Progression Analysis of Disease with Survival using Topological Data Analysis
Extract intervals from filter function output values.
plot dsga
Optimal SVHT Coefficient with Known Noise Level
Optimal SVHT Coefficient with Unknown Noise Level
check_arg_mapper
check_filter_values
check_full_data
check_gene_selection
check_vectors
Get clusters for all data level
Generate disease component matrix.
Get clusters for a particular data level
Computes the adjacency matrix.
Survival analysis based on gene expression levels.
Rectangular Matrix Denoiser.
Disease-Specific Genomic Analysis
Flatten normal tissues
gene_selection_classes.default
gene_selection_classes.dsga_object
Gene selection and filter function
Private gene_selection_
Gene selection based on variability and the relationship to survival.
Gene Structure Survival using Topological Data Analysis (GSSTDA).
Incomplete Marcenko-Pastur Integral
Extract Information about Nodes
Filtering function
Map to color
Mapper object
Median of the Marcenko-Pastur Distribution
one_D_Mapper
Plot mapper
results dsga
Samples in levels
Mapper-based survival analysis with transcriptomics data is designed to carry out. Mapper-based survival analysis is a modification of Progression Analysis of Disease (PAD) where survival data is taken into account in the filtering function. More details in: J. Fores-Martos, B. Suay-Garcia, R. Bosch-Romeu, M.C. Sanfeliu-Alonso, A. Falco, J. Climent, "Progression Analysis of Disease with Survival (PAD-S) by SurvMap identifies different prognostic subgroups of breast cancer in a large combined set of transcriptomics and methylation studies" <doi:10.1101/2022.09.08.507080>.