scEGOT

class scegot.CellStateGraph(G, scegot, threshold=0.05, mode='pca', cluster_names=None, node_ids=None, merge_clusters_by_name=False, x_reverse=False, y_reverse=False, require_parent=False)

Bases: object

plot_cell_state_graph(layout='normal', y_position='name', cluster_names=None, gene_names=None, gene_pick_num=5, plot_title='Cell State Graph', save=False, save_path=None)

Plot the cell state graph with the given graph object.

Parameters

layout{‘normal’, ‘hierarchy’}, optional

The layout of the graph, by default “normal”

When ‘normal’, the graph is plotted in PCA or UMAP space.
When ‘hierarchy’, the graph is plotted with the day on the x-axis and the cluster on the y-axis.

y_positionstr or dict, optional

Determines the y-axis position of nodes when layout is ‘hierarchy’, by default “name”.

‘name’: Sort nodes alphabetically by name.
‘weight’: Sort nodes by their weight.
dict: A dictionary mapping node names to y-axis positions.

This parameter is ignored when layout is ‘normal’.

cluster_nameslist of list of str

Custom names for the clusters, by default None.

1st dimension is the number of days, 2nd dimension is the number of gmm components in each day. When the attribute merge_clusters_by_name is True, clusters to be merged must be given the same new name.

If None, the attribute cluster_names is used.

gene_nameslist of str, optional

List of gene names to use, by default None If None, all gene names (self.scegot.gene_names) will be used. You can pass on any list of gene names you want to use, not limited to TF genes.

gene_pick_numint, optional

The number of genes to show in each node and edge, by default 5

plot_titlestr, optional

Title of the plot, by default “Cell State Graph”

savebool, optional

If True, save the output image, by default False

save_pathstr, optional

Path to save the output image, by default None If None, the image will be saved as ‘./cell_state_graph.png’

plot_simple_cell_state_graph(layout='normal', y_position='name', cluster_names=None, node_weight_annotation=False, edge_weight_annotation=False, save=False, save_path=None)

Plot the cell state graph with the given graph object in a simple way.

Parameters

layout{‘normal’, ‘hierarchy’}, optional

The layout of the graph, by default “normal”.

When “normal”, the graph is plotted in PCA or UMAP space.
When “hierarchy”, the graph is plotted with the day on the x-axis and the cluster on the y-axis.

y_positionstr or dict, optional

Determines the y-axis position of nodes when layout is “hierarchy”, by default “name”.

“name”: Sort nodes alphabetically by name.
“weight”: Sort nodes by their weight.
dict: A dictionary mapping node names to y-axis positions.

This parameter is ignored when layout is “normal”.

cluster_nameslist of list of str, optional

Custom names for the clusters, by default None.

1st dimension is the number of days, 2nd dimension is the number of gmm components in each day. When the attribute merge_clusters_by_name is True, clusters to be merged must be given the same new name.

If None, the attribute cluster_names is used.

node_weight_annotationbool, optional

If True, display the weight of each node, by default False.

edge_weight_annotationbool, optional

If True, display the weight of each edge, by default False.

savebool, optional

If True, save the output image, by default False.

save_pathstr, optional

Path to save the output image, by default None. If None, the image will be saved as ‘./simple_cell_state_graph.png’.

Raises

ValueError: This error is raised in the following cases: - When ‘layout’ is not ‘normal’ or ‘hierarchy’. - When ‘y_position’ is a string but not ‘name’ or ‘weight’.
TypeError: When ‘y_position’ is not a string or dict (if layout is ‘hierarchy’).

reverse_graph(x=False, y=False)

Reverse the graph layout along the specified axes.

Parameters

xbool, optional: If True, reverse the x-axis of the graph layout, by default False.
ybool, optional: If True, reverse the y-axis of the graph layout, by default False.

set_cluster_names(cluster_names)

Set new cluster names for the cell state graph.

Parameters

cluster_nameslist of list of str: New names for the clusters. 1st dimension is the number of days, 2nd dimension is the number of gmm components in each day. Merged clusters must have the same name when ‘merge_clusters_by_name’ is True.

Returns

list of list of str: The new cluster names.

update_cluster_names(cluster_names_map, day=None)

Update cluster names for the cell state graph based on a mapping dictionary.

Parameters

cluster_names_mapdict: A dictionary mapping old cluster names to new cluster names.
dayint, optional: The specific day to update cluster names for, by default None. If None, update cluster names for all days.

Returns

list of list of str: The updated cluster names.

scegot.integrate_data(input_data_dict, adata_day_key=None, recode_params={}, recode_fit_transform_params={})

Integrate multiple data using iRECODE.

