quickDendro: quickDendro

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

Description

Ceates a dendrogram of the genes from the pathway of interest.

Usage

quickDendro(filt_df, miRNA_exp, mRNA_exp, distmeth, hclustmeth,
pathwayname)

Arguments

filt_df	Dataframe from the matrixFilter function.
miRNA_exp	miRNA data from using the diffExpressRes function on miRNA data.
mRNA_exp	mRNA data from using the diffExpressRes function on miRNA data.
distmeth	Dist method for hierarchical clustering. Default is "maximum".
hclustmeth	Hclust method for hierarchical clustering. Default is "ward.D".
pathwayname	Character which is the name of pathway of interest. Default is "Pathway".

Value

A dendrogram with the genes on the Y axis and the distances on the X axis.

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)

quickDendro(filt_df=MAE[[11]], miRNA_exp=MAE[[9]],
            mRNA_exp=MAE[[10]], pathwayname = "Test")

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