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Sum Intensity Analysis

Sum intensity analysis measures normalized line intensity along a line scan kymograph ROI and detects transient peaks from a functional reporter (like GCaMP). The analysis operates on a rectangular ROI crop with time along rows and space along columns.

Input data

Sum intensity analysis expects image data organized as a line scan kymograph. The ROI should cover the spatial region used to estimate the mean line intensity trace. Peak detection uses normalized intensity (sum_intensity / spatial_pixel_count) so ROIs with different widths remain more comparable.

Signal pipeline

The backend pipeline:

  1. Compute row sums over the spatial dimension, with optional rolling averaging.
  2. Normalize by spatial pixel count.
  3. Optionally median-filter the normalized trace.
  4. Optionally apply single-exponential detrending for photobleaching.
  5. Estimate a scalar F0 baseline (percentile or manual).
  6. Compute df/f0 and its time derivative.
  7. Detect onsets (derivative threshold by default), enforce a refractory period, refine peaks, and measure fractional peak widths.

Detection parameters

Detection parameters define preprocessing (filtering, detrending, F0 baseline) and peak detection (threshold, refractory period, search windows). Parameters are grouped in the GUI as Preprocessing and Peak Detection.

Built-in detection presets provide starting parameter sets:

Preset Typical use
fast Rapid transients
medium General-purpose starting point
slow Slower rise and decay kinetics

Presets are copied into the analysis when selected; editing controls afterward does not change the built-in preset registry. Manual F0 uses baseline_method="manual" and a user-supplied manual_f0_baseline value.

For the full parameter schema and defaults, see the Sum Intensity Analysis API (get_detection_param_schema).

Results

Sum intensity analysis stores summary values in the AcqImage JSON sidecar and writes tabular output to a CSV file.

For a source file named my_file.tif, sum intensity analysis saves:

my_file.tif.json
my_file.tif.sum_intensity.csv

The JSON sidecar includes detection parameters and summary values for each analyzed ROI. Typical summary values include:

  • num_peaks
  • f0_baseline
  • baseline_method
  • detection_source
  • peak_amplitude_mean
  • peak_amplitude_median
  • peak_events (per-event onset, peak, width, and kinetics measurements)

The CSV file stores per-timepoint traces (for example df_f_signal, d_df_f_signal) and onset/peak marker columns (is_onset, is_peak).

Event-level features (prominence, rise/decay kinetics, area under curve) are documented in the backend feature schema (get_feature_schema on SumIntensityAnalysis).

Programmatic use

Sum intensity analysis can be run from the GUI or from Python code using the same acqstore backend.

See the Sum Intensity Analysis API and the Sum Intensity Analysis Notebook.