MnM is an R-package that includes the code for running the newly developed machine-learning algorithm M&M, as short for Minority & Majority classifier. This classifier enables the classification of pediatric tumor (sub)types based on RNA-seq data. Besides the creation of the classifier models, MnM contains many functions to visualize M&M's performance on reference and test cohorts.

With over 120 documented tumor types and 250 tumor subtypes, identifying the correct malignancy during the diagnostic procedure remains a challenging but crucial process within pediatric oncology. M&M allows for inclusion of many rare pediatric malignancies with as few as three available samples, occurring with rates of less than once for every 500 children diagnosed with cancer. M&M is designed to assist pathologists during the diagnostic process to reduce inter-observer variability and help recognizing rare pediatric malignancies.

library(remotes)
remotes::install_github("princessmaximacenter/MnM/", dependencies = T)

Please see the supplied tutorial, vignettes, and documentation within R on how to properly use all functions. If you wish to familiarize yourself with MnM, the tutorial is the best starting point.

RNA TPM-normalized count data and metadata of the reference cohort and test cohort can be obtained from ArrayExpress (accession E-MTAB-14038). The final resulting pre-trained models, which can be used to classify new incoming RNA-samples, can be obtained from Zenodo in the near future.

In case of questions, suggestions or additional comments, please reach out to us via p.kemmeren[AT]prinsesmaximacentrum.nl (orcid ID: 0000-0003-2237-7354).