ValidationT: Validate the targets of a miRNA using transfection data

In miRLAB: Dry lab for exploring miRNA-mRNA relationships

Description

Given the predicted target of a miRNA, the function returns a list of targets that are confirmed based on the curated transfection data. Users need to download the file logFC.imputed.rda from nugget.unisa.edu.au/Thuc/miRLAB/ and place it in the working directory (this file is obtained from the TargetScoreData package)

Usage

ValidationT(topkList, LFC)

Arguments

topkList	a matrix with 3 columns. The first column is the miRNA name, the second contains the target mRNAs, and the third contains the correlation values/ causal effects/ scores
LFC	the log fold change threshold. The targets that have the absolute value of log fold change greater than the LFC will be regarded as the confirmed targets.

Value

a matrix in the same format of the input matrix put only contains the confirmed interactions.

References

1. Le, T.D., Zhang, J., Liu, L., and Li, J. (2015) Ensemble Methods for miRNA Target Prediction from Expression Data, under review.

2. Li Y, Goldenberg A, Wong K and Zhang Z (2014). A probabilistic approach to explore human microRNA targetome using microRNA-overexpression data and sequence information. Bioinformatics, 30(5), pp. 621-628. http://dx.doi.org/10.1093/bioinformatics/btt599.

Examples

print("ps=Pearson(dataset, cause=1:35, effect=36:1189)")
print("miR200aTop100=bRank(ps, 11, 100, TRUE)")
print("miR200aTop100Confirmed = ValidationT(miR200aTop100, 1.0)")

miRLAB documentation built on Nov. 8, 2020, 5:45 p.m.