dynamo-in-silico-perturbation

name

dynamo-in-silico-perturbation

description

Perform in silico gene perturbation on a dynamo vector-field AnnData to predict cell fate diversion after single or multi-gene activation or suppression, then visualize the results with streamline or quiver plots. Use when running dyn.pd.perturbation, predicting transcription factor perturbation effects, simulating gene knockdown or overexpression in scRNA-seq data, reproducing 502_perturbation_tutorial.ipynb, or choosing among pertubation_method, perturb_mode, and emb_basis branches.

Dynamo In Silico Gene Perturbation

Goal

Given a dynamo AnnData with a fitted PCA-space vector field and Jacobian, apply expression-level changes to one or more genes and project the resulting velocity shift into a low-dimensional embedding to predict cell fate diversion. Visualize the perturbed trajectories with streamline or cell-wise vector plots.

Quick Workflow

1. Confirm the input AnnData has VecFld_pca, jacobian_pca, X_pca, and the target embedding (e.g., X_umap) precomputed.
2. Call dyn.pd.perturbation(adata, genes, expression, emb_basis=...) to compute the perturbation effect and project it to the embedding space.
3. Visualize with dyn.pl.streamline_plot(adata, basis="{emb_basis}_perturbation", ...).
4. Optionally use dyn.pl.cell_wise_vectors for quiver-style single-cell vector plots.
5. Repeat steps 2–4 for each perturbation scenario (single gene, multi-gene, mixed activation/suppression).

Interface Summary

• dyn.pd.perturbation(adata, genes, expression=10, perturb_mode='raw', cells=None, zero_perturb_genes_vel=False, pca_key=None, PCs_key=None, pca_mean_key=None, basis='pca', emb_basis='umap', jac_key='jacobian_pca', X_pca=None, delta_Y=None, projection_method='fp', pertubation_method='j_delta_x', J_jv_delta_t=1, delta_t=1, add_delta_Y_key=None, add_transition_key=None, add_velocity_key=None, add_embedding_key=None) — core perturbation call; writes perturbed embedding and velocity into adata.obsm.

  Note on typo: the parameter is spelled pertubation_method (one r) in the source — use that exact spelling.

• dyn.pl.streamline_plot(adata, basis='umap', ..., method='gaussian', vector='velocity', save_show_or_return='show', **streamline_kwargs) — after perturbation, call with basis='{emb_basis}_perturbation' to visualize diverted cell trajectories.

• dyn.pl.cell_wise_vectors(adata, basis='umap', ..., vector='velocity', projection='2d', quiver_size=1, quiver_length=None, save_show_or_return='show') — single-cell quiver vectors; works with perturbation basis.

Read references/source-grounding.md for full inspected signatures and storage key evidence.

Stage Selection

• Single-gene suppression: pass one gene string and a single negative value, e.g. genes='GATA1', expression=[-100].
• Single-gene activation: pass one gene string and a single positive value, e.g. genes='KLF1', expression=[100].
• Multi-gene simultaneous perturbation: pass a list of gene names and matching list of expression values.
• Mixed activation + suppression: combine positive and negative values in the expression list.
• Use perturb_mode='raw' (default) to shift expression by the given absolute value. Use perturb_mode='z_score' when expression units are z-scores.
• Use pertubation_method='j_delta_x' (default) for the standard linearized Jacobian approach. Use 'f_x_prime' or 'f_x_prime_minus_f_x_0' when you want direct vector-field evaluation instead of Jacobian approximation.
• Restrict perturbation to a cell subset by passing cells=<index_array>.

Read references/branch-selection.md for details on all pertubation_method branches and when to use them.

Input Contract

• AnnData with VecFld_pca in .uns (PCA-space vector field must already be fitted).
• adata.uns['jacobian_pca'] must exist with keys jacobian, regulators, effectors, and cell_idx. Run dyn.vf.jacobian(adata, basis='pca') first if absent.
• adata.uns['PCs'] and adata.uns['pca_mean'] (or adata.obsm['X_pca']) must exist for PCA projection.
• adata.obsm['X_{emb_basis}'] must exist for the target embedding projection (default X_umap).
• The hematopoiesis sample dataset (dyn.sample_data.hematopoiesis()) already satisfies all these requirements.
• Gene names in genes must appear in both adata.var_names and the Jacobian regulators/effectors arrays.

