Post-processing of TRANSPIRE results¶
[1]:
import TRANSPIRE
import pandas as pd
import numpy as np
1. Load data, define comparisons, and generate synthetic translocations¶
[2]:
f = 'mydata.csv'
df = TRANSPIRE.data.import_data.load_data(f)
comparisons = [('uninfected', 'infected')]
synthetic_translocations = TRANSPIRE.data.generate_translocations.make_translocations(df, comparisons)
mapping, mapping_r = TRANSPIRE.utils.get_mapping(df)
synthetic_translocations.head()
[2]:
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| accession_A | gene name_A | localization_A | condition_A | accession_B | gene name_B | localization_B | condition_B | label | ||||||||||||
| O00115 | DNASE2 | ER/Golgi/Lysosome | uninfected | O00115 | DNASE2 | ER/Golgi/Lysosome | infected | ER/Golgi/Lysosome to ER/Golgi/Lysosome | 0.121081 | 0.378059 | 0.287889 | 0.142575 | 0.056295 | 0.014235 | 0.091988 | 0.249011 | 0.334238 | 0.181254 | 0.104927 | 0.041090 |
| O00116 | AGPS | Peroxisome | infected | ER/Golgi/Lysosome to Peroxisome | 0.121081 | 0.378059 | 0.287889 | 0.142575 | 0.056295 | 0.014235 | 0.035467 | 0.087956 | 0.190615 | 0.228672 | 0.353767 | 0.095804 | ||||
| O00151 | PDLIM1 | PM/Cytosol | infected | ER/Golgi/Lysosome to PM/Cytosol | 0.121081 | 0.378059 | 0.287889 | 0.142575 | 0.056295 | 0.014235 | 0.298505 | 0.273591 | 0.136350 | 0.124446 | 0.101276 | 0.051836 | ||||
| O00161 | SNAP23 | PM/Cytosol | infected | ER/Golgi/Lysosome to PM/Cytosol | 0.121081 | 0.378059 | 0.287889 | 0.142575 | 0.056295 | 0.014235 | 0.303401 | 0.275456 | 0.219409 | 0.094259 | 0.065571 | 0.027320 | ||||
| O00186 | STXBP3 | PM/Cytosol | infected | ER/Golgi/Lysosome to PM/Cytosol | 0.121081 | 0.378059 | 0.287889 | 0.142575 | 0.056295 | 0.014235 | 0.251250 | 0.279922 | 0.220812 | 0.102742 | 0.089897 | 0.047200 |
2. Load TRANSPIRE predictions¶
[3]:
predictions = TRANSPIRE.data.import_data.load_predictions('results.csv')
3. Perform GO enrichment analysis on translocating protiens¶
Make GOAnalyzer to perform enrichment analyses (facilitated by GOATOOLS)
[4]:
# define a background set of genes to assess enrichment against
background = df.index.get_level_values('accession').unique()
GO_analyzer = TRANSPIRE.go_enrichment.GOAnalyzer(background, species = '9606', alpha = 0.05, method = 'fdr_bh')
EXISTS: go-basic.obo
EXISTS: gene2go
go-basic.obo: fmt(1.2) rel(2020-05-02) 47,240 GO Terms
HMS:0:00:03.614330 336,356 annotations, 20,586 genes, 18,410 GOs, 1 taxids READ: gene2go
Load BP Gene Ontology Analysis ...
fisher module not installed. Falling back on scipy.stats.fisher_exact
95% 3,051 of 3,196 population items found in association
Load CC Gene Ontology Analysis ...
fisher module not installed. Falling back on scipy.stats.fisher_exact
98% 3,147 of 3,196 population items found in association
Load MF Gene Ontology Analysis ...
fisher module not installed. Falling back on scipy.stats.fisher_exact
95% 3,032 of 3,196 population items found in association
Perform GO enrichment analysis for translocating proteins
[20]:
translocation_accs = predictions.index.get_level_values('accession_A')[predictions['passes cutoff?']].unique()
translocation_enrichment = GO_analyzer.get_enrichment(translocation_accs)
Run BP Gene Ontology Analysis: current study set of 728 IDs ...
