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.read_csv("data_Nestorowa.tsv.gz", index_col=0, sep="\t")
nestorowa.iloc[1:5, 1:5] 
#                HSPC_031  HSPC_037  LT-HSC_001  HSPC_001
# 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
boolean_trace = pd.read_csv("boolean_dynamics.csv", index_col=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

synthetic_scrna_pseudocounts = scbool_nestorowa.sample_counts(boolean_trace) 

Contributors

• Gustavo Magaña López