Parameters

input_data_dictdict: A dictionary where keys are data names and values are input data (list of pd.DataFrame or AnnData).
adata_day_keystr, optional: Name of the key in AnnData.obs for day names, by default None. Should be specified when values of input_data_dict are AnnData.
recode_paramsdict, optional: paramaters passed to the screcode.RECODE constructor, by default {}
recode_fit_transform_paramsdict, optional: paramaters for RECODE.fit_transform(), by default {}

Raises

ValueError

When ‘X’ is AnnData and ‘adata_day_key’ is not specified.

TypeError

This error is raised in the following cases:

When input_data_dict is not a dict.
When values of input_data_dict is neither list of pd.DataFrame nor AnnData.

Returns

(list of pd.DataFrame) or AnnData: Integrated data.

scegot.is_notebook()

Check if the code is running in a Jupyter notebook or not.

Returns

bool: True if the code is running in a Jupyter notebook, False otherwise.

class scegot.scEGOT(X, day_names=None, verbose=True, adata_day_key=None)

Bases: object

animate_gene_expression(target_gene_name, mode='pca', interpolate_interval=11, n_samples=5000, x_range=None, y_range=None, c_range=None, x_label=None, y_label=None, cmap='gnuplot2', save=False, save_path=None)

Calculate interpolation between all timepoints and create animation colored by gene expression level.

Parameters

target_gene_namestr: Gene name to plot expression level.
mode{‘pca’, ‘umap’}, optional: The space to plot gene expression levels, by default “pca”
interpolate_intervalint, optional: Number of frames to interpolate between two timepoints, by default 11 This is the total number of frames at both timepoints and the number of frames between these. Note that both ends are included.
n_samplesint, optional: Number of samples to generate, by default 5000
x_rangelist or tuple of float of shape (2,), optional: Range of the x-axis, by default None
y_rangelist or tuple of float of shape (2,), optional: Range of the y-axis, by default None
c_rangelist or tuple of float of shape (2,), optional: Range of the color bar, by default None
x_labelstr, optional: Label of the x-axis, by default None
y_labelstr, optional: Label of the y-axis, by default None
cmapstr, optional: String of the colormap, by default “gnuplot2”
savebool, optional: If True, save the output image, by default False
save_pathstr, optional: Path to save the output image, by default None If None, the image will be saved as ‘./interpolate_video.gif’

Raises

ValueError: When ‘mode’ is not ‘pca’ or ‘umap’.

animatie_interpolated_distribution(x_range=None, y_range=None, interpolate_interval=11, cmap='gnuplot2', save=False, save_path=None)

Export an animation of the interpolated distribution between GMM models.

Parameters

x_rangelist or tuple of float of shape (2,), optional: Restrict the X axis range, by default None
y_rangelist or tuple of float of shape (2,), optional: Restrict the Y axis range, by default None
interpolate_intervalint, optional: The number of frames to interpolate between two timepoints, by default 11 This is the total number of frames at both timepoints and the number of frames between these. Note that both ends are included.
cmapstr, optional: String of matplolib colormap name, by default “gnuplot2”
savebool, optional: If True, save the output animation, by default False
save_pathstr, optional: Path to save the output animation, by default None If None, the animation will be saved as ‘./cell_state_video.gif’

apply_umap(n_neighbors, n_components=2, random_state=None, min_dist=0.1, umap_other_params={})

Fit self.X_pca to UMAP and return the transformed data.

Parameters

n_neighborsfloat: The size of local neighborhood used for manifold approximation. Passed to the ‘n_neighbors’ parameter of the UMAP class.
n_componentsint, optional: The dimension of the space to embed into, by default 2 Passed to the ‘n_components’ parameter of the UMAP class.
random_stateint, RandomState instance or None, optional: Fix the random seed for reproducibility, by default None Passed to the ‘random_state’ parameter of the UMAP class.
min_distfloat, optional: The effective minimum distance between embedded points, by default 0.1 Passed to the ‘min_dist’ parameter of the UMAP class.
umap_other_paramsdict, optional: Other parameters for UMAP, by default {}

Returns

list of pd.DataFrame of shape (n_samples, n_components of UMAP): UMAP-transformed data.
umap.umap_.UMAP: UMAP instance fitted to the input data.

bures_wasserstein_distance(m_0, m_1, sigma_0, sigma_1)

calculate_cell_velocities()

Calculate cell velocities between each day.

