Features of priority for the next release

[burtonrj]

• User should be able to load a CSV file or pass a pandas dataframe to add_new_sample method of Experiment when populating an experiment with data
• Often the optimal parameters for a clustering algorithm used to 'gate' single cell data in two-dimensions differ from one sample to the next, it would be great to provide the option to perform hyperparameter search - parameters chosen that minimise the Hausdorff distance between newly clustered populations and the populations originally defined
• Improve coverage of unit tests
• Generate a new class structure similar to CellClassifier but inspired by https://www.pnas.org/content/117/35/21373 - should be able to merge the data from multiple subjects using create_ref_sample, label single cells with their origin, and train a classifier to predict the origin of a single cell (i.e. did it originate from a diseased individual), then inspect the model for populations that contribute to that prediction
• Port FlowAI for cleaning data prior to entry with add_new_sample

[burtonrj]

1. Reading csv files has been addressed read in csv (or pandas DataFrame) #13 in release 2.0
2. Optimising parameters with parameter search for auto gates is provided in release 2.0

Short term priorities:

1. Increase test coverage
2. Implement a multi-instance learning approach similar to CellCNN and @hzc363 https://www.pnas.org/content/117/35/21373

Long term priorities:

1. Create a lightweight clone of CytoPy that swaps out mongoengine for PeeWee ORM, granting the use of SQLite for those that cannot host a MongoDB service on their local machine or on Mongo Atlas
2. Graphical user interface deployed with Electron JS to expose CytoPy to scientists without training in Python

Porting FlowAI is unnecessary and would add technical debt, therefore it won't be included in any future release