MBMethPred

MBMethPred is a user-friendly package developed for the accurate prediction of medulloblastoma subgroups using DNA methylation beta values. It incorporates seven machine learning models, including Random Forest, K-Nearest Neighbors, Support Vector Machine, Linear Discriminant Analysis, Extreme Gradient Boosting, Naive Bayes, and a neural network model specifically designed for the complexities of medulloblastoma data. The package provides streamlined workflows for data preprocessing, feature selection, model training, cross-validation, and prediction. This vignette offers detailed explanations, examples, and resulting outputs for each functionality. The MBMethPred package was tested on an Ubuntu machine equipped with an Intel Core i5-6200U processor and 16GB RAM.

Citation

Sharif Rahmani E, Lawarde A, Lingasamy P, Moreno SV, Salumets A and Modhukur V (2023), MBMethPred: a computational framework for the accurate classiﬁcation of childhood medulloblastoma subgroups using data integration and AI-based approaches. Front. Genet. 14:1233657. doi: 10.3389/fgene.2023.1233657

Installation

install.packages("MBMethPred")
or
remotes::install_github("sharifrahmanie/MBMethPred")

{r setup, include = FALSE} require(MBMethPred)

Input file for prediction

The ReadMethylFile is a function for reading DNA methylation beta values files and using them as new data for prediction by every model. The input for this function should be either CSV or TSV file format.

Usage

set.seed(1234)
fac <- ncol(Data1)
NewData <- sample(data.frame(t(Data1[,-fac])),10)
NewData <- cbind(rownames(NewData), NewData)
colnames(NewData)[1] <- "ID"
write.csv(NewData, "NewData.csv", quote = FALSE, row.names = FALSE)
methyl <- ReadMethylFile(File = "NewData.csv")

This function has only one argument, the File. The first column of the File is the CpG methylation probe that starts with cg characters and is followed by a number (e.g., cg100091). Other columns are samples with methylation beta values. All columns in the data frame should have a name.

Box plot

The BoxPlot function draws a box plot out of DNA methylation beta values or other data frames.

Usage

```{r fig.width= 8, fig.height=5}

data <- Data2[1:20,] data <- cbind(rownames(data), data) colnames(data)[1] <- “ID” BoxPlot(File = data, Projname = NULL)


This function has two arguments as follows:

* `File`    A data frame with the first column as ID. 
* `Projname` A string to name the plot.

## t-SNE 3D plot
The `TSNEPlot` function draws a 3D t-SNE plot for the DNA methylation dataset using the K-means clustering technique. This function has two arguments `File` (any matrices) and `NCluster` ( number of clusters for K-Means clustering). 

### Usage 

```{r}
data <- data.frame(t(Data2[1:100,]))
data <- cbind(rownames(data), data)
colnames(data)[1] <- "ID"
TSNEPlot(File = data, NCluster = 4)

An R window will appear with a 3D projection of the t-SNE result. The plot object can be saved with the next line of code.

rgl.snapshot('tsne3d.png', fmt = 'png')

Input file for similarity network fusion (SNF)

Using ReadSNFData function, one can read files (any matrices with CSV or TSV format) and feed them into the similarity network fusion (SNF) function (from the SNFtools package).

Usage

data(Data2) # Gene expression 
Data2 <- cbind(rownames(Data2), Data2)
colnames(Data2)[1] <- "ID"
write.csv(Data2, "Data2.csv", row.names = FALSE)
Data2 <- ReadSNFData(File = "Data2.csv")

Similarity network fusion (SNF)

The SimilarityNetworkFusion is a function to perform the SNF function (from the SNFtool package) and output clusters.

Usage

data(RLabels) # Real labels
data(Data2) # Methylation
data(Data3) # Gene expression
snf <- SimilarityNetworkFusion(Files = list(Data2, Data3),
                               NNeighbors  = 13,
                               Sigma = 0.75,
                               NClusters = 4,
                               CLabels = c("Group4", "SHH", "WNT", "Group3"),
                               RLabels = RLabels,
                               Niterations = 60)
snf

This function has several arguments as follows:

Files A list of data frames created using the ReadSNFData function.
NNeighbors The number of nearest neighbors.
Sigma The variance for local model.
NClusters The number of clusters.
CLabels A string vector to name the clusters. Optional.
RLabels The actual label of samples to calculate the Normalized Mutual Information (NMI) score. Optional.
Niterations The number of iterations for the diffusion process.

Support vector machine model

The SupportVectorMachineModel is a function to train a support vector machine model to classify medulloblastoma subgroups using DNA methylation beta values (Illumina Infinium HumanMethylation450). Prediction is followed by training if new data is provided.

Model metrics, including accuracy, precision, sensitivity F1-Score, specificity, and AUC_average can be calculated for the test dataset using the ModelMetrics function, which calculates the average of the above parameters from the result of the ConfusionMatrix function.

The prediction result on new data can be accessed through the NewDataPredictionResult function, which calculates every prediction’s mode across the number of cross-validation folds.