STREAM2.

API

Import stream2 as:

import stream2 as st2

Configuration for STREAM2

settings.set_figure_params([context, style, ...])	Set global parameters for figures.
settings.set_workdir([workdir])	Set working directory.

Reading

read_csv(filename[, delimiter, ...])	Read .csv file.
read_h5ad(filename[, backed, as_sparse, ...])	Read .h5ad-formatted hdf5 file.
read_10X_output(file_path[, assay])
read_mtx(filename[, dtype])	Read .mtx file.

See more at anndata

Preprocessing

pp.log_transform(adata)	Return the natural logarithm of one plus the input array, element-wise.
pp.normalize(adata[, method, scale_factor, ...])	Normalize count matrix.
pp.cal_qc_rna(adata[, expr_cutoff])	Calculate quality control metrics.
pp.filter_genes(adata[, min_n_cells, ...])	Filter out features based on different metrics.
pp.pca(adata[, n_components, algorithm, ...])	perform Principal Component Analysis (PCA)
pp.select_variable_genes(adata[, layer, ...])	Select highly variable genes.

Tools

tl.dimension_reduction(adata[, n_neighbors, ...])	perform dimension reduction
tl.seed_graph(adata[, obsm, layer, ...])	Seeding the initial elastic principal graph.
tl.learn_graph(adata[, method, obsm, layer, ...])	Learn principal graph.
tl.infer_pseudotime(adata, source[, target, ...])	Infer pseudotime :param adata: Annotated data matrix.
tl.add_path(adata, source, target[, ...])
tl.del_path(adata, source, target[, ...])
tl.get_weights(adata[, obsm, layer, ...])
tl.extend_leaves(adata[, Mode, ControlPar, ...])
tl.refit_graph(adata[, use_weights, ...])	Refit graph to data
tl.project_graph(adata[, to_basis, key])

Plotting

pl.pca_variance_ratio(adata[, log, ...])	Plot the variance ratio.
pl.variable_genes(adata[, show_texts, ...])	Plot highly variable genes.
pl.violin(adata[, list_obs, list_var, ...])	Violin plot.
pl.graph(adata[, comp1, comp2, color, ...])	Plot principal graph.
pl.dimension_reduction(adata[, color, ...])	Plot coordinates in low dimensions.
pl.stream_sc(adata[, source, key, color, ...])	Generate stream plot at single cell level (aka, subway map plots)
pl.stream(adata[, source, key, color, ...])	Generate stream plot at density level