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We benchmarked SciBet in terms of accuracy and speed, against scmap6 and Seurat v37, with the aforementioned cross-validation strategy and E-test as the feature selection method. We found that SciBet achieved the best performance in most of the 14 benchmarking datasets, compared to the other methods (Fig. 2c for all datasets together and Supplementary Fig. 2 of each dataset separately). Finally, we evaluated how these classifiers behaved in speed using 4 simulated datasets comprising 500 genes along with 1000, 10,000, 20,000 and 50,000 cells, respectively.
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For 26 of the 42 datasets, we downloaded raw scRNA-seq fastq files, and estimated the gene-level expression abundance with kallisto23 and human genome reference hg19 (downloaded from UCSC). Fast, robust and technology-independent computational methods are needed for supervised cell type annotation of single-cell RNA sequencing data. We present SciBet, a supervised cell type identifier that accurately predicts cell identity for newly sequenced cells with order-of-magnitude speed advantage. We enable web client deployment of SciBet for rapid local computation without uploading local data to the server. Facing the exponential growth in the size of single cell RNA datasets, this user-friendly and cross-platform tool can be widely useful for single cell type identification. At OddsSurfer, we are committed to responsible gambling and offer tools to help you stay in control and keep gambling fun.
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