acore.drift_correction.cpca_drift_correction module

acore.drift_correction.cpca_drift_correction module#

Functions for metabolomics drift correction by Common Principal Components Analysis (CPCA).

check_missingness(df: DataFrame, rows_to_check: list)[source]#

This function checks for NAs in the data frame inside some user-provided rows.

Parameters:

  • df (samples as rows, features as columns)

  • rows_to_check (list of rows that should be checked for missingness)

Returns:

Boolean

Return type:

True if there is missingness, False if there is no missingness.

run_cpca_drift_correction(df: DataFrame, sample_rows, qc_rows, n_comps: int = 1) → DataFrame[source]#

Corrects technical drift using Common Principal Components Analysis (CPCA). Adapted from m-baralt/metabolomics_incident_diabetes.

Parameters:

  • df (samples as rows, features as columns)

  • sample_rows (list of sample row indices)

  • qc_rows (list of QC row indices)

  • n_comps (number of common principal components to remove (default 1))

Returns:

  • Full input DataFrame with corrected values applied to the intensity rows

  • (sample_rows + qc_rows). Rows outside those arguments are returned unchanged.

cpca_centroid(df: DataFrame, sample_rows, qc_rows: list, log_transform: bool = True)[source]#