96% 701 of 727 study items found in association
100% 727 of 728 study items found in population(3196)
Calculating 6,820 uncorrected p-values using fisher_scipy_stats
6,820 GO terms are associated with 3,049 of 3,196 population items
3,239 GO terms are associated with 701 of 727 study items
METHOD fdr_bh:
30 GO terms found significant (< 0.05=alpha) ( 26 enriched + 4 purified): statsmodels fdr_bh
260 study items associated with significant GO IDs (enriched)
4 study items associated with significant GO IDs (purified)
Run CC Gene Ontology Analysis: current study set of 728 IDs ...
99% 722 of 727 study items found in association
100% 727 of 728 study items found in population(3196)
Calculating 1,109 uncorrected p-values using fisher_scipy_stats
1,109 GO terms are associated with 3,145 of 3,196 population items
622 GO terms are associated with 722 of 727 study items
METHOD fdr_bh:
35 GO terms found significant (< 0.05=alpha) ( 28 enriched + 7 purified): statsmodels fdr_bh
639 study items associated with significant GO IDs (enriched)
92 study items associated with significant GO IDs (purified)
Run MF Gene Ontology Analysis: current study set of 728 IDs ...
96% 699 of 727 study items found in association
100% 727 of 728 study items found in population(3196)
Calculating 2,192 uncorrected p-values using fisher_scipy_stats
2,192 GO terms are associated with 3,030 of 3,196 population items
908 GO terms are associated with 699 of 727 study items
METHOD fdr_bh:
5 GO terms found significant (< 0.05=alpha) ( 5 enriched + 0 purified): statsmodels fdr_bh
573 study items associated with significant GO IDs (enriched)
0 study items associated with significant GO IDs (purified)
[21]:
translocation_enrichment
[21]:
| NS | enrichment | name | ratio_in_study | ratio_in_pop | p_uncorrected | depth | study_count | p_fdr_bh | study_items | |
|---|---|---|---|---|---|---|---|---|---|---|
| GO accession | ||||||||||
| GO:0006413 | BP | e | translational initiation | 67/727 | 103/3196 | 1.138456e-20 | 3 | 67 | 7.764267e-17 | 1654, 1964, 1965, 1968, 1973, 1981, 1982, 3646... |
| GO:0000184 | BP | e | nuclear-transcribed mRNA catabolic process, no... | 53/727 | 83/3196 | 5.171506e-16 | 10 | 53 | 1.763483e-12 | 1981, 2935, 3646, 3921, 5515, 5976, 6122, 6124... |
| GO:0006614 | BP | e | SRP-dependent cotranslational protein targetin... | 50/727 | 81/3196 | 2.685849e-14 | 12 | 50 | 6.105829e-11 | 3921, 6122, 6124, 6125, 6133, 6134, 6135, 6137... |
| GO:0019083 | BP | e | viral transcription | 48/727 | 77/3196 | 5.258122e-14 | 4 | 48 | 8.965097e-11 | 3921, 6122, 6124, 6125, 6133, 6134, 6135, 6137... |
| GO:0006886 | BP | e | intracellular protein transport | 65/727 | 131/3196 | 6.139768e-12 | 8 | 65 | 8.374644e-09 | 160, 161, 162, 163, 164, 372, 378, 381, 400, 1... |
| GO:0006890 | BP | e | retrograde vesicle-mediated transport, Golgi t... | 28/727 | 39/3196 | 7.885321e-11 | 6 | 28 | 8.962982e-08 | 372, 378, 381, 1314, 1315, 3831, 5861, 6836, 6... |
