miRSM: miRSM
In miRSM: Inferring miRNA sponge modules in heterogeneous data

Description Usage Arguments Value Author(s) References Examples

Identify miRNA sponge modules using sensitivity canonical correlation (SCC), sensitivity distance correlation (SDC), sensitivity RV coefficient (SRVC), and sponge module (SM) methods.

miRSM(
  miRExp,
  ceRExp,
  mRExp,
  miRTarget,
  CandidateModulegenes,
  typex = "standard",
  typez = "standard",
  nperms = 100,
  method = c("SCC", "SDC", "SRVC"),
  num_shared_miRNAs = 3,
  pvalue.cutoff = 0.05,
  MC.cutoff = 0.8,
  SMC.cutoff = 0.1,
  RV_method = c("RV", "RV2", "RVadjMaye", "RVadjGhaziri"),
  BCmethod = "BCPlaid"
)

`miRExp`	A SummarizedExperiment object. miRNA expression data: rows are samples and columns are miRNAs.
`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.
`miRTarget`	A SummarizedExperiment object. Putative miRNA-target binding information.
`CandidateModulegenes`	List object: a list of candidate miRNA sponge modules. Only for the SCC, SDC and SRVC methods.
`typex`	The columns of x unordered (type='standard') or ordered (type='ordered'). Only for the SCC method.
`typez`	The columns of z unordered (type='standard') or ordered (type='ordered'). Only for the SCC method.
`nperms`	The number of permutations. Only for the SCC method.
`method`	The method selected to identify miRNA sponge modules, including 'SCC', 'SDC', 'SRVC' and 'SM'.
`num_shared_miRNAs`	The number of common miRNAs shared by a group of ceRNAs and mRNAs. Only for the SCC, SDC and SRVC methods.
`pvalue.cutoff`	The p-value cutoff of significant sharing of common miRNAs by a group of ceRNAs and mRNAs or significant correlation.
`MC.cutoff`	The cutoff of matrix correlation (canonical correlation, distance correlation and RV coefficient). Only for the SCC, SDC and SRVC methods.
`SMC.cutoff`	The cutoff of sensitivity matrix correlation (sensitivity canonical correlation, sensitivity distance correlation and sensitivity RV coefficient). Only for the SCC, SDC and SRVC methods.
`RV_method`	the method of calculating RV coefficients. Select one of 'RV', 'RV2', 'RVadjMaye' and 'RVadjGhaziri' methods. Only for the SRVC method.
`BCmethod`	Specification of the biclustering method, including 'BCBimax', 'BCCC', 'BCPlaid' (default), 'BCQuest', 'BCSpectral', 'BCXmotifs'. Only for the SM method.

List object: Sensitivity correlation, and genes of miRNA sponge modules.

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

Witten DM, Tibshirani R, Hastie T. A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics. 2009, 10(3):515-34.

Szekely GJ, Rizzo ML. Partial distance correlation with methods for dissimilarities. Annals of Statistics. 2014, 42(6):2382-2412.

Szekely GJ, Rizzo ML, Bakirov NK. Measuring and Testing Dependence by Correlation of Distances, Annals of Statistics, 2007, 35(6):2769-2794.

Robert P, Escoufier Y. A unifying tool for linear multivariate statistical methods: the RV-Coefficient. Applied Statistics, 1976, 25(3):257-265.

Smilde AK, Kiers HA, Bijlsma S, Rubingh CM, van Erk MJ. Matrix correlations for high-dimensional data: the modified RV-coefficient. Bioinformatics, 2009, 25(3):401-405.

Maye CD, Lorent J, Horgan GW. Exploratory analysis of multiple omics datasets using the adjusted RV coefficient". Stat Appl Genet Mol Biol., 2011, 10, 14.

EIGhaziri A, Qannari EM. Measures of association between two datasets; Application to sensory data, Food Quality and Preference, 2015, 40(A):116-124.

data(BRCASampleData)
modulegenes_igraph <- module_igraph(ceRExp[, seq_len(10)], 
    mRExp[, seq_len(10)])
# Identify miRNA sponge modules using sensitivity RV coefficient (SRVC)
miRSM_igraph_SRVC <- miRSM(miRExp, ceRExp, mRExp, miRTarget,
                        modulegenes_igraph, method = "SRVC",
                        SMC.cutoff = 0.01, RV_method = "RV")