scmorph API ============= .. module:: scmorph Import ``scmorph`` as: .. code-block:: python import scmorph as sm Reading and writing data: ``io`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. currentmodule:: scmorph.io ``scmorph`` can read data from a variety of sources, including data exported by CellProfiler. Once loaded in, all data is treated as an :doc:`AnnData ` object. This has the advantage of being a fast, standard format that can be used with many existing single-cell tools, such as :doc:`scanpy `. .. note:: If you would like to learn more about the ``h5ad`` file format, please see :doc:`anndata `, which is used to read and write these files. .. note:: scmorph only processes continuous, non-radial features, i.e. features like number of nearest neighbors (discrete), X/Y coordinates (discrete and unfinformative) and BoundingBox (rotation-sensitive) are discarded. You may see a warning message about this: consider this an information rather than as an error. .. autosummary:: :toctree: generated/ read read_cellprofiler_csv read_cellprofiler_batches read_sql make_AnnData split_feature_names Preprocessing: ``pp`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. currentmodule:: scmorph.pp Preprocessing tools that do not produce output, but modify the data to prepare it for downstream analysis. Basic Preprocessing ------------------- .. autosummary:: :toctree: generated/ drop_na scale scale_by_batch Batch Effects ------------------- Tools to remove batch effects from single-cell morphological data. .. autosummary:: :toctree: generated/ remove_batch_effects Feature Selection ------------------- Tools to reduce number of features based on correlations. .. autosummary:: :toctree: generated/ select_features corr Aggregation ------------------- Tools to compare aggregate profiles. Additionally, different distance metrics are available. For a simple aggregation, use ``aggregate``. For a statistically robust distance metric, use ``aggregate_mahalanobis``. .. autosummary:: :toctree: generated/ aggregate aggregate_ttest tstat_distance aggregate_pc aggregate_mahalanobis Dimensionality-reduction ---------------------------- Tools to perform dimensionality-reduction. .. autosummary:: :toctree: generated/ pca neighbors umap Quality Control: ``qc`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. currentmodule:: scmorph.qc Tools to filter cells and images based on quality control metrics and morphological profiles. For cells, unsupervised filtering is done using :doc:`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. .. autosummary:: :toctree: generated/ filter_outliers read_image_qc qc_images Visualization: ``pl`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. currentmodule:: scmorph.pl Tools to plot data, often from dimensionality-reduction techniques. Most of these functions are wrappers around :doc:`scanpy ` functions. .. autosummary:: :toctree: generated/ pca umap cumulative_density ridge_plot Datasets: ``datasets`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. currentmodule:: scmorph.datasets Datasets that are included with ``scmorph`` for testing and demonstration purposes. Currently, this includes various versions of the data in :cite:p:`Rohban2017`. .. autosummary:: :toctree: generated/ rohban2017 rohban2017_minimal rohban2017_imageQC rohban2017_minimal_csv