SingleCell

class brisc.SingleCell(source=None, /, *, X=None, obs=None, var=None, obsm=None, varm=None, obsp=None, varp=None, uns=None, X_key=None, assay=None, obs_columns=None, var_columns=None, num_threads=-1)

A single-cell dataset.

Has slots for:

• X: a scipy sparse array of counts per cell and gene

• obs: a polars DataFrame of cell metadata

• var: a polars DataFrame of gene metadata

• obsm: a dictionary of NumPy arrays and polars DataFrames of cell metadata

• varm: a dictionary of NumPy arrays and polars DataFrames of gene metadata

• uns: a dictionary of scalars (strings, numbers or Booleans) or NumPy arrays, or nested dictionaries thereof

• num_threads: the default number of threads to use for operations on the dataset that support multithreading (which can be overridden by individual functions)

as well as obs_names and var_names, aliases for obs[:, 0] and var[:, 0].

Parameters:

• source : str | Path | 'AnnData' | None

• X : sparse.csr_array | sparse.csc_array | sparse.csr_matrix | sparse.csc_matrix | Literal[False] | None

• obs : pl.DataFrame | None

• var : pl.DataFrame | None

• obsm : dict[str, np.ndarray | pl.DataFrame] | Literal[False] | None

• varm : dict[str, np.ndarray | pl.DataFrame] | Literal[False] | None

• obsp : dict[str, sparse.csr_array | sparse.csc_array | sparse.csr_matrix | sparse.csc_matrix] | Literal[False] | None

• varp : dict[str, sparse.csr_array | sparse.csc_array | sparse.csr_matrix | sparse.csc_matrix] | Literal[False] | None

• uns : UnsDict | Literal[False] | None

• X_key : str | None

• assay : str | None

• obs_columns : str | Iterable[str]

• var_columns : str | Iterable[str]

• num_threads : int | np.integer

I/O

SingleCell.__init__	Load a SingleCell dataset from a file, or create one from an in-memory AnnData object or count matrix + metadata.
SingleCell.save	Save this SingleCell dataset to a file.
SingleCell.ls	Print the fields in an .h5ad file.
SingleCell.read_obs	Load just obs from an .h5ad file as a polars DataFrame.
SingleCell.read_var	Load just var from an .h5ad file as a polars DataFrame.
SingleCell.read_obsm	Load just obsm from an .h5ad file as a dictionary of Numpy arrays or DataFrames.
SingleCell.read_varm	Load just varm from an .h5ad file as a dictionary of Numpy arrays or DataFrames.
SingleCell.read_obsp	Load just obsp from an .h5ad file as a dictionary of sparse arrays.
SingleCell.read_varp	Load just varp from an .h5ad file as a dictionary of sparse arrays.
SingleCell.read_uns	Load just uns from an .h5ad file as a dictionary.
SingleCell.to_scanpy	Converts this SingleCell dataset to an AnnData object, the representation used by Scanpy.
SingleCell.from_seurat	Create a SingleCell dataset from a Seurat object that has already been loaded into memory via the ryp Python-R bridge.
SingleCell.to_seurat	Convert this SingleCell dataset to a Seurat object in the R workspace of the ryp Python-R bridge.
SingleCell.from_sce	Create a SingleCell dataset from a SingleCellExperiment object that has already been loaded into memory via the ryp Python-R bridge.
SingleCell.to_sce	Convert this SingleCell dataset to a SingleCellExperiment object in the R workspace of the ryp Python-R bridge.

Properties

SingleCell.X	The count matrix, as a sparse array.
SingleCell.obs	A Polars DataFrame of metadata for each cell.
SingleCell.var	A Polars DataFrame of metadata for each gene.
SingleCell.obsm	A dictionary of 2D NumPy arrays, where the length of each array's first dimension is the number of cells.
SingleCell.varm	A dictionary of 2D NumPy arrays, where the length of each array's first dimension is the number of genes.
SingleCell.obsp	A dictionary of 2D sparse arrays, where the length and width of each array is the number of cells.
SingleCell.varp	A dictionary of 2D sparse arrays, where the length and width of each array is the number of genes.
SingleCell.uns	A dictionary of miscellaneous metadata.
SingleCell.obs_names	A shortcut to access the first column of obs.
SingleCell.var_names	A shortcut to access the first column of var.
SingleCell.num_threads	The default number of threads used for this SingleCell dataset's operations.
SingleCell.shape	a length-2 tuple where the first element is the number of cells, and the second is the number of genes.

