Quality Control: qc

Tools to filter cells and images based on quality control metrics and morphological profiles. For cells, unsupervised filtering is done using pyod through filter_outliers. For images, semi-supervised filtering is done using machine-learning methods trained on image-level data and a subset of labels with qc_images.

While the former can be performed on any dataset, it is likely not as accurate and may remove underrepresented cell types.

filter_outliers(adata[, outliers, fraction, ...])	Filter outlier observations from an AnnData object.
read_image_qc(filename[, meta_cols, ...])	Read image metrics from csv file
qc_images(adata, qc[, classifier, ...])	Perform cell-QC based on image metrics, if needed using a classifier and a subset of labeled images.