# API

Import the ehrapy API as follows:

```python

import ehrapy as ep

```

You can then access the respective modules like:

```python

ep.pl.cool_fancy_plot()

```

```{eval-rst}

.. currentmodule:: ehrapy

```

## Reading and writing

```{eval-rst}

.. module:: ehrapy

```

```{eval-rst}

.. autosummary::

    :toctree: io

    :nosignatures:

    io.read_csv

    io.read_h5ad

    io.read_fhir

    io.write

```

## Data

```{eval-rst}

.. autosummary::

    :toctree: data

    :nosignatures:

    data.mimic_2

    data.mimic_2_preprocessed

    data.mimic_3_demo

    data.diabetes_130_raw

    data.diabetes_130_fairlearn

    data.heart_failure

    data.chronic_kidney_disease

    data.breast_tissue

    data.cervical_cancer_risk_factors

    data.dermatology

    data.echocardiogram

    data.heart_disease

    data.hepatitis

    data.statlog_heart

    data.thyroid

    data.breast_cancer_coimbra

    data.parkinson_dataset_with_replicated_acoustic_features

    data.parkinsons

    data.parkinsons_disease_classification

    data.parkinsons_telemonitoring

```

## Preprocessing

Any transformation of the data matrix that is not a tool.

Other than tools, preprocessing steps usually don’t return an easily interpretable annotation, but perform a basic transformation on the data matrix.

### Basic preprocessing

```{eval-rst}

.. autosummary::

    :toctree: preprocessing

    :nosignatures:

    preprocessing.encode

    preprocessing.pca

    preprocessing.regress_out

    preprocessing.subsample

    preprocessing.balanced_sample

    preprocessing.highly_variable_features

    preprocessing.winsorize

    preprocessing.clip_quantile

    preprocessing.summarize_measurements

```

### Quality control

```{eval-rst}

.. autosummary::

    :toctree: preprocessing

    :nosignatures:

    preprocessing.qc_metrics

    preprocessing.qc_lab_measurements

    preprocessing.mcar_test

    preprocessing.detect_bias

```

### Imputation

```{eval-rst}

.. autosummary::

    :toctree: preprocessing

    :nosignatures:

    preprocessing.explicit_impute

    preprocessing.simple_impute

    preprocessing.knn_impute

    preprocessing.miss_forest_impute

    preprocessing.mice_forest_impute

```

### Normalization

```{eval-rst}

.. autosummary::

    :toctree: preprocessing

    :nosignatures:

    preprocessing.log_norm

    preprocessing.maxabs_norm

    preprocessing.minmax_norm

    preprocessing.power_norm

    preprocessing.quantile_norm

    preprocessing.robust_scale_norm

    preprocessing.scale_norm

    preprocessing.offset_negative_values

```

### Dataset Shift Correction

Partially overlaps with dataset integration. Note that a simple batch correction method is available via `pp.regress_out()`.

```{eval-rst}

.. autosummary::

    :toctree: preprocessing

    :nosignatures:

    preprocessing.combat

```

### Neighbors

```{eval-rst}

.. autosummary::

    :toctree: preprocessing

    :nosignatures:

    preprocessing.neighbors

```

## Tools

Any transformation of the data matrix that is not preprocessing.

In contrast to a preprocessing function, a tool usually adds an easily interpretable annotation to the data matrix, which can then be visualized with a corresponding plotting function.

### Embeddings

```{eval-rst}

.. autosummary::

    :toctree: tools

    :nosignatures:

    tools.tsne

    tools.umap

    tools.draw_graph

    tools.diffmap

    tools.embedding_density

```

### Clustering and trajectory inference

```{eval-rst}

.. autosummary::

    :toctree: tools

    :nosignatures:

    tools.leiden

    tools.dendrogram

    tools.dpt

    tools.paga

```

### Feature Ranking

```{eval-rst}

.. autosummary::

    :toctree: tools

    :nosignatures:

    tools.rank_features_groups

    tools.filter_rank_features_groups

    tools.rank_features_supervised

```

### Dataset integration

```{eval-rst}

.. autosummary::

    :toctree: tools

    :nosignatures:

    tools.ingest

```

### Natural language processing

```{eval-rst}

.. autosummary::

    :toctree: tools

    :nosignatures:

    tools.annotate_text

    tools.get_medcat_annotation_overview

    tools.add_medcat_annotation_to_obs

