dvpio.read.omics.read_precursor_table

dvpio.read.omics.read_precursor_table(path, reader_type, *, intensity_column=None, protein_id_column=None, raw_name_column=None, reader_kwargs=None, **kwargs)

Warning: This function is experimental and may change in future versions

Parse proteomics precursor reports to the anndata.AnnData format

Supported formats include

• AlphaDIA alphadia_parquet (.parquet) alphadia_tsv (.tsv)

• DIANN diann (.tsv)

• MaxQuant

• MSFragger msfragger

• Sage sage_parquet (.parquet), sage_tsv (.tsv)

• Spectronaut

see dvpio.read.omics.available_reader() for a complete list

Parameters:

• path (str) – Path to proteomics report

• reader_type (str) – Name of engine output, pass the method name of the corresponding reader. You can list all available readers with the dvpio.read.omics.available_reader() helper function

• intensity_column (str | None (default: None)) – Column name of precursor intensity in report

• protein_id_column (str | None (default: None)) – Column name of feature (i.e. protein group) in report

• raw_name_column (str | None (default: None)) – Column names of individual samples in report.

• reader_kwargs (Mapping[str, Any] | None (default: None)) – Optional keyword arguments passed to alphabase.psm_reader.psm_reader.PSMReaderBase

• kwargs (Any) – Passed to spatialdata.models.TableModel.parse()

Return type:

AnnData

Returns:

anndata.AnnData AnnData object that can be further processed with scVerse packages.

• adata.X

  Stores values of the intensity_column argument the report as sparse matrix of shape observations x features

• adata.obs

  Stores observations

• adata.var

  Stores features

Example

from dvpio.io.read.omics import read_report, available_reader

print(available_reader())
> ['alphadia', 'alphadia_parquet', 'alphapept', 'diann', 'maxquant', ...]

path = ...
adata = read_precursor_table(
    path,
    reader_type="diann",
    intensity_column="Precursor.Normalised",
    raw_name_column="File.Name",
    protein_id_column="Protein.Names"
)