dens_univ: Compute KDE and JSD Compute kernel density estimate (KDE) of...

In sealx017/DenVar: What the Package Does (One Line, Title Case)

Compute KDE and JSD Compute kernel density estimate (KDE) of a marker expression using the function, dens_univ, compute Jensen Shannon Distance (JSD) between two computed KDEs using the function, jensen_shannon_dist, compute the KDE of all the patients of the dataset using the function, Array_KDE, and compute Jensen Shannon Distance matrix between all the patients using the function, JSD_matrix.

Description

Compute KDE and JSD Compute kernel density estimate (KDE) of a marker expression using the function, dens_univ, compute Jensen Shannon Distance (JSD) between two computed KDEs using the function, jensen_shannon_dist, compute the KDE of all the patients of the dataset using the function, Array_KDE, and compute Jensen Shannon Distance matrix between all the patients using the function, JSD_matrix.

Usage

dens_univ(x, ngrids = 1024, min_coef = 0, max_coef = 1)

Arguments

x	is a list of marker expression values in different cells of a patient
ngrids	is the number of grids used in KDE, default is m = 1024
px	is the KDE of first marker
py	is the KDE of second marker
Data	is the dataset having one column named "SampleID" with the patient IDs and one column with marker expression values

Value

The function, dens_univ returns KDE and the grid-points as a list, the function, jensen_shannon_dist returns the JSD between two densities the function, Array_KDE returns KDE (and the grid-points) of all the patients in a form of a 3d array, and the function, JSD_matrix returns the JSD distance between all the images in a matrix form.

sealx017/DenVar documentation built on July 22, 2024, 8:57 p.m.