# Classifiers methods

## Classifying gliomas samples with `gliomaClassifier`

Classifying glioma samples with DNA methylation array based on:

Ceccarelli, Michele, et al. “Molecular profiling reveals biologically discrete subsets and pathways of progression in diffuse glioma.” Cell 164.3 (2016): 550-563. (https://doi.org/10.1016/j.cell.2015.12.028)

Possible classifications are:

- Mesenchymal-like
- Classic-like
- G-CIMP-high
- G-CIMP-low
- LGm6-GBM
- Codel

## Data

The input data can be either a Summarized Experiment object of a matrix (samples as columns, probes as rows) from the following platforms:

- HM27
- HM450
- EPIC array.

In this example we will retrieve two samples from TCGA and classify them expecting the same result as the paper.

    query <- GDCquery(
      project = "TCGA-GBM",
      data.category = "DNA methylation",
      barcode = c("TCGA-06-0122","TCGA-14-1456"),
      platform = "Illumina Human Methylation 27",
      legacy = TRUE
    )
    GDCdownload(query)
    data.hg19 <- GDCprepare(query)

    assay(data.hg19)[1:5,1:2]

## Function

    classification <- gliomaClassifier(data.hg19)

## Results

The classfier will return a list of 3 data frames:

1. Sample final classification
2. Each model final classification
3. Each class probability of classification

    names(classification)
    classification$final.classification
    classification$model.classifications
    classification$model.probabilities

## Comparing results with paper

    TCGAquery_subtype("GBM") %>%
     dplyr::filter(patient %in% c("TCGA-06-0122","TCGA-14-1456")) %>%
     dplyr::select("patient","Supervised.DNA.Methylation.Cluster")

    ## gbm subtype information from:doi:10.1016/j.cell.2015.12.028