mBatchNet: an interactive web server for diagnosis, correction, and benchmarking of batch effects in microbiome data

Area of Science:

• Microbiome analysis
• Bioinformatics
• Computational biology

Background:

• Reproducible microbiome analysis and cross-study integration are critical.
• Existing batch-correction algorithms differ in assumptions, inputs, parameters, and outputs, making practical application and comparison challenging.

Purpose of the Study:

• To present mBatchNet, an interactive web server for applying and evaluating batch-correction methods in microbiome studies.
• To streamline the process of diagnosing and correcting batch effects within a unified workflow.

Main Methods:

• Developed an interactive web server (mBatchNet) with a Python/Dash front end and Python/R back-end scripts.
• Integrated multiple batch-correction methods, including microbiome-specific and general-purpose algorithms.
• Implemented validation of feature tables and metadata, batch-target association flagging, and pre- and post-correction diagnostics.

Main Results:

• mBatchNet supports a range of batch-correction methods and provides comprehensive evaluation metrics.
• The server validates inputs, identifies batch-target associations, and offers detailed diagnostic outputs.
• A case study using 16S rRNA data demonstrated method-dependent differences in batch attenuation and phenotype preservation.

Conclusions:

• mBatchNet facilitates the comparison of different batch-correction strategies for microbiome data.
• The tool aids in achieving reproducible microbiome analysis and enhances cross-study integration.
• mBatchNet highlights the importance of method selection for effective batch-effect management.