Data access

SingleCell.cell	Get the row of X corresponding to a single cell, based on the cell's name in obs_names.
SingleCell.gene	Get the column of X corresponding to a single gene, based on the gene's name in var_names.

Manipulation

SingleCell.set_obs_names	Sets a column as the new first column of obs, i.e. the obs_names.
SingleCell.set_var_names	Sets a column as the new first column of var, i.e. the var_names.
SingleCell.set_num_threads	Return a new SingleCell dataset with a different default number of threads.
SingleCell.make_obs_names_unique	Make obs_names unique by appending '-1' to the second occurence of a given name, '-2' to the third occurrence, and so on, where '-' can be switched to a different string via the separator argument.
SingleCell.make_var_names_unique	Make var_names unique by appending '-1' to the second occurence of a given name, '-2' to the third occurrence, and so on, where '-' can be switched to a different string via the separator argument.
SingleCell.filter_obs	Equivalent to df.filter() from polars, but applied to both obs/obsm and X.
SingleCell.filter_var	Equivalent to df.filter() from polars, but applied to both var/varm and X.
SingleCell.select_obs	Equivalent to df.select() from polars, but applied to obs.
SingleCell.select_var	Equivalent to df.select() from polars, but applied to var.
SingleCell.select_obsm	Subsets obsm to the specified key(s).
SingleCell.select_varm	Subsets varm to the specified key(s).
SingleCell.select_obsp	Subsets obsp to the specified key(s).
SingleCell.select_varp	Subsets varp to the specified key(s).
SingleCell.select_uns	Subsets uns to the specified key(s).
SingleCell.with_columns_obs	Equivalent to df.with_columns() from polars, but applied to obs.
SingleCell.with_columns_var	Equivalent to df.with_columns() from polars, but applied to var.
SingleCell.with_obsm	Adds one or more keys to obsm, overwriting existing keys with the same names if present.
SingleCell.with_varm	Adds one or more keys to varm, overwriting existing keys with the same names if present.
SingleCell.with_obsp	Adds one or more keys to obsp, overwriting existing keys with the same names if present.
SingleCell.with_varp	Adds one or more keys to varp, overwriting existing keys with the same names if present.
SingleCell.with_uns	Adds one or more keys to uns, overwriting existing keys with the same names if present.
SingleCell.drop_X	Create a new SingleCell dataset with X removed, to reduce memory use.
SingleCell.drop_obs	Create a new SingleCell dataset with columns and more_columns removed from obs.
SingleCell.drop_var	Create a new SingleCell dataset with columns and more_columns removed from var.
SingleCell.drop_obsm	Create a new SingleCell dataset with keys and more_keys removed from obsm.
SingleCell.drop_varm	Create a new SingleCell dataset with keys and more_keys removed from varm.
SingleCell.drop_obsp	Create a new SingleCell dataset with keys and more_keys removed from obsp.
SingleCell.drop_varp	Create a new SingleCell dataset with keys and more_keys removed from varp.
SingleCell.drop_uns	Create a new SingleCell dataset with keys and more_keys removed from uns.
SingleCell.rename_obs	Create a new SingleCell dataset with column(s) of obs renamed.
SingleCell.rename_var	Create a new SingleCell dataset with column(s) of var renamed.
SingleCell.rename_obsm	Create a new SingleCell dataset with key(s) of obsm renamed.
SingleCell.rename_varm	Create a new SingleCell dataset with key(s) of varm renamed.
SingleCell.rename_obsp	Create a new SingleCell dataset with key(s) of obsp renamed.
SingleCell.rename_varp	Create a new SingleCell dataset with key(s) of varp renamed.
SingleCell.rename_uns	Create a new SingleCell dataset with key(s) of uns renamed.
SingleCell.cast_X	Cast X to the specified data type.
SingleCell.cast_obs	Cast column(s) of obs to the specified data type(s).
SingleCell.cast_var	Cast column(s) of var to the specified data type(s).
SingleCell.join_obs	Left-join obs with another DataFrame, using the same logic as polars.DataFrame.join().
SingleCell.join_var	Left-join var with another DataFrame, using the same logic as polars.DataFrame.join().
SingleCell.subsample_obs	Subsample a specific number or fraction of cells.
SingleCell.subsample_var	Subsample a specific number or fraction of genes.
SingleCell.tocsr	Make a copy of this SingleCell dataset, converting X to a csr_array.
SingleCell.tocsc	Make a copy of this SingleCell dataset, converting X to a csc_array.

