pertpy.tools.Sccoda

pertpy.tools.Sccoda#

class Sccoda(*args, **kwargs)[source]#

Statistical model for single-cell differential composition analysis with specification of a reference cell type.

For further information, see scCODA is a Bayesian model for compositional single-cell data analysis (Büttner, Ostner et al., NatComms, 2021)

Methods table#

credible_effects(data[, modality_key, est_fdr])

Decides which effects of the scCODA model are credible based on an adjustable inclusion probability threshold.

get_effect_df(data[, modality_key])

Get effect dataframe as printed in the extended summary.

get_intercept_df(data[, modality_key])

Get intercept dataframe as printed in the extended summary.

get_node_df(data[, modality_key])

Get node effect dataframe as printed in the extended summary of a tascCODA model.

load(adata, type[, generate_sample_level, ...])

Prepare a MuData object for subsequent processing.

make_arviz(data[, modality_key, rng_key, ...])

Creates arviz object from model results for MCMC diagnosis.

model(counts, covariates, n_total, ...)

Implements scCODA model in numpyro.

plot_boxplots(data, feature_name, *[, ...])

Grouped boxplot visualization.

plot_draw_effects(data, covariate, *[, ...])

Plot a tree with colored circles on the nodes indicating significant effects with bar plots which indicate leave-level significant effects.

plot_draw_tree(data, *[, modality_key, ...])

Plot a tree using input ete4 tree object.

plot_effects_barplot(data, *[, ...])

Barplot visualization for effects.

plot_effects_umap(mdata, effect_name, ...[, ...])

Plot a UMAP visualization colored by effect strength.

plot_rel_abundance_dispersion_plot(data, *)

Plots total variance of relative abundance versus minimum relative abundance of all cell types for determination of a reference cell type.

plot_stacked_barplot(data, feature_name, *)

Plots a stacked barplot for all levels of a covariate or all samples (if feature_name=="samples").

prepare(data, formula[, ...])

Handles data preprocessing, covariate matrix creation, reference selection, and zero count replacement for scCODA.

run_hmc(data[, modality_key, num_samples, ...])

Run standard Hamiltonian Monte Carlo sampling (Neal, 2011) to infer optimal model parameters.

run_nuts(data[, modality_key, num_samples, ...])

Run No-U-turn sampling (Hoffman and Gelman, 2014), an efficient version of Hamiltonian Monte Carlo sampling to infer optimal model parameters.

set_fdr(data, est_fdr[, modality_key])

Direct posterior probability approach to calculate credible effects while keeping the expected FDR at a certain level.

set_init_mcmc_states(rng_key, ref_index, ...)

Sets initial MCMC state values for scCODA model.

summary(data[, extended, modality_key])

Printing method for the summary.

summary_prepare(sample_adata[, est_fdr])

Generates summary dataframes for intercepts, effects and node-level effect (if using tree aggregation).

Methods#

Sccoda.credible_effects(data, modality_key='coda', est_fdr=None)[source]#

Decides which effects of the scCODA model are credible based on an adjustable inclusion probability threshold.

Note: Parameter est_fdr has no effect for spike-and-slab LASSO selection method.

Parameters:

  • data (AnnData | MuData) – AnnData object or MuData object.

  • modality_key (str, default: 'coda') – If data is a MuData object, specify which modality to use.

  • est_fdr (float, default: None) – Estimated false discovery rate. Must be between 0 and 1.

Return type:

Series

Returns:

Credible effect decision series which includes boolean values indicate whether effects are credible under inc_prob_threshold.

Examples

>>> import pertpy as pt
>>> haber_cells = pt.dt.haber_2017_regions()
>>> sccoda = pt.tl.Sccoda()
>>> mdata = sccoda.load(haber_cells,
>>>                     type="cell_level",
>>>                     generate_sample_level=True,
>>>                     cell_type_identifier="cell_label",
>>>                     sample_identifier="batch",
>>>                     covariate_obs=["condition"])
>>> mdata = sccoda.prepare(mdata, formula="condition", reference_cell_type="Endocrine")
>>> sccoda.run_nuts(mdata, num_warmup=100, num_samples=1000, rng_key=42)
>>> credible_effects = sccoda.credible_effects(mdata).
Sccoda.get_effect_df(data, modality_key='coda')#

Get effect dataframe as printed in the extended summary.

Parameters:

  • data (AnnData | MuData) – AnnData object or MuData object.

  • modality_key (str, default: 'coda') – If data is a MuData object, specify which modality to use.

Return type:

DataFrame

Returns:

Effect data frame.

Examples

>>> import pertpy as pt
>>> haber_cells = pt.dt.haber_2017_regions()
>>> sccoda = pt.tl.Sccoda()
>>> mdata = sccoda.load(haber_cells, type="cell_level", generate_sample_level=True, cell_type_identifier="cell_label",                 sample_identifier="batch", covariate_obs=["condition"])
>>> mdata = sccoda.prepare(mdata, formula="condition", reference_cell_type="Endocrine")
>>> sccoda.run_nuts(mdata, num_warmup=100, num_samples=1000, rng_key=42)
>>> effects = sccoda.get_effect_df(mdata)
Sccoda.get_intercept_df(data, modality_key='coda')#

Get intercept dataframe as printed in the extended summary.

