DaMiR.corrplot: Correlation Plot

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

Description

This function easily draws the correlation plot of surrogate variables (sv) and variables.

Usage

DaMiR.corrplot(sv, df, type = c("pearson", "spearman"),
  sig.level = 1e-04)

Arguments

sv	The matrix of sv identified by DaMiR.SV function
df	A data frame with class and known variables; at least one column with 'class' label must be included
type	Type of correlation metric to be applied; default is "pearson"
sig.level	The significance level of the correlation; default is 0.0001

Details

Factorial variables are allowed. They will be tranformed as numeric before applying the rcorr function of Hmisc.The corrplot function, which draws the plot, marks with a cross all the correlations that do not reach the significance threshold defined in the sig.level argument.This plot allows the user to identify those sv that present significant correlations with either technical and biological known variables. Notably, none of the sv should present signifcant correlation with "class" variable.

Value

A correlation plot between sv and known variables.

Author(s)

Mattia Chiesa, Luca Piacentini

See Also

DaMiR.SV

Examples

# use example data:
data(df)
data(sv)
# Draw correlation plot:
DaMiR.corrplot(sv=sv, df=df, type = "pearson", sig.level=0.01)

DaMiRseq documentation built on Nov. 8, 2020, 5:53 p.m.