Biological Graph Signal Processing for Spatial Data Analysis
BioGSP: Biological Graph Signal Processing for Spatial Data Analysis
Calculate Graph Laplacian Matrix
Check K-band limited property of signals
Compare different kernel families
Compute SGWT filters
Calculate cosine similarity between two vectors
Demo function for SGWT
Fast eigendecomposition of Laplacian matrix
Find knee point in a curve
Graph Fourier Transform
Hello function for SGWT package demonstration
Inverse Graph Fourier Transform
Initialize SGWT object
Plot Fourier modes (eigenvectors) from SGWT object
Plot SGWT decomposition results
Print method for SGWT objects
Run SGCC weighted similarity analysis in Fourier domain
Run SGWT forward and inverse transforms for all signals
Build spectral graph for SGWT object
Generate automatic scales for SGWT
Analyze SGWT energy distribution across scales in Fourier domain
Forward SGWT transform (single or batch)
Get a unified kernel family (low-pass and band-pass) by kernel_type
Inverse SGWT transform (single or batch)
Global variables used in ggplot2 aesthetics
Simulate checkerboard pattern
Simulate Moving Circles Pattern
Simulate Multiple Center Patterns with Fixed Centers
Simulate Multi-center Multi-scale Concentric Ring Patterns
Simulate Stripe Patterns
Visualize checkerboard pattern
Visualize Moving Circles Pattern
Visualize Multi-center Multi-scale Concentric Ring Patterns
Visualize SGWT kernels and scaling functions
Visualize similarity in low vs non-low frequency space
Visualize Stripe Pattern Simulation Results
Implementation of Graph Signal Processing (GSP) methods including Spectral Graph Wavelet Transform (SGWT) for analyzing spatial patterns in biological data. Based on Hammond, Vandergheynst, and Gribonval (2011) <doi:10.1016/j.acha.2010.04.005>. Provides tools for multi-scale analysis of biology spatial signals, including forward and inverse transforms, energy analysis, and visualization functions tailored for biological applications. Biological application example is on Stephanie, Yao, Yuzhou (2024) <doi:10.1101/2024.12.20.629650>.