py-Monocle3

omicverse-facing wrapper for Bio-Babel/Monocle3-python — a comprehensive Python port of R monocle3 (Cao et al. Nature 2019 / Trapnell et al. Nat Biotech 2014).

Rather than duplicate Bio-Babel's excellent work, this package re-exports their full API under the pymonocle3 namespace + adds omicverse-conformant parity-validation harness so the trajectory benchmark treats it like the other ports.

Install

pip install pymonocle3-bio

This pulls in monocle3-python + its transitive deps (hnswlib, leidenalg, openTSNE, umap-learn, pheatmap-python, igraph).

Quick-start

import pymonocle3 as m3

adata = m3.load_packer_embryo()
m3.preprocess_cds(adata, num_dim=50)
m3.reduce_dimension(adata)
m3.cluster_cells(adata)
m3.learn_graph(adata)
m3.order_cells(adata, root_pr_nodes=["Y_1"])
m3.plot_cells(adata, color_cells_by="pseudotime")

Function coverage

This package re-exports the full Bio-Babel/Monocle3-python API:

• preprocess_cds, align_cds, reduce_dimension, cluster_cells
• learn_graph, order_cells, pseudotime, principal_graph
• graph_test, top_markers, find_gene_modules, aggregate_gene_expression
• plot_cells, plot_cells_3d
• transfer_cell_labels, fix_missing_cell_labels
• All accessors / projections / data loaders

See Bio-Babel/Monocle3-python for the full R parity report.

License

Artistic-2.0, matching upstream monocle3 / monocle3-python.

Citation

Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019). Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol 32, 381–386 (2014).