MM2S (version 1.0.6)

MM2S.mouse: MM2S Prediction of Mouse Medulloblastoma Samples

Description

This function generates MM2S subtype predictions for Mouse samples of interest. Users are provided the option to save this the predictions as a XLS file.

Usage

MM2S.mouse(InputMatrix,parallelize,seed,dir)

Arguments

InputMatrix

Matrix containing normalized gene expression data, with genes in rows and samples in columns. Gene IDs MUST be represented as Entres Gene IDs.

parallelize

Option to set number of cores to run ssGSEA calculations in parallel. Default is 1 (no parallelization)

seed

[integer] has to be specified by the user.

dir

Option to save MM2S subtype predictions only if the user wants to and specifies the dir.

Value

Predictions

MM2S Percent Confidence Predictions of Human Subtypes (Group3, Group4, Normal, Sonic hedgehog (SHH),Wingless (WNT)) for a given sample.

MM2S_Subtype

List of Sample names the designated Human MB subtype from MM2S classification.

RankMatrixTesting

ssGSEA rank matrix of the test data, using selected genesets common between the test data and training set. These genesets are based on a filtered list using the top24 common genesets, for each subtype

RankMatrixTraining

ssGSEA rank matrix of the trained MM2S human data, using selected genesets common between the test data and training set. These genesets are based on a filtered list using the top24 common genesets, for each subtype

References

Gendoo, D. M., Smirnov, P., Lupien, M. & Haibe-Kains, B. Personalized diagnosis of medulloblastoma subtypes across patients and model systems. Genomics, doi:10.1016/j.ygeno.2015.05.002 (2015)

Manuscript URL: http://www.sciencedirect.com/science/article/pii/S0888754315000774

See Also

MM2S.human,PredictionsHeatmap,PCARender

Examples

# NOT RUN {
# Load Mouse gene expression data 
# Data contains selected samples from a heterogenous WNT Mouse model
data(WNT_Mouse_Expr)
# Generate Subtype Predictions
SubtypePreds<-MM2S.mouse(InputMatrix=WNT_Mouse_Expr[2:3],parallelize=2, seed = 12345)

# Generate Heatmap of Predictions
PredictionsHeatmap(InputMatrix=SubtypePreds$Predictions, pdf_output=TRUE,pdfheight=5,pdfwidth=5)
# Generate projections of the selected genesets from 
# Mouse model onto the training set, using Principal Component Analysis (PCA)
PCARender(GSVAmatrixTesting=SubtypePreds$RankMatrixTesting,
GSVAmatrixTraining=SubtypePreds$RankMatrixTraining)
# }