Returns

pd.DataFrame: Cell velocities between each day. The rows are ordered as follows: when the number of days is N and the number of cells in each day is M_1, M_2, …, M_N, [day1_cell1 -> day1_cell2 -> … -> day1_cellM_1 -> day2cell1 -> … -> day(N-1)cellM_N]

calculate_grns(selected_clusters=None, alpha_range=(-2, 2), cv=3, ridge_cv_fit_intercept=False, ridge_fit_intercept=False)

Calculate gene regulatory networks (GRNs) between each day.

Parameters

selected_clusterslist of list of int of shape (n_days, 2), optional: Specify the clusters to calculate GRNs, by default None If None, all clusters will be used. The list should be like [[day1’s index, selected cluster number], [day2’s index, selected cluster number], …].
alpha_rangetuple or list of float of shape (2,), optional: Range of alpha values for Ridge regression, by default (-2, 2)
cvint, optional: Number of cross-validation folds, by default 3 This parameter is passed to RidgeCV’s ‘cv’ parameter.
ridge_cv_fit_interceptbool, optional: Whether to calculate the intercept in RidgeCV, by default False This parameter is passed to RidgeCV’s ‘fit_intercept’ parameter.
ridge_fit_interceptbool, optional: Whether to calculate the intercept in Ridge, by default False This parameter is passed to Ridge’s ‘fit_intercept’ parameter.

Returns

list of pd.DataFrame: Gene regulatory networks between each day. The rows and columns are gene names. Each element of the list corresponds to the GRN between day i and day i + 1.
list of RidgeCV objects: RidgeCV objects used to calculate GRNs. Each element of the list corresponds to the RidgeCV object between day i and day i + 1.

calculate_mut_st(gmm_source, gmm_target, t)

calculate_normalized_solutions(gmm_models, reg=0.01, numItermax=5000, method='sinkhorn_epsilon_scaling', tau=100000000.0, stopThr=1e-09, sinkhorn_other_params={})

calculate_solution(gmm_source, gmm_target, reg=0.01, numItermax=5000, method='sinkhorn_epsilon_scaling', tau=100000000.0, stopThr=1e-09, sinkhorn_other_params={})

calculate_solutions(gmm_models, reg=0.01, numItermax=5000, method='sinkhorn_epsilon_scaling', tau=100000000.0, stopThr=1e-09, sinkhorn_other_params={})

calculate_waddington_potential(n_neighbors=100, knn_other_params={})

Calculate Waddington potential of each sample.

Parameters

n_neighborsint, optional: Number of neighbors for rach sample, by default 100 This parameter is passed to ‘kneighbors_graph’ function.
knn_other_paramsdict, optional: Other parameters for ‘kneighbors_graph’ function, by default {}

Returns

np.ndarray of shape (sum of n_samples of each day - n_samples of the last day,): Waddington potential of each sample.

create_separated_data(data_names, min_cluster_size=2, return_cluster_names=False, cluster_names=None, original_covariances_weight=0)

Create separated data for each data name.

Parameters

data_nameslist of str: List of prefixes to identify datasets. Cells with names starting with these strings will be extracted into separate scEGOT objects.
min_cluster_sizeint, optional: Minimum number of cells required to retain a cluster, by default 2. Clusters smaller than this threshold will be removed in the separated objects.
return_cluster_namesbool, optional: If True, also return the cluster names for each separated object, by default False.
cluster_nameslist of list of str, optional: Custom names for the clusters in the original object, by default None. 1st dimension is the number of days, 2nd dimension is the number of gmm components in each day. If None, names are automatically generated by generate_cluster_names_with_day() method.
original_covariances_weightfloat, optional: Weight factor for blending the original GMM covariances with recalculated ones, by default 0. The new covariance is calculated as: new_cov = original_cov * weight + recalculated_cov * (1 - weight). - 0.0: Use only covariances calculated from the separated data. - 1.0: Use only covariances of the original object.

Returns

dict: A dictionary where keys are data_names and values are the corresponding separated scEGOT objects.
dict: A dictionary where keys are data_names and values are lists of cluster names. These names correspond to the cluster names of the original object. This will be returned only when ‘return_cluster_names’ is True.

egot(pi_0, pi_1, mu_0, mu_1, S_0, S_1, reg=0.01, numItermax=5000, method='sinkhorn_epsilon_scaling', tau=100000000.0, stopThr=1e-09, sinkhorn_other_params={})

fit_gmm(n_components_list, covariance_type='full', max_iter=2000, n_init=10, random_state=None, gmm_other_params={})

fit_predict_gmm(n_components_list, covariance_type='full', max_iter=2000, n_init=10, random_state=None, gmm_other_params={})

Fit GMM models with each day’s data and predict labels for them.

