| name | bioconductor-qfeatures |
| description | 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 assa |
| when_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
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