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Changelog

Source: NEWS.md

MicrobiomeStat 1.4.1

Additional Improvements

Complete Normalization Optimization for Barplot Functions

  • Extended normalization skip to proportion data type:
    • All barplot functions (single, pair, long) now skip normalization for proportion data
    • ggplot2’s position="fill" handles proportion conversion automatically
    • Eliminates redundant TSS normalization for proportion data
    • Preserves pre-computed feature.agg.list for all data types
    • Improved user messaging for proportion data type

Consistent Behavior Across All Barplot Functions

  • Unified normalization strategy:
    • count data: Skip normalization, let ggplot2 handle conversion
    • proportion data: Skip additional normalization, already in proportion format
    • other data: Skip normalization, user-processed data
    • All functions now have identical normalization handling logic

Performance and Compatibility Enhancements

  • Optimal performance: Eliminated all unnecessary normalization steps in barplot functions
  • Enhanced fake feature.ann compatibility: All barplot functions work seamlessly with placeholder annotations
  • Data integrity: Consistent preservation of pre-computed aggregations across all functions
  • Better user experience: Clear, informative messages for all data types

MicrobiomeStat 1.4.0

Major Improvements

Enhanced Normalization Handling in Visualization and Analysis Functions

  • Optimized normalization strategy for barplot visualization:
    • Removed unnecessary TSS normalization in generate_taxa_barplot_single for count data
    • ggplot2’s position="fill" automatically handles proportion conversion for barplots
    • Preserves pre-computed feature.agg.list to avoid re-aggregation with fake feature.ann
    • Improved performance by eliminating redundant normalization steps
    • Added informative messages about normalization handling
  • Fixed normalization issues in differential abundance testing:
    • Removed redundant pre-normalization in generate_taxa_test_single for count data
    • LinDA function handles normalization internally based on feature.dat.type parameter
    • Fixed hardcoded feature.dat.type = "proportion" in LinDA calls - now passes user-specified type
    • Preserves pre-computed feature.agg.list when using fake feature.ann scenarios
    • Ensures appropriate zero-value handling based on actual data type

Technical Enhancements

  • Improved parameter validation:
    • Added match.arg() validation for feature.dat.type parameter
    • Better error handling and user feedback
  • Enhanced compatibility with fake feature annotation scenarios:
    • Functions now work correctly when using placeholder feature.ann with pre-computed feature.agg.list
    • Prevents incorrect re-aggregation that could lead to wrong analysis results
    • Maintains data integrity across different usage patterns

Bug Fixes

  • Fixed color mapping issues in barplot visualization:
    • Improved color palette assignment for better ggplot2 compatibility
    • Reduced warning messages about color scale mismatches

MicrobiomeStat 1.3.9

Bug Fixes

Fixed distance filtering in generate_beta_test_single

  • Fixed issue where all distances in dist.obj were tested instead of only those specified in dist.name:
    • Added filtering to ensure only requested distance metrics are processed when dist.obj is provided
    • Added validation to check if all requested distances are available in dist.obj
    • Clear error messages when requested distances are not available
    • Improved code clarity with better variable naming (snake_case)
    • Fixed logical operators from & to && for proper scalar evaluation

MicrobiomeStat 1.3.6

Major Enhancements

Enhanced Data Aggregation Function

  • Completely redesigned mStat_aggregate_data function:
    • Added meta.handle.conflict parameter with three strategies:
      • "first" (default): Use first record’s metadata, issue warnings for conflicts
      • "stop": Stop execution immediately when metadata conflicts are detected
      • "summarise": Calculate mean for numeric variables, check consistency for non-numeric
    • Fixed hardcoded subject.var limitation - now accepts any column name as subject variable
    • Preserved original feature names during aggregation (no longer modified)
    • Enhanced conflict detection with detailed reporting of which groups and variables have conflicts
    • Improved data integrity validation and error handling

Technical Improvements

  • Robust conflict detection: Comprehensive metadata consistency checking across all variables
  • Detailed error reporting: Clear identification of conflicting groups and variables
  • Backward compatibility: All existing code continues to work with default settings
  • Enhanced validation: Stricter data integrity checks prevent silent errors

User Experience

  • Flexible subject variable: No longer restricted to columns named “subject”
  • Informative warnings: Detailed conflict reports help users identify data quality issues
  • Professional error handling: Clear, actionable error messages for data problems
  • Strategy-based processing: Choose the most appropriate conflict handling for your analysis

Notes

This major update transforms mStat_aggregate_data from a basic aggregation tool into a sophisticated, configurable data processing function. The new conflict handling strategies ensure data quality and provide transparency in how metadata inconsistencies are managed, making the entire analysis pipeline more robust and trustworthy.

MicrobiomeStat 1.3.3

Bug Fixes and Improvements

Negative Data Handling

  • Fixed negative value handling in generate_taxa_heatmap_single:
    • Added automatic detection of negative values in “other” data type
    • Disabled abundance filtering when negative values are detected
    • Updated prevalence calculation to use != 0 instead of > 0 for proper negative value handling
    • Enhanced mStat_filter function to support -Inf abundance filter for negative data
  • Fixed negative value handling in generate_taxa_test_single:
    • Added automatic detection of negative values in “other” data type
    • Implemented linear model (lm) analysis for “other” data type instead of LinDA
    • Updated prevalence calculation to properly handle negative values
    • Added perform_lm_analysis helper function for robust linear model analysis

Enhanced Features

  • Improved data type handling: Functions now automatically detect and appropriately handle log-transformed or other preprocessed data containing negative values
  • User-friendly messaging: Added informative messages when negative values are detected and filtering is adjusted
  • Backward compatibility: All changes maintain full compatibility with existing positive data workflows

Technical Improvements

  • Enhanced error handling and validation for edge cases
  • Improved code documentation and inline comments
  • Added comprehensive test coverage for negative data scenarios

Notes

These improvements specifically address issues encountered when analyzing log-transformed microbiome data or other preprocessed data containing negative values. The package now provides robust analysis capabilities for both traditional count data and transformed abundance data.

MicrobiomeStat 1.3.2

Previous version features and bug fixes…

MicrobiomeStat 1.3.1

Previous version features and bug fixes…

MicrobiomeStat 1.3.0

Previous version features and bug fixes…