bioconductor-qfeatures

name: bioconductor-qfeaturesdescription: The QFeatures infrastructure enables the management and processing of quantitative features for high-throughput mass spectrometry assays. It provides a familiar Bioconductor user experience to manages quantitative data across different assawhen_to_use: "Use when: Multi-level Quantitative MS Management: Managing multi-level quantitative mass spectrometry data (e.g., PSMs, peptides, and proteins) within a single, integrated object.; Hierarchical Feature Aggregation: Performing hierarchical aggregation of quantitative features (e.g., aggregating PSMs to peptides, or peptides to proteins) using aggregateFeatures.; Data Visualization: Visualizing the hierarchic. Not for: For general-purpose multi-omics integration without hierarchical mass spectrometry relationships, use MultiAssayExperiment directly.; For single-assay data without hierarchical processing steps or multiple linked sets, use SummarizedExperiment instea"user-invocable: false

QFeatures

Dependencies & Environment

Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.

• Version: 1.22.0 · Bioconductor: 3.23 · R: ≥ 4.6
• Depends: MultiAssayExperiment
• Imports: S4Vectors, IRanges, SummarizedExperiment, BiocGenerics, ProtGenerics, AnnotationFilter, lazyeval, Biobase, MsCoreUtils, igraph, plotly, tidyr, tidyselect, reshape2
• Install: BiocManager::install("QFeatures")

When to Use

• Multi-level Quantitative MS Management: Managing multi-level quantitative mass spectrometry data (e.g., PSMs, peptides, and proteins) within a single, integrated object.
• Hierarchical Feature Aggregation: Performing hierarchical aggregation of quantitative features (e.g., aggregating PSMs to peptides, or peptides to proteins) using aggregateFeatures.
• Data Visualization: Visualizing the hierarchical relationships between assays using plot or exploring the data interactively with display.
• Tabular Data Conversion: Converting tabular data from identification and quantification software (e.g., MaxQuant, Proteome Discoverer) into a structured object using readQFeatures.

When NOT to Use

• For general-purpose multi-omics integration without hierarchical mass spectrometry relationships, use MultiAssayExperiment directly.
• For single-assay data without hierarchical processing steps or multiple linked sets, use SummarizedExperiment instead.
• For raw sequence or genomic range manipulation, use GenomicRanges or IRanges instead, as QFeatures is designed for quantitative feature matrices.

Data Requirements

• Input Format: Tabular data (e.g., data.frame) containing quantitative columns and feature annotations.
• Structure: Rows represent features (e.g., quantified PSMs), and columns represent samples or multiplexing tags (e.g., TMT labels).
• Sample Annotations: Sample metadata provided as a DataFrame to the colData argument, containing at least the quantitative column names.

Key Parameters

• quantCols: A vector of column names or indices identifying the quantitative values in the input table.
• name: The name to assign to a newly created assay or set (e.g., "psms" or "peptides").
• fun: The mathematical function used for feature aggregation in aggregateFeatures (e.g., colMeans).
• runCol: The column name in the assay data used to split the table into multiple sets for multi-run experiments (e.g., "FileName").
• colData: A DataFrame containing sample annotations to be matched with the quantitative columns.
• removeEmptyCols: Logical indicating whether to automatically detect and remove columns containing only NAs.
• interactive: Logical passed to plot to explore the hierarchy of assays through an interactive plotly graph.

Best Practices

• Initialize your workflow by importing tabular data using readQFeatures and specifying the quantCols.
• Aggregate features to higher levels (e.g., PSMs to peptides) using aggregateFeatures with an appropriate summary function like colMeans.
• Use plot to visualize the hierarchical relationships between assays via the internal AssayLinks.
• Extract quantitative data using assay() and metadata using rowData() or colData() for downstream exploration.
• Use longForm() to convert the QFeatures object into a long table format, which is ideal for ggplot2 visualization.

Common Pitfalls

• Missing values during aggregation: aggregateFeatures will warn if row data contain missing values. Fix: Read the manual page regarding the effects of missing values on data aggregation before proceeding.
• Overcrowded hierarchy plots: Datasets with hundreds of batches can lead to an overcrowded plot. Fix: Use plot(hl, interactive = TRUE) to navigate the tree interactively.
• Empty samples from unused labels: Missing label channels filled with NAs can cause issues. Fix: Set removeEmptyCols = TRUE in readQFeatures to automatically drop them.
• Multi-set sample mixing: A quantification column might contain data from multiple samples across different MS runs. Fix: Provide a runCol to readQFeatures to split the table into multiple sets.

Alternatives

• MultiAssayExperiment: A general-purpose container for multi-omics data, which QFeatures builds upon and extends for MS data.
• SummarizedExperiment: The standard single-assay container used as the building block for individual QFeatures sets.
• SingleCellExperiment: Used internally by readQFeatures before converting the data to a QFeatures object.

Citations

• Gentleman, Robert C., Vincent J. Carey, Douglas M. Bates, Ben Bolstad, Marcel Dettling, Sandrine Dudoit, Byron Ellis, et al. 2004. "Bioconductor: Open Software Development for Computational Biology and Bioinformatics." Genome Biol 5 (10): 80.
• Gu, Zuguang, Roland Eils, and Matthias Schlesner. 2016. "Complex Heatmaps Reveal Patterns and Correlations in Multidimensional Genomic Data." Bioinformatics 32 (18): 2847-9.

References

• Homepage: https://bioconductor.org/packages/QFeatures
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/QFeatures/inst/doc/QFeatures.html

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