Parameters:

  • data (AnnData | MuData) – AnnData object or MuData object.

  • modality_key (str, default: 'coda') – If data is a MuData object, specify which modality to use.

Return type:

DataFrame

Returns:

Intercept data frame.

Examples

>>> import pertpy as pt
>>> haber_cells = pt.dt.haber_2017_regions()
>>> sccoda = pt.tl.Sccoda()
>>> mdata = sccoda.load(haber_cells, type="cell_level", generate_sample_level=True, cell_type_identifier="cell_label",                 sample_identifier="batch", covariate_obs=["condition"])
>>> mdata = sccoda.prepare(mdata, formula="condition", reference_cell_type="Endocrine")
>>> sccoda.run_nuts(mdata, num_warmup=100, num_samples=1000, rng_key=42)
>>> intercepts = sccoda.get_intercept_df(mdata)
Sccoda.get_node_df(data, modality_key='coda')#

Get node effect dataframe as printed in the extended summary of a tascCODA model.

Parameters:

  • data (AnnData | MuData) – AnnData object or MuData object.

  • modality_key (str, default: 'coda') – If data is a MuData object, specify which modality to use.

Return type:

DataFrame

Returns:

Node effect data frame.

Examples

>>> import pertpy as pt
>>> adata = pt.dt.tasccoda_example()
>>> tasccoda = pt.tl.Tasccoda()
>>> mdata = tasccoda.load(
>>>     adata, type="sample_level",
>>>     levels_agg=["Major_l1", "Major_l2", "Major_l3", "Major_l4", "Cluster"],
>>>     key_added="lineage", add_level_name=True
>>> )
>>> mdata = tasccoda.prepare(
>>>     mdata, formula="Health", reference_cell_type="automatic", tree_key="lineage", pen_args={"phi" : 0}
>>> )
>>> tasccoda.run_nuts(mdata, num_samples=1000, num_warmup=100, rng_key=42)
>>> node_effects = tasccoda.get_node_df(mdata)
Sccoda.load(adata, type, generate_sample_level=True, cell_type_identifier=None, sample_identifier=None, covariate_uns=None, covariate_obs=None, covariate_df=None, modality_key_1='rna', modality_key_2='coda')[source]#

Prepare a MuData object for subsequent processing. If type is “cell_level”, then create a compositional analysis dataset from the input adata.

When using type="cell_level", adata needs to have a column in adata.obs that contains the cell type assignment. Further, it must contain one column or a set of columns (e.g. subject id, treatment, disease status) that uniquely identify each (statistical) sample. Further covariates (e.g. subject age) can either be specified via addidional column names in adata.obs, a key in adata.uns, or as a separate DataFrame.

Parameters:

  • adata (AnnData) – AnnData object.

  • type (Literal['cell_level', 'sample_level']) – Specify the input adata type, which could be either a cell-level AnnData or an aggregated sample-level AnnData.

  • generate_sample_level (bool, default: True) – Whether to generate an AnnData object on the sample level or create an empty AnnData object.

  • cell_type_identifier (str, default: None) – If type is “cell_level”, specify column name in adata.obs that specifies the cell types.

  • sample_identifier (str, default: None) – If type is “cell_level”, specify column name in adata.obs that specifies the sample.

  • covariate_uns (str | None, default: None) – If type is “cell_level”, specify key for adata.uns, where covariate values are stored.

  • covariate_obs (list[str] | None, default: None) – If type is “cell_level”, specify list of keys for adata.obs, where covariate values are stored.

  • covariate_df (DataFrame | None, default: None) – If type is “cell_level”, specify dataFrame with covariates.

  • modality_key_1 (str, default: 'rna') – Key to the cell-level AnnData in the MuData object.

  • modality_key_2 (str, default: 'coda') – Key to the aggregated sample-level AnnData object in the MuData object.

Return type:

MuData

Returns:

mudata.MuData object with cell-level AnnData (mudata[modality_key_1]) and aggregated sample-level AnnData (mudata[modality_key_2]).

Examples

>>> import pertpy as pt
>>> haber_cells = pt.dt.haber_2017_regions()
>>> sccoda = pt.tl.Sccoda()
>>> mdata = sccoda.load(haber_cells,
>>>                     type="cell_level",
>>>                     generate_sample_level=True,
>>>                     cell_type_identifier="cell_label",
>>>                     sample_identifier="batch", covariate_obs=["condition"])
Sccoda.make_arviz(data, modality_key='coda', rng_key=None, num_prior_samples=500, use_posterior_predictive=True)[source]#

Creates arviz object from model results for MCMC diagnosis.

Parameters:

  • data (AnnData | MuData) – AnnData object or MuData object.

  • modality_key (str, default: 'coda') – If data is a MuData object, specify which modality to use.

  • rng_key (int | None, default: None) – The rng state used for the prior simulation. If None, a random state will be selected.

  • num_prior_samples (int, default: 500) – Number of prior samples calculated.

  • use_posterior_predictive (bool, default: True) – If True, the posterior predictive will be calculated.

