scvelo - RNA velocity generalized through dynamical modeling.

API

Import scVelo as:

import scvelo as scv

After reading the data or loading an in-built dataset (scv.datasets.*), the typical workflow consists of subsequent calls of preprocessing (scv.pp.*), analysis tools (scv.tl.*) and plotting (scv.pl.*). Further, several utilities (scv.utils.*) are provided to facilitate data analysis.

Preprocessing (pp)

Basic preprocessing (gene selection and normalization)

pp.filter_genes(data[, min_counts, ...])

Filter genes based on number of cells or counts.

pp.filter_genes_dispersion(data[, flavor, ...])

Extract highly variable genes.

pp.normalize_per_cell(data[, ...])

Normalize each cell by total counts over all genes.

pp.log1p(data[, copy])

Logarithmize the data matrix.

pp.filter_and_normalize(data[, min_counts, ...])

Filtering, normalization and log transform.

Moments (across nearest neighbors in PCA space)

pp.neighbors(adata[, n_neighbors, n_pcs, ...])

Compute a neighborhood graph of observations.

pp.moments(data[, n_neighbors, n_pcs, mode, ...])

Computes moments for velocity estimation.

Tools (tl)

Velocity estimation

tl.velocity(data[, vkey, mode, fit_offset, ...])

Estimates velocities in a gene-specific manner.

tl.velocity_graph(data[, vkey, xkey, tkey, ...])

Computes velocity graph based on cosine similarities.

tl.velocity_embedding(data[, basis, vkey, ...])

Projects the single cell velocities into any embedding.

Dynamical modeling

tl.recover_dynamics(data[, var_names, ...])

Recovers the full splicing kinetics of specified genes.

tl.differential_kinetic_test(data[, ...])

Test to detect cell types / lineages with different kinetics.

Dynamical genes

tl.rank_velocity_genes(data[, vkey, ...])

Rank genes for velocity characterizing groups.

tl.rank_dynamical_genes(data[, n_genes, ...])

Rank genes by likelihoods per cluster/regime.

Pseudotime and trajectory inference

tl.terminal_states(data[, vkey, modality, ...])

Computes terminal states (root and end points).

tl.velocity_pseudotime(adata[, vkey, ...])

Computes a pseudotime based on the velocity graph.

tl.latent_time(data[, vkey, min_likelihood, ...])

Computes a gene-shared latent time.

tl.paga(adata[, groups, vkey, ...])

PAGA graph with velocity-directed edges.

Further tools

tl.velocity_clusters(data[, vkey, ...])

Computes velocity clusters via louvain on velocities.

tl.velocity_confidence(data[, vkey, copy])

Computes confidences of velocities.

tl.score_genes_cell_cycle(adata[, s_genes, ...])

Score cell cycle genes.

Inference from metabolic labeling information

inference.get_labeling_time_mask(adata, ...)

Get number of neighbors required to include n_nontrivial_counts counts per labeling time.

inference.get_labeling_times(adata, time_key)

Get labeling times in dataset.

inference.get_n_neighbors(adata, ...[, ...])

Get number of neighbors required to include n_nontrivial_counts counts per labeling time.

inference.get_obs_dist_argsort(adata, ...)

Calculate argsorted pairwise distances per labeling_time_point.

inference.get_parameters(adata, use_rep, ...)

Estimates parameters of splicing kinetics from metabolic labeling data.

Plotting (pl)

Base scatter plot

pl.scatter([adata, basis, x, y, vkey, ...])

Scatter plot along observations or variables axes.

Velocity embeddings

pl.velocity_embedding(adata[, basis, vkey, ...])

Scatter plot of velocities on the embedding.

pl.velocity_embedding_grid(adata[, basis, ...])

Scatter plot of velocities on a grid.

pl.velocity_embedding_stream(adata[, basis, ...])

Stream plot of velocities on the embedding.

Velocity graph

pl.velocity(adata[, var_names, basis, vkey, ...])

Phase and velocity plot for set of genes.

pl.velocity_graph(adata[, basis, vkey, ...])

Plot of the velocity graph.

pl.paga(adata[, basis, vkey, color, layer, ...])

Plot PAGA graph with velocity-directed edges.

Further plotting

pl.proportions(adata[, groupby, layers, ...])

Plot pie chart of spliced/unspliced proprtions.

pl.heatmap(adata, var_names[, sortby, ...])

Plot time series for genes as heatmap.

pl.hist(arrays[, alpha, bins, color, ...])

Plot a histogram.

Datasets

datasets.pancreas([file_path])

Pancreatic endocrinogenesis.

datasets.dentategyrus([file_path, adjusted])

Dentate Gyrus neurogenesis.

datasets.forebrain([file_path])

Developing human forebrain.

datasets.dentategyrus_lamanno([file_path])

Dentate Gyrus neurogenesis.

datasets.gastrulation([file_path])

Mouse gastrulation.

datasets.gastrulation_e75([file_path])

Mouse gastrulation subset to E7.5.

datasets.gastrulation_erythroid([file_path])

Mouse gastrulation subset to erythroid lineage.

datasets.bonemarrow([file_path])

Human bone marrow.

datasets.pbmc68k([file_path])

Peripheral blood mononuclear cells.

datasets.simulation([n_obs, n_vars, alpha, ...])

Simulation of mRNA splicing kinetics.

Utils

Get data by key

get_df(data[, keys, layer, index, columns, ...])

Get dataframe for a specified adata key.

Get gene info

utils.gene_info(name[, fields])

Retrieve gene information from biothings client.

Data preparation

utils.cleanup(adata[, clean, keep, inplace])

Delete not needed attributes.

utils.clean_obs_names(adata[, alphabet, ...])

Clean up the obs_names.

utils.merge(adata, ldata[, copy])

Merge two annotated data matrices.

utils.show_proportions(adata[, layers, use_raw])

Proportions of abundances of modalities in layers.

Getters

utils.get_moments(adata[, layer, ...])

Computes moments for a specified layer.

utils.get_transition_matrix(adata[, vkey, ...])

Computes cell-to-cell transition probabilities.

utils.get_cell_transitions(adata[, ...])

Simulate cell transitions.

utils.get_extrapolated_state(adata[, vkey, ...])

Get extrapolated cell state.

Converters

utils.convert_to_ensembl([gene_names])

Retrieve ensembl IDs from a list of gene names.

utils.convert_to_gene_names([ensembl_names])

Retrieve gene names from ensembl IDs.

Least squares and correlation

utils.leastsq(x, y[, fit_offset, perc, ...])

Solves least squares X*b=Y for b.

utils.vcorrcoef(X, y[, mode, axis])

Pearsons/Spearmans correlation coefficients.

utils.test_bimodality(x[, bins, kde, plot])

Test for bimodal distribution.

Settings

set_figure_params([style, dpi, dpi_save, ...])

Set resolution/size, styling and format of figures.