypi.org/project/ccAF/). We proved that ccAFv2 has enhanced accuracy, flexibility, and adaptability across various experimental conditions, establishing ccAFv2 as a powerful tool for dissecting complex biological systems, unraveling cellular heterogeneity, and deciphering the molecular mechanisms by which proliferation and quiescence affect cellular processes.单细胞转录组学（single-cell transcriptomics）已揭示了细胞异质性的广阔图景，其中细胞周期是重要的组成部分。我们利用经全面表征的人类神经干细胞的单细胞RNA测序（single cell RNA-seq, scRNA-seq）数据，训练了一款高分辨率细胞周期分类器（ccAFv2）。该分类器的核心特性在于：可将细胞划分为6种细胞周期状态（G1、晚G1、S、S/G2、G2/M以及M/早G1）与1种类似静息状态的G0状态，同时内置可调参数以过滤置信度较低的分类结果。即便相较于其他当前顶尖的同类方法，ccAFv2分类器在可分类更多细胞周期状态（含G0状态）的前提下，仍能达到相当或更优的分类性能。我们通过将ccAFv2成功应用于人类与小鼠的细胞、细胞核及空间转录组学（spatial transcriptomics）数据的分类任务，并兼容多种标准化方法与基因标识符，验证了其广泛适用性。我们还提供了可从单细胞或细胞核数据中回归去除细胞周期表达特征的方法，以挖掘潜在的生物学信号。该分类器可通过两种方式使用：一是作为与Seurat整合的R包（链接：https://github.com/plaisier-lab/ccafv2_R），二是作为与Scanpy整合的PyPI包（链接：https://pypi.org/project/ccAF/）。我们的研究证实，ccAFv2在多种实验条件下均具备更优的准确性、灵活性与适配性，使其成为解析复杂生物系统、阐明细胞异质性以及揭示增殖与静息状态如何调控细胞过程的分子机制的强有力工具。