pertpy.tools.WilcoxonTest

pertpy.tools.WilcoxonTest#

class WilcoxonTest(adata, *, mask=None, layer=None, **kwargs)[source]#

Perform a unpaired or paired Wilcoxon test.

(the former is also known as “Mann-Whitney U test”, the latter as “wilcoxon signed rank test”)

Attributes table#

data

Get the data matrix from anndata this object was initalized with (X or layer).

Methods table#

compare_groups(adata, column, baseline, ...)

Perform a comparison between groups.

plot_fold_change(results_df, *[, var_names, ...])

Plot a metric from the results as a bar chart, optionally with additional information about paired samples in a scatter plot.

plot_ma(results_df, *[, mean_col, ...])

Creates an MA plot of the mean expression against the log2 fold change.

plot_multicomparison_fc(results_df, *[, ...])

Plot a matrix of log2 fold changes from the results.

plot_paired(adata, results_df, groupby, ...)

Creates a pairwise expression plot from a Pandas DataFrame or Anndata.

plot_volcano(data, *[, log2fc_threshold, ...])

Creates a volcano plot from a pandas DataFrame or Anndata.

Attributes#

WilcoxonTest.data#

Get the data matrix from anndata this object was initalized with (X or layer).

Methods#

classmethod WilcoxonTest.compare_groups(adata, column, baseline, groups_to_compare, *, paired_by=None, mask=None, layer=None, n_jobs=None, fit_kwargs=mappingproxy({}), test_kwargs=mappingproxy({}))#

Perform a comparison between groups.

Parameters:

  • adata (AnnData) – Data with observations to compare.

  • column (str) – Column in adata.obs that contains the groups to compare.

  • baseline (str) – Reference group.

  • groups_to_compare (str | Sequence[str]) – Groups to compare against the baseline. If None, all other groups are compared.

  • paired_by (str | None, default: None) – Column in adata.obs to use for pairing. If None, an unpaired test is performed.

  • mask (str | None, default: None) – Mask to apply to the data.

  • layer (str | None, default: None) – Layer to use for the comparison.

  • n_jobs (int | None, default: None) – Number of jobs to distribute the variables over, passed to joblib.Parallel. None means one job, -1 all available cores. TTest and WilcoxonTest test all variables at once and only use several jobs if the data does not fit into memory in one block.

  • fit_kwargs (Mapping, default: mappingproxy({})) – Unused argument for compatibility with the MethodBase interface, do not specify.

  • test_kwargs (Mapping, default: mappingproxy({})) – Additional kwargs passed to the test function.

Return type:

DataFrame

WilcoxonTest.plot_fold_change(results_df, *, var_names=None, n_top_vars=15, padj_threshold=0.01, padj_col='adj_p_value', log2fc_col='log_fc', symbol_col='variable', y_label='Log2 fold change', figsize=(10, 5), return_fig=False, **barplot_kwargs)#

Plot a metric from the results as a bar chart, optionally with additional information about paired samples in a scatter plot.

Parameters:

  • results_df (DataFrame) – DataFrame with results from DE analysis.

  • var_names (Sequence[str] | None, default: None) – Variables to plot. If None, the top n_top_vars variables based on the log2 fold change are plotted.

  • n_top_vars (int, default: 15) – Number of top variables to plot. The top and bottom n_top_vars variables are plotted, respectively.

  • padj_threshold (float, default: 0.01) – Only variables with adjusted p-values below this threshold are included in the plot.

  • padj_col (str, default: 'adj_p_value') – Column name of adjusted p-values.

  • log2fc_col (str, default: 'log_fc') – Column name of log2 Fold-Change values.

  • symbol_col (str, default: 'variable') – Column name of gene IDs.

  • y_label (str, default: 'Log2 fold change') – Label for the y-axis.

  • figsize (tuple[int, int], default: (10, 5)) – Size of the figure.

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

  • **barplot_kwargs – Additional arguments for seaborn.barplot.