Minimal Execution Patterns

Single-gene suppression

import dynamo as dyn

adata = dyn.sample_data.hematopoiesis()

gene = "GATA1"
dyn.pd.perturbation(adata, gene, [-100], emb_basis="umap")
dyn.pl.streamline_plot(adata, color=["cell_type", gene], basis="umap_perturbation")

Single-gene activation

gene = "KLF1"
dyn.pd.perturbation(adata, gene, [100], emb_basis="umap")
dyn.pl.streamline_plot(adata, color=["cell_type", gene], basis="umap_perturbation")

Multi-gene mixed perturbation

selected_genes = ["SPI1", "GATA1"]
expr_vals = [-100, -15]
dyn.pd.perturbation(adata, selected_genes, expr_vals, emb_basis="umap")
dyn.pl.streamline_plot(adata, color=["cell_type"] + selected_genes, basis="umap_perturbation")

Restrict to a cell subset

hsc_idx = dyn.tl.select_cell(adata, "cell_type", "HSC")
dyn.pd.perturbation(adata, "GATA1", [-100], emb_basis="umap", cells=hsc_idx)

Custom perturbation method

# Use direct vector-field evaluation instead of Jacobian linearization
dyn.pd.perturbation(
    adata, "GATA1", [-100],
    emb_basis="umap",
    pertubation_method="f_x_prime_minus_f_x_0",  # note: one 'r' in 'pertubation'
)

Cell-wise quiver visualization

dyn.pl.cell_wise_vectors(
    adata,
    basis="umap_perturbation",
    color="cell_type",
    quiver_size=1,
    save_show_or_return="show",
)

Validation

After dyn.pd.perturbation(adata, genes, expression, emb_basis='umap'), confirm:

• adata.obsm['X_umap_perturbation'] exists and has shape (n_obs, 2)
• adata.obsm['velocity_umap_perturbation'] exists and has shape (n_obs, 2)
• adata.obsm['j_delta_x_perturbation'] exists (or the key matching your pertubation_method)
• adata.obsp['perturbation_transition_matrix'] exists (sparse matrix)
• Terminal log includes: you can use dyn.pl.streamline_plot(adata, basis='umap_perturbation') to visualize the perturbation vector

After dyn.pl.streamline_plot(adata, basis='umap_perturbation'):

• A matplotlib figure appears with streamlines overlaid on the UMAP perturbation embedding
• Streamlines show directional bias consistent with the expected cell fate diversion

Constraints

• Jacobian must cover the perturbed genes: if a gene is not in adata.uns['jacobian_pca']['regulators'], the Jacobian row for that gene will be zero and the perturbation will have no effect. Run dyn.vf.jacobian(adata, regulators=gene_list, effectors=gene_list, basis='pca') with an explicit gene list if needed.
• expression can be a scalar or a list; if a list, it must be the same length as genes.
• Large absolute expression values (e.g., ±100) saturate the perturbation in PCA space; they are conventional in the tutorial but not physically bounded.
• Each dyn.pd.perturbation call overwrites adata.obsm['X_{emb_basis}_perturbation'] and adata.obsm['velocity_{emb_basis}_perturbation'] for that basis. Save or rename intermediate results between scenarios if needed.
• The hematopoiesis dataset uses human gene names (all-caps, e.g., GATA1). Confirm gene naming convention for non-hematopoiesis datasets.

Resource Map

• Read references/source-grounding.md for full inspected signatures and storage key details.
• Read references/branch-selection.md for pertubation_method and perturb_mode branch options.
• Read references/source-notebook-map.md to trace 502_perturbation_tutorial.ipynb sections to skill resources.
• Read references/compatibility.md for known API traps, including the pertubation_method typo and Jacobian prerequisite.
• Use assets/acceptance.json for the bounded smoke path used by local acceptance.