DaMiR.Allplot: Quality assessment and visualization of expression data

In BioinfoMonzino/DaMiRseq: Data Mining for RNA-seq data: normalization, feature selection and classification

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"),
  what = c("all", "all_w_PCA", "MDS", "PCA", "heatmap", "RLEbox", "distr", "avg_distr")
)

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"
what	A character string specifing the plots to be shown 'all', 'all_w_PCA', 'MDS','PCA','heatmap','RLEbox', 'distr', 'avg_distr' are allowed; default is "all"

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])

BioinfoMonzino/DaMiRseq documentation built on Aug. 22, 2021, 3:11 p.m.