DaMiR.Allplot {DaMiRseq} | R Documentation

## Quality assessment and visualization of expression data

### Description

This is a helper function to easily draw (1) clustering dendrogram and heatmap of a sample-per-sample correlation matrix, (2) multidimensional scaling plots (MDS), (3) relative log expression (RLE) boxplots of expression data, (4) a sample-by-sample expression value distribution, and (5) a class average expression value distribution

### Usage

    DaMiR.Allplot(data, df, type = c("spearman", "pearson"))

### Arguments

`data` | A SummarizedExperiment object or a matrix or a data.frame where rows and cols should be, respectively, observations and features
`df` | A data frame with class and known variables (or a subset of them); at least one column with 'class' label must be included
`type` | A character string specifing the metric to be applied to correlation analysis. Either "spearman" or "pearson" is allowed; default is "spearman"

### Details

Please be sure that NAs are not present in `df`'s columns. Plots will not be drawn in the presence of NAs.

### Value

A dendrogram and heatmap, MDS plot(s), a RLE boxplot, a sample-by-sample expression value distribution, and a class average expression value distribution

### Author(s)

Mattia Chiesa, Luca Piacentini

### Examples

    # use example data:
    data(data_norm)
    data(df)
    # Draw clustering dendrogram and heatmap, MDS, RLE boxplot:
    DaMiR.Allplot(data=data_norm, df=df[,5,drop=FALSE])

[Package DaMiRseq version 2.2.0 Index]