Parameters

n_components_listlist of int: Each element corresponds to the number of components of the GMM model for each day. Passed to the ‘n_components’ parameter of the GaussianMixture class.
covariance_type{‘full’, ‘tied’, ‘diag’, ‘spherical’}, optional: String describing the type of covariances parameters to use, by default “full” Passed to the ‘covariance_type’ parameter of the GaussianMixture class.
max_iterint, optional: The number of EM iterations to perform, by default 2000 Passed to the ‘max_iter’ parameter of the GaussianMixture class.
n_initint, optional: The number of initializations to perform, by default 10 Passed to the ‘n_init’ parameter of the GaussianMixture class.
random_stateint, RandomState instance or None, optional: Controls the random seed given at each GMM model initialization, by default None Passed to the ‘random_state’ parameter of the GaussianMixture class.
gmm_other_paramsdict, optional: Other parameters for GMM, by default {}

Returns

list of GaussianMixture instances: The length of the list is the same as the number of days. Each element is a GMM instance fitted to the corresponding day’s data.
list of np.ndarray: List of GMM labels. Each element is the predicted labels for the corresponding day’s data.

gaussian_mixture_density(mu, sigma, alpha, x)

generate_cluster_names_with_day(cluster_names=None)

get_gaussian_map(m_0, m_1, sigma_0, sigma_1, x)

get_gmm_means()

get_positive_gmm_mean_gene_values_per_cluster(gmm_means, cluster_names=None)

make_cell_state_graph(cluster_names, mode='pca', threshold=0.05)

Warning

make_cell_state_graph() was deprecated in version 0.3.0 and will be removed in future versions. Use make_cell_state_graph_object() instead.

Compute cell state graph and build a networkx graph object.

Parameters

cluster_names2D list of str: 1st dimension is the number of days, 2nd dimension is the number of gmm components in each day. Can be generaged by ‘generate_cluster_names’ method.
mode{‘pca’, ‘umap’}, optional: The space to build the cell state graph, by default “pca”
thresholdfloat, optional: Threshold to filter edges, by default 0.05 Only edges with edge_weights greater than this threshold will be included.

Returns

nx.classes.digraph.DiGraph: Networkx graph object of the cell state graph

Raises

ValueError: When ‘mode’ is not ‘pca’ or ‘umap’.

make_cell_state_graph_object(cluster_names=None, mode='pca', threshold=0.05, merge_clusters_by_name=False, x_reverse=False, y_reverse=False, require_parent=False)

Compute cell state graph and build a CellStateGraph object.

Parameters

cluster_names2D list of str, optional

Cluster names for each GMM cluster in each day. 1st dimension is the number of days, 2nd dimension is the number of gmm components in each day.

If merge_clusters_by_name is True, clusters with the same name will be merged.

If None, generated by generate_cluster_names_with_day() method.

mode{‘pca’, ‘umap’}, optional

The space to build the cell state graph, by default ‘pca’

thresholdfloat, optional

Threshold to filter edges, by default 0.05 Only edges with edge_weights greater than this threshold will be included.

merge_clusters_by_namebool, optional

If True, clusters with the same name will be merged, by default False

x_reversebool, optional

If True, reverse the X axis direction, by default False

y_reversebool, optional

If True, reverse the Y axis direction, by default False

require_parentbool, optional

If True, ensure that each cluster in the target day has at least one incoming edge from the source day, by default False

Returns

scegot.CellStateGraph: scegot.CellStateGraph object of the cell state graph

Raises

ValueError

This error is raised in the following cases:

When ‘mode’ is not ‘pca’ or ‘umap’.
When the length of ‘cluster_names’ is not the same as the number of days.
When the length of the second dimension of ‘cluster_names’ is not the same as the number of GMM components in each day.

make_interpolation_data(gmm_source, gmm_target, t, columns=None, n_samples=2000, seed=0)

Make interpolation data between two timepoints.

Parameters

gmm_sourceGaussianMixture: GMM model of the source timepoint.
gmm_targetGaussianMixture: GMM model of the target timepoint.
tfloat: Interpolation ratio. 0 <= t <= 1. 0 is the source timepoint, 1 is the target timepoint. If you specify 0.5, the data will be interpolated halfway between the source and target timepoints.
columnslist of str, optional: Columns names of the output data, by default None
n_samplesint, optional: Number of samples to generate, by default 2000
seedint, optional: Random seed, by default 0

Returns

pd.DataFrame: Interpolated data between two timepoints.

merge_cluster_names_by_pathway(last_day_cluster_names, n_merge_iter=None, merge_method='pattern', threshold=0.05, n_clusters_list=None, **kmeans_kwargs)

Merge cluster names based on cell state graph pathways.

