Single-cell and spatial multiomic inference of gene regulatory networks using SCRIPro

Bioinformatics, 2024 · DOI: https://doi.org/10.1093/bioinformatics/btae466 · Published: July 18, 2024

Simple Explanation

SCRIPro is a new computer program that helps scientists understand how genes are controlled in individual cells, especially when using multiple types of data from those cells. It works by first grouping cells with similar characteristics and then figuring out which master control genes (transcription regulators) are most important in each group. SCRIPro can use different kinds of data, including information about gene activity and the structure of DNA in the cell, to make more accurate predictions about gene control.

Study Duration

Not specified

Participants

Human and mouse single-cell and spatial multiomics data

Evidence Level

Not specified

Key Findings

1
SCRIPro accurately reconstructs cell type-specific, stage-specific, and region-specific gene regulatory networks (GRNs).
2
SCRIPro outperforms existing motif-based methods.
3
SCRIPro emerges as a streamlined and fast method capable of reconstructing TR activities and GRNs for both single-cell and spatial multiomic data.

Research Summary

SCRIPro is a computational framework designed to predict TR activity and reconstruct TR-centered GRNs for both single-cell and spatial multiomic data. SCRIPro addresses the challenge of sparse single-cell or spatial multiomic signals by employing a density clustering approach that considers either expression or spatial similarities. SCRIPro combines TR-target importance from epigenomic data with TR-target expression from transcriptomic data to construct the GRNs.

Practical Implications

Improved GRN Inference

SCRIPro's ability to integrate diverse data types and consider spatial context leads to more accurate and comprehensive GRN reconstruction.

Enhanced Understanding of Cell Fate

By identifying key TRs and their regulons, SCRIPro can provide insights into the mechanisms driving cell differentiation and development.

Disease Mechanism Discovery

The ability to identify tumor-specific GRNs makes SCRIPro a valuable tool for understanding the molecular basis of diseases like cancer.

Study Limitations

1
Performance relies heavily on the quality of public ChIP-seq datasets.
2
Precision at the single-cell level is still being established due to challenges related to background noise and data quality.
3
Approach is limited by the coverage of spatial-omics data and the number of L-R pairs in the database.

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