Structural

SingleCell.copy	Make a copy of this SingleCell dataset.
SingleCell.concat_obs	Concatenate one or more other SingleCell datasets with this one, cell-wise.
SingleCell.concat_var	Concatenate one or more other SingleCell datasets with this one, gene-wise.
SingleCell.split_by_obs	The opposite of concat_obs(): splits a SingleCell dataset into a dictionary of SingleCell datasets, one per unique value of a column of obs.
SingleCell.split_by_var	The opposite of concat_var(): splits a SingleCell dataset into a dictionary of SingleCell datasets, one per unique value of a column of var.

Analysis

SingleCell.qc_metrics	Adds quality-control metrics to obs for each cell: the sum of counts across all genes (num_counts), the number of genes with non-zero expression (num_genes), and the fraction of counts that are mitochondrial (mito_fraction).
SingleCell.qc	Adds a Boolean column to obs indicating which cells passed quality control (QC), or subsets to these cells if subset=True.
SingleCell.find_doublets	Find doublets using cxds (co-expression-based doublet scoring).
SingleCell.get_sample_covariates	Get a DataFrame of sample-level covariates, i.e. the columns of obs that are the same for all cells within each sample.
SingleCell.pseudobulk	Pseudobulk a SingleCell dataset with sample ID and cell type columns.
SingleCell.hvg	Select highly variable genes using the same approach as Seurat.
SingleCell.normalize	Normalize this SingleCell dataset's counts.
SingleCell.pca	Compute principal components (PCs) across cells.
SingleCell.neighbors	Calculate the num_neighbors nearest neighbors of each cell.
SingleCell.shared_neighbors	Calculate the shared nearest neighbor graph of this dataset's cells.
SingleCell.harmonize	Harmonize this SingleCell dataset with other datasets, or harmonize multiple batches of the same dataset, with Harmony2.
SingleCell.cluster	Cluster cells into cell types using Leiden clustering.
SingleCell.label_transfer_from	Transfer cell-type labels from another dataset to this one, using the two datasets' Harmony embeddings from harmonize().
SingleCell.umap	Calculate a two-dimensional embedding of this SingleCell dataset with UMAP (Uniform Manifold Approximation and Projection), suitable for plotting with plot_embedding().
SingleCell.pacmap	Calculate a two-dimensional embedding of this SingleCell dataset suitable for plotting with plot_embedding().
SingleCell.localmap	Calculate a two-dimensional embedding of this SingleCell dataset suitable for plotting with plot_embedding().
SingleCell.find_markers	Find "marker genes" that distinguish each cell type from all other cell types.
SingleCell.plot_heatmap	Plot a heatmap of the count of each combination of two categorical columns, x and y.
SingleCell.plot_markers	Make a dot plot of a set of marker genes of interest across cell types.
SingleCell.plot_umap	Plot a UMAP embedding created with umap().
SingleCell.plot_pacmap	Plot a PaCMAP embedding created with pacmap().
SingleCell.plot_localmap	Plot a LocalMAP embedding created with localmap().
SingleCell.plot_embedding	Plot the specified 2D embedding.

Utility

SingleCell.skip_qc	Skips QC, but allows the dataset to be used by downstream functions that require QCed data.
SingleCell.peek_obs	Print a row of obs (the first row, by default) with each column on its own line.
SingleCell.peek_var	Print a row of var (the first row, by default) with each column on its own line.
SingleCell.pipe	Apply a function to a SingleCell dataset.
SingleCell.pipe_X	Apply a function to a SingleCell dataset's X.
SingleCell.pipe_obs	Apply a function to a SingleCell dataset's obs.
SingleCell.pipe_var	Apply a function to a SingleCell dataset's var.
SingleCell.pipe_obsm	Apply a function to a SingleCell dataset's obsm.
SingleCell.pipe_obsm_key	Apply a function to a specific key in a SingleCell dataset's obsm.
SingleCell.pipe_varm	Apply a function to a SingleCell dataset's varm.
SingleCell.pipe_varm_key	Apply a function to a specific key in a SingleCell dataset's varm.
SingleCell.pipe_obsp	Apply a function to a SingleCell dataset's obsp.
SingleCell.pipe_obsp_key	Apply a function to a specific key in a SingleCell dataset's obsp.
SingleCell.pipe_varp	Apply a function to a SingleCell dataset's varp.
SingleCell.pipe_varp_key	Apply a function to a specific key in a SingleCell dataset's varp.
SingleCell.pipe_uns	Apply a function to a SingleCell dataset's uns.
SingleCell.pipe_uns_key	Apply a function to a specific key in a SingleCell dataset's uns.