Recent advances in single-cell transcriptome sequencing and computational analysis methods have improved our understanding of cellular heterogeneity. However, associating different cell subsets with phenotypes remains challenging. Recently, Ren et al. introduced PENCIL, a supervised learning framework incorporating gene selection to discern phenotype-relevant cells. To assess PENCIL’s reproducibility and transferability, we conducted a comprehensive evaluation across 12 single-cell RNA sequencing datasets representing four distinct phenotypes. We identified a few caveats with the original version of PENCIL, such as sensitivity to input perturbation, the correction of which contributed to PENCIL’s enhanced reproducibility. We highlight that boosting PENCIL’s cell subsets identification with gene set variation analysis creates a cytotoxic T cell immunotherapy response signature (CyTIR) predictive of immune checkpoint blockade response in skin cancer across multiple datasets, with an area under curve >0.75 and accuracy >0.71. Overall, our assessments enhance PENCIL’s reproducibility and utility, further extending its potential for identifying phenotype-relevant cell subsets in diverse biomedical applications.

单细胞转录组测序和计算分析方法的最新进展提高了我们对细胞异质性的理解。然而，将不同的细胞亚群与表型联系起来仍然具有挑战性。最近，任等人。引入了 PENCIL，一个监督学习框架，结合了基因选择来辨别表型相关细胞。为了评估 PENCIL 的重现性和可转移性，我们对代表四种不同表型的 12 个单细胞 RNA 测序数据集进行了全面评估。我们发现了 PENCIL 原始版本的一些注意事项，例如对输入扰动的敏感性，其修正有助于 PENCIL 增强的再现性。我们强调，通过基因集变异分析增强 PENCIL 的细胞亚群识别，创建了一个细胞毒性 T 细胞免疫治疗反应特征 (CyTIR)，可预测多个数据集中皮肤癌的免疫检查点阻断反应，曲线下面积 >0.75，准确度 >0.71。总体而言，我们的评估增强了 PENCIL 的重现性和实用性，进一步扩展了其在不同生物医学应用中识别表型相关细胞亚群的潜力。