Changelog

Source: NEWS.md

NULISAseqR 1.5.0 (2026-03-14)

Changes

New Features

  • loadNULISAseq() - Now accepts a pre-built list structure in addition to file paths, enabling reprocessing of data with sample exclusions without re-parsing XML files
  • get_reverse_curve_targets() - New exported helper to identify reverse curve targets (Curve_Quant starting with “R”)
  • get_noDetectability_targets() - New exported helper to identify targets with the XML noDetectability modifier

Enhancements

  • Reverse curve & noDetectability target handling - Reverse curve targets are now fully excluded from detectability and labeled “High Abundance”; rare-case targets with XML noDetectability modifier have individual detectability computed but are excluded from summary statistics (mean, sd, median, min, max, # detectable targets)
  • detectability_summary() - Added exclude_targets parameter; consolidated and centralized detectability formatting logic to avoid duplication; “High Abundance” label now shown only for PLASMA/SERUM matrix types; detectability set to NA for non-plasma/serum sample types; returns numeric columns by default (format=FALSE) to preserve downstream computation
  • writeNULISAseq() - IC target(s) now placed at the bottom rows of the RQ data sheet when include_IC_counts = TRUE
  • Target name sorting - Applied case-insensitive sorting (tolower) in quantifiability() and the QC report skeleton to ensure consistent ordering across platforms
  • Batch effect QC - Revised batch effect messaging; non-RC noDetectability targets kept in batch effect assessment; guarded against edge cases
  • DESCRIPTION - Minimum R version now declared (required for native pipe usage in lmNULISAseq.R); removed LazyData field; added withr to Suggests

Bug Fixes

  • Batch effect table - Fixed crash on pagination and PCA legend truncation in QC report
  • detectability_summary() - Fixed apply() dimension drop in detectability output table; fixed rowSums NA handling and guarded against empty target sets in aggregation
  • QCFlagTarget - Fixed incorrect exclusion of non-RC noDetectability targets from detectability calculations
  • Failed_Targets Run QC - Non-RC noDetectability targets now correctly included in Failed_Targets for CV criterion only
  • Well position - Corrected zero-padding for well position values
  • loadNULISAseq() - Fixed handling of AQ projects with list input; properly recreates numericCovariates for list inputs
  • Namespace fixes - Added explicit tibble:: prefix for column_to_rownames()

Testing

  • test-loadNULISAseq.R - New tests for list input support, AQ project equivalence, and coverage of all output fields
  • test-reverse-curve-detectability.R - New test suite for reverse curve and noDetectability target handling
  • test-writeNULISAseq.R - Expanded to test entire sheets for both RQ and AQ output; added case-insensitive formatting test; improved robustness to platform differences in string handling and floating point arithmetic

Infrastructure

  • Removed vignettes folder from build
  • Added Neuro220 XML files for testing
  • Updated CI workflows for hybrid branch pattern

NULISAseqR 1.4.2 (2026-02-15)

Changes

Enhancements

  • render_QC_report() - Improved function parameter ordering and defaults:
    • xml_files parameter moved to first position for more intuitive usage
    • Added default values for output_filename (“NULISAseq_QC_Report.html”) and output_dir (current working directory)
    • Added default value for dataDir (current working directory)
    • Simplified Rmd_input_file path construction using system.file()
  • lod() - Enhanced documentation and parameter handling:
    • Improved parameter ordering (moved data_matrix before blanks)
    • Enhanced roxygen documentation with clearer return value descriptions
    • Added filtering to ensure targetNoOutlierDetection only includes targets present in data_matrix

Bug Fixes

  • quantifiability() - Fixed sample subsetting issue that could cause errors when sample lists don’t match between AQ data and sample information:
    • Now uses intersect() to find common samples between Data_AQ_aM and SampleNames
    • Correctly calculates sample counts for overall and subgroup quantifiability
    • Prevents errors when processing data with mismatched sample lists
  • loadNULISAseq() - Added calculation of LOD_pgmL (limit of detection in pg/mL units) from XML data for AQ assays
  • targetBoxplot() - Fixed parameter naming in lod() function call to use data_matrix= explicitly

Testing

  • New comprehensive test suites added to ensure code quality and reliability:
    • test-importNULISAseq.R - Tests for importNULISAseq() function with and without NULISAseqAQ package, including fallback mode validation and AQ data consistency checks
    • test-reverse-curve.R - Tests for reverse curve target handling, including correlation validation, data transformation verification, and NPQ value consistency between loadNULISAseq() and importNULISAseq()
    • test-writeNULISAseq.R - Tests for Excel output generation with both RQ-only and AQ data, including validation of sheet structure, column names, and specific data values
  • Test infrastructure improvements:
    • Moved test fixtures from inst/rmarkdown/templates/nulisaseq/skeleton/ to tests/testthat/fixtures/ for better organization
    • Removed unnecessary .gitignore file from skeleton template directory

NULISAseqR 1.4.1 (2026-01-16)

Changes

Enhancements

  • importNULISAseq() - Improved robustness and flexibility for handling NULISAseq data files:
    • Added validation of internal AUTO_PLATE IDs with duplicate detection before processing
    • Enhanced parameter mapping for excludeSamples, excludeTargets, and control parameters (IC, IPC, SC, NC, Bridge, Calibrator) using prioritized keys (user-provided plate names, internal IDs, or fallback names)
    • Improved error handling with clear messages when duplicate plate IDs are detected with named exclusions
  • get_internal_plate_id() - New utility function to extract AUTO_PLATE ID from NULISAseq XML file headers

NULISAseqR 1.4.0 (2026-01-11)

Overview

Version 1.4.0 represents a major expansion of the NULISAseqR package, introducing new analytical capabilities, enhanced visualization tools, and improved data processing functions.

