Package index • NULISAseqR

Data Import & Loading Functions to load NULISAseq XML files and sample metadata
`importNULISAseq()`	Import and Process Multiple NULISAseq Runs
`loadNULISAseq()`	Read NULISAseq XML, perform normalization, and QC
`readNULISAseq()`	Read NULISAseq XML
`readCovariates()`	Read Covariates from XML File
`readCovariateFile()`	Read Covariate file and add to NULISAseq object
Data Normalization Normalize proteomic data using internal controls (IC) and inter-plate controls (IPC)
`intraPlateNorm()`	NULISAseq Intra-Plate Normalization
`interPlateNorm()`	NULISAseq Inter-Plate Normalization
`intraCV()`	Calculate Intra-plate Coefficients of Variation
`interCV()`	Calculate Inter-plate Coefficients of Variation
Quality Control Quality control criteria, flagging, and reporting functions
`render_QC_report()`	Render NULISAseq QC HTML Report
`QCSampleCriteria()`	QCSampleCriteria
`QCPlateCriteria()`	QCPlateCriteria
`QCTargetCriteria()`	QCTargetCriteria
`QCFlagSample()`	Write Sample QC table
`QCFlagPlate()`	Write Plate QC table
`QCFlagTarget()`	Write Target QC table
`sampleQCplot()`	Creates Plots Showing Sample-Specific Quality Control Metrics
`targetQCplot()`	Generate a Target QC Plot
`QCplateHeatmap()`	Generate plate heatmaps showing values relative to plate median
`plateSummary()`	NULISAseq Plate Summary
Protein Metrics Calculate protein detection rates, quantification metrics, and quality measures
`detectability()`	Calculate Detectability for a Set of Targets and Samples
`detectability_summary()`	Summarize Detectability Across Multiple Runs and Sample Groups
`quantifiability()`	Calculate Quantifiability for Each Target for a Set of AQ Runs
`lod()`	Calculate Limits of Detection
`CV_AQ()`	Calculate Coefficient of Variation for Each Target for a Set of AQ Runs
`CV_AQ_Hist()`	Make a CV Histogram for AQ Data
Statistical Analysis Differential expression testing and predictive modeling functions
`lmNULISAseq()`	Linear Regression Model for NULISAseq Data - targets as outcome
`lmerNULISAseq()`	Linear Mixed Effect Model for NULISAseq Data - targets as outcome
`lmNULISAseq_predict()`	Linear Regression Model for NULISAseq Data - targets as predictors
`lmerNULISAseq_predict()`	Linear Mixed Effect Model for NULISAseq Data - targets as predictor
`glmNULISAseq_predict()`	Generalized Linear Model for NULISAseq Data - targets as predictors
`glmerNULISAseq_predict()`	Generalized Linear Mixed Effects Model for NULISAseq Data - targets as predictors
`permutation_anova()`	Permutation-Based ANOVA with Post-Hoc Pairwise Comparisons
Visualization Create plots for exploratory analysis and publication-quality figures
`volcanoPlot()`	Volcano plot for NULISAseq differential expression test
`sampleBoxplot()`	Generate Sample Distribution Boxplots
`targetBoxplot()`	Target Boxplot
`generate_heatmap()`	Generate Heatmap for NULISAseq Data with ComplexHeatmap
`generate_pca()`	Generate PCA Biplot for NULISAseq Data with PCAtools
`plateHeatmap()`	Draw Plate Heatmap
`plot_plateLayout()`	Plot Plate Layout with Well Type Coloring
`spaghetti_plot()`	Spaghetti plot for visualizing longitudinal NULISAseq data
`quantHist()`	Make a Quantifiability Histogram
Data Export Write NULISAseq data and QC information to files
`writeNULISAseq()`	Write NULISAseq data in long format Excel file
`writeUpdatedXML()`	Write Updated XML
`QC2XML()`	Write Processed XML from QC table
XML Processing Utilities Low-level functions for XML file manipulation
`getXMLVersion()`	Get XML Version from XML File
`insertCovariatesXML()`	Inserts Covariates into XML
`readQCXMLNode()`	Parse QC XML Nodes into a Data Frame
`readQCThresholdXMLNode()`	Read and Parse QC Attributes from XML Nodes
`readXML_remove_duplicate_attributes()`	Read and Parse XML, Removing Duplicate Attributes
`removeBarcodeAndSamples()`	Removes BarcodeB nodes with a specified attribute and their associated Sample nodes.
`splitXML()`	Split XML file based on attribute and create a ZIP archive
Helper Functions Utility functions for data processing and visualization
`alamarColorPalette()`	Make color palette based on Alamar colors
`calcSampleTargetNAs()`	Identify Missing Target Entries in Sample Curves
`column_summary_stats()`	Generate Summary Statistics for Columns of a Matrix or Data Frame
`fill_predictors()`	Fill missing predictors with NA values
`numSCs()`	Calculate the number of SCs (Sample Controls) in the dataset
`outliers_mad()`	Detect Outliers using MAD (Median Absolute Deviation)
`renameDuplicateNames()`	Rename Duplicates in a List with Incrementing Values
`renameSC()`	Rename Sample Controls (SC) and Absolute Quantification Sample Controls (AQSC)
`safe_extract_matrix()`	Safely extract and combine model statistics into a matrix
`typeSummary()`	Well Type Summary
Package Documentation Package-level documentation
`NULISAseqR`	NULISAseqR Package

Reference

Data Import & Loading

Data Normalization

Quality Control

Protein Metrics

Statistical Analysis

Visualization

Data Export

XML Processing Utilities

Helper Functions

Package Documentation