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Single-cell regulome data analysis by SCRAT.

Zhicheng Ji¹, Weiqiang Zhou¹, Hongkai Ji¹

¹Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, USA.

Bioinformatics (Oxford, England)

|May 16, 2017

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Analyzing sparse single-cell regulome data is challenging. SCRAT, a new toolbox, helps identify cell subpopulations and their distinct regulatory features, enabling deeper insights into cell heterogeneity.

Area of Science:

Genomics
Computational Biology
Cell Biology

Background:

Single-cell technologies enable regulome analysis at the individual cell level.
Single-cell regulome data is characterized by high sparsity and discreteness, posing analytical challenges.
A lack of user-friendly software tools hinders the analysis of this complex data.

Purpose of the Study:

To introduce SCRAT (Single-Cell Regulome Analysis Toolbox), a user-friendly software with a graphical interface.
To facilitate the analysis of cell heterogeneity using single-cell regulome data.
To enable summarization and identification of regulatory activities across different cellular features.

Main Methods:

Development of SCRAT, a toolbox with a graphical user interface.

Related Experiment Videos

Implementation of functionalities to summarize regulatory activities based on user-defined features (gene sets, TF binding motifs).

Utilizing SCRAT to identify cell subpopulations, infer their identities, and discover distinguishing regulatory features.

Main Results:

SCRAT provides a convenient way to summarize regulatory activities.
The toolbox allows for the identification of distinct cell subpopulations within heterogeneous samples.
SCRAT aids in inferring cell identities and discovering key regulatory features differentiating subpopulations.

Conclusions:

SCRAT addresses the need for user-friendly tools in single-cell regulome data analysis.
The toolbox empowers researchers to study cell heterogeneity by analyzing regulatory landscapes.
SCRAT facilitates the discovery of cell-specific regulatory mechanisms and transcription factor activities.