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

x_test (Optional[ndarray]) – Array of shape (n_samples,) used for confidence interval calculation. If None, use x_test.
kwargs (Any) – Keyword arguments for underlying model’s confidence method or for default_confidence_interval().

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

x_test (Optional[ndarray]) – Array of shape (n_samples,) used for confidence interval calculation. If None, use x_test.
kwargs (Any) – Keyword arguments for underlying model’s confidence method or for default_confidence_interval().

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.