scBoolSeq

scRNA-Seq data binarisation and synthetic generation from Boolean dynamics.

Installation

Pip

pip install scboolseq

Conda

conda install -c conda-forge -c colomoto scboolseq

Docker

scBoolSeq is included in the ColoMoTo Docker distribution.

Usage

Python API

Here a minimal example is presented, using the same dataset as the CLI usage guide. For further information, please check the documentation.

import pandas as pd
from scboolseq import scBoolSeq
read in the normalized expression data
nestorowa = pd.readcsv("dataNestorowa.tsv.gz", index_col=0, sep="\t")
nestorowa.iloc[1:5, 1:5] 
               HSPC031  HSPC037  LT-HSC001  HSPC001
Kdm3a          6.877725  0.000000    0.000000  0.000000
Coro2b         0.000000  6.913384    8.178374  9.475577
8430408G22Rik  0.000000  0.000000    0.000000  0.000000
Clec9a         0.000000  0.000000    0.000000  0.000000
#
NOTE : here, genes are rows and observations are columns
scbool_nest = scBoolSeq()
## 
Binarization
 ##

scBoolSeq expects genes to be columns, thus we transpose the DataFrame.
scbool_nest.fit(nestorowa.T) # compute binarization criteria
binarized = scbool_nestorowa.binarize(nestorowa.T) binarized.iloc[1:5, 1:5]  
            Kdm3a  Coro2b  8430408G22Rik  Phf6
 
HSPC_031      1.0     NaN            NaN   0.0
 
HSPC_037      0.0     1.0            NaN   0.0
 
LT-HSC_001    0.0     1.0            NaN   1.0
 
HSPC_001      0.0     1.0            NaN   1.0

## 
Synthetic RNA-Seq generation from Boolean states
 ##

We load in a boolean trace obtained from the simulation of a Boolean model
booleantrace = pd.readcsv("booleandynamics.csv", indexcol=0)
boolean_trace
            Kdm3a  Coro2b  8430408G22Rik  Phf6
init          1.0     0.0            1.0   0.0
transient_1   0.0     1.0            1.0   0.0
transient_2   0.0     1.0            0.0   1.0
stable_state  0.0     1.0            1.0   1.0
syntheticscrnapseudocounts = scboolnestorowa.samplecounts(boolean_trace)

Contributors

• Gustavo Magaña López