The Cancer Galaxy serves as a hub for tools commonly used in analysis of cancer datasets. This server currently features tools for: Multiplex Tissue Imaging with Galaxy-ME, QIIME2, IOBIO Interactive tools, and machine learning with Galaxy-ML. This server is a collaborative effort and we welcome any suggestions or requests for making tools related to cancer analysis available on this server. We also welcome contributions to the development of new tools, workflows or trainings!
Are you new to Galaxy, or returning after a long time, and looking for help to get started? Take an interactive tour:
All of the Galaxy tools for MCMICRO have been developed by the Goecks lab at the Moffitt Cancer Center and the Oregon Health and Science University.
Tool | Description | Reference |
---|---|---|
basic_illumination | BaSiC shading correction for use with Ashlar | Peng et al. 2017 |
ashlar | ASHLAR: Alignment by Simultaneous Harmonization of Layer/Adjacency Registration | Muhlich et al. 2021 |
coreograph | Dearray of Tissue Microarrays | Coreograph Github |
unmicst | UnMICST - Universal Models for Identifying Cells and Segmenting Tissue | UnMICST info |
s3segmenter | S3segmenter: Generates single cell (nuclei and cytoplasm) label masks | S3Segmenter github |
quantification | MCQuant: Single cell quantification | MCQUant github |
A Hands-on Galaxy-ME Tutorial highlights an example analysis of a tissue microarray. Two workflows are currently available to process your samples using the MCMICRO Galaxy pipeline: