cellrank.models.GAM#

class cellrank.models.GAM(adata, n_knots=6, spline_order=3, distribution='gamma', link='log', max_iter=2000, expectile=None, grid=None, spline_kwargs=mappingproxy({}), **kwargs)[source]#

Fit Generalized Additive Models (GAMs) using pygam.

Parameters:
  • adata (AnnData) – Annotated data object.

  • n_knots (Optional[int]) – Number of knots.

  • spline_order (int) – Order of the splines, e.g., \(3\) for cubic splines.

  • distribution (Literal['normal', 'binomial', 'poisson', 'gamma', 'gaussian', 'inv_gauss']) – Name of the distribution. Available distributions can be found here.

  • link (Literal['identity', 'logit', 'inverse', 'log', 'inverse-squared']) – Name of the link function. Available link functions can be found here.

  • max_iter (int) – Maximum number of iterations for optimization.

  • expectile (Optional[float]) – Expectile for ExpectileGAM. This forces the distribution to be 'normal' and link function to 'identity'. Must be in \((0, 1)\).

  • grid (Union[str, Mapping[str, Any], None]) – Whether to perform a grid search. Keys correspond to a parameter names and values to range to be searched. If 'default', use the default grid. If None, don’t perform a grid search.

  • spline_kwargs (Mapping[str, Any]) – Keyword arguments for s().

  • kwargs (Any) – Keyword arguments for the GAM.

Attributes table#

adata

Annotated data object.

conf_int

Array of shape (n_samples, 2) containing the lower and upper bound of the confidence interval.

model

Underlying model.

prepared

Whether the model is prepared for fitting.

shape

Number of cells in adata.

w

Filtered weights of shape (n_filtered_cells,) used for fitting.

w_all

Unfiltered weights of shape (n_cells,).

x

Filtered independent variables of shape (n_filtered_cells, 1) used for fitting.

x_all

Unfiltered independent variables of shape (n_cells, 1).

x_hat

Filtered independent variables used when calculating default confidence interval, usually same as x.

x_test

Independent variables of shape (n_samples, 1) used for prediction.

y

Filtered dependent variables of shape (n_filtered_cells, 1) used for fitting.

y_all

Unfiltered dependent variables of shape (n_cells, 1).

y_hat

Filtered dependent variables used when calculating default confidence interval, usually same as y.

y_test

Prediction values of shape (n_samples,) for x_test.

Methods table#

confidence_interval([x_test])

Calculate the confidence interval.

copy()

Return a copy of self.

default_confidence_interval([x_test])

Calculate the confidence interval, if the underlying model has no method for it.

fit([x, y, w])

Fit the model.

plot([figsize, same_plot, hide_cells, perc, ...])

Plot the smoothed gene expression.

predict([x_test, key_added])

Run the prediction.

prepare(gene, lineage, time_key[, backward, ...])

Prepare the model to be ready for fitting.

read(fname[, adata, copy])

De-serialize self from a file.

write(fname[, write_adata])

Serialize self to a file using pickle.

Attributes#

adata#

GAM.adata#

Annotated data object.

conf_int#

GAM.conf_int#

Array of shape (n_samples, 2) containing the lower and upper bound of the confidence interval.

model#

GAM.model#

Underlying model.

prepared#

GAM.prepared#

Whether the model is prepared for fitting.

shape#

GAM.shape#

Number of cells in adata.

w#

GAM.w#

Filtered weights of shape (n_filtered_cells,) used for fitting.

w_all#

GAM.w_all#

Unfiltered weights of shape (n_cells,).

x#

GAM.x#

Filtered independent variables of shape (n_filtered_cells, 1) used for fitting.

x_all#

GAM.x_all#

Unfiltered independent variables of shape (n_cells, 1).

x_hat#

GAM.x_hat#

Filtered independent variables used when calculating default confidence interval, usually same as x.

x_test#

GAM.x_test#

Independent variables of shape (n_samples, 1) used for prediction.

y#

GAM.y#

Filtered dependent variables of shape (n_filtered_cells, 1) used for fitting.

y_all#

GAM.y_all#

Unfiltered dependent variables of shape (n_cells, 1).

y_hat#

GAM.y_hat#

Filtered dependent variables used when calculating default confidence interval, usually same as y.

y_test#

GAM.y_test#

Prediction values of shape (n_samples,) for x_test.

