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:

Sample final classification
Each model final classification
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