MM2S (version 1.0.6)

PCARender: Principal Component Analysis (PCA) Projections of ssGSEA Rank Matrix for Test Samples Onto the MM2S Training Set

Description

This function projects the ssGSEA ranked matrix for the Test Data onto the ssGSEA ranked matrix of the MM2S training set. The projections are done using ssGSEA ranked matrix containing the selected genesets common to both the Training set and test data.

Usage

PCARender(GSVAmatrixTesting,GSVAmatrixTraining)

Arguments

GSVAmatrixTesting

Matrix of ranked enrichment scores for the tested datasets or data sample, containing the sample(s) in rows and genesets in columns

GSVAmatrixTraining

Matrix of ranked enrichment scores for the training datasets, containing the samples in rows and genesets in columns

Value

3 PDF files of projected test data onto the MM2S training set, using PCA (Principal Component Analysis) based on the selected genesets: PC1-PC2: Projection of the ssGSEA rank matrix from the testing set onto the training data, first and second principal component PC2-PC3: Projection of the ssGSEA rank matrix from the testing set onto the training data, second and third principal component Lattice: Lattice matrix with the projections onto PCA1-PC3

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

Examples

# NOT RUN {
# Running raw expression data through MM2S
# load Mouse gene expression data for the potential WNT mouse model
data(WNT_Mouse_Expr)
SubtypePreds<-MM2S.mouse(InputMatrix=WNT_Mouse_Expr[2:3],parallelize=1, seed = 12345)
# Generate Heatmap
PCARender(GSVAmatrixTesting=SubtypePreds$RankMatrixTesting,
GSVAmatrixTraining=SubtypePreds$RankMatrixTraining)
# }