API

High-level functions for processing data, training and evaluating models and plotting results.

Data

`data.create_dataset`(params)	Create a `NNDataset`.
`data.load_example_data`([data_dir])	Download example data to `data_dir`.
`data.load_example_experiment`([experiment_dir])	Download example experiment to `experiment_dir`.

`tl.run_experiments`(exps[, mode])	Execute experiments.
`tl.create_cluster_data`(experiment_dir[, ...])	Create (subsampled) data for clustering.
`tl.prepare_full_dataset`(experiment_dir[, ...])	Prepare all data for clustering by predicting cluster-rep.
`tl.project_cluster_data`(experiment_dir, ...)	Project existing clustering to new data.
`tl.load_full_data_dict`(exp[, keys, ...])	Load mpp_datas used in experiment in a dict.
`tl.get_clustered_cells`(mpp_datas, cl, ...[, ...])	Get num_objs example cells for each mpp_data.
`tl.project_cluster_data`(experiment_dir, ...)	Project existing clustering to new data.
`tl.add_clustering_to_adata`(data_dir, ...[, ...])	Add clustering to adata.
`tl.extract_features`(params)	Extract features from clustered dataset using `FeaturesExtractor`.
`tl.thresholded_count`(df[, threshold])	Count largest CSL objects per cell.
`tl.thresholded_median`(df[, threshold])	Calculate median area of large CSL objects per cell.

`pl.plot_mean_intensity`(adata[, groupby, ...])	Show per cluster intensity of each channel.
`pl.get_intensity_change`(adata, groupby[, ...])	Get data for plotting intensity comparison with `plot_intensity_change()`.
`pl.plot_intensity_change`(adata, ...[, ...])	Plot mean intensity differences between perturbations or clusters.
`pl.plot_mean_size`(adata[, groupby_row, ...])	Plot mean cluster sizes per cell, grouped by different columns in obs.
`pl.plot_size_change`(adata[, groupby_row, ...])	Plot mean intensity differences between perturbations and clusters.
`pl.plot_object_stats`(adata, group_key[, ...])	Barplot of object statistics.
`pl.plot_co_occurrence`(adata, cluster1, ...)	Plot co-occurrence for one cluster-cluster pairs.
`pl.plot_co_occurrence_grid`(adata, condition)	Plot co-occurrence for all cluster-cluster pairs in a grid.
`pl.annotate_img`(img[, annotation, from_col, ...])	Annotate cluster image.

`constants.campa_config`	Configuration for CAMPA.
`utils.load_config`(config_file)	Load configuration file and return configuration object.
`utils.merged_config`(config1, config2)	Update config1 with config2.
`utils.init_logging`([level])	Set up logging for CAMPA.