.williams_2018

proteopy.datasets.williams_2018(zero_to_na=False, fill_na=None)[source]

Load Williams 2018 mouse multi-tissue proteomics dataset.

Download, process and format as an AnnData object the peptide-level SWATH-MS dataset from Williams et al. (2018) [1] quantifying protein expression across five tissues in eight genetically diverse BXD mouse strains. Only the whole cell fraction is included; peptide intensities from different charge states are summed per peptide sequence. By default, missing values are represented as np.nan.

Missing values and zeros are kept distinct, because they mean different things:

  • A zero is a measurement, and is preserved as 0.0.

  • A missing value is the absence of one. Charge-state summation propagates it, so a peptide is quantified in a sample only if every one of its charge states was quantified there; a partially measured group yields np.nan rather than a partial total.

Pass zero_to_na=True to treat zeros as missing instead.

Sample annotation (.obs) includes:

  • sample_id: Unique sample identifier

  • tissue: Tissue type (Brain, BAT, Heart, Liver, Quad)

  • mouse_id: BXD mouse strain identifier

Variable annotation (.var) includes:

  • peptide_id: Peptide sequence (matches .var_names)

  • protein_id: UniProt protein identifier

  • gene_id: Gene symbol

Data are sourced from the Elsevier supplementary archive (DOI: 10.1074/mcp.RA118.000554).

Parameters:

  • zero_to_na (bool, optional) – If True, zeros in .X are treated as missing values (NaN). Mutually exclusive with fill_na.

  • fill_na (float | int | None, optional) – If not None, replace np.nan in .X with this value. Mutually exclusive with zero_to_na.

Returns:

AnnData object with peptide-level quantification data. .X contains peptide intensities (samples x peptides).

Return type:

ad.AnnData

Raises:

urllib.error.URLError – If download from the Elsevier CDN fails.

Examples

>>> import proteopy as pr
>>> adata = pr.datasets.williams_2018()
>>> adata
AnnData object with n_obs x n_vars
    obs: 'sample_id', 'tissue', 'mouse_id'
    var: 'peptide_id', 'protein_id', 'gene_id'

References