Returns:

arviz_data with all MCMC information

Return type:

arviz.InferenceData

Examples

>>> import pertpy as pt
>>> haber_cells = pt.dt.haber_2017_regions()
>>> sccoda = pt.tl.Sccoda()
>>> mdata = sccoda.load(haber_cells,
>>>                     type="cell_level",
>>>                     generate_sample_level=True,
>>>                     cell_type_identifier="cell_label",
>>>                     sample_identifier="batch",
>>>                     covariate_obs=["condition"])
>>> mdata = sccoda.prepare(mdata, formula="condition", reference_cell_type="Endocrine")
>>> sccoda.run_nuts(mdata, num_warmup=100, num_samples=1000, rng_key=42)
>>> arviz_data = sccoda.make_arviz(mdata, num_prior_samples=100)
Sccoda.model(counts, covariates, n_total, ref_index, sample_adata)[source]#

Implements scCODA model in numpyro.

Parameters:

  • counts (ndarray) – Count data array

  • covariates (ndarray) – Covariate matrix

  • n_total (ndarray) – Number of counts per sample

  • ref_index – Index of reference feature

  • sample_adata (AnnData) – Anndata object with cell counts as sample_adata.X and covariates saved in sample_adata.obs.

Returns:

predictions (see numpyro documentation for details on models)

Sccoda.plot_boxplots(data, feature_name, *, modality_key='coda', y_scale='relative', plot_facets=False, add_dots=False, cell_types=None, args_boxplot=None, args_swarmplot=None, palette='Blues', show_legend=True, level_order=None, figsize=None, dpi=100, return_fig=False)#

Grouped boxplot visualization.

The cell counts for each cell type are shown as a group of boxplots with intra–group separation by a covariate from data.obs.

Parameters:

  • data (AnnData | MuData) – AnnData object or MuData object

  • feature_name (str) – The name of the feature in data.obs to plot

  • modality_key (str, default: 'coda') – If data is a MuData object, specify which modality to use.

  • y_scale (Literal['relative', 'log', 'log10', 'count'], default: 'relative') – Transformation to of cell counts. Options: “relative” - Relative abundance, “log” - log(count), “log10” - log10(count), “count” - absolute abundance (cell counts).

  • plot_facets (bool, default: False) – If False, plot cell types on the x-axis. If True, plot as facets.

  • add_dots (bool, default: False) – If True, overlay a scatterplot with one dot for each data point.

  • cell_types (list | None, default: None) – Subset of cell types that should be plotted.

  • args_boxplot (dict | None, default: None) – Arguments passed to sns.boxplot.

  • args_swarmplot (dict | None, default: None) – Arguments passed to sns.swarmplot.

  • figsize (tuple[float, float] | None, default: None) – Figure size.

  • dpi (int | None, default: 100) – Dpi setting.

  • palette (str | Colormap | None, default: 'Blues') – The seaborn color map (name) for the barplot.

  • show_legend (bool | None, default: True) – If True, adds a legend.

  • level_order (list[str], default: None) – Custom ordering of bars on the x-axis.

  • return_fig (bool, default: False) – if True, returns figure of the plot, that can be used for saving.

Return type:

Figure | None

Returns:

If return_fig is True, returns the figure, otherwise None.

Examples

>>> import pertpy as pt
>>> haber_cells = pt.dt.haber_2017_regions()
>>> sccoda = pt.tl.Sccoda()
>>> mdata = sccoda.load(haber_cells, type="cell_level", generate_sample_level=True, cell_type_identifier="cell_label",                 sample_identifier="batch", covariate_obs=["condition"])
>>> sccoda.plot_boxplots(mdata, feature_name="condition", add_dots=True)
Preview:
../../_images/sccoda_boxplots.png
Sccoda.plot_draw_effects(data, covariate, *, modality_key='coda', tree='tree', show_legend=None, show_leaf_effects=False, tight_text=False, show_scale=False, units='px', figsize=(None, None), dpi=100, save=False, return_fig=False)#

Plot a tree with colored circles on the nodes indicating significant effects with bar plots which indicate leave-level significant effects.

Parameters:

  • data (AnnData | MuData) – AnnData object or MuData object.

  • covariate (str) – The covariate, whose effects should be plotted.

  • modality_key (str, default: 'coda') – If data is a MuData object, specify which modality to use.

  • tree (str, default: 'tree') – A ete4 tree object or a str to indicate the tree stored in .uns.

  • show_legend (bool | None, default: None) – If show legend of nodes significant effects or not. Defaults to False if show_leaf_effects is True.

  • show_leaf_effects (bool | None, default: False) – If True, plot bar plots which indicate leave-level significant effects.

  • tight_text (bool | None, default: False) – When False, boundaries of the text are approximated according to general font metrics, producing slightly worse aligned text faces but improving the performance of tree visualization in scenes with a lot of text faces.

  • show_scale (bool | None, default: False) – Include the scale legend in the tree image or not.

  • units (Literal[‘px’, ‘mm’, ‘in’] | None, default: 'px') – Unit of image sizes. “px”: pixels, “mm”: millimeters, “in”: inches.

