Small changes for FlashLFQ writer (#131)

Fixed retention time division by 60.
Time is required in minutes for FlashLFQ, it's already in minutues

Co-authored-by: William Fondrie <fondriew@gmail.com>

mokapot/writers/flashlfq.py

@@ -0,0 +1,138 @@
+"""This module writes data in the generic format for FlashLFQ.
+
+Details about the format can be found here:
+https://github.com/smith-chem-wisc/FlashLFQ/wiki/Identification-Input-Formats#generic
+"""
+
+from pathlib import Path
+import logging
+
+import pandas as pd
+
+LOGGER = logging.getLogger(__name__)
+
+
+def to_flashlfq(conf, out_file="mokapot.flashlfq.txt"):
+    """Save confidenct peptides for quantification with FlashLFQ.
+
+    `FlashLFQ <https://github.com/smith-chem-wisc/FlashLFQ>`_ is an open-source
+    tool for label-free quantification. For mokapot to save results in a
+    compatible format, a few extra columns are required to be present, which
+    specify the MS data file name, the theoretical peptide monoisotopic mass,
+    the retention time, and the charge for each PSM. If these are not present,
+    saving to the FlashLFQ format is disabled.
+
+    Note that protein grouping in the FlashLFQ results will be more accurate if
+    proteins were added for analysis with mokapot.
+
+    Parameters
+    ----------
+    conf : Confidence object or tuple of Confidence objects
+        One or more :py:class:`~mokapot.confidence.LinearConfidence` objects.
+    out_file : str, optional
+        The output file to write.
+
+    Returns
+    -------
+    str
+        The path to the saved file.
+
+    """
+    try:
+        assert not isinstance(conf, str)
+        iter(conf)
+    except TypeError:
+        conf = [conf]
+    except AssertionError:
+        raise ValueError("'conf' should be a Confidence object, not a string.")
+
+    flashlfq = pd.concat([_format_flashlfq(c) for c in conf])
+    flashlfq.to_csv(str(out_file), sep="\t", index=False)
+    return out_file
+
+
+def _format_flashlfq(conf):
+    """Format peptides for quantification with FlashLFQ
+
+    If proteins are provided, use the mokapot protein groups. Else,
+    use the protein_column.
+
+    Parameters
+    ----------
+    conf : a LinearConfidence object
+        A :py:class:`~mokapot.confidence.LinearConfidence` object.
+
+    Returns
+    -------
+    pandas.DataFrame
+        The peptides in FlashLFQ format.
+    """
+    # Do some error checking for the required columns:
+    required = ["filename", "calcmass", "rt", "charge"]
+    missing = [c for c in required if conf._optional_columns[c] is None]
+    if missing:
+        missing = ", ".join([c + "_column" for c in missing])
+        raise ValueError(
+            "The following parameters must be specified when loading a "
+            "collection of PSMs in order to save them in FlashLFQ format: "
+            f"{missing}"
+        )
+
+    if conf._has_proteins:
+        proteins = conf._proteins
+    elif conf._protein_column is not None:
+        proteins = conf._protein_column
+    else:
+        proteins = None
+
+    # Get parameters
+    peptides = conf.peptides
+    filename_column = conf._optional_columns["filename"]
+    peptide_column = conf._peptide_column
+    mass_column = conf._optional_columns["calcmass"]
+    rt_column = conf._optional_columns["rt"]
+    charge_column = conf._optional_columns["charge"]
+    eval_fdr = conf._eval_fdr
+
+    # Create FlashLFQ dataframe
+    passing = peptides["mokapot q-value"] <= eval_fdr
+
+    out_df = pd.DataFrame()
+    out_df["File Name"] = peptides.loc[passing, filename_column].apply(
+        lambda x: Path(x).name
+    )
+
+    seq = peptides.loc[passing, peptide_column]
+    base_seq = (
+        seq.str.replace(r"[\[\(].*?[\]\)]", "", regex=True)
+        .str.replace(r"^.*?\.", "", regex=True)
+        .str.replace(r"\..*?$", "", regex=True)
+    )
+
+    out_df["Base Sequence"] = base_seq
+    out_df["Full Sequence"] = seq
+    out_df["Peptide Monoisotopic Mass"] = peptides.loc[passing, mass_column]
+    out_df["Scan Retention Time"] = peptides.loc[passing, rt_column]
+    out_df["Precursor Charge"] = peptides.loc[passing, charge_column]
+
+    if isinstance(proteins, str):
+        # TODO: Add delimiter sniffing.
+        prots = peptides.loc[passing, proteins].str.replace("\t", "; ", regex=False)
+    elif proteins is None:
+        prots = ""
+    else:
+        prots = base_seq.map(proteins.peptide_map.get)
+        shared = pd.isna(prots)
+        prots.loc[shared] = base_seq[shared].map(proteins.shared_peptides.get)
+
+    out_df["Protein Accession"] = prots
+    missing = pd.isna(out_df["Protein Accession"])
+    num_missing = missing.sum()
+    if num_missing:
+        LOGGER.warning(
+            "- Discarding %i peptides that could not be mapped to protein " "groups",
+            num_missing,
+        )
+        out_df = out_df.loc[~missing, :]
+
+    return out_df