433 lines (329 with data), 8.3 kB
API
Import the ehrapy API as follows:
import ehrapy as ep
You can then access the respective modules like:
ep.pl.cool_fancy_plot()
.. currentmodule:: ehrapy
Reading and writing
.. module:: ehrapy
.. autosummary::
:toctree: io
:nosignatures:
io.read_csv
io.read_h5ad
io.read_fhir
io.write
Data
.. 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
.. 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
.. autosummary::
:toctree: preprocessing
:nosignatures:
preprocessing.qc_metrics
preprocessing.qc_lab_measurements
preprocessing.mcar_test
preprocessing.detect_bias
Imputation
.. autosummary::
:toctree: preprocessing
:nosignatures:
preprocessing.explicit_impute
preprocessing.simple_impute
preprocessing.knn_impute
preprocessing.miss_forest_impute
preprocessing.mice_forest_impute
Normalization
.. 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().
.. autosummary::
:toctree: preprocessing
:nosignatures:
preprocessing.combat
Neighbors
.. 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
.. autosummary::
:toctree: tools
:nosignatures:
tools.tsne
tools.umap
tools.draw_graph
tools.diffmap
tools.embedding_density
Clustering and trajectory inference
.. autosummary::
:toctree: tools
:nosignatures:
tools.leiden
tools.dendrogram
tools.dpt
tools.paga
Feature Ranking
.. autosummary::
:toctree: tools
:nosignatures:
tools.rank_features_groups
tools.filter_rank_features_groups
tools.rank_features_supervised
Dataset integration
.. autosummary::
:toctree: tools
:nosignatures:
tools.ingest
Natural language processing
.. autosummary::
:toctree: tools
:nosignatures:
tools.annotate_text
tools.get_medcat_annotation_overview
tools.add_medcat_annotation_to_obs
Survival Analysis
.. 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
.. autosummary::
:toctree: tools
:nosignatures:
tools.causal_inference
Cohort Tracking
.. 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
.. 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
.. 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.
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.
.. autosummary::
:toctree: plot
:nosignatures:
plot.pca
plot.pca_loadings
plot.pca_variance_ratio
plot.pca_overview
Embeddings
.. autosummary::
:toctree: plot
:nosignatures:
plot.tsne
plot.umap
plot.diffmap
plot.draw_graph
plot.embedding
plot.embedding_density
Branching trajectories and pseudotime
.. autosummary::
:toctree: plot
:nosignatures:
plot.dpt_groups_pseudotime
plot.dpt_timeseries
plot.paga
plot.paga_path
plot.paga_compare
Feature Ranking
.. 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
.. autosummary::
:toctree: plot
:nosignatures:
plot.ols
plot.kaplan_meier
plot.cox_ph_forestplot
Causal Inference
.. autosummary::
:toctree: plot
:nosignatures:
plot.causal_effect
AnnData utilities
.. 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.
import ehrapy as ep
ep.settings.set_figure_params(dpi=150)
Please refer to the Scanpy settings documentation
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:
import ehrapy as ep
ep.print_versions()