  • figsize (tuple[float, float] | None, default: (None, None)) – Figure size.

  • dpi (int | None, default: 100) – Dots per inches.

  • save (str | bool, default: False) – Save the tree plot to a file. You can specify the file name here.

  • return_fig (bool, default: False) – if True, returns figure of the plot, that can be used for saving.

Return type:

Tree | None

Returns:

Depending on save, returns ete4.core.tree.Tree and ete4.treeview.TreeStyle (save = ‘output.png’) or plot the tree inline (save = False).

Examples

>>> import pertpy as pt
>>> adata = pt.dt.tasccoda_example()
>>> tasccoda = pt.tl.Tasccoda()
>>> mdata = tasccoda.load(
>>>     adata, type="sample_level",
>>>     levels_agg=["Major_l1", "Major_l2", "Major_l3", "Major_l4", "Cluster"],
>>>     key_added="lineage", add_level_name=True
>>> )
>>> mdata = tasccoda.prepare(
>>>     mdata, formula="Health", reference_cell_type="automatic", tree_key="lineage", pen_args=dict(phi=0)
>>> )
>>> tasccoda.run_nuts(mdata, num_samples=1000, num_warmup=100, rng_key=42)
>>> tasccoda.plot_draw_effects(mdata, covariate="Health[T.Inflamed]", tree="lineage")
Preview:
../../_images/tasccoda_draw_effects.png
Sccoda.plot_draw_tree(data, *, modality_key='coda', tree='tree', tight_text=False, show_scale=False, units='px', figsize=(None, None), dpi=100, save=False, return_fig=False)#

Plot a tree using input ete4 tree object.

Parameters:

  • data (AnnData | MuData) – AnnData object or MuData object.

  • modality_key (str, default: 'coda') – If data is a MuData object, specify which modality to use.

  • tree (str, default: 'tree') – A ete4 tree object or a str to indicate the tree stored in .uns.

  • tight_text (bool, default: False) – When False, boundaries of the text are approximated according to general font metrics, producing slightly worse aligned text faces but improving the performance of tree visualization in scenes with a lot of text faces.

  • show_scale (bool | None, default: False) – Include the scale legend in the tree image or not.

  • units (Literal[‘px’, ‘mm’, ‘in’] | None, default: 'px') – Unit of image sizes. “px”: pixels, “mm”: millimeters, “in”: inches.

  • figsize (tuple[float, float] | None, default: (None, None)) – Figure size.

  • dpi (int | None, default: 100) – Dots per inches.

  • save (str | bool, default: False) – Save the tree plot to a file. You can specify the file name here.

  • return_fig (bool, default: False) – if True, returns figure of the plot, that can be used for saving.

Return type:

Tree | None

Returns:

Depending on save, returns ete4.core.tree.Tree and ete4.treeview.TreeStyle (save = ‘output.png’) or plot the tree inline (save = False)

Examples

>>> import pertpy as pt
>>> adata = pt.dt.tasccoda_example()
>>> tasccoda = pt.tl.Tasccoda()
>>> mdata = tasccoda.load(
>>>     adata, type="sample_level",
>>>     levels_agg=["Major_l1", "Major_l2", "Major_l3", "Major_l4", "Cluster"],
>>>     key_added="lineage", add_level_name=True
>>> )
>>> mdata = tasccoda.prepare(
>>>     mdata, formula="Health", reference_cell_type="automatic", tree_key="lineage", pen_args=dict(phi=0)
>>> )
>>> tasccoda.run_nuts(mdata, num_samples=1000, num_warmup=100, rng_key=42)
>>> tasccoda.plot_draw_tree(mdata, tree="lineage")
Preview:
../../_images/tasccoda_draw_tree.png
Sccoda.plot_effects_barplot(data, *, modality_key='coda', covariates=None, parameter='log2-fold change', plot_facets=True, plot_zero_covariate=True, plot_zero_cell_type=False, palette=<matplotlib.colors.ListedColormap object>, level_order=None, args_barplot=None, figsize=None, dpi=100, return_fig=False)#

Barplot visualization for effects.

The effect results for each covariate are shown as a group of barplots, with intra–group separation by cell types. The covariates groups can either be ordered along the x-axis of a single plot (plot_facets=False) or as plot facets (plot_facets=True).

Parameters:

  • data (AnnData | MuData) – AnnData object or MuData object.

  • modality_key (str, default: 'coda') – If data is a MuData object, specify which modality to use.

  • covariates (str | list | None, default: None) – The name of the covariates in data.obs to plot.

  • parameter (Literal['log2-fold change', 'Final Parameter', 'Expected Sample'], default: 'log2-fold change') – The parameter in effect summary to plot.

  • plot_facets (bool, default: True) – If False, plot cell types on the x-axis. If True, plot as facets.

  • plot_zero_covariate (bool, default: True) – If True, plot covariate that have all zero effects. If False, do not plot.

  • plot_zero_cell_type (bool, default: False) – If True, plot cell type that have zero effect. If False, do not plot.

  • figsize (tuple[float, float] | None, default: None) – Figure size.

  • dpi (int | None, default: 100) – Figure size.

