module_clust: module_clust

Description Usage Arguments Value Author(s) References Examples

View source: R/miRSM.R

Description

Identification of gene modules from matched ceRNA and mRNA expression data using a series of clustering packages, including stats, flashClust, dbscan, subspace, mclust, SOMbrero and ppclust packages.

Usage

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module_clust(
  ceRExp,
  mRExp,
  cluster.method = "kmeans",
  num.modules = 10,
  num.ModuleceRs = 2,
  num.ModulemRs = 2
)

Arguments

ceRExp

A SummarizedExperiment object. ceRNA expression data: rows are samples and columns are ceRNAs.

mRExp

A SummarizedExperiment object. mRNA expression data: rows are samples and columns are mRNAs.

cluster.method

Specification of the clustering method, including 'kmeans'(default), 'hclust', 'dbscan' , 'clique', 'gmm', 'som' and 'fcm'.

num.modules

Parameter of the number of modules to be identified for the 'kmeans', 'hclust', 'gmm' and 'fcm' methods. Parameter of the number of intervals for the 'clique' method. For the 'dbscan' and 'som' methods, no need to set the parameter.

num.ModuleceRs

The minimum number of ceRNAs in each module.

num.ModulemRs

The minimum number of mRNAs in each module.

Value

GeneSetCollection object: a list of module genes.

Author(s)

Junpeng Zhang (https://www.researchgate.net/profile/Junpeng_Zhang3)

References

Forgy EW. Cluster analysis of multivariate data: efficiency vs interpretability of classifications. Biometrics, 1965, 21:768-769.

Hartigan JA, Wong MA. Algorithm AS 136: A K-means clustering algorithm. Applied Statistics, 1979, 28:100-108.

Lloyd SP. Least squares quantization in PCM. Technical Note, Bell Laboratories. Published in 1982 in IEEE Transactions on Information Theory, 1982, 28:128-137.

MacQueen J. Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, eds L. M. Le Cam & J. Neyman, 1967, 1, pp.281-297. Berkeley, CA: University of California Press.

Langfelder P, Horvath S. Fast R Functions for Robust Correlations and Hierarchical Clustering. Journal of Statistical Software. 2012, 46(11):1-17.

Ester M, Kriegel HP, Sander J, Xu X. A density-based algorithm for discovering clusters in large spatial databases with noise, Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96), 1996, 96(34): 226-231.

Campello RJGB, Moulavi D, Sander J. Density-based clustering based on hierarchical density estimates, Pacific-Asia conference on knowledge discovery and data mining. Springer, Berlin, Heidelberg, 2013: 160-172.

Agrawal R, Gehrke J, Gunopulos D, Raghavan P. Automatic subspace clustering of high dimensional data for data mining applications. In Proc. ACM SIGMOD, 1998.

Scrucca L, Fop M, Murphy TB, Raftery AE. mclust 5: clustering, classification and density estimation using Gaussian finite mixture models The R Journal 8/1, 2016, pp. 205-233.

Kohonen T. Self-Organizing Maps. Berlin/Heidelberg: Springer-Verlag, 3rd edition, 2001.

Dunn JC. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 1973, 3(3):32-57.

Bezdek JC. Cluster validity with fuzzy sets. Journal of Cybernetics, 1974, 3: 58-73.

Bezdek JC. Pattern recognition with fuzzy objective function algorithms. Plenum, NY, 1981.

Examples

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data(BRCASampleData)
modulegenes_clust <- module_clust(ceRExp[, seq_len(30)],
    mRExp[, seq_len(30)])

zhangjunpeng411/miRSM documentation built on Nov. 28, 2020, 7 a.m.