miRSM: miRSM

In zhangjunpeng411/miRSM: Inferring miRNA sponge modules in heterogeneous data

miRSM

Description

Identify miRNA sponge modules using sensitivity canonical correlation (SCC), sensitivity distance correlation (SDC), sensitivity RV coefficient (SRVC), sensitivity similarity index (SSI), sensitivity generalized coefficient of determination (SGCD), sensitivity Coxhead's or Rozeboom's coefficient (SCRC), and sponge module (SM) methods.

Usage

miRSM(
  miRExp = NULL,
  ceRExp,
  mRExp = NULL,
  miRTarget,
  CandidateModulegenes,
  typex = "standard",
  typez = "standard",
  nperms = 100,
  method = c("SCC", "SDC", "SRVC", "SM", "SSI", "SGCD", "SCRC"),
  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",
  CRC_method = c("Coxhead", "Rozeboom")
)

Arguments

miRExp	NULL (default) or 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	NULL (default) or 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, SRVC, SSI, SGCD and SCRC 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', 'SM', 'SSI', 'SGCD' and 'SCRC'.
num_shared_miRNAs	The number of common miRNAs shared by a group of ceRNAs and mRNAs. Only for the SCC, SDC, SRVC, SSI, SGCD and SCRC 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, SRVC, SSI, SGCD and SCRC methods.
SMC.cutoff	The cutoff of sensitivity matrix correlation (sensitivity canonical correlation, sensitivity distance correlation and sensitivity RV coefficient). Only for the SCC, SDC, SRVC, SSI, SGCD and SCRC methods when miRExp is not NULL.
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.
CRC_method	the method of calculating matrix correlation. Select one of 'Coxhead' and 'Rozeboom' methods. Only for the SCRC method.

Value

List object: Group competition of miRNA sponge modules, and miRNA sponge modules.

Author(s)

Junpeng Zhang (https://www.researchgate.net/profile/Junpeng-Zhang-2)

References

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.

Indahl UG, Næs T, Liland KH. A similarity index for comparing coupled matrices. Journal of Chemometrics. 2018; 32:e3049.

Yanai H. Unification of various techniques of multivariate analysis by means of generalized coefficient of determination (GCD). Journal of Behaviormetrics, 1974, 1(1): 45-54.

Coxhead P. Measuring the relationship between two sets of variables. British journal of mathematical and statistical psychology, 1974, 27(2): 205-212.

Rozeboom WW. Linear correlations between sets of variables. Psychometrika, 1965, 30(1): 57-71.

Examples

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")

zhangjunpeng411/miRSM documentation built on Feb. 9, 2024, 6:14 p.m.