matrixFilter {TimiRGeN} | R Documentation

## matrixFilter

### Description

Filters out miR-mRNA interactions based on how many times an interaction has been predicted and/ or validated. miR-mRNA interactions can also be filtered by correlations of expression values (log2fc or ave exp). Negatively correlating miR-mRNA interactions can be filtered for, and degree of correlation is also a filterable parameter.

### Usage

    matrixFilter(MAE, miningMatrix, negativeOnly, predictedOnly,
                        threshold, maxCor)

### Arguments

`MAE` | MultiAssayExperiment to store the output of matrixFilter. It is recommended to use the same MAE which stores the results from dataMiningMatrix.
`miningMatrix` | A large correlation matrix which has miR-mRNA validation information from targetscans, mirdb and mirtarbase. This is output from dataMiningMatrix, and should be stored as an assay within the MAE used in the dataMiningMatrix function.
`negativeOnly` | TRUE or FALSE. Should only negatively correlating miR-mRNA interactions be retrieved? Default is TRUE.
`predictedOnly` | TRUE or FALSE. Should only predicted interactions should be retrieved? Default is TRUE.
`threshold` | Integer from 0 to 3. How many databases should a miR-mRNA interaction be found in? If predictedOnly = TRUE, then maximum threshold is 2.
`maxCor` | Number from -1 to 1. What is the highest average correlation that is allowed? Default is -0.5. The lower the maxCor, the stricter the filtering.

### Value

Filtered miR-mRNA interactions that are specific for a signalling pathway of interest and the input data. Output will be stored as an assay in the input MAE.

### Examples

    Int_matrix <- data.frame(row.names = c("mmu-miR-320-3p:Acss1",
                                          "mmu-miR-27a-3p:Odc1"),
                            corr = c(-0.9191653, 0.7826041),
                            miR = c("mmu-miR-320-3p", "mmu-miR-27a-3p"),
                            mRNA = c("Acss1", "Odc1"),
                            miR_Entrez = c(NA, NA),
                            mRNA_Entrez = c(68738, 18263),
                            TargetScan = c(1, 0),
                            miRDB = c(0, 0),
                            Predicted_Interactions = c(1, 0),
                            miRTarBase = c(0, 1),
                            Pred_Fun = c(1, 1))

    MAE <- MultiAssayExperiment()

    MAE <- matrixFilter(MAE, miningMatrix = Int_matrix, negativeOnly = TRUE,
                            threshold = 1, predictedOnly = FALSE)

[Package TimiRGeN version 1.2.0 Index]