miRLAB (version 1.2.2)

IDA: miRNA target prediction with the IDA method

Description

Calculate the causal effect of each pair of miRNA-mRNA,and return a matrix of causal effects with columns are miRNAs and rows are mRNAs.

Usage

IDA(datacsv, cause, effect, pcmethod = "original", alpha = 0.05,
  targetbinding = NA)

Arguments

datacsv

the input dataset in csv format

cause

the column range that specifies the causes (miRNAs), e.g. 1:35

effect

the column range that specifies the effects (mRNAs), e.g. 36:2000

pcmethod

choose different versons of the PC algorithm, including "original" (default) "stable", and "stable.fast"

alpha

significance level for the conditional independence test, e.g. 0.05.

targetbinding

the putative target, e.g. "TargetScan.csv". If targetbinding is not specified, only expression data is used. If targetbinding is specified, the prediction results using expression data with be intersected with the interactions in the target binding file.

Value

• A matrix that includes the causal effects. Columns are miRNAs, rows are mRNAs.

References

1. Le, T.D., Liu, L., Tsykin, A., Goodall, G.J., Liu, B., Sun, B.Y. and Li, J. (2013) Inferring microRNA-mRNA causal regulatory relationships from expression data. Bioinformatics, 29, 765-71.

2. Zhang, J., Le, T.D., Liu, L., Liu, B., He, J., Goodall, G.J. and Li, J. (2014) Identifying direct miRNA-mRNA causal regulatory relationships in heterogeneous data. J. Biomed. Inform., 52, 438-47.

3. Maathuis, H.M., Colombo, D., Kalisch, M. and Buhlmann, P. (2010) Predicting causal effects in large-scale systems from observational data. Nat. Methods, 7, 247-249.

4. Maathuis, H.M., Kalisch, M. and Buhlmann, P. (2009) Estimating high-dimensional intervention effects from observational data. Ann. Stat., 37, 3133-3164.

Examples

dataset=system.file("extdata", "ToyEMT.csv", package="miRLAB")
results=IDA(dataset, 1:3, 4:18)