pyproteonet.metrics.differential_expression.find_des

pyproteonet.metrics.differential_expression.find_des(dataset: Dataset, molecule: str, columns: str | List[str], nominator_samples: List[str], denominator_samples: List[str], max_pvalue=0.05, min_fc=2, is_log: bool = False) → Tuple[DataFrame, DataFrame, DataFrame]

Find differentially expressed molecules usign a t-test and multitest correction (benjamin hochberg).

Parameters:

  • dataset (Dataset) – The dataset to find differentially expressed molecules in.

  • molecule (str) – The molecule type to find differentially expressed molecules for.

  • columns (Union[str, List[str]]) – The value column(s) containing the abundance values of potentially differentially expressed molecules.

  • nominator_samples (List[str]) – List of samples names to use as nominator when computing fold change.

  • denominator_samples (List[str]) – List of samples names to use as denominator when computing fold change.

  • max_pvalue (float, optional) – P value to as significance threshold . Defaults to 0.05.

  • min_fc (int, optional) – Minimum fold change required to be considered as differentially expressed. Works for both increase and decrease in abundance (e.g. a min. fold change of 2 results in both a fold change of 2 and 0.5 being considered as potentially differentially expressed.). Defaults to 2.

  • is_log (bool, optional) – Whether the column values are logarithmized. Defaults to False.

Returns:

Tuple of three dataframes representing differential expressions, p-values and fold changes.

Return type:

Tuple(pd.DataFrame, pd.DataFrame, pd.DataFrame)