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  • Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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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). 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.
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  • We curated the cell label by unifying the major cell type annotated by different publications (e.g., considering both “T lymphoid cells” and “T cells” as “T cells”).
  • We used the HomoloGene databases provided by NCBI (Build 68) to identify homologous genes between human and mouse, and kept only genes that have one-to-one correspondence, which serves as a look-up table.

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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. We present SciBet, a single-cell classifier that accurately predicts cell identity for newly sequenced cells or cell clusters. 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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