Return type:

Figure | None

Returns:

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

Examples

>>> # Example with EdgeR
>>> import pertpy as pt
>>> adata = pt.dt.zhang_2021()
>>> adata.layers["counts"] = adata.X.copy()
>>> ps = pt.tl.PseudobulkSpace()
>>> pdata = ps.compute(adata, target_col="Patient", groups_col="Cluster", layer_key="counts", mode="sum")
>>> edgr = pt.tl.EdgeR(pdata, design="~Efficacy+Treatment")
>>> edgr.fit()
>>> res_df = edgr.test_contrasts(
...     edgr.contrast(column="Treatment", baseline="Chemo", group_to_compare="Anti-PD-L1+Chemo")
... )
>>> edgr.plot_fold_change(res_df)
Preview:
../../_images/de_fold_change.png
WilcoxonTest.plot_ma(results_df, *, mean_col=None, log2fc_col='log_fc', padj_col='adj_p_value', padj_threshold=0.05, log2fc_threshold=0.0, log_x=None, colors=('lightgray', '#D62728'), x_label=None, y_label='Log2 fold change', figsize=(5, 5), return_fig=False, **kwargs)#

Creates an MA plot of the mean expression against the log2 fold change.

The pertpy equivalent of edgeR’s plotSmear and DESeq2’s plotMA. Every variable is one point, so the plot shows how the fold change depends on expression strength and which variables the test called significant.

Parameters:

  • results_df (DataFrame) – DataFrame with results from DE analysis.

  • mean_col (str | None, default: None) – Column name of the mean expression. If None, PyDESeq2’s baseMean and edgeR’s logCPM are detected automatically.

  • log2fc_col (str, default: 'log_fc') – Column name of log2 Fold-Change values.

  • padj_col (str, default: 'adj_p_value') – Column name of adjusted p-values.

  • padj_threshold (float, default: 0.05) – Variables with an adjusted p-value below this threshold are highlighted as significant.

  • log2fc_threshold (float, default: 0.0) – Variables must additionally exceed this absolute log2 fold change to be highlighted. The thresholds are drawn as dashed lines if it is not zero.

  • log_x (bool | None, default: None) – Whether to log-scale the x-axis. If None, it is scaled for mean columns on a linear scale such as baseMean but not for those already on a log scale such as logCPM.

  • colors (Sequence[str], default: ('lightgray', '#D62728')) – Colors for [non-significant, significant] variables.

  • x_label (str | None, default: None) – Label for the x-axis.

  • y_label (str, default: 'Log2 fold change') – Label for the y-axis.

  • figsize (tuple[int, int], default: (5, 5)) – Size of the figure.

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

  • **kwargs – Additional arguments for seaborn.scatterplot.

Return type:

Figure | None

Returns:

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

Examples

>>> # Example with EdgeR
>>> import pertpy as pt
>>> adata = pt.dt.zhang_2021()
>>> adata.layers["counts"] = adata.X.copy()
>>> ps = pt.tl.PseudobulkSpace()
>>> pdata = ps.compute(adata, target_col="Patient", groups_col="Cluster", layer_key="counts", mode="sum")
>>> edgr = pt.tl.EdgeR(pdata, design="~Efficacy+Treatment")
>>> edgr.fit()
>>> res_df = edgr.test_contrasts(
...     edgr.contrast(column="Treatment", baseline="Chemo", group_to_compare="Anti-PD-L1+Chemo")
... )
>>> edgr.plot_ma(res_df)
Preview:
../../_images/de_ma.png
WilcoxonTest.plot_multicomparison_fc(results_df, *, n_top_vars=15, contrast_col='contrast', log2fc_col='log_fc', pvalue_col='adj_p_value', symbol_col='variable', marker_size=100, figsize=(10, 2), x_label='Contrast', y_label='Gene', return_fig=False, **heatmap_kwargs)#

Plot a matrix of log2 fold changes from the results.

Parameters:

  • results_df (DataFrame) – DataFrame with results from DE analysis.

  • n_top_vars (default: 15) – Number of top variables to plot per group.

  • contrast_col (str, default: 'contrast') – Column in results_df containing information about the contrast.

