Release notes

Dynamo Ver 1.1.0

Feature Changes

  • Following new function are added, exported or documented in API / class page:

    • Preprocessing: pp.convert2symbol, pp.filter_cells, pp.filter_gene, pp.filter_genes_by_pattern, pp.normalize_cells, pp.scale, pp.log1p, pp.pca

    • Kinetic parameters and RNA/protein velocity: tl.recipe_deg_data, tl.recipe_kin_data, tl.recipe_mix_kin_deg_data, tl.recipe_one_shot_data, tl.velocity_N

    • Labeling Velocity recipes: tl.infomap, tl.leiden, tl.louvain, tl.scc

    • Clustering: tl.run_scvelo, tl.run_velocyto, tl.vlm_to_adata

    • Converter and helper: vf.graphize_vecfld, vf.vector_field_function

    • Vector field reconstruction: vf.FixedPoints, vf.VectorField2D, vf.assign_fixedpoints

    • Beyond RNA velocity: vf.jacobian, vf.sensitivity

    • Vector field ranking: vf.rank_cells, vf.rank_genes, vf.rank_expression_genes, vf.rank_jacobian_genes, vf.rank_s_divergence_genes, vf.rank_sensitivity_genes

    • Vector field clustering and graph: vf.cluster_field, vf.streamline_clusters

    • Prediction pd.andecestor, pd.get_init_path, pd.least_action, pd.perturbation, pd.rank_perturbation_cell_clusters, pd.rank_perturbation_cells, pd.rank_perturbation_genes, pd.state_graph, pd.tree_model

    • Preprocessing plot: pl.biplot, pl.loading, pl.highest_frac_genes, pl.bubble

    • Space plot: pl.space

    • Kinetics plot: pl.sensitivity_kinetics

    • Vector field plots: pl.cell_wise_vectors_3d, pl.plot_fixed_points_2d

    • differential geometry plots: pl.acceleration

    • Regulatory network plots pl.arcPlot, pl.circosPlot, pl.circosPlotDeprecated, pl.hivePlot

    • fate plots pl.fate

    • heatmap plots pl.causality, pl.comb_logic, pl.plot_hill_function, pl.response

    • Predictions plots pl.lap_min_time

    • External functionality ext.normalize_layers_pearson_residuals, ext.select_genes_by_pearson_residuals, ext.sctransform

  • More differential geometry analyses

    • include the switch mode in rank_jacobian_genes

    • added calculation of sensitivity matrix and relevant ranking

  • most probable path and in silico perturbation prediction

    • implemented least action path optimization (can be done in high dimensional space) with analytical Jacobian

    • include genetic perturbation prediction by either changing the vector field function or simulate genetic perturbation via analytical Jacobian

  • preprocessor class implementation

    • extensible modular preprocess steps

    • support following recipes: monocle (dynamo), seurat (seurat V3 flavor), sctransform (seurat), pearson residuals and pearson residuals for feature selection, combined with monocle recipe (ensure no negative values)

    • following recipes tested on zebrafish dataset to make implemetation results consistent:

    • monocle, seurat, pearson residuals

  • CDlib integration

    • leiden, louvain, infomap community detection for cell clustering

    • wrappers in dyn.tl.* for computing clusters

    • wrappers in dyn.pl.* for plotting

Tutorial Updates on Readthedocs

  • human HSC hematopoiesis RNA velocity analysis tutorials

  • in silico perturbation and least action path (LAP) predictions tutorials on HSC dataset

  • differential geometry analysis on HSC dataset

    • Molecular mechanism of megakaryocytes

    • Minimal network for basophil lineage commitment

    • Cell-wise analyses: dominant interactions

  • gallery: Pancreatic endocrinogenesis differential geometry

Sample Dataset Updates

CI/CD Updates

  • update dynamo testing and pytest structure

  • test building workflow on 3.7, 3.8, 3.9 (3.6 no longer tested on github building CI)

Performance Improvements

API Changes

  • preprocess

  • pp.pca -> pca.pca_monocle

  • Native implementation of various graphical calculus using Numpy without using igraph.

Other Changes

  • general code refactor and bug fixing

  • pl.scatters refactor