Download from journal (ovarian cancer proteome)

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Download from journal (ovarian cancer proteome)#

Download the ovarian cancer proteome data from the journal’s website. It was provided as supplementary data. See the article here:

Fabian Coscia, Ernst Lengyel, Jaikumar Duraiswamy, Bradley Ashcroft, Michal Bassani-Sternberg, Michael Wierer, Alyssa Johnson, Kristen Wroblewski, Anthony Montag, S. Diane Yamada, Blanca López-Méndez, Jakob Nilsson, Andreas Mund, Matthias Mann, Marion Curtis, Multi-level Proteomics Identifies CT45 as a Chemosensitivity Mediator and Immunotherapy Target in Ovarian Cancer, Cell, Volume 175, Issue 1, 2018,

https://doi.org/10.1016/j.cell.2018.08.065.

%pip install acore openpyxl

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Note: you may need to restart the kernel to use updated packages.

from pathlib import Path

import pandas as pd

import acore.io

Specify the proteome file’s url

furl_pg = "https://ars.els-cdn.com/content/image/1-s2.0-S0092867418311668-mmc2.xlsx"

Load it using an acore function

local_filename = Path(furl_pg).name
acore.io.download_file(furl_pg, local_filename)

Open the excel file from the supplementary data given in the article.

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data = pd.read_excel(
    local_filename,
    sheet_name="SupplementaryTable2_PatientProt",
)
data
Gene names Protein names Peptides Patient02 Patient24 Patient08 Patient03 Patient13 Patient18 Patient25 ... Potential contaminant Razor unique peptides Unique peptides Molweight kDa Score Qvalue Sequence coverage MSMS Count Protein IDs Majority protein Ids
0 A1BG Alpha-1B-glycoprotein 18 31.692 32.113 31.097 30.995 32.037 31.855 30.773 ... NaN 18 18 54.253 181.040 0.000 52.500 405 P04217;P04217-2;M0R009;CON__Q2KJF1 P04217;P04217-2;M0R009
1 A1CF APOBEC1 complementation factor 6 28.418 27.401 28.432 28.365 NaN NaN NaN ... NaN 5 5 58.871 3.773 0.000 15.000 14 Q9NQ94-6;Q9NQ94;F8W9F8;Q9NQ94-5;Q9NQ94-3;Q9NQ9... Q9NQ94-6;Q9NQ94;F8W9F8;Q9NQ94-5;Q9NQ94-3;Q9NQ9...
2 A2M Alpha-2-macroglobulin 70 33.330 33.009 31.600 33.827 33.761 34.705 32.751 ... NaN 70 55 163.290 323.310 0.000 61.500 1,933 P01023;F8W7L3;H0YFH1;F5H1E8 P01023
3 A2ML1 Alpha-2-macroglobulin-like protein 1 17 24.592 25.149 25.151 22.606 23.823 25.289 NaN ... NaN 17 17 161.100 23.239 0.000 12.400 23 A8K2U0;H0YGG5;A8K2U0-2;H0YH14 A8K2U0
4 A4GALT Lactosylceramide 4-alpha-galactosyltransferase 1 24.250 24.902 NaN 23.793 NaN 25.377 NaN ... NaN 1 1 40.499 2.600 0.001 3.400 1 Q9NPC4 Q9NPC4
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
9,022 NaN Ig heavy chain V-I region V35 3 25.947 29.286 28.306 26.100 29.893 29.193 27.692 ... NaN 2 1 13.009 8.840 0.000 32.500 32 P23083 P23083
9,023 NaN Ig lambda chain V-III region LOI 5 27.607 27.957 29.456 28.598 31.016 28.310 24.011 ... NaN 4 4 11.935 4.096 0.000 57.700 21 P80748 P80748
9,024 NaN NaN 10 28.401 27.551 29.775 27.193 29.931 30.401 27.139 ... NaN 10 1 26.080 52.160 0.000 56.700 87 Q29967;P05538;H0Y7Y7;Q5SQ91;Q5SR05;A2ADX3;A0A0... Q29967
9,025 NaN NaN 29 24.711 24.391 25.486 NaN NaN NaN 25.195 ... NaN 1 0 111.020 4.131 0.000 30.000 24 Q5T8P6-2 Q5T8P6-2
9,026 NaN NaN 29 28.975 29.047 28.924 30.021 29.517 27.910 29.448 ... NaN 29 0 110.660 92.914 0.000 29.800 224 Q5T8P6-3;Q5T8P6-5 Q5T8P6-3;Q5T8P6-5

9027 rows × 39 columns

We will use the first protein in the a protein group as identifier, which we verify to be unique.