  • log2fc_col (str, default: 'log_fc') – Column in results_df containing the log2 fold change.

  • pvalue_col (str, default: 'adj_p_value') – Column in results_df containing the p-value. Can be used to switch between adjusted and unadjusted p-values.

  • symbol_col (str, default: 'variable') – Column in results_df containing the gene symbol.

  • marker_size (int, default: 100) – Size of the biggest marker for significant variables.

  • figsize (tuple[int, int], default: (10, 2)) – Size of the figure.

  • x_label (str, default: 'Contrast') – Label for the x-axis.

  • y_label (str, default: 'Gene') – Label for the y-axis.

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

  • **heatmap_kwargs – Additional arguments for seaborn.heatmap.

Return type:

Figure | None

Returns:

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

Examples

>>> # Example with EdgeR
>>> import pertpy as pt
>>> adata = pt.dt.zhang_2021()
>>> adata.layers["counts"] = adata.X.copy()
>>> ps = pt.tl.PseudobulkSpace()
>>> pdata = ps.compute(adata, target_col="Patient", groups_col="Cluster", layer_key="counts", mode="sum")
>>> edgr = pt.tl.EdgeR(pdata, design="~Efficacy+Treatment")
>>> res_df = edgr.compare_groups(pdata, column="Efficacy", baseline="SD", groups_to_compare=["PR", "PD"])
>>> edgr.plot_multicomparison_fc(res_df)
Preview:
../../_images/de_multicomparison_fc.png
WilcoxonTest.plot_paired(adata, results_df, groupby, pairedby, *, var_names=None, n_top_vars=15, layer=None, pvalue_col='adj_p_value', symbol_col='variable', n_cols=4, panel_size=(5, 5), show_legend=True, size=10, y_label='expression', pvalue_template=<function MethodBase.<lambda>>, boxplot_properties=None, palette=None, return_fig=False)#

Creates a pairwise expression plot from a Pandas DataFrame or Anndata.

Visualizes a panel of paired scatterplots per variable.

Parameters:

  • adata (AnnData) – AnnData object, can be pseudobulked.

  • results_df (DataFrame) – DataFrame with results from a differential expression test.

  • groupby (str) – .obs column containing the grouping. Must contain exactly two different values.

  • pairedby (str) – .obs column containing the pairing (e.g. “patient_id”). If None, an independent t-test is performed.

  • var_names (Sequence[str] | None, default: None) – Variables to plot.

  • n_top_vars (int, default: 15) – Number of top variables to plot.

  • layer (str | None, default: None) – Layer to use for plotting.

  • pvalue_col (str, default: 'adj_p_value') – Column name of the p values.

  • symbol_col (str, default: 'variable') – Column name of gene IDs.

  • n_cols (int, default: 4) – Number of columns in the plot.

  • panel_size (tuple[int, int], default: (5, 5)) – Size of each panel.

  • show_legend (bool, default: True) – Whether to show the legend.

  • size (int, default: 10) – Size of the points.

  • y_label (str, default: 'expression') – Label for the y-axis.

  • pvalue_template (default: <function MethodBase.<lambda> at 0x751ae1a66ca0>) – Template for the p-value string displayed in the title of each panel.

  • boxplot_properties (default: None) – Additional properties for the boxplot, passed to seaborn.boxplot.

  • palette (default: None) – Color palette for the line- and stripplot.

