Hyperparameters
Note
If not done yet, you may be interested in reading our advice to improve your knowledge table. The default Scyan parameters should work well for most cases, so you should first check your knowledge table.
Main parameters
We provide some help to choose scyan.Scyan initialization parameters. We listed below the most important ones.
prior_stdis probably one of the most important parameters. Its default value should work for most of the usage, but it can be changed if needed. A lowprior_std(e.g.,0.2) will help better separate the populations, but it may be too stringent, and some small populations may disappear. In contrast, a highprior_std(e.g.,0.35) increases the chances of having a large diversity of populations, but their separation may be less clear. We recommend to start with a medium value such as0.25or0.3and reducing it afterwards if it already captures all the populations.- Reducing the
temperaturecan help better capture small populations (e.g.,0.25). batch_refis the reference batch we use to align distributions. By default, we use the batch where we have the most cells, but you can choose your own reference. For that, please choose a batch that is representative of the diversity of populations you want to annotate; it can help the batch effect correction.- To improve batch effect correction, we recommend to let the model run longer. This can be done by changing the parameters of the
model.fit()method: for instance, one can increase thepatience(e.g., 6) and/or decrease themin_delta(e.g., 0.5). continuous_covariatesandcategorical_covariatescan be provided to the model if you have some. For instance, if you changed one antibody, you can add a categorical covariate telling which samples have been measured with which antibody. Any covariate may help the model annotations and batch effect correction.
Hyperparameter search
If you want to automate the choice of the hyperparameters, you can also run a hyperparameter optimization with Hydra. See how to configure your project and run Hydra.