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.

Predicts the presence of signal peptides in eukaryotic protein
    using hidden semi-Markov models. The implemented algorithm can be accessed from
    both the command line and GUI.

When multiple Cox proportional hazard models are performed on clinical data 
    (month or year and status) and a set of differential expressions of genes, 
    the results (Hazard risks, z-scores and p-values) can be used to create gene-expression signatures. 
    Weights are calculated using the survival p-values of genes and are utilized to calculate expression 
    values of the signature across the selected genes in all patients in a cohort. A Single or multiple 
    univariate or multivariate Cox proportional hazard survival analyses of the patients in one cohort 
    can be performed by using the gene-expression signature and visualized using our survival plots.

Extract the signed backbones of intrinsically dense weighted networks based on the significance filter and vigor filter as described in the following paper. Please cite it if you find this software useful in your work.	
	Furkan Gursoy and Bertan Badur. "Extracting the signed backbone of intrinsically dense weighted networks." Journal of Complex Networks. <arXiv:2012.05216>.