# Protocol for obtaining cancer type and subtype predictions using subSCOPE

## TL;DR

This paper introduces a protocol using subSCOPE to classify cancer types and subtypes from various -omics data.

## Contribution

A protocol is introduced for cancer subtype prediction using subSCOPE with multiple -omics data types.

## Key findings

- subSCOPE can classify cancer subtypes using five -omics data types.
- The protocol allows selection of specific cancer types and data types for prediction.
- It provides subtype-level classification for non-TCGA cancer samples across 26 cohorts and 106 subtypes.

## Abstract

We present a protocol for obtaining cancer type and subtype predictions using a machine learning method (subSCOPE). We describe steps for data preparation, subSCOPE setup, and running subSCOPE inference on prepared data. The protocol supports five -omics data types as input (DNA methylation, gene expression, microRNA [miRNA] expression, point mutations, and copy-number variants) and allows individual cancer type and data type selection. For non-The Cancer Genome Atlas (TCGA) cancer samples, it provides subtype-level classification across 26 different TCGA cancer cohorts and 106 subtypes.

For complete details on the use and execution of this protocol, please refer to Ellrott et al.1

•Classify -omics data into one of 106 subtypes across 26 human cancers with subSCOPE•Use gene expression, miRNA, mutation, copy-number variation, or methylation data•Specify choice of data types and cancer types for prediction if desired•Obtain confidence values associated with each prediction

Classify -omics data into one of 106 subtypes across 26 human cancers with subSCOPE

Use gene expression, miRNA, mutation, copy-number variation, or methylation data

Specify choice of data types and cancer types for prediction if desired

Obtain confidence values associated with each prediction

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

We present a protocol for obtaining cancer type and subtype predictions using a machine learning method (subSCOPE). We describe steps for data preparation, subSCOPE setup, and running subSCOPE inference on prepared data. The protocol supports five -omics data types as input (DNA methylation, gene expression, microRNA [miRNA] expression, point mutations, and copy-number variants) and allows individual cancer type and data type selection. For non-The Cancer Genome Atlas (TCGA) cancer samples, it provides subtype-level classification across 26 different TCGA cancer cohorts and 106 subtypes.