subtype_snf: Subtypes patients using Similarity Network Fusion

In agapow/subtypr: Robust and Validated Patient Subtyping for Precision Medicine

Description

Subtypes patients using Similarity Network Fusion

Usage

subtype_snf(data_list, minimal_return = FALSE, cluster_number, K = 20,
  alpha = 0.5, t = 20, spectral_clust_type = 3, just_fuse = FALSE)

Arguments

data_list	a list of data matrices with continuous data of format samples x features (with the same number of samples).
minimal_return	logical, if TRUE, the result of the function will just be what's needed to evaluate the goodness of the partition, i.e. the partition and the element for internal metrics.
cluster_number	The supposed or previously infered number of clusters.
K	Number of neighbors in K-nearest neighbors part of the algorithm of fusion and for the computation of the affinity matrix (same parameter for both process).
alpha	Variance for the local model (for the Gaussian kernel of the affinity matrix). Recommended values are between 0.3 and 0.8.
t	Number of iterations for the diffusion process.
spectral_clust_type	The type of spectral clustering, see spectralClustering for more information.
just_fuse	wetherto just integrate matrices and return the fused matrix or not

Value

a result list containing:

• $partition: The predicted partition

• $element_for_metric: The name of the element in the result list. containing the data to be used with internal metrics.

• $affinity_fused: The fused affinity matrix returned by the function SNF.

See Also

SNF, spectralClustering, affinityMatrix.

agapow/subtypr documentation built on May 5, 2019, 1:33 a.m.