Parameters

last_day_cluster_nameslist of str

Cluster names for the last day. Clusters with the same name will be merged.

The length of the list should be equal to the number of clusters in the last day.

n_merge_iterint, optional

Number of preceding days to trace back and merge cluster names, starting from the last day, by default (the number of days - 1).

Must be an integer in the range from 1 to (the number of days - 1).

merge_method{‘pattern’, ‘kmeans’}, optional

Method to merge nodes, by default ‘pattern’.

‘pattern’: Merges nodes that share the same connection pattern to the next day’s nodes.
‘kmeans’: Merges nodes based on the edge weights to the next day’s nodes using K-Means.

thresholdfloat, optional

Threshold to filter edges, by default 0.05. Edges with weights below this value are ignored.

This parameter is used only when merge_method is ‘pattern’.

n_clusters_listlist of int, optional

List specifying the number of merged clusters for each day.

The length of the list must equal to the number of days or (the number of days - 1). If None, defaults to the minimum of (original cluster count, 4) for each day.

This parameter is used only when merge_method is ‘kmeans’.

**kmeans_kwargsdict

Arbitrary keyword arguments passed to sklearn.cluster.KMeans.

This parameter is used only when merge_method is ‘kmeans’.

Returns

list of list of str: Merged cluster names for each day.

Raises

ValueError

This error is raised in the following cases:

When ‘n_merge_iter’ is not an integer within the valid range (1 to number of days - 1).
When ‘merge_method’ is not one of ‘pattern’ or ‘kmeans’.

plot_cell_state_graph(G, cluster_names, tf_gene_names=None, tf_gene_pick_num=5, save=False, save_path=None)

Warning

scEGOT.plot_cell_state_graph() was deprecated in version 0.3.0 and will be removed in future versions. Use CellStateGraph.plot_cell_state_graph() instead.

Plot the cell state graph with the given graph object.

Parameters

Gnx.classes.digraph.DiGraph: Networkx graph object of the cell state graph.
cluster_nameslist of list of str: 1st dimension is the number of days, 2nd dimension is the number of gmm components of each day. Can be generaged by ‘generate_cluster_names’ method.
tf_gene_nameslist of str, optional: List of transcription factor gene names to use, by default None If None, all gene names (self.gene_names) will be used. You can pass on any list of gene names you want to use, not limited to TF genes.
tf_gene_pick_numint, optional: The number of genes to show in each node and edge, by default 5
savebool, optional: If True, save the output image, by default False
save_path_type_, optional: Path to save the output image, by default None If None, the image will be saved as ‘./cell_state_graph.png’

plot_cell_velocity(velocities, mode='pca', color_points='gmm', size_points=30, cmap='tab20', cluster_names=None, save=False, save_path=None)

Plot cell velocities in 2D space.

Parameters

velocitiespd.DataFrame: Cell velocities calculated by ‘calculate_cell_velocities’ method.
mode{‘pca’ or ‘umap’}, optional: The space to plot cell velocities, by default “pca”
color_points{‘gmm’ or ‘day’}, optional: Color points by GMM clusters or days, by default “gmm”
size_pointsint, optional: Size of points, by default 30
cmapstr, optional: String of matplolib colormap name, by default “tab20”
cluster_nameslist of str of shape (sum of gmm components), optional: List of gmm cluster names, by default None Used when ‘color_points’ is ‘gmm’. You need to flatten the list of lists of gmm cluster names before passing it.
savebool, optional: If True, save the output image, by default False
save_pathstr, optional: Path to save the output image, by default None If None, the image will be saved as ‘./cell_velocity.png’

Raises

ValueError: This error is raised in the following cases: - When ‘mode’ is not ‘pca’ or ‘umap’. - When ‘color_points’ is not ‘gmm’ or ‘day’. - When ‘color_points’ is ‘gmm’ and ‘cluster_names’ is None.

plot_fold_change(cluster_names, cluster1, cluster2, tf_gene_names=None, threshold=1.0, save=False, save_path=None)

Plot fold change between two clusters.

Parameters

cluster_nameslist of list of str: 1st dimension is the number of days, 2nd dimension is the number of gmm components in each day. Can be generaged by ‘generate_cluster_names’ method.
cluster1str: Cluster name of denominator.
cluster2str: Cluster name of numerator.
tf_gene_nameslist of str, optional: List of transcription factor gene names to use, by default None If None, all gene names (self.gene_names) will be used. You can pass on any list of gene names you want to use, not limited to TF genes.
thresholdfloat, optional: Threshold to filter labels, by default 1.0 Only genes with fold change greater than this threshold will be plotted its label.
savebool, optional: If True, save the output image, by default False
save_pathstr, optional: Path to save the output image, by default None If None, the image will be saved as ‘./fold_change.png’

plot_gene_expression_2d(gene_name, mode='pca', col=None, save=False, save_path=None)

Plot gene expression levels in 2D space.

