pyrovelocity.analysis#

analyze#

cytotrace#

pyrovelocity.analysis.cytotrace.compute_gcs(mat, count, top_n_genes=200)[source]#

Compute gene set enrichment scores by correlating gene count and gene expression

Parameters:

  • mat (ndarray) – Matrix of shape (n_genes, n_cells)

  • count (ndarray) – Gene count

  • top_n_genes (int, optional) – Number of genes to select. Defaults to 200.

Return type:

ndarray

pyrovelocity.analysis.cytotrace.compute_similarity1(A)[source]#

Compute pairwise correlation of all columns in matrices A

adapted from https://github.com/ikizhvatov/efficient-columnwise-correlation/blob/master/columnwise_corrcoef_perf.py

Parameters:

A (ndarray) – matrix of shape (n, t)

Returns:

correlation matrix of shape (t, t)

Return type:

ndarray

pyrovelocity.analysis.cytotrace.compute_similarity2(O, P)[source]#

Compute pearson correlation between two matrices O and P.

Parameters:

  • O (ndarray) – matrix of shape (n, t)

  • P (ndarray) – matrix of shape (n, m)

Returns:

correlation matrix of shape (t, m)

Return type:

ndarray

pyrovelocity.analysis.cytotrace.cytotrace_sparse(adata, layer='raw', cell_count=20, top_n_features=200, skip_regress=False)[source]#

optimized version

Return type:

dict[str, ndarray]

pyrovelocity.analysis.cytotrace.diffused(markov, gcs, ALPHA=0.9)[source]#

Compute diffused gene set enrichment scores

Parameters:

  • markov (ndarray) – Markov state transition matrix

  • gcs (ndarray) – gene set enrichment scores

  • ALPHA (float, optional) – Defaults to 0.9.

Returns:

_description_

Return type:

ndarray

pyrovelocity.analysis.cytotrace.threshold_and_normalize_similarity_matrix(sim)[source]#

Transform a dense similarity matrix into a sparse, normalized version.

Parameters:

sim (ndarray) – Similarity matrix

Returns:

Thresholded and normalized similarity matrix

Return type:

ndarray