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data["first_prot"] = data["Majority protein Ids"].str.split(";").str[0]
data["first_prot"].nunique() == data["Majority protein Ids"].nunique()
data = data.set_index("first_prot")
assert data.index.is_unique
data
Gene names Protein names Peptides Patient02 Patient24 Patient08 Patient03 Patient13 Patient18 Patient25 ... Potential contaminant Razor unique peptides Unique peptides Molweight kDa Score Qvalue Sequence coverage MSMS Count Protein IDs Majority protein Ids
first_prot
P04217 A1BG Alpha-1B-glycoprotein 18 31.692 32.113 31.097 30.995 32.037 31.855 30.773 ... NaN 18 18 54.253 181.040 0.000 52.500 405 P04217;P04217-2;M0R009;CON__Q2KJF1 P04217;P04217-2;M0R009
Q9NQ94-6 A1CF APOBEC1 complementation factor 6 28.418 27.401 28.432 28.365 NaN NaN NaN ... NaN 5 5 58.871 3.773 0.000 15.000 14 Q9NQ94-6;Q9NQ94;F8W9F8;Q9NQ94-5;Q9NQ94-3;Q9NQ9... Q9NQ94-6;Q9NQ94;F8W9F8;Q9NQ94-5;Q9NQ94-3;Q9NQ9...
P01023 A2M Alpha-2-macroglobulin 70 33.330 33.009 31.600 33.827 33.761 34.705 32.751 ... NaN 70 55 163.290 323.310 0.000 61.500 1,933 P01023;F8W7L3;H0YFH1;F5H1E8 P01023
A8K2U0 A2ML1 Alpha-2-macroglobulin-like protein 1 17 24.592 25.149 25.151 22.606 23.823 25.289 NaN ... NaN 17 17 161.100 23.239 0.000 12.400 23 A8K2U0;H0YGG5;A8K2U0-2;H0YH14 A8K2U0
Q9NPC4 A4GALT Lactosylceramide 4-alpha-galactosyltransferase 1 24.250 24.902 NaN 23.793 NaN 25.377 NaN ... NaN 1 1 40.499 2.600 0.001 3.400 1 Q9NPC4 Q9NPC4
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
P23083 NaN Ig heavy chain V-I region V35 3 25.947 29.286 28.306 26.100 29.893 29.193 27.692 ... NaN 2 1 13.009 8.840 0.000 32.500 32 P23083 P23083
P80748 NaN Ig lambda chain V-III region LOI 5 27.607 27.957 29.456 28.598 31.016 28.310 24.011 ... NaN 4 4 11.935 4.096 0.000 57.700 21 P80748 P80748
Q29967 NaN NaN 10 28.401 27.551 29.775 27.193 29.931 30.401 27.139 ... NaN 10 1 26.080 52.160 0.000 56.700 87 Q29967;P05538;H0Y7Y7;Q5SQ91;Q5SR05;A2ADX3;A0A0... Q29967
Q5T8P6-2 NaN NaN 29 24.711 24.391 25.486 NaN NaN NaN 25.195 ... NaN 1 0 111.020 4.131 0.000 30.000 24 Q5T8P6-2 Q5T8P6-2
Q5T8P6-3 NaN NaN 29 28.975 29.047 28.924 30.021 29.517 27.910 29.448 ... NaN 29 0 110.660 92.914 0.000 29.800 224 Q5T8P6-3;Q5T8P6-5 Q5T8P6-3;Q5T8P6-5

