Changelog

Source: NEWS.md

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