Yao Qiuming, Jia Yinglu, Ma Pengchong
University of Nebraska-Lincoln.
niversity of Nebraska-Lincoln.
Res Sq. 2024 Jan 17:rs.3.rs-3844718. doi: 10.21203/rs.3.rs-3844718/v1.
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.
评估所有细胞簇的标记基因可能既耗时又缺乏系统的策略。通过一个统一的计算平台简化这一过程，该平台能够自动进行识别和基准测试，将大大提高效率并确保公平评估。因此，我们开发了一个新颖的计算平台cellMarkerPipe（https://github.com/yao-laboratory/cellMarkerPipe），用于从单细胞RNA测序数据中自动识别细胞类型特异性标记基因，并结合全面的评估方案。CellMarkerPipe自适应地整合了一系列常用的和最先进的工具，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在单个标记基因选择方面的整体可靠性能，COSG则展现出值得称赞的速度和相当的功效。此外，我们还证明了我们的方法在实际医学数据集中的关键作用。这个通用的开源管道是简化细胞标记基因识别和评估方面的一项重大进展，适用于细胞生物学和医学研究领域的广泛应用。
