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Based on our assumption that ΔS of genes varies according to the nonuniformity of the expression among pre-defined cell groups, genes with larger ΔS tended to be more informative. Thus, for the training set we ordered genes based on their ΔS and selected genes with top ΔS for the downstream classification. During my review, I tested the customer support at Scibet Casino, and the site has multiple channels.
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The detailed information including the accession codes and publication citations for all datasets can be seen in Supplementary Information. 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.
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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. A Training set Pre-process by calculating the mean gene expression form the original expression matrix. 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.
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