Custom Igor Pro toolkit for analyzing electrophysiological recordings exported from PatchMaster.
- Batch processing of traces using prefix-based selection
- Baseline correction and linear drift removal
- Peak amplitude extraction (corrected and raw)
- Passive membrane property estimation (Cm, Rs)
- Organized output structure for downstream analysis
-
Copy all
.ipffiles into your Igor Pro User Procedures folder -
Restart Igor Pro (or recompile procedures)
-
Open the panel from the menu:
Analysis → Nanion → Panel
The core analysis pipeline follows these steps:
Trace selection → Baseline correction → Linear detrending → Peak extraction → Passive property estimation
Experiments must be exported from PatchMaster using the following settings:
- Export one group at a time
- Format: Igor
- Enable:
- Trace relative to sweep
- Allow data access
- Export mode: Traces and Stimulus
These settings ensure compatibility with the analysis pipeline and proper reconstruction of traces in Igor Pro.
- Open the exported file by double-clicking the PatchMaster-generated file (
.pxp) - This will load all waves into the root of the Data Browser (typically unorganized)
- The prefix corresponds to the PatchMaster group name
- All matching traces will be selected for analysis
Root/
├─ Trace/ # Raw traces
├─ Stimulus / # Stimulus
├─ Analysis/ # Output results
└─ Packages/
└─ Wave_prefix
Results are saved in the Analysis/ folder:
- peak_net → corrected peak amplitudes.
- Netpeak → [corrected, fit, raw] values.
- cm → membrane capacitance.
- rs → series resistance.
- Netpeak_index → index of maximum response.
- Some dependencies (e.g.,
Wave_prefix) are intentionally implicit - Folder structure is partially hardcoded to match experimental organization
- Certain legacy functions are preserved separately for reference
- Igor Pro
- PatchMaster-exported data
- Consistent trace naming convention
- Parameterization of folder structure
- Improved validation of dependencies
- Modularization of analysis steps
- Export compatibility (e.g., CSV, HDF5 for Python/R workflows)