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benchmark_cv.py
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(font="Arial")
sns.set_theme(style="ticks")
palette_dict = {
"publication": sns.color_palette()[9],
"diaumpire": sns.color_palette()[6],
"diaumpire hybrid": sns.color_palette()[8],
"spectronaut": sns.color_palette()[4],
"encyclopedia": sns.color_palette()[5],
"diann": sns.color_palette()[3],
"maxdia": sns.color_palette()[2],
"msfraggerdia": sns.color_palette()[0],
"msfragger hybrid": sns.color_palette()[1]
}
def pick_normalize_ecoli_precursors(aa, annotation2_path, is_spectronaut):
if is_spectronaut:
annotation2 = pd.read_csv(annotation2_path, sep="\t", index_col=None, na_values="NA", header=0, usecols=["PG.ProteinGroups", "PEP.StrippedSequence", "EG.ModifiedSequence", "FG.Charge"])
annotation2["Precursor.Id"] = annotation2["EG.ModifiedSequence"] + annotation2["FG.Charge"].apply(str)
annotation2.rename(columns={"PG.ProteinGroups": "Protein.Group", "PEP.StrippedSequence": "Stripped.Sequence"}, inplace=True)
else:
annotation2 = pd.read_csv(annotation2_path, sep="\t", index_col=None, na_values="NA", header=0, usecols=["Precursor.Id", "Protein.Group", "Stripped.Sequence"])
annotation2.drop_duplicates(subset="Precursor.Id", keep="first", inplace=True)
annotation2 = annotation2.loc[:, ["Precursor.Id", "Protein.Group", "Stripped.Sequence"]]
annotation2["Protein.Group"] = annotation2["Protein.Group"].str.split(";", expand=True, regex=False)[0]
annotation2 = annotation2.merge(annotation, how="left", left_on="Protein.Group", right_on="Entry")
annotation2.set_index("Precursor.Id", drop=True, inplace=True)
aa.index.name = "Precursor.Id"
aa = aa.merge(annotation2, how="left", on="Precursor.Id")
aa = aa[aa["Organism"] == "Escherichia coli (strain K12)"]
aa = aa[~aa["Stripped.Sequence"].isin(human_ecoli_overlap_peptide)]
aa.drop(["Protein.Group", "Stripped.Sequence", "Organism"], axis=1, inplace=True)
median_column = aa.median(axis=0, skipna=True)
median_all = median_column.median(axis=0, skipna=True)
aa = aa * median_all / median_column
return aa
def translate_spectronaut_peptides(aa):
aa.index = aa.index.str.\
replace("[Acetyl (Protein N-term)]", "(UniMod:1)", regex=False).str.\
replace("[Oxidation (M)]", "(UniMod:35)", regex=False).str.\
replace("[Carbamidomethyl (C)]", "(UniMod:4)", regex=False).str.\
replace("[Phospho (STY)]", "(UniMod:21)", regex=False).str.\
replace("_", "", regex=False).str. \
replace(".", "", regex=False)
return aa
def process(input_df, tool_name):
xx = input_df.dropna(axis=0, thresh=4)
input_cv = pd.DataFrame({"cv": np.nanstd(xx, 1) * 100 / np.nanmean(xx, 1), "tool": tool_name})
return input_cv
os.chdir(r"G:\Dropbox\papers_Fengchao\msfragger_dia\script\results\benchmark")
annotation = pd.read_csv(r"G:\Dropbox\papers_Fengchao\msfragger_dia\script\code\MSFragger-DIA-manuscript\uniprot.tab", sep="\t", index_col=0, na_values="NA", header=0, usecols=["Entry", "Organism"])
human_ecoli_overlap_peptide = []
with open("human_ecoli_overlap_peptides.txt") as f:
for line in f.readlines():
human_ecoli_overlap_peptide.append(line.strip())
# precursor level
spectronaut_14_path = r"spectronaut\14\\"
spectronaut_17_path = r"spectronaut\17\\"
diann_path = r"diann\\"
msfraggerdia_path = r"msfraggerdia\\"
msfraggerdia_hybrid_path = r"msfraggerdiadda\\"
spectronaut_14 = pd.read_csv(spectronaut_14_path + "precursor_maxlfq.tsv", sep="\t", index_col=0, na_values="NA", header=0)
spectronaut_17 = pd.read_csv(spectronaut_17_path + "precursor_maxlfq.tsv", sep="\t", index_col=0, na_values="NA", header=0)
diann = pd.read_csv(diann_path + "precursor_maxlfq.tsv", sep="\t", index_col=0, na_values="NA", header=0)
msfraggerdia = pd.read_csv(msfraggerdia_path + "precursor_maxlfq.tsv", sep="\t", index_col=0, na_values="NA", header=0)
msfraggerdia_hybrid = pd.read_csv(msfraggerdia_hybrid_path + "precursor_maxlfq.tsv", sep="\t", index_col=0, na_values="NA", header=0)
spectronaut_14.dropna(how="all", inplace=True)
spectronaut_17.dropna(how="all", inplace=True)
diann.dropna(how="all", inplace=True)
msfraggerdia.dropna(how="all", inplace=True)
msfraggerdia_hybrid.dropna(how="all", inplace=True)
spectronaut_14 = pick_normalize_ecoli_precursors(spectronaut_14, spectronaut_14_path + "20230227_181426_directDIA_LymphEcoli_Report.tsv", True)
spectronaut_17 = pick_normalize_ecoli_precursors(spectronaut_17, spectronaut_17_path + "20230227_180032_SN17_RealDilutionSeries_Report.tsv", True)
diann = pick_normalize_ecoli_precursors(diann, diann_path + "diann-output.tsv", False)
msfraggerdia = pick_normalize_ecoli_precursors(msfraggerdia, msfraggerdia_path + "diann-output.tsv", False)
msfraggerdia_hybrid = pick_normalize_ecoli_precursors(msfraggerdia_hybrid, msfraggerdia_hybrid_path + "diann-output.tsv", False)
spectronaut_14 = translate_spectronaut_peptides(spectronaut_14)
spectronaut_17 = translate_spectronaut_peptides(spectronaut_17)
spectronaut_14.drop_duplicates(keep="first", inplace=True)
spectronaut_17.drop_duplicates(keep="first", inplace=True)
diann.drop_duplicates(keep="first", inplace=True)
msfraggerdia.drop_duplicates(keep="first", inplace=True)
msfraggerdia_hybrid.drop_duplicates(keep="first", inplace=True)
conditions = ["1-06"]
condition_patterns = [r"Lymph_Ecoli_1-6_\d+"]
for idx, p in enumerate(condition_patterns):
spectronaut_14_sub = spectronaut_14.filter(regex=p, axis=1)
spectronaut_17_sub = spectronaut_17.filter(regex=p, axis=1)
diann_sub = diann.filter(regex=p, axis=1)
msfraggerdia_sub = msfraggerdia.filter(regex=p, axis=1)
msfraggerdia_hybrid_sub = msfraggerdia_hybrid.filter(regex=p, axis=1)
spectronaut_14_cv = process(spectronaut_14_sub, "Spectronaut\n14")
spectronaut_17_cv = process(spectronaut_17_sub, "Spectronaut\n17")
diann_cv = process(diann_sub, "DIA-NN\nlib-free")
msfraggerdia_cv = process(msfraggerdia_sub, "FP-MSF")
msfraggerdia_hybrid_cv = process(msfraggerdia_hybrid_sub, "FP-MSF hybrid")
x = pd.concat([spectronaut_14_cv, spectronaut_17_cv, diann_cv, msfraggerdia_cv, msfraggerdia_hybrid_cv])
plt.figure(figsize=(6, 6), dpi=300)
sns_plot = sns.violinplot(x="tool", y="cv", data=x, inner=None, linewidth=0.5, width=1, cut=0, palette=[palette_dict["spectronaut"], palette_dict["spectronaut"], palette_dict["diann"], palette_dict["maxdia"], palette_dict["msfraggerdia"]])
sns.boxplot(x="tool", y="cv", data=x, showfliers=False, width=0.3, linewidth=1, boxprops={'zorder': 2, 'facecolor': 'white'}, ax=sns_plot)
sns_plot.set_ylim([0, 100])
sns_plot.set(xlabel=None, ylabel="coefficient of variation (%)")
sns_plot.figure.savefig("benchmark_cv_precursor_" + conditions[idx] + ".pdf")