quickHClust {TimiRGeN} | R Documentation

## quickHClust

### 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[1:50,]

    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)

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

[Package TimiRGeN version 1.2.0 Index]