omicverse.single.gene_trends

class omicverse.single.gene_trends(adata, pseudotime, var_names)

Fit and visualize smooth feature trends along pseudotime.

Parameters:

• adata (AnnData) – AnnData containing pseudotime and feature matrix.

• pseudotime (str) – adata.obs key used for temporal ordering.

• var_names (sequence of str) – Feature names to model along pseudotime.

Returns:

Initializes trend analysis configuration.

Return type:

None

Examples

>>> gt_obj = ov.single.gene_trends(adata_aucs, "pt_via", var_name)

__init__(adata, pseudotime, var_names)

Initialize the gene_trends analysis based on pseudotime

Parameters:

• adata (AnnData object)

• pseudotime (str, the column name of pseudotime in adata.obs)

• var_names (list, the list of gene name to calculate)

Methods

__init__(adata, pseudotime, var_names)	Initialize the gene_trends analysis based on pseudotime
cal_border_cell(adata, pseudotime, cluster_key)	Calculate the border cell of each cluster
calculate([n_convolve])	Calculate the trends of gene with pseudotime
get_border_gene(adata, cluster_key, ...[, ...])	Get the border gene between two clusters
get_heatmap()	Get the data of heatmap of trends
get_kendalltau()	Get the kendalltau of trends
get_kernel_gene(adata, cluster_key, cluster)	Get the kernel gene of cluster
get_linregress()	Get the linregress of trends
get_multi_border_gene(adata, cluster_key[, ...])	Get the border gene between two clusters for all clusters
get_multi_kernel_gene(adata, cluster_key[, ...])	Get the kernel gene of cluster for all clusters
get_special_border_gene(adata, cluster_key, ...)	Get the special border gene between two clusters
get_special_kernel_gene(adata, cluster_key, ...)	Get the special kernel gene of cluster
plot_trend([figsize, max_threshold, color, ...])	Plot the trends of gene with pseudotime