NEWS

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CrcBiomeScreen 1.1.0

CrcBiomeScreen 0.99.0 (2025-10-21)

Overview

CrcBiomeScreen is an R package designed to streamline microbiome-based colorectal cancer (CRC) screening workflows.
It provides standardized functions for preprocessing, taxonomic data handling, machine learning model training,
and cross-cohort validation — supporting reproducible and interpretable microbiome analysis for biomarker discovery.

This version marks the first public release of the package, submitted to Bioconductor.

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New Features

Data Processing

• SplitTaxas() now automatically detects taxonomy separators
  (supports |, ., _, and ;) to handle common formats from MetaPhlAn, QIIME, etc.
  Adds:

  • OriginalTaxa column to retain the raw taxonomy string.
  • Special handling for uncultured and unclassified taxa
    (e.g. converts f__Rikenellaceae|g__unclassified → Rikenellaceae_unclassified).

• KeepTaxonomicLevel() filters data at a user-defined rank (e.g., Genus, Family, Species)
  and automatically collapses lower-level abundances.
  Handles multiple nested unclassified levels (e.g., D_2__Clostridia.D_3__uncultured.D_4__uncultured).

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Machine Learning

• TrainModel() serves as a unified interface for training Random Forest (RF) and XGBoost classifiers.

  • Integrates with internal preprocessing and class-weight balancing.
  • Uses withr::with_seed() for local reproducibility instead of set.seed().
  • Adds support for both training and external validation workflows.

• EvaluateRF() and EvaluateXGBoost() now return standardized performance metrics
  (AUC, accuracy, recall, F1) and store model outputs in the main object structure.

• ValidateModelOnData() supports model evaluation across independent datasets.

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Data Object Structure

• Introduced the CrcBiomeScreenObject class to store:
  • Absolute and relative abundance tables
  • Taxonomic annotations
  • Model training results
  • Validation data and predictions
  • Visual outputs (e.g., ROC, variable importance)

This ensures data provenance and reproducibility across the full workflow.

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Utility and Visualization

• Added built-in plotting functions for:
  • Model ROC curves (PlotAUC)

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Documentation and Testing

• Added comprehensive vignette:

  • Demonstrates preprocessing, genus-level filtering, model training, and validation.
  • Includes examples for multiple taxonomy formats and classifier comparisons.

• Implemented unit tests under tests/testthat/ for key components:

  • Taxa splitting
  • Level filtering
  • Model reproducibility

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Technical and Compliance Updates

• Adopted MIT license.
• Added BugReports and URL fields in DESCRIPTION.
• Removed unnecessary system files (.Rproj, .DS_Store).
• Replaced all instances of set.seed() with withr::with_seed() for Bioconductor compliance.
• Reduced hard-coded dependencies and improved optional imports.

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Future Plans

• Add support for additional classifiers.
• Integrate feature interpretation.
• Provide reproducible benchmarking across public CRC datasets.

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Version 0.99.13

• Fixed vignette and example issues
• Temporarily disabled XGBoost execution due to compatibility issues with caret
• Improved documentation and S4 accessor usage

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Version 0.99.14(2026-04-10)

• runnable toy workflow added to vignette
• examples updated for TrainModels, ValidateModelOnData, RunScreening
• documentation for included datasets completed

Version 0.99.15(2026-04-14)

• Standardized the format of the vignette and documentation

Maintainer: Chengxin Li (University of Leeds)
Date: 2025-10-21
License: MIT
Repository: https://github.com/omicsForestry/CrcBiomeScreen