9027 rows × 39 columns

Filter intensity values for patients

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pgs = data.filter(like="Patient")
pgs
Patient02 Patient24 Patient08 Patient03 Patient13 Patient18 Patient25 Patient14 Patient05 Patient20 ... Patient17 Patient04 Patient10 Patient12 Patient06 Patient09 Patient15 Patient22 Patient19 Patient01
first_prot
P04217 31.692 32.113 31.097 30.995 32.037 31.855 30.773 30.435 30.219 31.078 ... 31.484 31.410 30.540 31.881 31.969 30.657 30.606 30.311 30.641 31.957
Q9NQ94-6 28.418 27.401 28.432 28.365 NaN NaN NaN 28.195 26.704 NaN ... 27.751 28.070 26.970 27.155 NaN 27.128 NaN NaN 28.844 27.237
P01023 33.330 33.009 31.600 33.827 33.761 34.705 32.751 32.463 32.468 34.063 ... 34.503 33.947 33.911 33.990 33.707 34.230 34.209 33.014 32.556 33.766
A8K2U0 24.592 25.149 25.151 22.606 23.823 25.289 NaN NaN 22.564 25.315 ... 23.818 23.039 24.601 23.756 22.169 NaN NaN NaN 21.944 NaN
Q9NPC4 24.250 24.902 NaN 23.793 NaN 25.377 NaN NaN NaN 24.377 ... NaN 24.354 NaN NaN 24.041 NaN NaN NaN NaN NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
P23083 25.947 29.286 28.306 26.100 29.893 29.193 27.692 28.491 27.293 25.503 ... 25.634 25.193 25.832 29.269 27.593 27.592 28.329 26.345 25.061 26.888
P80748 27.607 27.957 29.456 28.598 31.016 28.310 24.011 28.122 27.931 NaN ... 27.485 25.081 28.539 28.899 29.836 26.667 26.651 26.829 29.803 28.780
Q29967 28.401 27.551 29.775 27.193 29.931 30.401 27.139 30.373 30.291 29.367 ... 29.994 27.299 27.989 27.364 30.685 29.753 29.954 31.376 29.947 29.021
Q5T8P6-2 24.711 24.391 25.486 NaN NaN NaN 25.195 26.393 26.618 24.658 ... 25.876 25.836 25.638 25.619 NaN 25.349 24.755 25.959 24.917 24.186
Q5T8P6-3 28.975 29.047 28.924 30.021 29.517 27.910 29.448 29.849 29.479 28.802 ... 29.284 29.756 29.984 28.604 29.132 28.876 29.945 30.301 29.669 29.075

9027 rows × 26 columns

There are two measurements for patient 11 in the data: 11 and 11B. In the methods of the paper it is stated:

“We required a minimum peptide ratio count of 1 to report a quantitative readout and averaged the results from duplicate measurements of the same sample.”

We will do this manually for patient 11 measurements.

pgs.filter(like="Patient11").describe()
Patient11 Patient11B
count 7,706.000 7,719.000
mean 28.067 27.924
std 2.945 2.863
min 16.638 17.883
25% 25.864 25.708
50% 28.004 27.808
75% 30.100 29.879
max 37.698 38.145 
pgs = pgs.assign(Patient11=lambda df: df.filter(like="Patient11").mean(axis=1)).drop(
    ["Patient11B"], axis=1
)

Keep the other information of protein groups as additional annotations on protein groups.

