| description |
|---|
How to get started with cellfinder |
- Make sure that cellfinder is installed (if not see Installation)
- Make sure that your data is organised as expected (see Data requirements)
{% hint style="info" %} Make sure you activate your conda environment before running cellfinder {% endhint %}
The cell detection via cellfinder can be run with a single terminal command (cellfinder):
cellfinder -s signal_channel_images -b background_channel_images -o /path/to/output_directory -x 2 -y 2 -z 5
Multiple channels can also be processed at once:
cellfinder -s first_signal_channel_images second_signal_channel_images -b background_channel_images -o /path/to/output_directory -x 2 -y 2 -z 5
However, there are many options to change what parts of the analysis are run, and how they are run. For instance, to register the brain to the allen reference atlas, use --register. I recommend looking through the Command line options.
{% hint style="warning" %} If you have any spaces in your file-path, please enclose it in quotation marks, otherwise cellfinder will interpret it as two inputs, separated by a space.
i.e. "/path/to/my data" not path/to/my data.
{% endhint %}
The deep learning network included with cellfinder to classify cells as real cells or artefacts was trained on a very specific dataset. You will very likely need to retrain this if the classification is incorrect on your data. See Training the network.