@@ -24,8 +24,10 @@ library(DT)

 <br>

 ## Classifying gliomas samples with `gliomaClassifier`

+<hr>

+

+Classifying glioma samples with DNA methylation array based on:

-Classify 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)

@@ -38,7 +40,8 @@ Possible classifications are:

 - LGm6-GBM

 - Codel

-### Data

+## Data

+<hr>

 The input data can be either a Summarized Experiment object of a matrix 

 (samples as columns, probes as rows) from the following platforms:

@@ -64,13 +67,15 @@ data.hg19 <- GDCprepare(query)

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

 ```

-### Function

+## Function

+<hr>

+

 ```{r, eval = TRUE}

 classification <- gliomaClassifier(data.hg19)

 ```

-### Results

-

+## Results

+<hr>

 The classfier will return a list of 3 data frames:

 1. Sample final classification

@@ -84,7 +89,8 @@ classification$model.classifications

 classification$model.probabilities

 ```

-### Comparing results with paper

+## Comparing results with paper

+<hr>

 ```{R}

 TCGAquery_subtype("GBM") %>%

  dplyr::filter(patient %in% c("TCGA-06-0122","TCGA-14-1456")) %>%

@@ -20,6 +20,14 @@ h1 {

   font-size: 28px;

 }

+h2 {

+  font-size: 28px;

+}

+

+.section.level2 h2 {

+    padding-top: 65px;

+    margin-top: -40px;

+}

 .section.level1 h1 {

     padding-top: 65px;

     margin-top: -40px;