quickHClust: quickHClust

View source: R/quickHClust.R

quickHClustR 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)

Krutik6/TimiRGeN documentation built on Jan. 27, 2024, 7:46 p.m.