SnapATAC (version 1.0.0)

runDiffusionMaps: Dimentionality Reduction by Diffusion Maps Algorithm

Description

This function takes a snap obj as input and runs diffusion maps for dimentionality reduction.

Usage

runDiffusionMaps(obj, input.mat = c("bmat", "pmat"), num.eigs = 20)

Arguments

obj

A snap obj

input.mat

Input matrix c("bmat", "pmat").

num.eigs

Number of eigenvectors to be computed [20].

Details

Diffusion Maps algorithm, a nonlinear dimensionality reduction technique that discovers low dimensional manifolds within high-dimensional datasets by performing harmonic analysis of a random walk constructed over the data to identify nonlinear collective variables containing the predominance of the variance in the data. We choose diffusion maps because it is highly robust to noise and perturbation, making it particuarly suited for analyzing sparse scATAC-seq dataset.

Examples

# NOT RUN {
data(demo.sp);
demo.sp = makeBinary(demo.sp);
demo.sp = runDiffusionMaps(
obj=demo.sp, 
input.mat="bmat", 
num.eigs=20
);
# }