New Features

Documentation & Installation

  • Added pkgdown website for package documentation
  • Added comprehensive vignette covering data loading, QC, differential expression, visualization, and case studies
  • Added MacOS and Windows installation instructions to documentation

Data Import & Export

  • importNULISAseq() - New streamlined function to import NULISAseq data from multiple XML files with improved error handling and validation
  • getXMLVersion() - Retrieve XML version information from NULISAseq files
  • Added support for XML v1.3.0 format compatibility in loadNULISAseq() which accommodates absolute quantification (AQ) NULISAseq assay panels

Quality Control

  • render_QC_report() - Generate automated quality control reports in HTML format
  • detectability_summary() - Summarize detectability across multiple runs and sample matrix types

Statistical Analysis

  • permutation_anova() - Perform permutation-based ANOVA testing for robust statistical inference
Single-Protein Prediction Models

Four new functions enable using single-target NPQ as a predictor in covariate-adjusted linear and logistic regression models:

  • lmNULISAseq_predict() - Predictions for continuous outcomes using linear regression models
  • lmerNULISAseq_predict() - Predictions for continuous outcomes from linear mixed-effects models for hierarchical data
  • glmNULISAseq_predict() - Predictions for binary/count outcomes using generalized linear models
  • glmerNULISAseq_predict() - Predictions for binary/count outcomes using generalized linear mixed-effects models for hierarchical data

Visualization Suite

Heatmaps
  • generate_heatmap() - Create publication-quality protein abundance heatmaps with ComplexHeatmap integration, supporting clustering, annotations, and custom color schemes
  • QCplateHeatmap() - Plate-level quality control heatmaps for identifying spatial patterns
Sample & Target Visualization
  • sampleBoxplot() - Boxplots showing sample distributions
  • sampleQCplot() - Comprehensive sample quality control plots with multiple metrics
  • plot_plateLayout() - Visual representation of plate layouts for experimental design
Dimensionality Reduction
  • generate_pca() - Principal component analysis with biplot generation and customizable aesthetics

NULISAseq Absolute Quantification (AQ) Analysis

  • targetQCplot() - Target-level QC visualizations for AQ assay performance monitoring
  • CV_AQ() - Calculate intra-plate and inter-plate coefficient of variation for AQ runs, with automatic handling of values outside the dynamic range
  • CV_AQ_Hist() - Visualize CV distributions for quality control monitoring
  • quantifiability() - Calculate quantifiability metrics across multiple runs
  • quantHist() - Histogram plots of quantifiability distributions
  • Added withinDR matrix to AQ output for dynamic range filtering

Enhanced Functions

Data Import/Export

  • readNULISAseq() - Improved XML parsing for better compatibility across file versions, enhanced error handling and validation
  • writeNULISAseq() - Refactored to utilize importNULISAseq function
  • writeUpdatedXML() - Now loads XML internally instead of requiring pre-loaded data

Visualization

  • volcanoPlot() - Major enhancements including:
    • Dual plotting mode: plot both unadjusted and FDR-adjusted p-values simultaneously with color coding (light colors for unadjusted significance, darker colors for FDR significance)
    • Flexible p-value input: accepts either single vector or named list with ‘unadj’ and ‘fdr’ p-values
    • Fold change thresholds: upper_log2FC_threshold and lower_log2FC_threshold parameters for labeling targets based on effect size
    • Automatic axis label adjustment based on p-value type (unadjusted vs FDR-adjusted)
    • Enhanced customization options for colors, fonts, and plot dimensions
  • targetBoxplot() - Better handling of normalized vs. unnormalized data
  • Target detectability boxplots margins adjusted to prevent clipping of long target names
  • alamarColorPalette() - Expanded color palette options

QC Report Improvements

  • Added batch effect QC section with configurable significance thresholds
  • Added ICC (Intraclass Correlation Coefficient) scatterplot for batch effect assessment
  • Added combined detectability calculation across plates when common sample matrix types exist
  • Both unnormalized and normalized sample boxplots now shown on all reports to better enable internal control QC checking
  • Improved QC plot formatting and spacing
  • Intra-plate CV “Overall” column renamed to “Average” for clarity
  • Detectability summary tables now show denominator (total targets per plate)
  • Interactive reactable tables allow row sorting by clicking on column names

Other Improvements

  • License updated to GPL-3

Bug Fixes

Data Processing

  • Fixed sample tag search to only search within Data subnodes, preventing false matches
  • Fixed NA handling in writeNULISAseq (empty cells vs “NA” string)
  • Fixed numeric covariate detection in linear model functions to properly identify NA values stored as strings

Quality Control

  • Fixed subsetting errors when data contains only 1 row or column (added drop=FALSE)
  • Fixed Sample QC percentage values (now multiplied by 100)
  • Fixed Target QC and CV table display issues

Statistical Models

  • Fixed drop=FALSE placement bug in predict model functions to prevent errors when dataset contains only 1 row or column

Visualization

  • Fixed special characters displaying as “|” in tables

Data Management

  • Fixed lazy-load database corruption by disabling LazyData
  • In QC report, fixed overall detectability weight calculation when sample matrices differ between plates

Breaking Changes

  • Version number updated from 1.2.0 to 1.4.0 (skipping 1.3.0 as standalone release)
  • Some function parameters may have changed order or names - please review documentation

Getting Help

Full Changelog: https://github.com/Alamar-Biosciences/NULISAseqR/compare/main…1.4