Methods#

confidence_interval#

GAM.confidence_interval(x_test=None, **kwargs)[source]#

Calculate the confidence interval.

Parameters:
Return type:

ndarray

Returns:

: Returns self and updates the following fields:

copy#

GAM.copy()[source]#

Return a copy of self.

Return type:

BaseModel

default_confidence_interval#

GAM.default_confidence_interval(x_test=None, **kwargs)#

Calculate the confidence interval, if the underlying model has no method for it.

This formula is taken from [DeSalvo, 1970], eq. 5.

Parameters:
Return type:

ndarray

Returns:

: Returns self and updates the following fields:

Also updates the following fields:

  • x_hat - Filtered independent variables used when calculating default confidence interval, usually same as x.

  • y_hat - Filtered dependent variables used when calculating default confidence interval, usually same as y.

fit#

GAM.fit(x=None, y=None, w=None, **kwargs)[source]#

Fit the model.

Parameters:
  • x (Optional[ndarray]) – Independent variables, array of shape (n_samples, 1). If None, use x.

  • y (Optional[ndarray]) – Dependent variables, array of shape (n_samples, 1). If None, use y.

  • w (Optional[ndarray]) – Optional weights of x, array of shape (n_samples,). If None, use w.

  • kwargs (Any) – Keyword arguments for underlying model’s fitting function.

Return type:

GAM

Returns:

: Fits the model and returns self.

plot#

GAM.plot(figsize=(8, 5), same_plot=False, hide_cells=False, perc=None, fate_prob_cmap=<matplotlib.colors.ListedColormap object>, cell_color=None, lineage_color='black', alpha=0.8, lineage_alpha=0.2, title=None, size=15, lw=2, cbar=True, margins=0.015, xlabel='pseudotime', ylabel='expression', conf_int=True, lineage_probability=False, lineage_probability_conf_int=False, lineage_probability_color=None, obs_legend_loc='best', dpi=None, fig=None, ax=None, return_fig=False, save=None, **kwargs)#

Plot the smoothed gene expression.

Parameters:
  • figsize (Tuple[float, float]) – Size of the figure.

  • same_plot (bool) – Whether to plot all trends in the same plot.

  • hide_cells (bool) – Whether to hide the cells.

  • perc (Optional[Tuple[float, float]]) – Percentile by which to clip the fate probabilities.

  • fate_prob_cmap (ListedColormap) – Colormap to use when coloring in the fate probabilities.

  • cell_color (Optional[str]) – Key in obs or var_names used for coloring the cells.

  • lineage_color (str) – Color for the lineage.

  • alpha (float) – Alpha value in \([0, 1]\) for the transparency of cells.

  • lineage_alpha (float) – Alpha value in \([0, 1]\) for the transparency lineage confidence intervals.

  • title (Optional[str]) – Title of the plot.

  • size (int) – Size of the points.

  • lw (float) – Line width for the smoothed values.

  • cbar (bool) – Whether to show the colorbar.

  • margins (float) – Margins around the plot.

  • xlabel (str) – Label on the x-axis.

  • ylabel (str) – Label on the y-axis.

  • conf_int (bool) – Whether to show the confidence interval.

  • lineage_probability (bool) – Whether to show smoothed lineage probability as a dashed line. Note that this will require 1 additional model fit.

  • lineage_probability_conf_int (Union[bool, float]) – Whether to compute and show smoothed lineage probability confidence interval.

  • lineage_probability_color (Optional[str]) – Color to use when plotting the smoothed lineage_probability. If None, it’s the same as lineage_color. Only used when show_lineage_probability = True.

