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.AnnDataformatSupported formats include
AlphaDIA
alphadia_parquet(.parquet)alphadia_tsv(.tsv)DIANN
diann(.tsv)MaxQuant
MSFragger
msfraggerSage
sage_parquet(.parquet),sage_tsv(.tsv)Spectronaut
see
dvpio.read.omics.available_reader()for a complete list- Parameters:
path (
str) – Path to proteomics reportreader_type (
str) – Name of engine output, pass the method name of the corresponding reader. You can list all available readers with thedvpio.read.omics.available_reader()helper functionintensity_column (
Optional[str] (default:None)) – Column name of precursor intensity in reportprotein_id_column (
Optional[str] (default:None)) – Column name of feature (i.e. protein group) in reportraw_name_column (
Optional[str] (default:None)) – Column names of individual samples in report.reader_kwargs (
Optional[Mapping[str,Any]] (default:None)) – Optional keyword arguments passed toalphabase.psm_reader.psm_reader.PSMReaderBasekwargs (
Any) – Passed tospatialdata.models.TableModel.parse()
- Return type:
- Returns:
anndata.AnnDataAnnData object that can be further processed with scVerse packages.- adata.X
Stores values of the
intensity_columnargument 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" )