quickTC {TimiRGeN} | R Documentation |
Plots miRNA:mRNA pair over timecourse.
quickTC(filt_df, pair, miRNA_exp, mRNA_exp, scale,Interpolation, timecourse)
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? |
Time course plot of selected pair.
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) quickTC(filt_df=MAE[[11]], pair=1, miRNA_exp=MAE[[9]], mRNA_exp=MAE[[10]], scale = FALSE)