SlipGURU Dipartimento di Informatica e Scienze dell'Informazione Università Degli Studi di Genova

PyCGH - a Comparative Genomic Hybridization toolkit


PyCGH is a Python library for the analysis of aCGH data. It consists mainly of three components:

  • A script which creates synthetic aCGH data, for testing purposes.
  • A python wrapper for the CGHNormaliter algorithm [CGHNormaliter], a preprocessing step required to normalizeCGH signals.
  • eFLLat, an algorithm which uses a dictionary learning approach to discover common patterns in aCGH data.

PyCGH requires the following python libraries:

  • numpy: the fundamental package for scientific computing.
  • matplotlib : a plotting library.
  • rpy2 : a library to include and use R code in a python program.

Since the function implementing the CGHNormaliter algorithm is actually a wrapper for the implementation in the R language, an appropriate interpreter for that language is required, as well as the library containing the implementation of the algorithm itself, which can be found at this link.

User Documentation

In this Section an overview of each component will be given.


[CGHNormaliter]B. van Houte, T. Binsl, H. Hettling, W. Pirovano and J. Heringa. CGHnormaliter: an iterative strategy to enhance normalization of array CGH data with imbalanced aberrations. BMC Genomics, 2009.


Current version: 0.0.1alpha

Get PyCGH from the Python Package Index, or install it with:

pip install --upgrade PyCGH
easy_install -U PyCGH

Latest documentation in pdf is also available.

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