Compute TPA and protein concentration

bin/compute_tpa.py

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+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import click
+import pandas as pd
+from pandas import DataFrame, Series
+from pyopenms import *
+
+from bin.compute_ibaq import print_help_msg, parse_uniprot_name
+from ibaq.ibaqpy_commons import PROTEIN_NAME, NORM_INTENSITY, SAMPLE_ID, CONDITION
+from ibaq.ibaqpy_commons import plot_distributions, plot_box_plot
+import numpy as np
+import os
+
+@click.command()
+@click.option("-f", "--fasta", help="Protein database")
+@click.option("-p", "--peptides", help="Peptide identifications with intensities following the peptide intensity output")
+@click.option("-r", "--ruler", help="Whether to use proteomicRuler", default=True)
+@click.option("-n", "--ploidy", help="ploidy number", default=2)
+@click.option("-c", "--cpc", help="cellular protein concentration(g/L)", default=200)
+@click.option("-o", "--output", help="Output file with the proteins and ibaq values")
+def tpa_compute(fasta: str, peptides: str, ruler: bool, ploidy: int, cpc: float, output: str) -> None:
+    """
+    This command computes the protein copies and concentrations according to a file output of peptides with the
+    format described in peptide_contaminants_file_generation.py.
+    :param fasta: Fasta file used to perform the peptide identification
+    :param peptides: Peptide intensity file.
+    :param ruler: Whether to compute protein copies, weight and concentration.
+    :param ploidy: ploidy number.
+    :param cpc: cellular protein concentration(g/L).
+    :param output: output format containing the ibaq values.
+    :return:
+    """
+    if peptides is None or fasta is None:
+        print_help_msg(ibaq_compute)
+        exit(1)
+
+    data = pd.read_csv(peptides, sep=",")
+    print(data.head())
+
+    res = pd.DataFrame(data.groupby([PROTEIN_NAME, SAMPLE_ID, CONDITION])[NORM_INTENSITY].sum())
+    res = res.reset_index()
+    proteins = res["ProteinName"].unique().tolist()
+    proteins = sum([i.split(";") for i in proteins], [])
+
+    # calculate molecular weight of quantified proteins
+    mw_dict = dict()
+    fasta_proteins = list()  # type: list[FASTAEntry]
+    FASTAFile().load(fasta, fasta_proteins)
+    for entry in fasta_proteins:
+        accession, name = entry.identifier.split("|")[1:]
+        if name in proteins:
+            mw = AASequence().fromString(entry.sequence).getMonoWeight()
+            mw_dict[name] = mw
+
+    # calculate TPA for every protein group
+    def get_protein_group_mw(group: str) -> float:
+        mw_list = [mw_dict[i] for i in group.split(";")]
+        return sum(mw_list)
+
+    res["MolecularWeight"] = res.apply(lambda x: get_protein_group_mw(x["ProteinName"]), axis=1)
+    res["MolecularWeight"] = res["MolecularWeight"].fillna(1)
+    res["MolecularWeight"] = res["MolecularWeight"].replace(0, 1)
+    res["TPA"] = res["NormIntensity"] / res["MolecularWeight"]
+    plot_distributions(res, "TPA", SAMPLE_ID, log2=True)
+    plot_box_plot(res, "TPA", SAMPLE_ID, log2=True,
+                    title="TPA Distribution", violin=False)
+
+    # calculate protein weight(ng) and concentration(nM)
+    if ruler:
+        avogadro = 6.02214129e23
+        average_base_pair_mass = 617.96 # 615.8771
+
+        organism = res.loc[0, "ProteinName"].split("_")[1].lower()
+        histone_df = pd.read_json(open(os.path.split(__file__)[0] + os.sep + "histones.json", "rb")).T
+        target_histones = histone_df[histone_df["name"] == organism.lower()]
+        genome_size = target_histones["genome_size"].values[0]
+        histones_list = target_histones["histone_entries"].values[0]
+
+        dna_mass = ploidy * genome_size * average_base_pair_mass / avogadro
+
+        def calculate(protein_intensity, histone_intensity, mw):
+            copy = (protein_intensity / histone_intensity) * dna_mass * avogadro / mw
+            # The number of moles is equal to the number of particles divided by Avogadro's constant
+            moles = copy * 1e9 / avogadro # unit nmol
+            weight = moles * mw # unit ng
+            return tuple([copy, moles, weight])
+
+        def proteomicRuler(df):
+            histone_intensity = df[df["ProteinName"].isin(histones_list)]["NormIntensity"].sum()
+            histone_intensity = histone_intensity if histone_intensity > 0 else 1
+            df[["Copy", "Moles[nmol]", "Weight[ng]"]] = df.apply(lambda x: calculate(x["NormIntensity"], histone_intensity, x["MolecularWeight"]), axis = 1, result_type="expand")
+            volume = df["Weight[ng]"].sum() * 1e-9 / cpc # unit L
+            df["Concentration[nM]"] = df["Moles[nmol]"] / volume # unit nM
+            return df
+
+        res = res.groupby(["Condition"]).apply(proteomicRuler)
+
+        plot_distributions(res, "Concentration[nM]", SAMPLE_ID, log2=True)
+        plot_box_plot(res, "Concentration[nM]", SAMPLE_ID, log2=True,
+                        title="Concentration[nM] Distribution", violin=False)
+        res.to_csv(output, index=False)
+
+if __name__ == '__main__':
+    tpa_compute()