# Bioconductor Qfeatures > 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 - Skill: `biomate-ai/bioconductor-qfeatures` (Agent Skill) - Install (CLI): `npx skillmds@latest add biomate-ai/bioconductor-qfeatures` - Raw SKILL.md: https://api.skillmd.com/api/skills/biomate-ai/bioconductor-qfeatures/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: biomate-ai (https://skillmd.com/u/biomate-ai) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/biomate-ai/bioconductor-qfeatures --- # 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 `NA`s. - **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 `NA`s 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 --- ## Run this on BioMate This skill is the **knowledge layer** — when, why, and how to use `qfeatures`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=qfeatures)** — free to start. ▶ **[Open `qfeatures` on BioMate →](https://www.biomate.ai?ref=kb&pkg=qfeatures)**