Intensity Color Representation

intensity-color-representation

Summary

Map mass spectrometry intensity values to a color scale in 2D peak map visualizations to enable intuitive perception of signal strength across m/z and retention time dimensions. This skill is essential for interactive and static peak map plots where color intensity conveys the third dimension (intensity/abundance) of the data.

When to use

When visualizing 2D peak maps (x=m/z, y=retention time or ion mobility, z=intensity) using pyOpenMS-viz with any plotting backend (matplotlib, bokeh, plotly). The skill is triggered when you have mass spectrometry data in a Pandas DataFrame with three numeric columns representing m/z, retention time (or ion mobility), and intensity, and you need to render the intensity dimension as a color scale rather than as explicit z-axis height.

When NOT to use

• Input data lacks a clear intensity dimension or has missing/invalid z values — intensity-to-color mapping requires complete, numeric intensity data.
• Visualization goal is 1D (chromatogram or spectrum only) — use 'chromatogram' or 'spectrum' plot kind instead of 'peakmap'.
• You need explicit numeric z-axis height (3D surface plot) rather than color; use peakmap with plot3d=True and plotly or matplotlib backends instead.

Inputs

• Pandas DataFrame with three numeric columns: m/z (x), retention_time or ion_mobility (y), intensity (z)

Outputs

• Interactive or static 2D peak map plot with intensity encoded as continuous color scale
• Optionally, marginal chromatogram and spectrum plots (if add_marginals=True)

How to apply

Structure your mass spectrometry data as a Pandas DataFrame with columns for m/z (x-axis), retention time or ion mobility (y-axis), and intensity (z-axis). Call DataFrame.plot() with kind='peakmap', specifying x and y column names, and rely on pyOpenMS-viz's automatic color mapping of the z (intensity) column to a continuous color scale. The color representation uses intensity values directly; select a backend (matplotlib for static, bokeh or plotly for interactive) via the backend parameter. Optionally enable marginal plots with add_marginals=True to display integrated chromatograms and spectra alongside the main peak map. Verify that the color scale spans from low (cool) to high (warm) colors, matching the min/max intensity range in your data.

Related tools

• pyOpenMS-viz (Primary plotting library that implements peak map visualization with automatic intensity-to-color mapping for Pandas DataFrames) — https://github.com/OpenMS/pyopenms_viz
• Pandas (Data manipulation and representation framework; holds mass spectrometry data as DataFrame with m/z, retention time, and intensity columns)
• bokeh (Interactive plotting backend for rendering peak maps with color-mapped intensity and hover tooltips)
• matplotlib (Static plotting backend for rendering peak maps with intensity-to-color mapping)
• plotly (Interactive plotting backend supporting both 2D and 3D peak maps with intensity color representation)

Examples

ms_data.plot(x="m/z", y="rt", z="intensity", kind="peakmap", backend="ms_bokeh", add_marginals=True)

Evaluation signals

• Color scale is continuous and monotonically maps low intensity → cool colors (blue) and high intensity → warm colors (red/yellow)
• Color bar or legend is present and labeled with intensity units (e.g., 'Intensity', '[counts]')
• All data points are rendered with no missing colors; NaN or zero intensity values do not cause rendering errors
• Marginal plots (if enabled) correctly reflect integrated intensity: chromatogram y-values match sum of intensities per RT bin; spectrum y-values match sum of intensities per m/z bin
• Interactive backends (bokeh, plotly) allow hover inspection of exact m/z, retention time, and intensity values corresponding to each colored pixel

Limitations

• Color mapping is automatically determined by pyOpenMS-viz; custom colormaps or manual intensity scale adjustment is not documented in the provided README or workflow steps.
• 3D peak maps (with explicit z-axis height) are supported only in matplotlib and plotly backends, not in bokeh; intensity-color representation in bokeh is limited to 2D.
• Missing or zero intensity values may render as transparent or a default color; the handling is backend-dependent and not explicitly specified in the README.
• Very large datasets with millions of points may suffer performance degradation in interactive backends; static matplotlib is more efficient for large matrices.

Evidence

• [readme] Visualization of various mass spectrometry data types, including 1D chromatograms, spectra, and 2D peak maps: "Visualization of various mass spectrometry data types, including 1D chromatograms, spectra, and 2D peak maps"
• [readme] Support for multiple plotting backends: matplotlib (static), bokeh and plotly (interactive): "Support for multiple plotting backends: matplotlib (static), bokeh and plotly (interactive)"
• [other] integrates seamlessly with various plotting library backends (matpotlib, bokeh and plotly): "integrates seamlessly with various plotting library backends (matpotlib, bokeh and plotly)"
• [other] Call the DataFrame.plot() method with x and y parameters specifying the m/z and retention time column names, kind='peakmap': "Call the DataFrame.plot() method with x and y parameters specifying the m/z and retention time column names, kind='peakmap'"
• [other] Optionally enable marginal plots by setting add_marginals=True to display marginal chromatograms and spectra: "Optionally enable marginal plots by setting add_marginals=True to display marginal chromatograms and spectra"