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.. automodule:: sccoda

API

We advise to import scCODA in a python session via:

import sccoda
dat = sccoda.util.cell_composition_data
ana = sccoda.util.compositional_analysis
viz = sccoda.util.data_visualization

The workflow in scCODA starts with reading in cell count data (dat) and visualizing them (viz) or synthetically generating cell count data (util.data_generation).

Data acquisition

Integrating data sources (dat) (scanpy or pandas)

.. autosummary::
    :toctree: .

    sccoda.util.cell_composition_data.from_pandas
    sccoda.util.cell_composition_data.from_scanpy
    sccoda.util.cell_composition_data.from_scanpy_dir
    sccoda.util.cell_composition_data.from_scanpy_list
    sccoda.util.cell_composition_data.read_anndata_one_sample

Synthetic data generation

.. autosummary::
    :toctree: .

    sccoda.util.data_generation.generate_case_control
    sccoda.util.data_generation.b_w_from_abs_change
    sccoda.util.data_generation.counts_from_first
    sccoda.util.data_generation.sparse_effect_matrix

Compositional data visualization

Compositional datasets can be plotted via the methods in util.data_visualization.

.. autosummary::
    :toctree: .

    sccoda.util.data_visualization.stacked_barplot
    sccoda.util.data_visualization.boxplots
    sccoda.util.data_visualization.stackbar

Model setup and inference

Using the scCODA model is easiest by generating an instance of ana.CompositionalAnalysis. By specifying the formula via the patsy syntax, many combinations and transformations of the covariates can be performed without redefining the covariate matrix. Also, the reference cell type needs to be specified in this step.

The scCODA model

.. autosummary::
    :toctree: .

    sccoda.util.comp_ana.CompositionalAnalysis
    sccoda.model.scCODA_model.CompositionalModel
    sccoda.model.scCODA_model.scCODAModel

Utility functions

.. autosummary::
    :toctree: .

    sccoda.util.helper_functions.sample_size_estimate

Result evaluation

Executing an inference method on a compositional model produces a sccoda.util.result_classes.CAResult object. This class extends the InferenceData class of arviz and supports all its diagnostic and plotting functionality.

.. autosummary::
    :toctree: .

    sccoda.util.result_classes.CAResult

Model comparison

sccoda.models.other_models contains implementations of several compositional methods frm microbiome analysis and non-compositional tests that can be used for comparison.

.. autosummary::
    :toctree: .

    sccoda.model.other_models.SimpleModel
    sccoda.model.other_models.scdney_model
    sccoda.model.other_models.HaberModel
    sccoda.model.other_models.CLRModel
    sccoda.model.other_models.TTest
    sccoda.model.other_models.CLRModel_ttest
    sccoda.model.other_models.ALDEx2Model
    sccoda.model.other_models.ALRModel_ttest
    sccoda.model.other_models.ALRModel_wilcoxon
    sccoda.model.other_models.AncomModel
    sccoda.model.other_models.DirichRegModel
    sccoda.model.other_models.BetaBinomialModel
    sccoda.model.other_models.ANCOMBCModel