Interface to 'sigma.js' graph visualization library including animations, plugins and shiny proxies.

Genomic alterations including single nucleotide substitution,
    copy number alteration, etc. are the major force for cancer
    initialization and development. Due to the specificity of molecular
    lesions caused by genomic alterations, we can generate characteristic
    alteration spectra, called 'signature' (Wang, Shixiang, et al. (2021)
    <DOI:10.1371/journal.pgen.1009557> & Alexandrov, Ludmil B., et al.
    (2020) <DOI:10.1038/s41586-020-1943-3> & Steele Christopher D., et al.
    (2022) <DOI:10.1038/s41586-022-04738-6>).  This package helps users to
    extract, analyze and visualize signatures from genomic alteration
    records, thus providing new insight into cancer study.

Several different sigmoid functions are implemented, including a wrapper function, SoftMax preprocessing and inverse functions.

Provides a classification framework to use expression patterns of pathways as features to identify similarity between biological samples. It provides a new measure for quantifying similarity between expression patterns of pathways.

A framework for the analysis and exploration of single-cell chromatin data.
    The 'Signac' package contains functions for quantifying single-cell chromatin data,
    computing per-cell quality control metrics, dimension reduction
    and normalization, visualization, and DNA sequence motif analysis.
    Reference: Stuart et al. (2021) <doi:10.1038/s41592-021-01282-5>.

An implementation of neural networks trained with flow-sorted gene expression data to classify cellular phenotypes in single cell RNA-sequencing data. See Chamberlain M et al. (2021) <doi:10.1101/2021.02.01.429207> for more details.

A set of signal processing functions originally written for 'Matlab' and 'Octave'.
  Includes filter generation utilities, filtering functions,
  resampling routines, and visualization of filter models. It also
  includes interpolation functions.