  • 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

>>> # Example with EdgeR
>>> import pertpy as pt
>>> adata = pt.dt.zhang_2021()
>>> adata.layers["counts"] = adata.X.copy()
>>> ps = pt.tl.PseudobulkSpace()
>>> pdata = ps.compute(adata, target_col="Patient", groups_col="Cluster", layer_key="counts", mode="sum")
>>> edgr = pt.tl.EdgeR(pdata, design="~Efficacy+Treatment")
>>> edgr.fit()
>>> res_df = edgr.test_contrasts(
...     edgr.contrast(column="Treatment", baseline="Chemo", group_to_compare="Anti-PD-L1+Chemo")
... )
>>> edgr.plot_paired(pdata, results_df=res_df, n_top_vars=8, groupby="Treatment", pairedby="Efficacy")
Preview:
../../_images/de_paired_expression.png
WilcoxonTest.plot_volcano(data, *, log2fc_threshold=0.75, padj_threshold=0.05, log2fc_col='log_fc', padj_col='adj_p_value', symbol_col='variable', to_label=5, s_curve=False, colors=None, varm_key=None, color_dict=None, shape_dict=None, size_col=None, fontsize=10, top_right_frame=False, figsize=(5, 5), legend_pos=(1.6, 1), point_sizes=(15, 150), shapes=None, shape_order=None, x_label=None, y_label=None, return_fig=False, log2fc_thresh=None, pval_thresh=None, pvalue_col=None, **kwargs)#

Creates a volcano plot from a pandas DataFrame or Anndata.

Parameters:

  • data (DataFrame | AnnData) – DataFrame or Anndata to plot.

  • log2fc_threshold (float, default: 0.75) – Threshold for log2 fold change significance.

  • padj_threshold (float, default: 0.05) – Adjusted p-values for significance.

  • log2fc_col (str, default: 'log_fc') – Column name of log2 Fold-Change values.

  • padj_col (str, default: 'adj_p_value') – Column name of adjusted p-values.

  • symbol_col (str, default: 'variable') – Column name of gene IDs.

  • varm_key (str | None, default: None) – Key in Anndata.varm slot to use for plotting if an Anndata object was passed.

  • size_col (str | None, default: None) – Column name to size points by.

  • point_sizes (tuple[int, int], default: (15, 150)) – Lower and upper bounds of point sizes.

  • to_label (int | list[str], default: 5) – Number of top genes or list of genes to label.

  • s_curve (bool, default: False) – Whether to use a reciprocal threshold for up and down gene determination.

  • color_dict (dict[str, list[str]] | None, default: None) – Dictionary for coloring dots by categories.

  • shape_dict (dict[str, list[str]] | None, default: None) – Dictionary for shaping dots by categories.

  • fontsize (int, default: 10) – Size of gene labels.

  • colors (list[str] | None, default: None) – Colors for [non-DE, up, down] genes. Defaults to [‘gray’, ‘#D62728’, ‘#1F77B4’].

  • top_right_frame (bool, default: False) – Whether to show the top and right frame of the plot.

  • figsize (tuple[int, int], default: (5, 5)) – Size of the figure.

  • legend_pos (tuple[float, float], default: (1.6, 1)) – Position of the legend as determined by matplotlib.

  • shapes (list[str] | None, default: None) – List of matplotlib marker ids.

  • shape_order (list[str] | None, default: None) – Order of categories for shapes.

  • x_label (str | None, default: None) – Label for the x-axis.

  • y_label (str | None, default: None) – Label for the y-axis.

  • log2fc_thresh (float | None, default: None) – Deprecated and will be removed in a future release. Use log2fc_threshold.

  • pval_thresh (float | None, default: None) – Deprecated and will be removed in a future release. Use padj_threshold.

  • pvalue_col (str | None, default: None) – Deprecated and will be removed in a future release. Use padj_col.

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

  • **kwargs – Additional arguments for seaborn.scatterplot.

Return type:

Figure | None

Returns:

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

Examples

>>> # Example with EdgeR
>>> import pertpy as pt
>>> adata = pt.dt.zhang_2021()
>>> adata.layers["counts"] = adata.X.copy()
>>> ps = pt.tl.PseudobulkSpace()
>>> pdata = ps.compute(adata, target_col="Patient", groups_col="Cluster", layer_key="counts", mode="sum")
>>> edgr = pt.tl.EdgeR(pdata, design="~Efficacy+Treatment")
>>> edgr.fit()
>>> res_df = edgr.test_contrasts(
...     edgr.contrast(column="Treatment", baseline="Chemo", group_to_compare="Anti-PD-L1+Chemo")
... )
>>> edgr.plot_volcano(res_df, log2fc_threshold=0)
Preview:
../../_images/de_volcano.png