MBMethPred introduction

Input file for prediction

The ReadMethylFile is a function for reading DNA methylation files and use them as new data for prediction by every model. The input for this function should be either CSV or TSV file format. Please uncomment the following lines and run the function.

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. While the first column is CpG methylation probs, starting with cg and followed by a number, other columns are samples with methylation values. All columns should be named.

Box plot

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

Usage


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

plot of chunk unnamed-chunk-3

This function has two arguments as follow:

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 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

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 (uncomment).

# 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). Please uncomment the following lines and run the function.

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 SNF function (from 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)

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snf
#>  [1] SHH    Group3 Group4 Group4 Group4 SHH    SHH    Group3 Group4 SHH   
#> [11] WNT    SHH    SHH    WNT    SHH    WNT    Group3 Group3 Group3 Group4
#> [21] Group4 Group3 Group3 Group3 Group4 Group4 Group4 Group3 Group3 SHH   
#> [31] SHH    SHH    SHH    SHH    Group4 Group3 SHH    Group4 Group4 Group3
#> [41] Group4 Group4 WNT    Group3 Group4 Group4 Group4 Group4 SHH    Group4
#> Levels: Group4 SHH WNT Group3

This function has several arguments as follow:

Files A list of data frames created using the ReadSNFData function or matrices.
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 the DNA methylation dataset (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.