  • palette (str | Colormap | None, default: <matplotlib.colors.ListedColormap object at 0x70182e107710>) – The seaborn color map (name) for the barplot.

  • level_order (list[str], default: None) – Custom ordering of bars on the x-axis.

  • args_barplot (dict | None, default: None) – Arguments passed to sns.barplot.

  • return_fig (bool, default: False) – if True, returns figure of the plot, that can be used for saving.

Return type:

Figure | None

Returns:

If return_fig is True, returns the figure, otherwise None.

Examples

>>> import pertpy as pt
>>> haber_cells = pt.dt.haber_2017_regions()
>>> sccoda = pt.tl.Sccoda()
>>> mdata = sccoda.load(haber_cells, type="cell_level", generate_sample_level=True, cell_type_identifier="cell_label",                 sample_identifier="batch", covariate_obs=["condition"])
>>> mdata = sccoda.prepare(mdata, formula="condition", reference_cell_type="Endocrine")
>>> sccoda.run_nuts(mdata, num_warmup=100, num_samples=1000, rng_key=42)
>>> sccoda.plot_effects_barplot(mdata)
Preview:
../../_images/sccoda_effects_barplot.png
Sccoda.plot_effects_umap(mdata, effect_name, cluster_key, *, modality_key_1='rna', modality_key_2='coda', color_map=None, palette=None, ax=None, return_fig=False, **kwargs)#

Plot a UMAP visualization colored by effect strength.

Effect results in .varm of aggregated sample-level AnnData (default is data[‘coda’]) are assigned to cell-level AnnData (default is data[‘rna’]) depending on the cluster they were assigned to.

Parameters:

  • mdata (MuData) – MuData object.

  • effect_name (str | list | None) – The name of the effect results in .varm of aggregated sample-level AnnData to plot

  • cluster_key (str) – The cluster information in .obs of cell-level AnnData (default is data[‘rna’]). To assign cell types’ effects to original cells.

  • modality_key_1 (str, default: 'rna') – Key to the cell-level AnnData in the MuData object.

  • modality_key_2 (str, default: 'coda') – Key to the aggregated sample-level AnnData object in the MuData object.

  • color_map (Colormap | str | None, default: None) – The color map to use for plotting.

  • palette (str | Sequence[str] | Colormap | None, default: None) – The color palette (name) to use for plotting.

  • ax (Axes, default: None) – A matplotlib axes object. Only works if plotting a single component.

  • return_fig (bool, default: False) – if True, returns figure of the plot, that can be used for saving.

  • **kwargs – All other keyword arguments are passed to scanpy.plot.umap()

Return type:

Figure | None

Returns:

If return_fig is True, returns the figure, otherwise None.

Examples

>>> import pertpy as pt
>>> import scanpy as sc
>>> import schist
>>> adata = pt.dt.haber_2017_regions()
>>> sc.pp.neighbors(adata)
>>> schist.inference.nested_model(adata, n_init=100, random_seed=5678)
>>> tasccoda_model = pt.tl.Tasccoda()
>>> tasccoda_data = tasccoda_model.load(adata, type="cell_level",
>>>                 cell_type_identifier="nsbm_level_1",
>>>                 sample_identifier="batch", covariate_obs=["condition"],
>>>                 levels_orig=["nsbm_level_4", "nsbm_level_3", "nsbm_level_2", "nsbm_level_1"],
>>>                 add_level_name=True)
>>> tasccoda_model.prepare(
>>>     tasccoda_data,
>>>     modality_key="coda",
>>>     reference_cell_type="18",
>>>     formula="condition",
>>>     pen_args=dict(phi=0, lambda_1=3.5),
>>>     tree_key="tree"
>>> )
>>> tasccoda_model.run_nuts(
...     tasccoda_data, modality_key="coda", rng_key=1234, num_samples=10000, num_warmup=1000
... )
>>> sc.tl.umap(tasccoda_data["rna"])
>>> tasccoda_model.plot_effects_umap(tasccoda_data,
>>>                         effect_name=["effect_df_condition[T.Salmonella]",
>>>                                      "effect_df_condition[T.Hpoly.Day3]",
>>>                                      "effect_df_condition[T.Hpoly.Day10]"],
>>>                                       cluster_key="nsbm_level_1",
>>>                         )
Preview:
../../_images/tasccoda_effects_umap.png
Sccoda.plot_rel_abundance_dispersion_plot(data, *, modality_key='coda', abundant_threshold=0.9, default_color='Grey', abundant_color='Red', label_cell_types=True, figsize=None, dpi=100, ax=None, return_fig=False)#

Plots total variance of relative abundance versus minimum relative abundance of all cell types for determination of a reference cell type.

If the count of the cell type is larger than 0 in more than abundant_threshold percent of all samples, the cell type will be marked in a different color.

Parameters:

  • data (AnnData | MuData) – AnnData or MuData object.

  • modality_key (str, default: 'coda') – If data is a MuData object, specify which modality to use.

  • abundant_threshold (float | None, default: 0.9) – Presence threshold for abundant cell types.

  • default_color (str | None, default: 'Grey') – Bar color for all non-minimal cell types.