Hide code cell source

 
meta_pgs = data.drop(pgs.columns, axis=1).drop("Patient11B", axis=1)
meta_pgs
Gene names Protein names Peptides Potential contaminant Razor unique peptides Unique peptides Molweight kDa Score Qvalue Sequence coverage MSMS Count Protein IDs Majority protein Ids
first_prot
P04217 A1BG Alpha-1B-glycoprotein 18 NaN 18 18 54.253 181.040 0.000 52.500 405 P04217;P04217-2;M0R009;CON__Q2KJF1 P04217;P04217-2;M0R009
Q9NQ94-6 A1CF APOBEC1 complementation factor 6 NaN 5 5 58.871 3.773 0.000 15.000 14 Q9NQ94-6;Q9NQ94;F8W9F8;Q9NQ94-5;Q9NQ94-3;Q9NQ9... Q9NQ94-6;Q9NQ94;F8W9F8;Q9NQ94-5;Q9NQ94-3;Q9NQ9...
P01023 A2M Alpha-2-macroglobulin 70 NaN 70 55 163.290 323.310 0.000 61.500 1,933 P01023;F8W7L3;H0YFH1;F5H1E8 P01023
A8K2U0 A2ML1 Alpha-2-macroglobulin-like protein 1 17 NaN 17 17 161.100 23.239 0.000 12.400 23 A8K2U0;H0YGG5;A8K2U0-2;H0YH14 A8K2U0
Q9NPC4 A4GALT Lactosylceramide 4-alpha-galactosyltransferase 1 NaN 1 1 40.499 2.600 0.001 3.400 1 Q9NPC4 Q9NPC4
... ... ... ... ... ... ... ... ... ... ... ... ... ...
P23083 NaN Ig heavy chain V-I region V35 3 NaN 2 1 13.009 8.840 0.000 32.500 32 P23083 P23083
P80748 NaN Ig lambda chain V-III region LOI 5 NaN 4 4 11.935 4.096 0.000 57.700 21 P80748 P80748
Q29967 NaN NaN 10 NaN 10 1 26.080 52.160 0.000 56.700 87 Q29967;P05538;H0Y7Y7;Q5SQ91;Q5SR05;A2ADX3;A0A0... Q29967
Q5T8P6-2 NaN NaN 29 NaN 1 0 111.020 4.131 0.000 30.000 24 Q5T8P6-2 Q5T8P6-2
Q5T8P6-3 NaN NaN 29 NaN 29 0 110.660 92.914 0.000 29.800 224 Q5T8P6-3;Q5T8P6-5 Q5T8P6-3;Q5T8P6-5

9027 rows × 13 columns

View non-numeric columns of protein group metadata.

Hide code cell source

 
meta_pgs.describe(exclude="number")
Gene names Protein names Potential contaminant Protein IDs Majority protein Ids
count 8905 8757 81 9027 9027
unique 8559 8560 1 9027 9027
top HLA-B Plectin + P04217;P04217-2;M0R009;CON__Q2KJF1 P04217;P04217-2;M0R009
freq 13 6 81 1 1

Get rid of potential contaminants (marked with a +, so non missing have NAN).

