Here we use marker genes G1, G2, and G3 along with a non-marker gene G4 as examples. B Using E-test to select cell type-specific genes for the downstream classification. Genes with total entropy difference larger than the predefined threshold will be kept.
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- For read count data generated by full-length sequencing technique, we calculated Transcript Per Million (TPM)20, added pseudo value one to handle 0 values and performed log-normalization.
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All scRNA-seq datasets in this paper were obtained from their public accessions. And we used the original cell type annotation provided by each publication as ground truth. For all datasets, we applied the common normalization methods as following. For read count data generated by full-length sequencing technique, we calculated Transcript Per Million (TPM)20, added pseudo value one to handle 0 values and performed log-normalization. Scibet, a new online betting site launched in 2024, operates under the Malta Gaming Authority (MGA). Their standard welcome bonus offers a 100% match up to €100, ideal for exploring their sports and market options.
Fig. 1. Overview of SciBet algorithm.
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Genes selected by E-test are used for the model training and prediction. C Training SciBet model by obtaining the parameters for the multinomial models of each cell type. For each cell type, the sum of all parameters belonging to different genes equals to 1, which represent the expression probability of different genes. D Calculating the likelihood function of a test cell using the trained SciBet model and annotating cell type for the test cell with maximum likelihood estimation. 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.
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