https://github.com/quantixed/superplot
Making SuperPlots in IGOR Pro
https://github.com/quantixed/superplot
Last synced: 3 months ago
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Making SuperPlots in IGOR Pro
- Host: GitHub
- URL: https://github.com/quantixed/superplot
- Owner: quantixed
- License: gpl-3.0
- Created: 2021-07-14T06:37:28.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2025-01-03T17:33:20.000Z (about 1 year ago)
- Last Synced: 2025-03-01T23:28:28.489Z (12 months ago)
- Language: IGOR Pro
- Size: 535 KB
- Stars: 1
- Watchers: 4
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# SuperPlot
Making SuperPlots in IGOR Pro.
[**Example**](#Example) | [**Workflow**](#Workflow) | [**Installation**](#Installation)
SuperPlots are a way to visualise data in a way that emphasises the
experimental reproducibility. You can read more about SuperPlots in the
[original paper](https://doi.org/10.1083/jcb.202001064):
Lord, S.J., Velle, K.B., Mullins, R.D. & Fritz-Laylin, L.K. (2020)
**SuperPlots: Communicating reproducibility and variability in cell
biology.** *J. Cell Biol.* 219(6):e202001064
## Example
An example SuperPlot generated using `SuperPlot.ipf` and the test data. Test data is taken from Lord _et al._ (2020)
## Workflow
Execute using _Macros > Make SuperPlot_ menu item.
Select the three waves that correspond to replicates, treatment/condition, and measurement using the panel.
Click **Do It** to make your SuperPlot.
### Settings
The SuperPlot can be customised using the settings on the right of the panel.
- **Width** controls how wide the each cluster of points is on the x-axis.
- **Alpa** controls the tranparency of the individual data points. Auto mode lets Igor decide the transparency depending on data density.
- **Add bars** will add bars to show the mean ± 1 standard deviation to each condition, calculated from the averages of replicates, not from data points. See example above.
- **Add stats** will add p-values from a t-test for two conditions, or from Dunnett's post-hoc test for three or more conditions (compared to the first condition).
### Using your own data
Assemble your data in three waves where each row is a masurement for the entire dataset:
1. replicates (numeric wave) indicating which replicate the measurement is from
2. condition (text wave) indicating which condition/treatment/group the measurement corresponds to
3. measurement (numeric wave) the measurement taken
The waves can be named whatever you like and you can make more than one SuperPlot in an Igor experiment.
Replicates and conditions are converted to numeric and text resepectively and either numeric or text waves can be used as input.
#### Generating lots of SuperPlots
A typical use case would be to make one SuperPlot at a time using the interactive mode described above, however if you want to generate many SuperPlots, programmatically, it is possible to do this using `SuperPlotHeadless()`.
This is intended for advanced users only (which is, let's face it all Igor users!).
Note, there is a current limit of 50 SuperPlots per experiment.
## Installation
To install, save a copy of `SuperPlot.ipf` and `PXPUtils.ipf` to *Wavemetrics/Igor Pro 9 User Files/User Procedures*
This directory is usually in `~/Documents/`
Open `SuperPlot.ipf` in Igor by double-clicking it from Finder or selecting *File > Open File > Procedure* in a new Igor experiment.
If the code does not auto-compile, click *Macros > Compile*
`PXPUtils.ipf` can be found [here](https://github.com/quantixed/PXPUtils).
--
### Compatability
This code was developed for IGOR Pro 9. It should work on IP8+.
### R version
An R package SuperPlotR is available [here](https://github.com/quantixed/SuperPlotR).