Hide code cell source

 
mask = meta_pgs["Potential contaminant"].isna()
pgs = pgs.loc[mask].T
pgs
first_prot P04217 Q9NQ94-6 P01023 A8K2U0 Q9NPC4 Q9NRG9 Q86V21 Q7RTV5 Q6PD74 A0A096LP25 ... P06889 P07951-2 P09493-3 P09493-8 P0DMW5 P23083 P80748 Q29967 Q5T8P6-2 Q5T8P6-3
Patient02 31.692 28.418 33.330 24.592 24.250 29.019 29.245 NaN 26.173 28.495 ... 25.704 32.095 30.611 NaN NaN 25.947 27.607 28.401 24.711 28.975
Patient24 32.113 27.401 33.009 25.149 24.902 29.400 28.924 NaN 24.706 27.410 ... 24.301 33.191 30.633 30.067 NaN 29.286 27.957 27.551 24.391 29.047
Patient08 31.097 28.432 31.600 25.151 NaN 29.252 26.575 NaN NaN 28.213 ... 23.590 32.968 31.439 28.936 NaN 28.306 29.456 29.775 25.486 28.924
Patient03 30.995 28.365 33.827 22.606 23.793 29.446 29.296 NaN 24.301 28.854 ... NaN 32.578 31.484 27.719 NaN 26.100 28.598 27.193 NaN 30.021
Patient13 32.037 NaN 33.761 23.823 NaN 28.945 27.177 NaN 24.637 28.921 ... NaN 34.461 32.920 28.884 NaN 29.893 31.016 29.931 NaN 29.517
Patient18 31.855 NaN 34.705 25.289 25.377 27.779 26.758 NaN 24.226 27.896 ... 24.383 33.344 31.891 27.841 NaN 29.193 28.310 30.401 NaN 27.910
Patient25 30.773 NaN 32.751 NaN NaN 28.813 28.589 NaN 24.396 28.315 ... NaN 30.589 29.244 24.012 NaN 27.692 24.011 27.139 25.195 29.448
Patient14 30.435 28.195 32.463 NaN NaN 29.564 28.233 NaN 25.189 30.207 ... 23.700 33.071 31.503 26.714 NaN 28.491 28.122 30.373 26.393 29.849
Patient05 30.219 26.704 32.468 22.564 NaN 28.719 29.265 23.019 25.624 29.525 ... NaN 30.435 29.425 25.293 NaN 27.293 27.931 30.291 26.618 29.479
Patient20 31.078 NaN 34.063 25.315 24.377 28.385 27.472 NaN 24.626 28.666 ... NaN 32.109 30.750 26.679 NaN 25.503 NaN 29.367 24.658 28.802
Patient21 30.912 NaN 33.287 NaN NaN 29.188 28.756 NaN NaN 28.396 ... NaN 33.239 31.750 27.464 NaN 29.800 29.206 30.769 NaN 30.355
Patient11 30.697 28.426 32.712 22.730 23.259 29.753 28.441 NaN 24.970 26.765 ... 23.929 32.397 30.945 27.180 19.654 25.578 27.736 26.040 25.767 30.056
Patient23 31.175 28.184 32.586 24.601 NaN 29.202 28.150 22.956 24.361 28.326 ... NaN 34.423 32.661 28.921 NaN 25.794 27.847 28.916 25.984 29.626
Patient16 30.172 27.839 32.827 30.132 NaN 29.329 28.857 NaN 22.695 29.167 ... 23.819 32.130 31.584 26.226 NaN 26.428 27.499 25.790 24.300 29.740
Patient07 30.991 27.558 32.742 21.468 NaN 28.534 25.345 NaN 24.528 28.065 ... NaN 33.581 32.539 28.420 NaN 27.072 23.853 28.670 24.996 28.872
Patient17 31.484 27.751 34.503 23.818 NaN 29.059 27.084 NaN 25.798 28.339 ... NaN 32.383 31.135 26.782 NaN 25.634 27.485 29.994 25.876 29.284
Patient04 31.410 28.070 33.947 23.039 24.354 29.062 28.640 NaN 26.040 28.909 ... NaN 32.452 31.515 25.965 20.206 25.193 25.081 27.299 25.836 29.756
Patient10 30.540 26.970 33.911 24.601 NaN 29.826 28.359 NaN 24.734 29.186 ... 25.039 33.270 31.977 28.255 20.992 25.832 28.539 27.989 25.638 29.984
Patient12 31.881 27.155 33.990 23.756 NaN 28.367 27.276 NaN 24.930 28.589 ... 26.967 33.719 32.595 28.613 NaN 29.269 28.899 27.364 25.619 28.604
Patient06 31.969 NaN 33.707 22.169 24.041 29.420 27.533 NaN NaN 28.196 ... NaN 33.199 31.595 26.842 NaN 27.593 29.836 30.685 NaN 29.132
Patient09 30.657 27.128 34.230 NaN NaN 29.205 27.669 NaN 25.924 28.201 ... NaN 31.685 31.324 25.805 NaN 27.592 26.667 29.753 25.349 28.876
Patient15 30.606 NaN 34.209 NaN NaN 28.476 26.708 NaN NaN 28.343 ... NaN 32.300 30.295 27.085 NaN 28.329 26.651 29.954 24.755 29.945
Patient22 30.311 NaN 33.014 NaN NaN 30.312 28.655 NaN 26.169 28.945 ... NaN 33.099 31.663 28.124 NaN 26.345 26.829 31.376 25.959 30.301
Patient19 30.641 28.844 32.556 21.944 NaN 28.835 27.235 NaN 23.146 29.000 ... 27.079 34.425 33.490 29.931 NaN 25.061 29.803 29.947 24.917 29.669
Patient01 31.957 27.237 33.766 NaN NaN 29.325 28.526 NaN 25.355 27.734 ... NaN 33.243 31.817 28.737 NaN 26.888 28.780 29.021 24.186 29.075

25 rows × 8946 columns

Patient metadata#

The patient metadata was only provided as a pdf file. We parsed it and saved it as a csv file. You can load it for our GitHub repository:

! ToDo: link file after it is available on the main branch

/usr/bin/sh: 1: ToDo:: not found

Done.

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