quickCrossCorr {TimiRGeN} | R Documentation

## quickCrossCorr

### Description

Plots a cross-correlation plot to compare the miRNA and mRNA of a selected pair. This is a useful test of the similarities between the the two time series. It tracks movement of two time series relative to one another to determine how well they match and at which point the best match occurs.

### Usage

    quickCrossCorr(filt_df, pair, miRNA_exp, mRNA_exp, scale,
    Interpolation, timecourse)

### Arguments

`filt_df` | Dataframe from the matrixFilter function.
`pair` | Interger representing the pair to be explored.
`miRNA_exp` | miRNA data from using the diffExpressRes function on miRNA data.
`mRNA_exp` | mRNA data from using the diffExpressRes function on miRNA data
`scale` | TRUE or FALSE. Should data be scales. Default is FALSE. If the correlation is based on Log2FC values scale should be TRUE.
`Interpolation` | TRUE or FALSE. Should the whole time course be interpolated over by a smooth spline? Default is FALSE. This is most useful for longer and regular time courses.
`timecourse` | If Iterpolation is TRUE, how many time points should be interpolated over?

### Value

A cross correlation plot.

### Examples

    library(org.Mm.eg.db)

    miR <- mm_miR[1:50,]

    mRNA <- mm_mRNA[1:100,]

    MAE <- startObject(miR = miR, mRNA = mRNA)

    MAE <- getIdsMir(MAE, assay(MAE, 1), orgDB = org.Mm.eg.db, 'mmu')

    MAE <- getIdsMrna(MAE, assay(MAE, 2), "useast", 'mmusculus', orgDB = org.Mm.eg.db)

    MAE <- diffExpressRes(MAE, df = assay(MAE, 1), dataType = 'Log2FC',
                         genes_ID = assay(MAE, 3),
                          idColumn = 'GENENAME',
                          name = "miRNA_log2fc")

    MAE <- diffExpressRes(MAE, df = assay(MAE, 2), dataType = 'Log2FC',
                         genes_ID = assay(MAE, 7),
                         idColumn = 'GENENAME',
                         name = "mRNA_log2fc")

    Filt_df <- data.frame(row.names = c("mmu-miR-145a-3p:Adamts15",
                                       "mmu-miR-146a-5p:Acy1"),
                         corr = c(-0.9191653, 0.7826041),
                         miR = c("mmu-miR-145a-3p", "mmu-miR-146a-5p"),
                         mRNA = c("Adamts15", "Acy1"),
                         miR_Entrez = c(387163, NA),
                         mRNA_Entrez = c(235130, 109652),
                         TargetScan = c(1, 0),
                         miRDB = c(0, 0),
                         Predicted_Interactions = c(1, 0),
                         miRTarBase = c(0, 1),
                         Pred_Fun = c(1, 1))

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

    quickCrossCorr(filt_df=MAE[[11]], pair=1, miRNA_exp=MAE[[9]],
                  mRNA_exp=MAE[[10]],scale = FALSE, Interpolation = FALSE)

[Package TimiRGeN version 1.4.0 Index]