Release notes

Dynamo Ver 1.3.0

Feature Changes

• The preprocessing module has been refactored:

  • Class Preprocessor is recommended for most preprocessing methods and recipes. pp.recipe_monocle, pp.recipe_velocyto has been deprecated (PR 497 PR 500). Check the tutorials here for more instructions.

  • Normalization has been refactored (PR 474 PR 475): pp.normalize_cell_expr_by_size_factors has been deprecated, and new APIs are:

    • pp.normalize_cell_expr_by_size_factors -> pp.calc_sz_factor, pp.normalize.

  • Gene selection has been refactored (PR 474). Now support genes selected by fano factors. APIs are pp.select_genes_monocle and pp.select_genes_by_seurat_recipe.

  • PCA has been refactored (PR 469). dyn.pp.pca_monocle has been deprecated. The new API is:

    • pp.pca_monocle -> pp.pca.

  • sctransform and pearson residuals recipe has been refactored (PR 510 PR 512). Now those advanced methods will only be performed on X layer. Other layers will get normalized by size factors.

  • Calculation of ntr rate and pp.cell_cycle_scores has been added to the Preprocessor (PR 513). To enable cell cycle scores, set parameter cell_cycle_score_enable to True when initializing the pp.Preprocessor.

  • Now the size factors normalization will normalize all layers with its own size factors by default (PR 521). To normalize the labeled data with total size factors, we need to set the total_szfactor to total_Size_Factor explicitly.

  • Multiple new features added, includes genes selection by fano factors (PR 474), external data integration methods (PR 473) and pp.regress_out (PR 470 PR 483 PR 484).

  • Created more tests for preprocessing module (PR 485).

  • Replaced adata.obsm["X"] with adata.obsm["X_pca"] (PR 514).

  • Removed some console output. They can still be displayed with DEBUG logging mode.

  • Other deprecated APIs include: pp.calc_sz_factor_legacy, pp.filter_cells_legacy, pp.filter_genes_by_outliers_legacy, pp.select_genes_monocle_legacy, pp.select_genes_by_dispersion_general, pp.cook_dist, pp.normalize_cell_expr_by_size_factors. More information can be found on our preprocessing tutorials.

DEBUG

• Fixed the bug that save_show_or_return flags not working (PR 414).

• Enabled the leiden algorithm to accept the resolution parameters (PR 441).

• Fixed the wrong attribute name of anndata object in utils_dimensionReduction.py (PR 458)`

• Fixed the dimensionality issue in moments.py (PR 461).

• Fixed part of the bug that h5ad file cannot be saved correctly (PR 467).

• Fixed the bug that pca_mean will be None under some circumstances (PR 482).

• Removing warning message for nxviz (PR 489).

• Corrected the norm log-likelihood function (PR 495).

• Removed deprecated parameters in gseapy functions (PR 496).

• Fixed the bugs that functions will raise error when no fixed points are found in vector field by sampling (PR 501).

• Removed unwanted operations in dimension reduction (PR 502).

Tutorial Updates on Readthedocs

• Documentation, Tutorials, and readthedocs update:

  • Update requirements for readthedocs (PR 466).

  • Update readme (PR 479).

  • Fixed documentation error caused by importing Literal (PR 486).

  • Fixed readthedocs error caused by the new version of urllib3 (PR 488).

Other Changes

• Docstring and type hints update:

  • Updated docstring and type hints for tools module (PR 419).

  • Updated docstring and type hints for vector field module (PR 434).

  • Updated the docstring and type hints for simulation and predicting module (PR 457).

  • Update the docstring and type hints for hzplot (PR 456).

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