Statistical visualization multi group

Statistical Visualization of Multi-Group Comparisons

Summary

Generate interactive visualizations (bar plots, violin plots, upset plots) to compare feature identification, quantification, and variation metrics across multiple DIA-MS search tools or experimental conditions. This skill enables rapid visual assessment of agreement and disagreement in analyte detection and measurement across different analysis pipelines.

When to use

When you have loaded search results from two or more DIA-MS analysis tools (e.g., DIA-NN and OpenSwath) or multiple experimental replicates and need to visualize and compare their feature identifications, log2 quantifications, and coefficient of variation values to assess consistency and tool/condition effects.

When NOT to use

• Input contains only a single search tool output (multi-group comparison requires ≥2 groups for meaningful visualization).
• Feature identifications have not been filtered to a high-confidence set (e.g., unfiltered all-hits list will obscure patterns).
• Analyte count is extremely large (>10,000 unique features) without prior aggregation or subset selection, as violin plots and upset plots become uninterpretable.

Inputs

• Search results file paths from multiple DIA-MS tools (e.g., DIA-NN output, OpenSwath OSW format)
• Tool metadata (software identifier, experiment label)
• Q-value cutoff threshold (e.g., 0.01 for 1% FDR)
• Analyte or feature list for selection

Outputs

• Identifications bar plot (Bokeh-rendered interactive plot)
• Log2 quantifications violin plot (per-tool distribution)
• Coefficient of variation violin plot (per-tool precision comparison)
• Upset comparisons plot (analyte overlap across tools)
• Summary table (CSV) with feature scores, Q-values, and tool-specific metadata

How to apply

After filtering search results at a specified Q-value cutoff (e.g., 1% Q-value threshold) to retain high-confidence identifications, extract feature identification counts, log2 quantification values, and coefficient of variation statistics for each analyte per tool or group. Use an interactive plotting framework (Bokeh-based InteractivePlotter in MassDash) to generate three complementary visualizations: (1) an identifications bar plot showing detection frequency per analyte across tools; (2) log2 quantifications violin plots to display distribution shape and central tendency for each tool/group; and (3) coefficient of variation violin plots to compare measurement precision. Construct an upset comparisons plot to reveal which analytes are detected by all tools (core set) versus tool-specific or pairwise-specific detections. Export the summary table with feature scores and metadata as CSV for downstream analysis or publication.

Related tools

• MassDash (Web-based dashboard that orchestrates search result loading, filtering, and interactive multi-group visualization using Bokeh plots and upset diagrams.) — https://github.com/Roestlab/massdash
• Streamlit (Graphical user interface framework that hosts the MassDash dashboard for interactive parameter control and real-time plot rendering.)
• Bokeh (Interactive plotting library that renders the main area visualizations including bar plots, violin plots, and upset plots with hover tools and legend interactivity.)
• ResultsLoader (MassDash module that loads search result file paths and tool metadata for each search tool to be compared.)
• OSWDataAccess (Tool-specific loader for OpenSwath (OSW) search results, enabling OpenSwath output integration into multi-group comparison workflows.)
• InteractivePlotter (MassDash module that generates identifications bar plots, log2 quantifications violin plots, coefficient of variation plots, and upset comparisons using Bokeh rendering.)

Evaluation signals

• Each visualization panel renders without errors and displays data for all groups/tools included in the comparison.
• Q-value filtering correctly removes analytes above the specified cutoff; verify by checking that all identifications in the summary table have Q-value ≤ threshold.
• Violin plots show non-empty distributions for log2 quantification and CV values per tool/group; presence of NaN or missing data points should be logged and investigated.
• Upset plot correctly captures set overlaps: the sum of all individual and intersection counts should equal the total analyte count after filtering.
• CSV export is valid and contains all rows from the summary table with matching feature counts and no truncated or corrupted fields.

Limitations

• Upset plots become difficult to interpret when the number of groups exceeds 4–5; consider subsetting or aggregating data.
• Performance may degrade if >10,000 analytes are included; pre-filtering to high-confidence or abundant features is recommended.
• Violin plots require sufficient replicates or measurements per group; groups with <3 observations may produce misleading visualizations.
• The visualization assumes that analytes are identified by the same naming convention across tools; mismatched identifiers will fragment the comparison.

Evidence

• [other] MassDash accepts the file path for search result files containing feature identification results, enabling users to load outputs from different tools (such as DIA-NN and OpenSwath) for comparison and visualization.: "MassDash accepts the file path for search result files containing feature identification results, enabling users to load outputs from different tools (such as DIA-NN and OpenSwath) for comparison and"
• [other] Apply Q-value filtering at the specified cutoff threshold using results filtering at Q-value to select high-confidence identifications.: "Apply Q-value filtering at the specified cutoff threshold using results filtering at Q-value to select high-confidence identifications."
• [other] Generate an identifications bar plot, log2 quantifications violin plot, and coefficient of variation violin plot using InteractivePlotter with Bokeh rendering.: "Generate an identifications bar plot, log2 quantifications violin plot, and coefficient of variation violin plot using InteractivePlotter with Bokeh rendering."
• [other] Construct an upset comparisons plot to show overlaps in identified analytes across different search tools.: "Construct an upset comparisons plot to show overlaps in identified analytes across different search tools."
• [other] The main area will be populated with interactive Bokeh figures: "The main area will be populated with interactive Bokeh figures"
• [other] Compile results into a summary table with feature scores and variables, then export as CSV.: "Compile results into a summary table with feature scores and variables, then export as CSV."