quickTC: quickTC

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

Description

Plots miRNA:mRNA pair over timecourse.

Usage

quickTC(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.
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 time courses.
timecourse	If Iterpolation is TRUE, how many time points should be interpolated over?

Value

Time course plot of selected pair.

Examples

library(org.Mm.eg.db)

miR <- mm_miR[1:100,]

mRNA <- mm_mRNA[1:200,]

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')

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)

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

TimiRGeN documentation built on April 17, 2021, 6:03 p.m.