Parameters

gene_namestr: Gene name to plot expression level.
mode{‘pca’, ‘umap’}, optional: The space to plot gene expression levels, by default “pca”
collist or tuple of str of shape (2,), optional: X and Y axis labels, by default None If None, the first two columns of the input data will be used.
savebool, optional: If True, save the output image, by default False
save_pathstr, optional: Path to save the output image, by default None If None, the image will be saved as ‘./pathway_single_gene_2d.png’

Raises

ValueError: When ‘mode’ is not ‘pca’ or ‘umap’.

plot_gene_expression_3d(gene_name, col=None, save=False, save_path=None)

Plot gene expression levels in 3D space.

Parameters

gene_namestr: Gene name to plot expression level.
collist or tuple of str of shape (2,), optional: X, Y, and Z axis labels, by default None If None, the first three columns of the input data will be used.
savebool, optional: If True, save the output image, by default False
save_pathstr, optional: Path to save the output image, by default None If None, the image will be saved as ‘./pathway_single_gene_3d.html’

plot_gmm_predictions(mode='pca', figure_labels=None, x_range=None, y_range=None, figure_titles_without_gmm=None, figure_titles_with_gmm=None, plot_gmm_means=False, cmap='plasma', save=False, save_paths=None)

Plot GMM predictions. Output images for the number of days. Each image contains two subplots: left one is in one color and right one is colored by GMM labels.

Parameters

mode{‘pca’, ‘umap’}, optional: The space to plot the GMM predictions, by default “pca”
figure_labelslist or tuple of str of shape (2,), optional: X and Y axis labels, by default None If None, the first two columns of the input data will be used.
x_rangelist or tuple of float of shape (2,), optional: Restrict the X axis range, by default None If None, the range will be automatically determined to include all data points.
y_rangelist or tuple of float of shape (2,), optional: Restrict the Y axis range, by default None If None, the range will be automatically determined to include all data points.
figure_titles_without_gmmlist or tuple of str of shape (n_days,), optional: List of figure titles of left subplots, by default None
figure_titles_with_gmmlist or tuple of str of shape (n_days,), optional: List of figure titles of right subplots, by default None
plot_gmm_meansbool, optional: If True, plot GMM mean points on the right subplots, by default False
cmapstr, optional: String of matplolib colormap name, by default “plasma”
savebool, optional: If True, save the output images, by default False
save_pathslist or tuple of str of shape (n_days), optional: List of paths to save the output images, by default None If None, the images will be saved as ‘./GMM_preds_{i + 1}.png’.

Raises

ValueError: When ‘mode’ is not ‘pca’ or ‘umap’.

plot_grn_graph(grns, ridge_cvs, selected_genes, threshold=0.01, save=False, save_paths=None, save_format='png')

Plot gene regulatory networks (GRNs) between each day.

Parameters

grnslist of pd.DataFrame: Gene regulatory networks between each day. The rows and columns are gene names.
ridge_cvslist of RidgeCV objects: RidgeCV objects used to calculate GRNs.
selected_geneslist of str: Gene names to plot GRNs.
thresholdfloat, optional: Threshold to plot edges, by default 0.01 If the absolute value of the edge weight is less than this value, the edge will not be plotted.
savebool, optional: If True, save the output image, by default False
save_pathsstr, optional: Paths to save the output images, by default None
save_formatstr, optional: Format of the output images, by default “png”

plot_interpolation_of_cell_velocity(velocities, mode='pca', color_streams=False, color_points='gmm', cluster_names=None, x_range=None, y_range=None, cmap='gnuplot2', linspace_num=300, save=False, save_path=None)

Warning

plot_interpolation_of_cell_velocity() was deprecated in version 0.3.0 and will be removed in future versions. Use plot_cell_velocity() instead.

