quickHClust: quickHClust

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

Description

Plots all the genes found in a particular cluster. The plots will contain the data (gray) and a smoothed line (red).

Usage

quickHClust(filt_df, miRNA_exp, mRNA_exp, distmeth,hclustmeth,
pathwayname, k, cluster)

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".
k	Integer. Number of clusters.
cluster	Integer. Which cluster to look into? Default is 1.

Value

Time course plots of each gene found in the cluster of interest, from the pathway of interest.

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)

quickHClust(filt_df=MAE[[11]], miRNA_exp=MAE[[9]],
            mRNA_exp=MAE[[10]], pathwayname = "Test", k = 2, cluster = 1)

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