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Analysis table#

The hand-off between preprocessing and statistics: one row per sample, one column per assay.

to_wide#

Turn the long NPX table into the analysis table.

to_wide(npx, *, values="NPX", verbose=True) -> pandas.DataFrame
Argument Type Default Meaning
npx DataFrame — the preprocessed long table (from prepare, or after add_sample_info)
values str "NPX" "NPX": the NPX values; "below_lod": True where the value is below the LOD, for compare_groups(below_lod=...)
verbose bool True print the report

What it does

  • control samples and control assays are left out;
  • a sample measured more than once (a bridge sample, or a repeat) gets the average of its NPX values, so every sample counts once;
  • each assay becomes one column, named after the assay; an assay name used by more than one OlinkID (the same assay on several Explore panels) gets the OlinkID added, e.g. IL6 (OID20101);
  • the columns of your sample metadata come first.

Output. A table with one row per sample (index sample, the tidy sample ID). The assay columns are listed in table.attrs["proteins"], so the statistics functions know which columns hold NPX values; the report is in table.attrs["wide_report"].

Errors. ValueError without an NPX column, for values="below_lod" without a LOD column, or when a sample-metadata column has the name of an assay.

Example

table = so.to_wide(so.prepare([so.example_file(1), so.example_file(2)],
                                so.example_samples(), on="sample_id"))

Analysis table
  averaged: 12 samples measured more than once (bridge samples or repeats) get the average of their NPX values
  result: 59 samples x 92 assays, plus 4 columns of sample metadata (sample_id, group, age, sex)

The lesson: Analysis table.