cellarr-se 0.1.0

TileDB-backed SummarizedExperiment using cellarr objects

cellarr-se

cellarr-se is a read-only, out-of-core coordinator for TileDB-backed genomic datasets. It wraps the cellarr-array and cellarr-frame primitives into a lazy, SummarizedExperiment-compatible interface, so you can slice large genomics datasets stored on disk without loading them into memory.

Single-cell and bulk RNA-seq datasets frequently exceed available RAM. cellarr-se keeps assay matrices and metadata tables on disk as TileDB arrays, performing synchronized lazy slices across all components only when you request them. The result is always a standard in-memory SummarizedExperiment object.

Install

pip install cellarr-se

Usage

Construction

CellArraySE wraps existing TileDB arrays and frames; it does not create them. Use cellarr-array and cellarr-frame to build the backing stores first.

from cellarr_se import CellArraySE

se = CellArraySE(
    assays={"counts": my_cell_array, "tpm": my_tpm_array},
    row_data=my_row_frame,   # gene annotations (CellArrayFrame)
    col_data=my_col_frame,   # sample annotations (CellArrayFrame)
)

Inspection

se.shape          # (n_genes, n_samples)
se.assay_names    # ["counts", "tpm"]
se.row_names      # pd.Index of gene identifiers
se.col_names      # pd.Index of sample identifiers
se.row_columns    # list of gene metadata fields
se.col_columns    # list of sample metadata fields

se.show()         # print a summary with the first 5 rows of each metadata table
repr(se)          # <CellArraySE: 20000x500 | counts, tpm>

Slicing

Bracket notation supports integer indices, slices, name strings, and lists:

# Positional slice
subset = se[0:100, 0:50]

# Single element
gene = se[5, 3]

# Lists of indices or names
subset = se[["BRCA1", "TP53"], ["sample_001", "sample_042"]]

For attribute-filtered access, use slice() with TileDB query strings:

# Filter rows and columns by metadata attributes
subset = se.slice(
    row_query="gene_type == 'protein_coding'",
    col_query="tissue == 'liver'",
)

# Combine query with explicit column selection
subset = se.slice(
    row_query="gene_type == 'protein_coding'",
    col_subset=slice(0, 50),
    assays=["counts"],
    row_columns=["gene_id", "gene_name"],
)

Both se[...] and se.slice(...) return a standard in-memory SummarizedExperiment.

Assay metadata

se.is_sparse("counts")        # True if backed by SparseCellArray
se.get_assay_type("counts")   # numpy dtype of the assay

Demo

A worked example covering construction, inspection, and slicing is available in the demo notebook.

Note

This project has been set up using BiocSetup and PyScaffold.