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- Most cells (~92%) could be correctly mapped based on SciBet (Fig. 3b), demonstrating the utility of SciBet in cross-species cell identification.
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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. While owned by L.C.S. Limited, the site has a licence from MGA, meaning it respects all regulations and ensures a pleasant and secure experience on the website. With our deep understanding of the sector from direct access to the latest insights, we are able to provide accurate, relevant, and unbiased content that our readers can rely on. If these seatbacks don’t bother you very much, then we strongly suggest trying out Scibet. Start by typing your email address and picking a password and currency.
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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. Our results demonstrated that SciBet out-performed scmap and Seurat v3 by orders of magnitude, with a classification speed of ~100,000 cells per second (Fig. 2e). We tested the performance of SciBet on two datasets, with both even and uneven cell type distributions. In addition, for a human pancreatic dataset11 with imbalanced proportions of cell types, SciBet could still provide the most discriminating power.
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Facing such explosive data growth, one major challenge is the reliable and rapid cell type identification given a newly sequenced cell. Such practice can become inefficient if aforementioned huge datasets are used as reference. To illustrate the performance and the scalability of the feature selection methods, we benchmarked E-test against F-test (one-way ANOVA) and M3Drop8 using the same classifier scmap. Our results showed that E-test consistently achieved the highest classification accuracy. In principle, E-test only needs linear operations and the computational time increases linearly with the numbers of cells and genes, in contrast to the quadratic increase for other gene selection methods. The time consumption and cell number relationship (Fig. 2b) confirmed this point, demonstrating the scalability of E-test for large datasets.