School of Computing, University of Nebraska Lincoln, 256 Avery Hall, Lincoln, NE, 68588, USA.
Department of Chemistry, University of Nebraska Lincoln, Hamilton Hall, Lincoln, NE, 68588, USA.
Sci Rep. 2024 Jun 7;14(1):13151. doi: 10.1038/s41598-024-63492-z.
Assessing marker genes from all cell clusters can be time-consuming and lack systematic strategy. Streamlining this process through a unified computational platform that automates identification and benchmarking will greatly enhance efficiency and ensure a fair evaluation. We therefore developed a novel computational platform, cellMarkerPipe ( https://github.com/yao-laboratory/cellMarkerPipe ), for automated cell-type specific marker gene identification from scRNA-seq data, coupled with comprehensive evaluation schema. CellMarkerPipe adaptively wraps around a collection of commonly used and state-of-the-art tools, including Seurat, COSG, SC3, SCMarker, COMET, and scGeneFit. From rigorously testing across diverse samples, we ascertain SCMarker's overall reliable performance in single marker gene selection, with COSG showing commendable speed and comparable efficacy. Furthermore, we demonstrate the pivotal role of our approach in real-world medical datasets. This general and opensource pipeline stands as a significant advancement in streamlining cell marker gene identification and evaluation, fitting broad applications in the field of cellular biology and medical research.
评估所有细胞群的标记基因可能既耗时又缺乏系统策略。通过一个自动识别和基准测试的统一计算平台来简化这个过程，将极大地提高效率并确保公平评估。因此，我们开发了一个新的计算平台，cellMarkerPipe（https://github.com/yao-laboratory/cellMarkerPipe），用于从 scRNA-seq 数据中自动识别细胞类型特异性标记基因，并结合了全面的评估方案。cellMarkerPipe自适应地围绕一系列常用和最先进的工具进行包装，包括 Seurat、COSG、SC3、SCMarker、COMET 和 scGeneFit。通过对不同样本的严格测试，我们确定了 SCMarker 在单标记基因选择中的整体可靠性能，COSG 表现出令人赞赏的速度和相当的效果。此外，我们还在真实的医学数据集上展示了我们方法的关键作用。这种通用的开源管道是简化细胞标记基因识别和评估的重要进展，适用于细胞生物学和医学研究领域的广泛应用。