Parameters

velocitiespd.DataFrame: Cell velocities calculated by ‘calculate_cell_velocities’ method.
mode{‘pca’, ‘umap’}, optional: The space to plot cell velocities, by default “pca”
color_streamsbool, optional: If True, color the streamlines by the speed of the cell velocities, by default False
color_points{‘gmm’ or ‘day’}, optional: Color points by GMM clusters or days, by default “gmm”
cluster_nameslist of str of shape (sum of gmm n_components), optional: List of gmm cluster names, by default None Used when ‘color_points’ is ‘gmm’. You need to flatten the list of lists of gmm cluster names before passing it.
x_rangetuple or list of float of shape (2,), optional: Limit of the x-axis, by default None
y_rangetuple or list of float of shape (2,), optional: Limit of the y-axis, by default None
cmapstr, optional: String of matplolib colormap name, by default “gnuplot2”
linspace_numint, optional: Number of points on each axis to interpolate, by default 300 linspace_num * linspace_num points will be interpolated.
savebool, optional: If True, save the output image, by default False
save_pathstr, optional: Path to save the output image, by default None If None, the image will be saved as ‘./interpolation_of_cell_velocity_gmm_clusters.png’

Raises

ValueError: This error is raised in the following cases: - When ‘mode’ is not ‘pca’ or ‘umap’. - When ‘color_points’ is not ‘gmm’ or ‘day’. - When ‘color_points’ is ‘gmm’ and ‘cluster_names’ is None.

plot_pathway_gene_expressions(cluster_names, pathway_names, selected_genes, save=False, save_path=None)

Plot gene expression levels within a pathway.

Parameters

cluster_nameslist of list of str: 1st dimension is the number of days, 2nd dimension is the number of gmm components in each day. Can be generaged by ‘generate_cluster_names’ method.
pathway_nameslist of str of shape (n_days,): List of cluster names included in the pathway. Specify like [‘day0’s cluster name’, ‘day1’s cluster name’, …, ‘dayN’s cluster name’].
selected_geneslist of str: List of gene names whose gene expression changes you want to track. Recommend using about 5 genes.
savebool, optional: If True, save the output image, by default False
save_pathstr, optional: Path to save the output image, by default None If None, the image will be saved as ‘./pathway_gene_expressions.png’

plot_pathway_mean_var(cluster_names, pathway_names, tf_gene_names=None, threshold=1.0, save=False, save_path=None)

Plot mean and variance of gene expression levels within a pathway.

Parameters

cluster_nameslist of list of str: 1st dimension is the number of days, 2nd dimension is the number of gmm components in each day. Can be generaged by ‘generate_cluster_names’ method.
pathway_nameslist of str of shape (n_days,): List of cluster names included in the pathway. Specify like [‘day0’s cluster name’, ‘day1’s cluster name’, …, ‘dayN’s cluster name’].
tf_gene_nameslist of str, optional: List of transcription factor gene names to use, by default None If None, all gene names (self.gene_names) will be used. You can pass on any list of gene names you want to use, not limited to TF genes.
thresholdfloat, optional: Threshold to filter labels, by default 1.0 Only genes with variance greater than this threshold will be plotted its label.
savebool, optional: If True, save the output image, by default False
save_pathstr, optional: Path to save the output image, by default None If None, the image will be saved as ‘./pathway_mean_var.png’

plot_pathway_single_gene_2d(gene_name, mode='pca', col=None, save=False, save_path=None)

plot_pathway_single_gene_3d(gene_name, col=None, save=False, save_path=None)

plot_simple_cell_state_graph(G, layout='normal', order=None, save=False, save_path=None)

Warning

scEGOT.plot_simple_cell_state_graph() was deprecated in version 0.3.0 and will be removed in future versions. Use CellStateGraph.plot_simple_cell_state_graph() instead.

Plot the cell state graph with the given graph object in a simple way.

Parameters

Gnx.classes.digraph.DiGraph: Networkx graph object of the cell state graph.
layout{‘normal’, ‘hierarchy’}, optional: The layout of the graph, by default “normal” When ‘normal’, the graph is plotted the same layout as the self.plot_cell_state_graph method. When ‘hierarchy’, the graph is plotted with the day on the x-axis and the cluster on the y-axis.
order{‘weight’, None}, optional: Order of nodes along the y-axis, by default None This parameter is only used when ‘layout’ is ‘hierarchy’. When ‘weight’, the nodes are ordered by the size of the nodes. When None, the nodes are ordered by the cluster number.
savebool, optional: If True, save the output image, by default False
save_pathstr, optional: Path to save the output image, by default None If None, the image will be saved as ‘./simple_cell_state_graph.png’

Raises

ValueError: When ‘layout’ is not ‘normal’ or ‘hierarchy’, or ‘order’ is not None or ‘weight’.

plot_true_and_interpolation_distributions(interpolate_index, mode='pca', n_samples=2000, t=0.5, plot_source_and_target=True, alpha_true=0.5, x_col_name=None, y_col_name=None, x_range=None, y_range=None, save=False, save_path=None)

Compare the true and interpolation distributions by plotting them.

