DoRC: Discovery of rare cells from ultralarge scRNAseq data The advent of dropletbased transcriptomics platforms has enabled parallel screening over thousands or millions of cells. One of the challenging issues is to identify the rare cells from the ultralarge scRNAseq data. Existing algorithms to find rare cells are time consuming or memoryexhausting. We propose an efficient and accurate method, Discovery of Rare Cells (DoRC). The rareness scores generated by DoRC can help biologists focus the downstream analyses only on a fraction of expression profiles within ultralarge scRNAseq data. We also demonstrate the efficacy of DoRC in delineating human blood dendritic cell subtypes using xe2x88xbc68k singlecell expression profiles of human blood cells. DoRC can recover artificially planted rare cells and is sensitive to cell type identities as well.

Detection of Cell Types from Singlecell RNAseq Data using Similarity via Kernel Preserving Learning Embedding The recent advances in singlecell sequencing techniques allow us to study biological issues on cell levels. Detecting cell types from scRNAseq data analysis is important and meaningful. However, highlevel noise and the nonlinearity and sparsity of scRNAseq data are great challenges. In this paper, we propose a celltype detection algorithm preserving the overall cell relations named POCR to analyze scRNAseq data. POCR utilizes a kernel embedding similarity measure to calculate celltocell similarity, by minimizing the reconstruction error of a kernel matrix, rather than the reconstruction error of the original data adopted by other similarity metrics. According to the scale of scRNAseq datasets, we select Gaussian kernel or linear kernel to calculate the embedding. We then adopt spectral clustering to detect the cell types based on the learned celltocell similarity. The results are further visualized to demonstrate the effectiveness of the celltype detection algorithm POCR. Further analysis shows that the learned similarity could improve the clustering and visualization of cell types in scRNAseq data. Our proposed algorithm is compared with five other stateoftheart cell subtype detection methods. The effectiveness of the algorithms is evaluated by two criteria: ARI and NMI. The experiments show that POCR achieves accurate and robust performance across different scRNAseq data. Our python implementation of POCR is available at https:github.comZeMingLiuPOCR.