msmu._stats.target_decoy_q
compute_fdr_q
compute_fdr_q(target_decoy)
Compute FDR and q-values for the picked target-decoy pairs. Args: picked_target_decoy (pd.DataFrame): DataFrame with 'score' and 'is_decoy' Returns: pd.DataFrame: DataFrame with 'is_decoy' and 'q_value' columns.
concat_target_decoy
concat_target_decoy(identification_df, decoy_df)
Concatenate target and decoy DataFrames with an 'is_decoy' column. Args: identification_df (pd.DataFrame): DataFrame containing target identifications. decoy_df (pd.DataFrame): DataFrame containing decoy identifications. Returns: pd.DataFrame: Concatenated DataFrame with 'is_decoy' column.
estimate_q_values
estimate_q_values(identification_df, decoy_df)
Estimate q-values for target and decoy identifications using target-decoy competition.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
identification_df
|
DataFrame
|
DataFrame containing target identifications with 'score' column. |
required |
decoy_df
|
DataFrame
|
DataFrame containing decoy identifications with 'score' column. |
required |
Returns:
| Type | Description |
|---|---|
tuple[DataFrame, DataFrame]
|
tuple[pd.DataFrame, pd.DataFrame]: Tuple of DataFrames (identification_with_q, decoy_with_q) |
retrieve_target_decoy_with_q_values
retrieve_target_decoy_with_q_values(identification_df, decoy_df, q_vals)
Retrieve target and decoy DataFrames with assigned q-values. Args: identification_df (pd.DataFrame): DataFrame containing target identifications. decoy_df (pd.DataFrame): DataFrame containing decoy identifications. q_vals (pd.DataFrame): DataFrame with 'is_decoy' and 'q_value' columns. Returns: tuple[pd.DataFrame, pd.DataFrame]: Tuple of DataFrames (identification_with_q, decoy_with_q)