Nanopore data analysis example
PyNanoLab could be used to nanopore spike signal analysis. By independent development and integrating algorithms in public references, PyNanoLab supports a variety of nanopore signal analysis such as single step, multiple step.
Then, we will give a simple nanopore analysis example to introduce the usage of PyNanoLab for nanopore or other general application.
1. Data import and Preprocessing
- Use the import action to import a *.abf file into the Data Folder
- Click the Data item in the folder tree, the imported file will be listed in the table list view.
- Double click the data item in table list view, the data will show as a child window in the mdi container.
Tips: colse the data window will not delete the data item.
In data view window, the left panel show the data sweeps and channels. The right panel show the current selected channels. There are two vertical cursor line used to select the data range. And there are two horizontal line used to set the baseline and threshold value for nanopore analysis.
The toolbar action from left to right:
- reset the data. This action restore the data to initinal state.
- PSD analysis. Power spectral density of the data in the two cursor.
- Export data to Table. Export the raw data in the cursor the table item.
- Detrent. Remove the linear trend of the raw data.
- Reverse. Reverse the raw data.
- Histogram. Get the all point histogram of the data in cursor.
- IV Curve. Get the voltage vs current curves. Only for multi sweep data.
- Drope data. Delete the rawdata between cursor.
- Bring cursor. Bring the cursor into the center of the window view.
- Auto scale data view.
- Nanopore Analysis. The entrance of the nanopore analysis.
- Denoise. Reduce the noise of the rawdata.
- Baseline Fitting. Find the baseline and flatten the data.
- Eval. Apply calculation to the raw data.
The denoise action will make the data have a better signal to noise.
As shown in denoise dialog, there have several methods to reduce the noise.The
Preview button show preview in the raw data. The
Apply button will apply the denoise result to the rawdata and close window.
The baseline fitting action could find the baseline and make the fluctuant data flatten.
After remove baseline:
Nanopore Signal Analysis
After version 2.3.0, the nanopore toolbox is recommended to analysis the nanopore signal. You could click the nanopore toolbox button at the toolbar of the data view window. The data in the two cursor will be transfered two the nanopore toolbox. As shown:
Max 5 nanopore toolbox panel could be opened simultaneously. The nanopore toolbox has a independent file, *.pnlmd, to save the nanopore data model. You could use the load button to load the model file directly. There are both automatic and manual mode in this toolbox.
Click the analysis button, you could open the parameters setting dialog.
The parameters shown in the dialog will change according to the analysis model you selected.
Preprocessing: use wavelets, lowpass filter etc. to dsenoise the signal
Baseline: set the reference baseline of the nanopore signal. We will calculate baseline for each signal.
Threhold: set the threhold of the signal, only the signasignal that exceeds the threshold will be extracted.
fluctuation: the diff value of the baseline's extremum, we use this value to find a signal.
Model: select the analysis model.
- singleStepOriginal: For single step signal analysis.
- singleStepReduced: For single step signal analysis, but ignor the rise time and restore the blockage current by charge conservation.
- singleStepAdept2: For single step signal analysis, using the function as MOSAIC software mentioned.
- multistepspnl: For multi step signal analysis, useing the the same method of the singleStepOriginal.
- multiStepAdept2: For multi step signal analysis, using the function as MOSAIC software mentioned.
- IMDS: For a longtime signal with many many steps. Use this method should cutsignal firstly.
- faradayStep: For a signal with a long and slow decay time, these signal are usually accompanied by a faraday process.
- multistepsKmeans: For multi step signal analysis, using the K-means cluster algorithm.
- extremejump: For single step signal analysis, all parameters is determined automaticly.
- cutsignal: just split the signal, not analysis the result of the signal. Subsequently used for manual analysis.
After analysis, click the Plot Fitdata, you could view the all fitdata.
We will also acquire a signal list to view the fit result one by one. The fitdata and result table will show in the front panel. If one signal is not correct, you could use the edit mode to modify the result.
Click the View Mode button, to change between the edit and view mode.
In edit mode, same infinite lines will show in the plot window, and drag the line you could change the corresponding result.
Click the reset button, you could delete current result and manual analysis this signal. In manual analysis, double click the point in the figure to label the start and stop of a step. And then click the Confirm button to generate the result automatic. Following, you could drag the infinite line to modify the result.
After analysis, Click the Export button to export the result table for all the signal.
If the signal is hard distinguished from baseline, you could click the Manual Model button to manual split the signal to the signal list.
First, click the Manual Model button entrance the split mode. The all rawdata with two cursor will draw in the plotting window. And then drag the cursor to set the range of a signal. Finally, click the addsignal button to add the signal in the signal list view.
We also integrate some addons in the side toolbar used to analysis the signal in batches. Such as the PSD, STFT etc.