Molecular heterogeneities and complex microenvironments bring great challenges for cancer diagnosis and treatment. Recent advances in single-cell RNA-sequencing (scRNA-seq) technology make it possible to study cancer cell heterogeneities and microenvironments at single-cell transcriptomic level. Here, we develop an R package named scCancer, which focuses on processing and analyzing scRNA-seq data for cancer research. Except basic data processing steps, this package takes several special considerations for cancer-specific features. Firstly, the package introduced comprehensive quality control metrics. Secondly, it used a data-driven machine learning algorithm to accurately identify major cancer microenvironment cell populations. Thirdly, it estimated a malignancy score to classify malignant (cancerous) and non-malignant cells. Then, it analyzed intra-tumor heterogeneities by key cellular phenotypes (such as cell cycle and stemness), gene signatures and cell–cell interactions. Besides, it provided multi-sample data integration analysis with different batch-effect correction strategies. Finally, user-friendly graphic reports were generated for all the analyses. By testing on 56 samples with 433 405 cells in total, we demonstrated its good performance. The package is available at: http://lifeome.net/software/sccancer/.

中文翻译：

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scCancer：用于自动处理癌症中单细胞 RNA-seq 数据的软件包

分子异质性和复杂的微环境给癌症的诊断和治疗带来了巨大的挑战。单细胞 RNA 测序 (scRNA-seq) 技术的最新进展使得在单细胞转录组水平上研究癌细胞异质性和微环境成为可能。在这里，我们开发了一个名为 scCancer 的 R 包，它专注于处理和分析用于癌症研究的 scRNA-seq 数据。除了基本的数据处理步骤外，该软件包还对癌症特定的特征进行了一些特殊考虑。首先，该软件包引入了全面的质量控制指标。其次，它使用数据驱动的机器学习算法来准确识别主要的癌症微环境细胞群。第三，它估计了一个恶性评分，以对恶性（癌性）和非恶性细胞进行分类。然后，它通过关键细胞表型（如细胞周期和干性）、基因特征和细胞间相互作用分析了肿瘤内的异质性。此外，它还提供了具有不同批次效应校正策略的多样本数据集成分析。最后，为所有分析生成了用户友好的图形报告。通过对总共 433 405 个电池的 56 个样品的测试，我们证明了其良好的性能。该软件包可从以下网址获得：http://lifeome.net/software/sccancer/。通过对总共 433 405 个电池的 56 个样品的测试，我们证明了其良好的性能。该软件包可从以下网址获得：http://lifeome.net/software/sccancer/。通过对总共 433 405 个电池的 56 个样品的测试，我们证明了其良好的性能。该软件包可从以下网址获得：http://lifeome.net/software/sccancer/。