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scBoolSeq: Linking scRNA-seq statistics and Boolean dynamics.

Gustavo Magaña-López¹, Laurence Calzone^2,3,4, Andrei Zinovyev⁵

¹Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, Talence, France.

Plos Computational Biology

|July 8, 2024

Summary

This summary is machine-generated.

scBoolSeq links single-cell RNA sequencing (scRNA-seq) data with Boolean models for cell fate. It enables accurate binarization of scRNA-seq and generation of realistic synthetic data, improving model inference and benchmarking.

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Area of Science:

Computational Biology
Systems Biology
Genomics

Background:

Boolean networks model gene/transcription factor activation states over time.
Bridging qualitative Boolean states with quantitative scRNA-seq data is crucial for data-driven biological models.
scRNA-seq binarization and synthetic data generation are key for Boolean model inference and validation.

Purpose of the Study:

To develop a method for bidirectional linking of scRNA-seq data and Boolean gene activation states.
To enable accurate binarization of scRNA-seq datasets for Boolean model inference.
To generate realistic synthetic scRNA-seq data from Boolean models for benchmarking.

Main Methods:

scBoolSeq analyzes scRNA-seq data to classify gene pseudocount distributions (unimodal, bimodal, zero-inflated).
It fits gene-dependent probabilistic models for dropout events.
The method performs scRNA-seq binarization and generates synthetic data via biased sampling and dropout simulation.

Main Results:

scBoolSeq successfully binarizes scRNA-seq data and generates synthetic datasets from Boolean traces.
A case study demonstrated scBoolSeq's utility in data-driven Boolean model inference.
Synthetic data from scBoolSeq better reproduced real scRNA-seq statistics (mean-variance, mean-dropout) compared to BoolODE.

Conclusions:

scBoolSeq provides a robust framework for integrating scRNA-seq data with Boolean models.
The method enhances the accuracy of Boolean model inference and the benchmarking of inference algorithms.
scBoolSeq facilitates a more quantitative and data-driven approach to understanding cell fate dynamics.