  • abundant_color (str | None, default: 'Red') – Bar color for cell types with abundant percentage larger than abundant_threshold.

  • label_cell_types (bool, default: True) – Label dots with cell type names.

  • figsize (tuple[float, float] | None, default: None) – Figure size.

  • dpi (int | None, default: 100) – Dpi setting.

  • ax (Axes | None, default: None) – A matplotlib axes object. Only works if plotting a single component.

  • return_fig (bool, default: False) – if True, returns figure of the plot, that can be used for saving.

Return type:

Figure | None

Returns:

If return_fig is True, returns the figure, otherwise None.

Examples

>>> import pertpy as pt
>>> haber_cells = pt.dt.haber_2017_regions()
>>> sccoda = pt.tl.Sccoda()
>>> mdata = sccoda.load(haber_cells, type="cell_level", generate_sample_level=True, cell_type_identifier="cell_label",                 sample_identifier="batch", covariate_obs=["condition"])
>>> mdata = sccoda.prepare(mdata, formula="condition", reference_cell_type="Endocrine")
>>> sccoda.run_nuts(mdata, num_warmup=100, num_samples=1000, rng_key=42)
>>> sccoda.plot_rel_abundance_dispersion_plot(mdata)
Preview:
../../_images/sccoda_rel_abundance_dispersion_plot.png
Sccoda.plot_stacked_barplot(data, feature_name, *, modality_key='coda', palette=<matplotlib.colors.ListedColormap object>, show_legend=True, level_order=None, figsize=None, dpi=100, return_fig=False)#

Plots a stacked barplot for all levels of a covariate or all samples (if feature_name==”samples”).

Parameters:

  • data (AnnData | MuData) – AnnData object or MuData object.

  • feature_name (str) – The name of the covariate to plot. If feature_name==”samples”, one bar for every sample will be plotted

  • modality_key (str, default: 'coda') – If data is a MuData object, specify which modality to use.

  • figsize (tuple[float, float] | None, default: None) – Figure size.

  • dpi (int | None, default: 100) – Dpi setting.

  • palette (str | Colormap | None, default: <matplotlib.colors.ListedColormap object at 0x70182e107710>) – The matplotlib color map (name) for the barplot.

  • show_legend (bool | None, default: True) – If True, adds a legend.

  • level_order (list[str], default: None) – Custom ordering of bars on the x-axis.

  • return_fig (bool, default: False) – if True, returns figure of the plot, that can be used for saving.

Return type:

Figure | None

Returns:

If return_fig is True, returns the Figure, otherwise None.

Examples

>>> import pertpy as pt
>>> haber_cells = pt.dt.haber_2017_regions()
>>> sccoda = pt.tl.Sccoda()
>>> mdata = sccoda.load(haber_cells, type="cell_level", generate_sample_level=True, cell_type_identifier="cell_label",                 sample_identifier="batch", covariate_obs=["condition"])
>>> sccoda.plot_stacked_barplot(mdata, feature_name="samples")
Preview:
../../_images/sccoda_stacked_barplot.png
Sccoda.prepare(data, formula, reference_cell_type='automatic', automatic_reference_absence_threshold=0.05, modality_key='coda')[source]#

Handles data preprocessing, covariate matrix creation, reference selection, and zero count replacement for scCODA.

Parameters:

  • data (AnnData | MuData) – Anndata object with cell counts as sample_adata.X and covariates saved in sample_adata.obs.

  • formula (str) – R-style formula for building the covariate matrix. Categorical covariates are handled automatically, with the covariate value of the first sample being used as the reference category. To set a different level as the base category for a categorical covariate, use “C(<CovariateName>, Treatment(‘<ReferenceLevelName>’))”

  • reference_cell_type (str, default: 'automatic') – Column name that sets the reference cell type. Reference the name of a column. If “automatic”, the cell type with the lowest dispersion in relative abundance that is present in at least 90% of samlpes will be chosen.

  • automatic_reference_absence_threshold (float, default: 0.05) – If using reference_cell_type = “automatic”, determine the maximum fraction of zero entries for a cell type to be considered as a possible reference cell type.

  • modality_key (str, default: 'coda') – If data is a MuData object, specify key to the aggregated sample-level AnnData object in the MuData object.

Return type:

AnnData | MuData

Returns:

Return an AnnData (if input data is an AnnData object) or return a MuData (if input data is a MuData object)

Specifically, parameters have been set:

  • adata.uns[“param_names”] or data[modality_key].uns[“param_names”]: List with the names of all tracked latent model parameters (through npy.sample or npy.deterministic)

  • adata.uns[“scCODA_params”][“model_type”] or data[modality_key].uns[“scCODA_params”][“model_type”]: String indicating the model type (“classic”)

  • adata.uns[“scCODA_params”][“select_type”] or data[modality_key].uns[“scCODA_params”][“select_type”]: String indicating the type of spike_and_slab selection (“spikeslab”)

Examples

>>> import pertpy as pt
>>> haber_cells = pt.dt.haber_2017_regions()
>>> sccoda = pt.tl.Sccoda()
>>> mdata = sccoda.load(haber_cells,
>>>                     type="cell_level",
>>>                     generate_sample_level=True,
>>>                     cell_type_identifier="cell_label",
>>>                     sample_identifier="batch",
>>>                     covariate_obs=["condition"])
>>> mdata = sccoda.prepare(mdata, formula="condition", reference_cell_type="Endocrine")
Sccoda.run_hmc(data, modality_key='coda', num_samples=20000, num_warmup=5000, rng_key=None, copy=False, *args, **kwargs)#

Run standard Hamiltonian Monte Carlo sampling (Neal, 2011) to infer optimal model parameters.

