prof.funct: Emprirical statistical depth
In AliciaNieto/fdaRNA: RNA-Seq and Microarray Preprocessing

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/prof.funct.R

Computes the sample statistical depth of a given sample using the procedure described in Nieto-Reyes (2011) and Nieto-Reyes and Cabrera (2020).

1	prof.funct(x)

`x`	A matrix where the columns are the sample elements and the rows the variables. The matrix can represent functional or multivariate data. It requires at least two variables. When applied to gene expression data, each column is a RNA-seq or microarray and the rows represent the genes.

The function returns a list containing the following components:

depth.vaA vector with the depth values of the sample elements.
deepest.eleThe identification number (column) of the deepest(s) elements.
distant.valIn the process of computing the depth values, these are ordered in pairs. There is a number of pairs equal to the integer part of the sample size divided by two. This vector contains the distance among each two elements of a pair, in decreasing order.

Alicia Nieto-Reyes and Javier Cabrera

Nieto-Reyes A. (2011) On the Properties of Functional Depth. In: Ferraty F. (eds) Recent Advances in Functional Data Analysis and Related Topics. Contributions to Statistics. Physica-Verlag HD.

Nieto-Reyes A, Cabrera J. Statistical depth based normalization and outlier detection of gene expression data. Preprint.

normalization and prof.funct

# Applies the "prof.funct" function to the Tissue dataset
p = prof.funct(Tissue)

# Gives the depth values of each of the 41 microarrays in the Tissue dataset
p$depth.val

# Gives the deepest microarray of the Tissue dataset, the one with highest depth value.
p$deepest.ele