FindOptimalSubspaceClustering: Finds the optimal subspace clustering (i.e. using Grassmann...

In ncats/MultiOmicsGraphPrediction: Network-Based Ensemble Prediction from IntLIM Predictors

Finds the optimal subspace clustering (i.e. using Grassmann Manifold Technique - see PMID 30329022) given two different modalities of data (e.g. gene and metabolite). It optimizes the cophenetic correlation of the hierarchicial clustering of the samples using a grid search.

Description

Finds the optimal subspace clustering (i.e. using Grassmann Manifold Technique - see PMID 30329022) given two different modalities of data (e.g. gene and metabolite). It optimizes the cophenetic correlation of the hierarchicial clustering of the samples using a grid search.

Usage

FindOptimalSubspaceClustering(
  type1Similarity,
  type2Similarity,
  eigStep = 10,
  alphaMin = 0,
  alphaMax = 1,
  alphaStep = 0.1
)

Arguments

type1Similarity	A cosine similarity matrix for the first data type, found using ComputeCosineSimilarity.
type2Similarity	A cosine similarity matrix for the second data type, found using ComputeCosineSimilarity.
eigStep	The number of eigenvectors to step by during the evaluation. Note that this must be less than the number of samples. Default = 10.
alphaMin	The lowest value of alpha to investigate. Default = 0.
alphaMax	The highest value of alpha to investigate. Default = 1.
alphaStep	The value of alpha to step by during the evalutation. Default = 0.1.

Value

A named list including the data projected onto the merged subspace, the optimal number of eigenvectors, the optimal alpha value, the clustering coefficient, and the dendrogram.

ncats/MultiOmicsGraphPrediction documentation built on Aug. 23, 2023, 9:19 a.m.