speciesot.speciesot.SpeciesOT

class speciesot.speciesot.SpeciesOT(data_object)

Bases: Data

Core class for geometric inference of cross-species transcriptomic correspondence using Gromov–Wasserstein optimal transport.

preprocessing()

Mask and preprocess expression data across species.

calculate_gene_distance_matrix()

Compute per-species gene–gene distance matrices.

gromov_wasserstein_ot()

Run Gromov–Wasserstein Optimal Transport (GWOT) computations.

plot_transcriptomic_discrepancy(raw_return_opt=False)

Compute and visualize transcriptomic discrepancy across species.

normalize_otp()

Normalize optimal transport plans (gene-to-gene correspondences).

dashboard(target_species_pairs, target_genes, top_n)

Generate a dashboard displaying top corresponding genes between species.

plot_corresponding_gene_expressions(target_species_pairs, spe_gene_dict, target_genes, top_n, title_fontsize=14)

Plot cross-species gene expression correspondences in a grid layout.

corresponding_gene_expressions_separated_heatmap(target_species_pairs, spe_gene_dict, target_genes, top_n, dataset1_bool=False, cbar_separate_opt=False, raw_return_opt=False)

Generate individual heatmaps for each target gene and its cross-species correspondences.

normalization()

Normalize expression data and reduce noise.

read_csv(verbose=False)

Read input CSVs and initialize AnnData objects.

read_tf()

Read human transcription factor genes from TFs/hTF.txt.