mirror of
https://github.com/chanzuckerberg/cellxgene.git
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e2d864a336
* move prepare into main CLI as subcommand * naming and formatting tweaks to better match launch subcommand
161 lines
6.4 KiB
Python
161 lines
6.4 KiB
Python
import click
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from numpy import unique, ndarray
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from scipy.sparse.csc import csc_matrix
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from os.path import isfile, isdir, splitext, expanduser, sep
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@click.command()
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@click.argument('data', nargs=1, metavar='<dataset: file or path to data>', required=True)
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@click.option('--layout', '-l', default=['umap', 'tsne'], multiple=True, type=click.Choice(['umap', 'tsne']),
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help='Layout algorithm', show_default=True)
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@click.option('--recipe', '-r', default='none', type=click.Choice(['none', 'seurat', 'zheng17']),
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help='Preprocessing to run.', show_default=True)
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@click.option('--output', '-o', default='', help='Save a new file to filename.', metavar='<filename>')
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@click.option('--plotting', '-p', default=False, is_flag=True, help='Whether to generate plots.', show_default=True)
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@click.option('--sparse', default=False, is_flag=True, help='Whether to force sparsity.', show_default=True)
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@click.option('--overwrite', default=False, is_flag=True, help='Allow file overwriting.', show_default=True)
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@click.option('--set-obs-names', default='', help='Named field to set as index for obs.', metavar='<name>')
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@click.option('--set-var-names', default='', help='Named field to set as index for var.', metavar='<name>')
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@click.option('--make-obs-names-unique', default=True, is_flag=True,
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help='Ensure obs index is unique.', show_default=True)
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@click.option('--make-var-names-unique', default=True, is_flag=True,
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help='Ensure var index is unique.', show_default=True)
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def prepare(data, layout, recipe, output, plotting, sparse, overwrite,
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set_obs_names, set_var_names, make_obs_names_unique, make_var_names_unique):
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"""Preprocesses data for use with cellxgene.
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This tool runs a series of scanpy routines for preparing a dataset
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for use with cellxgene. It loads data from different formats
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(h5ad, loom, or a 10x directory), runs dimensionality reduction,
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computes nearest neighbors, computes a layout, performs clustering,
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and saves the results. Includes additional options for naming
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annotations, ensuring sparsity, and plotting results."""
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# collect slow imports here to make CLI startup more responsive
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click.echo('[cellxgene] Starting CLI...')
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import matplotlib
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matplotlib.use('Agg')
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import scanpy.api as sc
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# scanpy settings
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sc.settings.verbosity = 0
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sc.settings.autosave = True
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# check args
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if sparse and not recipe == 'none':
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raise click.UsageError('Cannot use a recipe when forcing sparsity')
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output = expanduser(output)
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if isfile(output) and not overwrite:
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raise click.UsageError(f'Cannot overwrite existing file {output}, try using the flag --overwrite')
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def load_data(data):
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if isfile(data):
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name, extension = splitext(data)
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if extension == '.h5ad':
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adata = sc.read_h5ad(data)
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elif extension == '.loom':
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adata = sc.read_loom(data)
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else:
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raise click.FileError(data, hint='does not have a valid extension [.h5ad | .loom]')
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elif isdir(data):
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if not data.endswith(sep):
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data += sep
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adata = sc.read_10x_mtx(data)
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else:
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raise click.FileError(data, hint='not a valid file or path')
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if not set_obs_names == '':
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if set_obs_names not in adata.obs_keys():
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raise click.UsageError(f'obs {set_obs_names} not found, options are: {adata.obs_keys()}')
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adata.obs_names = adata.obs[set_obs_names]
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if not set_var_names == '':
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if set_var_names not in adata.var_keys():
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raise click.UsageError(f'var {set_var_names} not found, options are: {adata.var_keys()}')
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adata.var_names = adata.var[set_var_names]
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if make_obs_names_unique:
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adata.obs_names_make_unique()
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if make_var_names_unique:
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adata.var_names_make_unique()
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if not adata._obs.index.is_unique:
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click.echo('Warning: obs index is not unique')
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if not adata._var.index.is_unique:
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click.echo('Warning: var index is not unique')
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return adata
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def make_sparse(adata):
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if (type(adata.X) is ndarray) and sparse:
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adata.X = csc_matrix(adata.X)
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def run_recipe(adata):
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if recipe == 'seurat':
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sc.pp.recipe_seurat(adata)
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elif recipe == 'zheng17':
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sc.pp.recipe_zheng17(adata)
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else:
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sc.pp.filter_cells(adata, min_genes=5)
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sc.pp.filter_genes(adata, min_cells=25)
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if sparse:
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sc.pp.scale(adata, zero_center=False)
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else:
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sc.pp.scale(adata)
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def run_pca(adata):
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if sparse:
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sc.pp.pca(adata, svd_solver='arpack', zero_center=False)
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else:
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sc.pp.pca(adata, svd_solver='arpack')
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def run_neighbors(adata):
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sc.pp.neighbors(adata)
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def run_louvain(adata):
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sc.tl.louvain(adata)
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def run_layout(adata):
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if len(unique(adata.obs['louvain'].values)) < 10:
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palette = 'tab10'
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else:
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palette = 'tab20'
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if 'umap' in layout:
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sc.tl.umap(adata)
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if plotting:
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sc.pl.umap(adata, color='louvain', palette=palette, save='_louvain')
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if 'tsne' in layout:
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sc.tl.tsne(adata)
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if plotting:
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sc.pl.tsne(adata, color='louvain', palette=palette, save='_louvain')
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def show_step(item):
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names = {
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'make_sparse': 'Ensuring sparsity',
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'run_recipe': 'Running preprocessing recipe "%s"' % recipe,
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'run_pca': 'Running PCA',
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'run_neighbors': 'Calculating neighbors',
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'run_louvain': 'Calculating clusters',
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'run_layout': 'Computing layout'
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}
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if item is not None:
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return names[item.__name__]
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steps = [make_sparse, run_recipe, run_pca, run_neighbors, run_louvain, run_layout]
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click.echo(f'[cellxgene] Loading data from {data}, please wait...')
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adata = load_data(data)
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click.echo('[cellxgene] Beginning preprocessing...')
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with click.progressbar(steps, label='[cellxgene] Progress', show_eta=False, item_show_func=show_step) as bar:
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for step in bar:
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step(adata)
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# saving
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if not output == '':
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click.echo(f'[cellxgene] Saving results to {output}...')
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adata.write(output)
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click.echo('[cellxgene] Success!')
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