matrixFilter {TimiRGeN} | R Documentation |
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.
matrixFilter(MAE, miningMatrix, negativeOnly, predictedOnly, threshold, maxCor)
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. |
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.
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)