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Analyze TPM counts and explore classes and subclasses of tumor types.
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OTTER is an ensemble of convolutional neural networks for the accurate identification and classification of tumour components from whole-transcriptome gene expression data.

It has been trained on expression data from a pan-cancer and normal tissue dataset of poly(A) RNA-Seq samples. HUGO or ENSEMBL gene expression tables (tsv) are compatible in input.


When using OTTER for your work please cite:

Comitani, Federico, Joshua O. Nash, Sarah Cohen-Gogo, Astra I. Chang, Timmy T. Wen, Anant Maheshwari, Bipasha Goyal et al. "Diagnostic classification of childhood cancer using multiscale transcriptomics." Nature Medicine 29, no. 3 (2023): 656-666.

Terms of use

RESEARCH USE ONLY: This website is intended for research purposes only. These analyses were not performed for the purposes of diagnosis, prophylaxis, or treatment.