Parameters

interpolate_indexint: Index of the timepoint to interpolate. 1 <= interpolate_index <= n_days - 2
mode{‘pca’, ‘umap’}, optional: The space to plot gene expression levels, by default “pca”
n_samplesint, optional: Number of samples to generate, by default 2000
tfloat, optional: Interpolation ratio, by default 0.5 If you want to interpolate halfway between the source and target timepoints, specify 0.5. Source timepoint is interpolate_index - 1, target timepoint is interpolate_index + 1.
plot_source_and_targetbool, optional: If True, plot the source and target timepoints, by default True
alpha_truefloat, optional: Transparency of the true data, by default 0.5
x_col_namestr, optional: Label of the x-axis, by default None
y_col_namestr, optional: Label of the y-axis, by default None
x_rangelist or tuple of float of shape (2,), optional: Range of the x-axis, by default None If None, the range will be automatically determined based on the data.
y_rangelist or tuple of float of shape (2,), optional: Range of the y-axis, by default None If None, the range will be automatically determined based on the data.
savebool, optional: If True, save the output image, by default False
save_pathstr, optional: Path to save the output image, by default None

Raises

ValueError: When ‘mode’ is not ‘pca’ or ‘umap’.

plot_waddington_potential(waddington_potential, mode='pca', gene_name=None, save=False, save_path=None)

Plot Waddington potential in 3D space.

Parameters

waddington_potentialnp.ndarray: Waddington potential of each sample. This array should be calculated by ‘calculate_waddington_potential’ method.
mode{‘pca’, ‘umap’}, optional: The space to plot Waddington potential, by default “pca”
gene_namestr, optional: Gene name to color the points, by default None If None, the points will be colored by Waddington potential. If specified, the points will be colored by the expression of the specified gene.
savebool, optional: If True, save the output image, by default False
save_pathstr, optional: Path to save the output image, by default None If None, the image will be saved as ‘./waddington_potential.html’

plot_waddington_potential_surface(waddington_potential, mode='pca', save=False, save_path=None)

Plot Waddington’s landscape in 3D space by using cellmap.

Parameters

waddington_potentialnp.ndarray: Waddington potential of each sample. This array should be calculated by ‘calculate_waddington_potential’ method
mode{‘pca’, ‘umap’}, optional: The space to plot Waddington potential, by default “pca”
savebool, optional: If True, save the output image, by default False
save_pathstr, optional: Path to save the output image, by default None If None, the image will be saved as ‘./wadding_potential_surface.html’

predict_gmm_label(X_item, gmm_model)

predict_gmm_labels(X, gmm_models)

preprocess(pca_n_components, recode_params={}, umi_target_sum=10000.0, pca_random_state=None, pca_other_params={}, apply_recode=True, apply_normalization_log1p=True, apply_normalization_umi=True, select_genes=True, n_select_genes=2000, hvg_method='dispersion')

Preprocess the input data.

Apply scRECODE, normalize, select highly variable genes, and apply PCA.

Parameters

pca_n_componentsint: Number of components to keep in PCA. Passed to the ‘n_components’ parameter of the PCA class.
recode_paramsdict, optional: Parameters for scRECODE, by default {}
umi_target_sumint or float, optional: Target sum for UMI normalization, by default 1e4
pca_random_stateint, RandomState instance or None, optional: Pass an int for reproducible results, by default None Passed to the ‘random_state’ parameter of the PCA class.
pca_other_paramsdict, optional: Parameters other than ‘n_components’ and ‘random_state’ for PCA, by default {}
apply_recodebool, optional: If True, apply scRECODE, by default True
apply_normalization_log1pbool, optional: If True, apply log1p normalization, by default True
apply_normalization_umibool, optional: If True, apply UMI normalization, by default True
select_genesbool, optional: If True, filter genes and select highly variable genes, by default True
n_select_genesint, optional: Number of highly variable genes to select, by default 2000 Used only when ‘select_genes’ is True.
hvg_method{‘dispersion’, ‘RECODE’}, optional: Method to select highly variable genes, by default ‘dispersion’ * ‘dispersion’: select genes based on dispersion. * ‘RECODE’: select genes based on scRECODE.

Raises

ValueError: If ‘hvg_method’ is not ‘dispersion’ or ‘RECODE’.

Returns

list of pd.DataFrame of shape (n_samples, n_components of PCA): Normalized, filtered, and PCA-transformed data.
sklearn.decomposition.PCA: PCA instance fitted to the input data.

replace_gmm_labels(converter)