  • obs_legend_loc (Optional[str]) – Location of the legend when cell_color corresponds to a categorical variable.

  • dpi (Optional[int]) – Dots per inch.

  • fig (Optional[Figure]) – Figure to use. If None, create a new one.

  • ax (Optional[Axes]) – Ax to use. If None, create a new one.

  • return_fig (bool) – If True, return the figure object.

  • save (Optional[str]) – Filename where to save the plot. If None, just shows the plots.

  • kwargs (Any) – Keyword arguments for legend().

Return type:

Optional[Figure]

Returns:

: Nothing, just plots the figure. Optionally saves it based on save.

predict#

GAM.predict(x_test=None, key_added='_x_test', **kwargs)[source]#

Run the prediction.

Parameters:
  • x_test (Optional[ndarray]) – Array of shape (n_samples,) used for prediction. If None, use x_test.

  • key_added (Optional[str]) – Attribute name where to save the x_test for later use. If None, don’t save it.

  • kwargs (Any) – Keyword arguments for underlying model’s prediction method.

Return type:

ndarray

Returns:

: Returns and updates the following fields:

prepare#

GAM.prepare(gene, lineage, time_key, backward=False, time_range=None, data_key='X', use_raw=False, threshold=None, weight_threshold=(0.01, 0.01), filter_cells=None, n_test_points=200)#

Prepare the model to be ready for fitting.

Parameters:
  • gene (str) – Gene in var_names.

  • lineage (Optional[str]) – Name of the lineage. If None, all weights will be set to \(1\).

  • time_key (str) – Key in obs where the pseudotime is stored.

  • backward (bool) – Direction of the process.

  • time_range (Union[float, Tuple[float, float], None]) –

    Specify start and end times:

    • tuple - it specifies the minimum and maximum pseudotime. Both values can be None, in which case the minimum is the earliest pseudotime and the maximum is automatically determined.

    • float - it specifies the maximum pseudotime.

  • data_key (Optional[str]) – Key in layers or 'X' for X. If use_raw = True, it’s always set to 'X'.

  • use_raw (bool) – Whether to access raw.

  • threshold (Optional[float]) – Consider only cells with weights > threshold when estimating the test endpoint. If None, use the median of the weights.

  • weight_threshold (Union[float, Tuple[float, float]]) – Set all weights below weight_threshold to weight_threshold if a float, or to the second value, if a tuple.

  • filter_cells (Optional[float]) – Filter out all cells with expression values lower than this threshold.

  • n_test_points (int) – Number of test points. If None, use the original points based on threshold.

Return type:

BaseModel

Returns:

: Nothing, just updates the following fields:

  • x - Filtered independent variables of shape (n_filtered_cells, 1) used for fitting.

  • y - Filtered dependent variables of shape (n_filtered_cells, 1) used for fitting.

  • w - Filtered weights of shape (n_filtered_cells,) used for fitting.

  • x_all - Unfiltered independent variables of shape (n_cells, 1).

  • y_all - Unfiltered dependent variables of shape (n_cells, 1).

  • w_all - Unfiltered weights of shape (n_cells,).

  • x_test - Independent variables of shape (n_samples, 1) used for prediction.

  • prepared - Whether the model is prepared for fitting.

read#

static GAM.read(fname, adata=None, copy=False)#

De-serialize self from a file.

Parameters:
  • fname (Union[str, Path]) – Path from which to read the object.

  • adata (Optional[AnnData]) – AnnData object to assign to the saved object. Only used when the saved object has adata and it was saved without it.

  • copy (bool) – Whether to copy adata before assigning it. If adata is a view, it is always copied.

Return type:

IOMixin

Returns:

: The de-serialized object.

write#

GAM.write(fname, write_adata=True)#

Serialize self to a file using pickle.

Parameters:
  • fname (Union[str, Path]) – Path where to save the object.

  • write_adata (bool) – Whether to save adata object.

Return type:

None

Returns:

: Nothing, just writes itself to a file.