Parameters:

  • data (AnnData | MuData) – AnnData object or MuData object.

  • modality_key (str, default: 'coda') – If data is a MuData object, specify which modality to use.

  • num_samples (int, default: 20000) – Number of sampled values after burn-in.

  • num_warmup (int, default: 5000) – Number of burn-in (warmup) samples.

  • rng_key (default: None) – The rng state used. If None, a random state will be selected.

  • copy (bool, default: False) – Return a copy instead of writing to adata.

  • *args – Additional args passed to numpyro HMC

  • **kwargs – Additional kwargs passed to numpyro HMC

Examples

>>> import pertpy as pt
>>> haber_cells = pt.dt.haber_2017_regions()
>>> sccoda = pt.tl.Sccoda()
>>> mdata = sccoda.load(haber_cells, type="cell_level", generate_sample_level=True, cell_type_identifier="cell_label",                 sample_identifier="batch", covariate_obs=["condition"])
>>> mdata = sccoda.prepare(mdata, formula="condition", reference_cell_type="Endocrine")
>>> sccoda.run_hmc(mdata, num_warmup=100, num_samples=1000)
Sccoda.run_nuts(data, modality_key='coda', num_samples=10000, num_warmup=1000, rng_key=0, copy=False, *args, **kwargs)[source]#

Run No-U-turn sampling (Hoffman and Gelman, 2014), an efficient version of Hamiltonian Monte Carlo sampling to infer optimal model parameters.

Parameters:

  • data (AnnData | MuData) – AnnData object or MuData object.

  • modality_key (str, default: 'coda') – If data is a MuData object, specify which modality to use.

  • num_samples (int, default: 10000) – Number of sampled values after burn-in.

  • num_warmup (int, default: 1000) – Number of burn-in (warmup) samples.

  • rng_key (int, default: 0) – The rng state used.

  • copy (bool, default: False) – Return a copy instead of writing to adata.

  • *args – Additional args passed to numpyro NUTS

  • **kwargs – Additional kwargs passed to numpyro NUTS

Returns:

Calls self.__run_mcmc

Examples

>>> import pertpy as pt
>>> haber_cells = pt.dt.haber_2017_regions()
>>> sccoda = pt.tl.Sccoda()
>>> mdata = sccoda.load(haber_cells,
>>>                     type="cell_level",
>>>                     generate_sample_level=True,
>>>                     cell_type_identifier="cell_label",
>>>                     sample_identifier="batch",
>>>                     covariate_obs=["condition"])
>>> mdata = sccoda.prepare(mdata, formula="condition", reference_cell_type="Endocrine")
>>> sccoda.run_nuts(mdata, num_warmup=100, num_samples=1000, rng_key=42).
Sccoda.set_fdr(data, est_fdr, modality_key='coda', *args, **kwargs)[source]#

Direct posterior probability approach to calculate credible effects while keeping the expected FDR at a certain level.

Note: Does not work for spike-and-slab LASSO selection method.

Parameters:

  • data (AnnData | MuData) – AnnData object or MuData object.

  • est_fdr (float) – Desired FDR value.

  • modality_key (str, default: 'coda') – If data is a MuData object, specify which modality to use.

  • args – passed to self.summary_prepare

  • kwargs – passed to self.summary_prepare

Returns:

Adjusts intercept_df and effect_df

Examples

>>> import pertpy as pt
>>> haber_cells = pt.dt.haber_2017_regions()
>>> sccoda = pt.tl.Sccoda()
>>> mdata = sccoda.load(haber_cells,
>>>                     type="cell_level",
>>>                     generate_sample_level=True,
>>>                     cell_type_identifier="cell_label",
>>>                     sample_identifier="batch",
>>>                     covariate_obs=["condition"])
>>> mdata = sccoda.prepare(mdata, formula="condition", reference_cell_type="Endocrine")
>>> sccoda.run_nuts(mdata, num_warmup=100, num_samples=1000, rng_key=42)
>>> sccoda.set_fdr(mdata, est_fdr=0.4).
Sccoda.set_init_mcmc_states(rng_key, ref_index, sample_adata)[source]#

Sets initial MCMC state values for scCODA model.

Parameters:

  • rng_key (None) – RNG value to be set

  • ref_index (ndarray) – Index of reference feature

  • sample_adata (AnnData) – Anndata object with cell counts as sample_adata.X and covariates saved in sample_adata.obs.

Return type:

AnnData

Returns:

Return AnnData object.

