Fitnoise is a Python 2 library for statistical analysis of RNA-Seq, PAT-Seq, and microarray data using linear models.
An R wrapper is provided to allow access from R.
Fitnoise uses the Theano deep-learning library for speed.
Fitnoise is developed by Dr. Paul Harrison for the RNA Systems Biology Laboratory, Monash University.
Overview:
-
Poster presented at Lorne Genome Conference 2015
(An earlier poster presented at ABiC 2014 describes the previous R based Fitnoise.)
Documentation:
- What is Fitnoise?
- How to use Fitnoise
- Assessing the quality of a fit
- Noise models available
- Control genes (and what to do if you don't have replicates)
Download:
Links:
This is the easiest way to try out Fitnoise.
The following creates a virtualenv in directory venv for both Python and R:
./make_virtualenv.sh venv
To freshen the virtualenv after pulling a new version of Fitnoise from github or hacking on the code:
./freshen.sh venv
On the python side, Fitnoise requires Theano. On the R side, Fitnoise requires rPython, and jsonlite, and limma.
Installing dependencies:
apt-get install python-pip python-numpy python-scipy r-base
# (or whichever package manager is appropriate to your Linux distribution)
# (MacOS users can use brew and Anaconda Python)
pip install --upgrade git+git://github.com/Theano/Theano.git
R
install.packages(c("rPython", "jsonlite"))
source("http://bioconductor.org/biocLite.R")
biocLite("limma")
To install Fitnoise with pip:
pip install --upgrade fitnoise
python -m fitnoise
# This prints out instructions to install the R component
Alternatively, to install Fitnoise from source:
python setup.py install
R CMD INSTALL fitnoise
Alternatively, to install the development version of Fitnoise directly from github:
pip install --upgrade 'git+https://github.com/pfh/fitnoise.git#egg=fitnoise'
R
install.packages("devtools")
devtools::install_github("pfh/fitnoise", subdir="fitnoise")
Fitnoise re-implements various features of the limma Bioconductor package: