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runtime.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 pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(font="Arial")
sns.set_theme(style="ticks")
os.chdir(r"G:\Dropbox\papers_Fengchao\msfragger_dia\script\results")
# ccrcc 20 desktop
diaumpire = [254, 17, 23, 10] # dia-umpire, msfragger, msbooster+percolator+filtering+easypqp, dia-nn
diann = [30, 332-30] # prediction, others
maxdia = [1255]
msfraggerdia = [65, 50, 10] # msfragger, msbooster+percolator+filtering+easypqp, dia-nn
xx = pd.DataFrame({"tool": ["FP-DIAU", "DIA-NN lib-free", "MaxDIA", "FP-MSF"],
"spectral library prediction": [0, diann[0], 0, 0],
"identification and quantification": [0, diann[1], 0, 0],
"pseudo-MS/MS generation": [diaumpire[0], 0, 0, 0],
"database searching": [diaumpire[1], 0, 0, msfraggerdia[0]],
"rescoring and FDR filtering": [diaumpire[2], 0, 0, msfraggerdia[1]],
"quantification": [diaumpire[3], 0, 0, msfraggerdia[2]],
"MaxDIA": [0, 0, maxdia[0], 0]
})
fig = xx.set_index("tool").plot(kind="bar", stacked=True, xlabel="", ylabel="run time (minutes)", linewidth=0).get_figure()
plt.xticks(rotation=0)
fig.savefig("runtime_ccrcc_20_desktop.pdf")
plt.figure()
# ccrcc 20 dev02
diaumpire = [292, 15, 34, 17] # dia-umpire, msfragger, msbooster+percolator+filtering+easypqp, dia-nn
diann = [10, 194] # prediction, others
maxdia = [550]
msfraggerdia = [43, 47, 18] # msfragger, msbooster+percolator+filtering+easypqp, dia-nn
xx = pd.DataFrame({"tool": ["FP-DIAU", "DIA-NN lib-free", "MaxDIA", "FP-MSF"],
"spectral library prediction": [0, diann[0], 0, 0],
"identification and quantification": [0, diann[1], 0, 0],
"pseudo-MS/MS generation": [diaumpire[0], 0, 0, 0],
"database searching": [diaumpire[1], 0, 0, msfraggerdia[0]],
"rescoring and FDR filtering": [diaumpire[2], 0, 0, msfraggerdia[1]],
"quantification": [diaumpire[3], 0, 0, msfraggerdia[2]],
"MaxDIA": [0, 0, maxdia[0], 0]
})
fig = xx.set_index("tool").plot(kind="bar", stacked=True, xlabel="", ylabel="run time (minutes)", linewidth=0).get_figure()
plt.xticks(rotation=0)
fig.savefig("runtime_ccrcc_20_dev02.pdf")
plt.figure()
# melanoma desktop
diaumpire = [132, 13, 13, 5] # dia-umpire, msfragger, msbooster+percolator+filtering+easypqp, dia-nn
diann = [1213, 2558-1213] # prediction, others
msfraggerdia = [141, 33, 5] # msfragger, msbooster+percolator+filtering+easypqp, dia-nn
xx = pd.DataFrame({"tool": ["FP-DIAU", "DIA-NN lib-free", "FP-MSF"],
"spectral library prediction": [0, diann[0], 0],
"identification and quantification": [0, diann[1], 0],
"pseudo-MS/MS generation": [diaumpire[0], 0, 0],
"database searching using MSFragger": [diaumpire[1], 0, msfraggerdia[0]],
"rescoring and FDR filtering": [diaumpire[2], 0, msfraggerdia[1]],
"quantification": [diaumpire[3], 0, msfraggerdia[2]],
})
fig = xx.set_index("tool").plot(kind="bar", stacked=True, xlabel="", ylabel="run time (minutes)", linewidth=0).get_figure()
plt.xticks(rotation=0)
fig.savefig("runtime_melanoma_phospho_desktop.pdf")
# melanoma dev02
diaumpire = [157, 11, 14, 11] # dia-umpire, msfragger, msbooster+percolator+filtering+easypqp, dia-nn
diann = [109, 1143-109] # prediction, others
msfraggerdia = [144, 36, 13] # msfragger, msbooster+percolator+filtering+easypqp, dia-nn
xx = pd.DataFrame({"tool": ["FP-DIAU", "DIA-NN lib-free", "FP-MSF"],
"spectral library prediction": [0, diann[0], 0],
"identification and quantification": [0, diann[1], 0],
"pseudo-MS/MS generation": [diaumpire[0], 0, 0],
"database searching using MSFragger": [diaumpire[1], 0, msfraggerdia[0]],
"rescoring and FDR filtering": [diaumpire[2], 0, msfraggerdia[1]],
"quantification": [diaumpire[3], 0, msfraggerdia[2]],
})
fig = xx.set_index("tool").plot(kind="bar", stacked=True, xlabel="", ylabel="run time (minutes)", linewidth=0).get_figure()
plt.xticks(rotation=0)
fig.savefig("runtime_melanoma_phospho_dev02.pdf")