MOESM3 of scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data

Additional file 3: Table S4. Prediction results of pancreatic cells without Seurat alignment. Table S6. Prediction results using Baron dataset as reference. Table S7. Classification performance of scmap-cluster using the Baron dataset as training. Table S8. Classification performance of scmap-cell using the Baron dataset as training. Table S9. Classification performance of caSTLe using the Baron dataset as training. Table S10. Classification performance of singleCellNet using the Baron dataset as training. Table S11. Classification performance of scID using the Baron dataset as training. Table S14. Differentially expressed genes between unassigned cells by scPred and remaining cord blood-derived cells. Table S15. Gene ontology overrepresentation results of overexpressed genes from unassigned cells.

附加文件3：表S4。未经过Seurat比对的胰腺细胞预测结果。
表S6：以Baron数据集为参考的预测结果。
表S7：以Baron数据集作为训练集时scmap-cluster的分类性能。
表S8：以Baron数据集作为训练集时scmap-cell的分类性能。
表S9：以Baron数据集作为训练集时caSTLe的分类性能。
表S10：以Baron数据集作为训练集时singleCellNet的分类性能。
表S11：以Baron数据集作为训练集时scID的分类性能。
表S14：经scPred鉴定为未分配的细胞与其余脐带血来源细胞间的差异表达基因。
表S15：未分配细胞中过表达基因的基因本体富集分析结果。