```

### Survival Analysis

```{eval-rst}

.. autosummary::

    :toctree: tools

    :nosignatures:

    tools.ols

    tools.glm

    tools.kaplan_meier

    tools.test_kmf_logrank

    tools.test_nested_f_statistic

    tools.cox_ph

    tools.weibull_aft

    tools.log_logistic_aft

    tools.nelson_aalen

    tools.weibull

```

### Causal Inference

```{eval-rst}

.. autosummary::

    :toctree: tools

    :nosignatures:

    tools.causal_inference

```

### Cohort Tracking

```{eval-rst}

.. autosummary::

    :toctree: tools

    :nosignatures:

    tools.CohortTracker

```

## Plotting

The plotting module `ehrapy.pl.\*` largely parallels the `tl.\*` and a few of the `pp.\*` functions.

For most tools and for some preprocessing functions, you will find a plotting function with the same name.

### Generic

```{eval-rst}

.. autosummary::

    :toctree: plot

    :nosignatures:

    plot.scatter

    plot.heatmap

    plot.dotplot

    plot.tracksplot

    plot.violin

    plot.stacked_violin

    plot.matrixplot

    plot.clustermap

    plot.ranking

    plot.dendrogram

    plot.catplot

```

### Quality Control and missing values

```{eval-rst}

.. autosummary::

    :toctree: plot

    :nosignatures:

    plot.missing_values_matrix

    plot.missing_values_barplot

    plot.missing_values_heatmap

    plot.missing_values_dendrogram

```

### Classes

Please refer to [Scanpy's plotting classes documentation](https://scanpy.readthedocs.io/en/stable/api.html#classes).

### Tools

Methods that extract and visualize tool-specific annotation in an AnnData object. For any method in module `tl`, there is a method with the same name in `pl`.

```{eval-rst}

.. autosummary::

    :toctree: plot

    :nosignatures:

    plot.pca

    plot.pca_loadings

    plot.pca_variance_ratio

    plot.pca_overview

```

### Embeddings

```{eval-rst}

.. autosummary::

    :toctree: plot

    :nosignatures:

    plot.tsne

    plot.umap

    plot.diffmap

    plot.draw_graph

    plot.embedding

    plot.embedding_density

```

### Branching trajectories and pseudotime

```{eval-rst}

.. autosummary::

    :toctree: plot

    :nosignatures:

    plot.dpt_groups_pseudotime

    plot.dpt_timeseries

    plot.paga

    plot.paga_path

    plot.paga_compare

```

### Feature Ranking

```{eval-rst}

.. autosummary::

    :toctree: plot

    :nosignatures:

    plot.rank_features_groups

    plot.rank_features_groups_violin

    plot.rank_features_groups_stacked_violin

    plot.rank_features_groups_heatmap

    plot.rank_features_groups_dotplot

    plot.rank_features_groups_matrixplot

    plot.rank_features_groups_tracksplot

    plot.rank_features_supervised

```

### Survival Analysis

```{eval-rst}

.. autosummary::

    :toctree: plot

    :nosignatures:

    plot.ols

    plot.kaplan_meier

    plot.cox_ph_forestplot

```

### Causal Inference

```{eval-rst}

.. autosummary::

    :toctree: plot

    :nosignatures:

    plot.causal_effect

```

## AnnData utilities

```{eval-rst}

.. autosummary::

    :toctree: anndata

    :nosignatures:

    anndata.infer_feature_types

    anndata.feature_type_overview

    anndata.replace_feature_types

    anndata.df_to_anndata

    anndata.anndata_to_df

    anndata.move_to_obs

    anndata.delete_from_obs

    anndata.move_to_x

    anndata.get_obs_df

    anndata.get_var_df

    anndata.get_rank_features_df

```

## Settings

A convenience object for setting some default {obj}`matplotlib.rcParams` and a

high-resolution jupyter display backend useful for use in notebooks.

An instance of the {class}`~scanpy._settings.ScanpyConfig` is available as `ehrapy.settings` and allows configuring ehrapy.

```python

import ehrapy as ep

ep.settings.set_figure_params(dpi=150)

```

Please refer to the [Scanpy settings documentation](https://scanpy.readthedocs.io/en/stable/api.html#settings)

for configuration options. ehrapy will adapt these in the future and update the documentation.

## Dependency Versions

ehrapy is complex software with many dependencies. To ensure a consistent runtime environment you should save all

versions that were used for an analysis. This comes in handy when trying to diagnose issues and to reproduce results.

Call the function via:

```python

import ehrapy as ep

ep.print_versions()

```