quickTCPred {TimiRGeN} | R Documentation |
Creates a regression plot over the time course, which compares data and a simulation which was predicted using the data. Data is based on the model formula used in the multiReg function and linearRegr function. R.squared and p value are also calculated and pasted into the plot.
quickTCPred(model, reg_df)
model |
Linear model which was generated by the linearRegr function. |
reg_df |
Matrix/assay which which was generated by the multiReg function. Should be in the MAE used during the multiReg function. |
A linear regression plot with data and a simulation.
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) MAE <- multiReg(MAE = MAE, gene_interest = "Adamts15", mRNAreg =TRUE, filt_df=MAE[[11]], miRNA_exp=MAE[[9]], mRNA_exp=MAE[[10]]) model1 <- linearRegr(mreg = MAE[[12]], colselect =2, colpair =3) summary(model1$regression) quickTCPred(model = model1, reg_df = MAE[[12]])