profile_binr

DEPRECATION/MIGRATION WARNING:

This package has been replaced by scBoolSeq which implements the generation of synthetic scRNA-Seq data biased by Boolean activation states as well as rich CLI. If any of your work relies on profile_binr, we invite you to migrate to scBoolSeq which has retained a similar API but has a richer set of features.

Legacy README:

The PROFILE methodology for the binarisation and normalisation of RNA-seq data.

This is a Python interface to a set of normalisation and binarisation functions for RNA-seq data originally written in R.

This software package is based on the methodology developed by Beal, Jonas; Montagud, Arnau; Traynard, Pauline; Barillot, Emmanuel; and Calzone, Laurence at Computational Systems Biology of Cancer team at Institut Curie (contact-sysbio@curie.fr). It generalizes and offers a Python interface of the original implementation in Rmarkdown notebooks available at https://github.com/sysbio-curie/PROFILE.

Installation

Using conda

The tool can be installed using the Conda package profile_binr in the colomoto channel. Note that some of its dependencies requires the conda-forge channel.

conda install -c conda-forge colomoto::profile_binr

Using pip

Requirements

• R (≥4.0)
• R packages:
  • mclust
  • diptest
  • moments
  • magrittr
  • tidyr
  • dplyr
  • tibble
  • bigmemory
  • doSNOW
  • foreach
  • glue

pip install profile_binr

Usage

A minimal example of the binarization suite. Take a look at notebooks/ for more details.

from profile_binr import ProfileBin
import pandas as pd

# your data is assumed to contain observations as
# rows and genes as columns
data = pd.read_csv("path/to/your/data.csv")
data.head()

# create the binarisation instance using the dataframe
# with the index containing the cell identifier
# and the columns being the gene names
probin = ProfileBin(data)

# compute the criteria used to binarise/normalise the data :
# This method uses a parallel implementation, you can specify the 
# number of workers with an integer
probin.fit(8) # train using 8 threads

# Look at the computed criteria
probin.criteria

# get binarised data (alternatively .binarise()):
my_bin = probin.binarize()
my_bin.head()

# idem for normalised data :
my_norm = probin.normalize()
my_norm.head()

References

• Béal J, Montagud A, Traynard P, Barillot E and Calzone L (2019) Personalization of Logical Models With Multi-Omics Data Allows Clinical Stratification of Patients. Front. Physiol. 9:1965. doi:10.3389/fphys.2018.01965