Examples

>>> import pertpy as pt
>>> haber_cells = pt.dt.haber_2017_regions()
>>> sccoda = pt.tl.Sccoda()
>>> mdata = sccoda.load(haber_cells,
>>>                     type="cell_level",
>>>                     generate_sample_level=True,
>>>                     cell_type_identifier="cell_label",
>>>                     sample_identifier="batch",
>>>                     covariate_obs=["condition"])
>>> mdata = sccoda.prepare(mdata, formula="condition", reference_cell_type="Endocrine")
>>> adata = sccoda.set_init_mcmc_states(rng_key=42, ref_index=0, sample_adata=mdata["coda"])
Sccoda.summary(data, extended=False, modality_key='coda', *args, **kwargs)[source]#

Printing method for the summary.

Parameters:

  • data (AnnData | MuData) – AnnData object or MuData object.

  • extended (bool, default: False) – If True, return the extended summary with additional statistics.

  • modality_key (str, default: 'coda') – If data is a MuData object, specify which modality to use.

  • args – Passed to az.summary

  • kwargs – Passed to az.summary

Examples

>>> import pertpy as pt
>>> haber_cells = pt.dt.haber_2017_regions()
>>> sccoda = pt.tl.Sccoda()
>>> mdata = sccoda.load(haber_cells, type="cell_level", generate_sample_level=True, cell_type_identifier="cell_label",                 sample_identifier="batch", covariate_obs=["condition"])
>>> mdata = sccoda.prepare(mdata, formula="condition", reference_cell_type="Endocrine")
>>> sccoda.run_nuts(mdata, num_warmup=100, num_samples=1000, rng_key=42)
>>> sccoda.summary(mdata)

Examples

>>> import pertpy as pt
>>> haber_cells = pt.dt.haber_2017_regions()
>>> sccoda = pt.tl.Sccoda()
>>> mdata = sccoda.load(haber_cells,
>>>                     type="cell_level",
>>>                     generate_sample_level=True,
>>>                     cell_type_identifier="cell_label",
>>>                     sample_identifier="batch",
>>>                     covariate_obs=["condition"])
>>> mdata = sccoda.prepare(mdata, formula="condition", reference_cell_type="Endocrine")
>>> sccoda.run_nuts(mdata, num_warmup=100, num_samples=1000, rng_key=42)
>>> sccoda.summary(mdata).
Sccoda.summary_prepare(sample_adata, est_fdr=0.05, *args, **kwargs)#

Generates summary dataframes for intercepts, effects and node-level effect (if using tree aggregation).

This function builds on and supports all functionalities from az.summary.

Parameters:

  • sample_adata (AnnData) – Anndata object with cell counts as sample_adata.X and covariates saved in sample_adata.obs.

  • est_fdr (float, default: 0.05) – Desired FDR value.

  • args – Passed to az.summary

  • kwargs – Passed to az.summary

Returns:

class:pandas.DataFrame, :class:pandas.DataFrame] or Tuple[:class:pandas.DataFrame, :class:pandas.DataFrame, :class:pandas.DataFrame]: Intercept, effect and node-level DataFrames

intercept_df

Summary of intercept parameters. Contains one row per cell type.

  • Final Parameter: Final intercept model parameter

  • HDI X%: Upper and lower boundaries of confidence interval (width specified via hdi_prob=)

  • SD: Standard deviation of MCMC samples

  • Expected sample: Expected cell counts for a sample with no present covariates. See the tutorial for more explanation

effect_df

Summary of effect (slope) parameters. Contains one row per covariate/cell type combination.

  • Final Parameter: Final effect model parameter. If this parameter is 0, the effect is not significant, else it is.

  • HDI X%: Upper and lower boundaries of confidence interval (width specified via hdi_prob=)

  • SD: Standard deviation of MCMC samples

  • Expected sample: Expected cell counts for a sample with only the current covariate set to 1. See the tutorial for more explanation

  • log2-fold change: Log2-fold change between expected cell counts with no covariates and with only the current covariate

  • Inclusion probability: Share of MCMC samples, for which this effect was not set to 0 by the spike-and-slab prior.

node_df

Summary of effect (slope) parameters on the tree nodes (features or groups of features). Contains one row per covariate/cell type combination.

  • Final Parameter: Final effect model parameter. If this parameter is 0, the effect is not significant, else it is.

  • Median: Median of parameter over MCMC chain

  • HDI X%: Upper and lower boundaries of confidence interval (width specified via hdi_prob=)

  • SD: Standard deviation of MCMC samples

  • Delta: Decision boundary value - threshold of practical significance

  • Is credible: Boolean indicator whether effect is credible

Return type:

Tuple[

Examples

>>> import pertpy as pt
>>> haber_cells = pt.dt.haber_2017_regions()
>>> sccoda = pt.tl.Sccoda()
>>> mdata = sccoda.load(haber_cells, type="cell_level", generate_sample_level=True, cell_type_identifier="cell_label",                 sample_identifier="batch", covariate_obs=["condition"])
>>> mdata = sccoda.prepare(mdata, formula="condition", reference_cell_type="Endocrine")
>>> sccoda.run_nuts(mdata, num_warmup=100, num_samples=1000, rng_key=42)
>>> intercept_df, effect_df = sccoda.summary_prepare(mdata["coda"])
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