matrixFilter: matrixFilter

In Krutik6/TimiRGeN: Time sensitive microRNA-mRNA integration, analysis and network generation tool

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)

Krutik6/TimiRGeN documentation built on Jan. 27, 2024, 7:46 p.m.