The goal of this project is to develop a model/function that can accurately predict amongst 4 molecular subtypes of Medulloblastoma, Sonic Hedgehog (SHH), WNT, Group 3, and Group 4 from RNA-Seq or microarray data (and potentially any transcriptomic data). These subtypes were first identified using Non-negative matrix factorization on microarray data (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4874239/). Since then, these subtypes are widely used in both research and clinical practice (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4105823/). It is important to note there are other studies that classify medulloblastoma's into many more subgroups(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6163053/). This classifier is not capable of that currently as we don't have adequete training data .
The classifier was built using mulitiple microarray and RNA-Seq datasets. As there is tremendous variability in the dynamic range and values of microarray (between different platforms) and RNA-Seq, gene ratio's instead of gene expression were used as features for the model. Discrimanating features (gene ratios) were identified using the limma package (https://academic.oup.com/nar/article/43/7/e47/2414268) and samples are classified using an un-weighted sum of normalized scores. The data was trained using 2 datasets and tested in 2 separate datasets. The classifier is still being refined but can currently differentiate between the 4 subtypes at a greater than 95% accuracy.
The results reflect testing among 2 separate datasets although more independent datasets are needed and further refinement of the model is necessary. The overall trend is that the model/function can discern WNT and SHH very well but has a tougher time with Group 3 and Group 4 which may be expected as Group 3 and Group 4 are not characterized as well.
