Joint Research Center for Computational Biomedicine, Institute for Computational Genomics, RWTH Aachen University, Aachen, Germany.
Department of Cell Biology, Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany.
BMC Bioinformatics. 2022 Jul 12;23(1):276. doi: 10.1186/s12859-022-04817-5.
Single-cell RNA sequencing (scRNA-seq) allows the detection of rare cell types in complex tissues. The detection of markers for rare cell types is useful for further biological analysis of, for example, flow cytometry and imaging data sets for either physical isolation or spatial characterization of these cells. However, only a few computational approaches consider the problem of selecting specific marker genes from scRNA-seq data.
Here, we propose sc2marker, which is based on the maximum margin index and a database of proteins with antibodies, to select markers for flow cytometry or imaging. We evaluated the performances of sc2marker and competing methods in ranking known markers in scRNA-seq data of immune and stromal cells. The results showed that sc2marker performed better than the competing methods in accuracy, while having a competitive running time.
单细胞 RNA 测序 (scRNA-seq) 允许在复杂组织中检测罕见的细胞类型。检测罕见细胞类型的标记物对于进一步的生物学分析很有用，例如，对于这些细胞的物理分离或空间特征的流式细胞术和成像数据集。然而，只有少数计算方法考虑从 scRNA-seq 数据中选择特定标记基因的问题。
在这里，我们提出了 sc2marker，它基于最大间隔索引和具有抗体的蛋白质数据库，用于选择流式细胞术或成像的标记物。我们评估了 sc2marker 和竞争方法在对免疫和基质细胞的 scRNA-seq 数据中的已知标记物进行排序的性能。结果表明，sc2marker 在准确性方面优于竞争方法，同时具有竞争的运行时间。