| GO:0006888 | BP | e | endoplasmic reticulum to Golgi vesicle-mediate... | 42/727 | 87/3196 | 1.485486e-07 | 6 | 42 | 1.447288e-04 | 372, 378, 1314, 1315, 1639, 1778, 1781, 2621, ... |
| GO:0006412 | BP | e | translation | 57/727 | 134/3196 | 2.370902e-07 | 7 | 57 | 2.021194e-04 | 207, 1615, 1915, 1981, 2935, 3921, 5610, 6122,... |
| GO:0043488 | BP | e | regulation of mRNA stability | 31/727 | 60/3196 | 9.324625e-07 | 9 | 31 | 6.359395e-04 | 207, 1981, 3315, 3842, 5578, 5684, 5689, 5700,... |
| GO:0032482 | BP | e | Rab protein signal transduction | 20/727 | 33/3196 | 2.926560e-06 | 8 | 20 | 1.814467e-03 | 5861, 5864, 5865, 5867, 5868, 5869, 5873, 5878... |
| GO:0060071 | BP | e | Wnt signaling pathway, planar cell polarity pa... | 26/727 | 51/3196 | 1.016250e-05 | 9 | 26 | 4.950587e-03 | 160, 161, 163, 1173, 1175, 5684, 5689, 5700, 5... |
| GO:0031397 | BP | e | negative regulation of protein ubiquitination | 11/727 | 15/3196 | 4.325101e-05 | 10 | 11 | 1.914097e-02 | 207, 857, 3301, 6188, 8878, 9532, 9636, 11261,... |
| GO:0001732 | BP | e | formation of cytoplasmic translation initiatio... | 10/727 | 13/3196 | 5.095329e-05 | 8 | 10 | 1.914097e-02 | 1983, 3646, 8661, 8662, 8663, 8667, 8668, 8894... |
| GO:0010972 | BP | e | negative regulation of G2/M transition of mito... | 20/727 | 38/3196 | 5.254492e-05 | 9 | 20 | 1.914097e-02 | 2273, 5684, 5689, 5700, 5702, 5704, 5705, 5706... |
| GO:0019886 | BP | e | antigen processing and presentation of exogeno... | 20/727 | 38/3196 | 5.254492e-05 | 5 | 20 | 1.914097e-02 | 160, 161, 162, 163, 164, 1173, 1175, 1639, 177... |
| GO:0070498 | BP | e | interleukin-1-mediated signaling pathway | 22/727 | 43/3196 | 5.332528e-05 | 7 | 22 | 1.914097e-02 | 5595, 5684, 5689, 5700, 5702, 5704, 5705, 5706... |
| GO:0006521 | BP | e | regulation of cellular amino acid metabolic pr... | 19/727 | 36/3196 | 7.715603e-05 | 6 | 19 | 2.505734e-02 | 1728, 5684, 5689, 5700, 5702, 5704, 5705, 5706... |
| GO:0042147 | BP | e | retrograde transport, endosome to Golgi | 17/727 | 31/3196 | 9.867668e-05 | 6 | 17 | 3.058977e-02 | 400, 1639, 6642, 6643, 7879, 8675, 8729, 9367,... |
| GO:0090263 | BP | e | positive regulation of canonical Wnt signaling... | 27/727 | 60/3196 | 1.269032e-04 | 8 | 27 | 3.615045e-02 | 857, 1613, 1654, 5684, 5689, 5700, 5702, 5704,... |
| GO:0033572 | BP | e | transferrin transport | 13/727 | 21/3196 | 1.272157e-04 | 9 | 13 | 3.615045e-02 | 392, 523, 526, 528, 529, 535, 1785, 7037, 9296... |
| GO:0090383 | BP | e | phagosome acidification | 10/727 | 14/3196 | 1.420635e-04 | 12 | 10 | 3.875492e-02 | 523, 526, 528, 529, 535, 7879, 9296, 9550, 513... |
| GO:0061418 | BP | e | regulation of transcription from RNA polymeras... | 20/727 | 40/3196 | 1.651179e-04 | 12 | 20 | 4.331169e-02 | 5684, 5689, 5700, 5702, 5704, 5705, 5706, 5707... |
| GO:0090090 | BP | e | negative regulation of canonical Wnt signaling... | 26/727 | 58/3196 | 1.883253e-04 | 8 | 26 | 4.756956e-02 | 857, 1601, 2010, 5684, 5689, 5700, 5702, 5704,... |
| GO:0045652 | BP | e | regulation of megakaryocyte differentiation | 7/727 | 8/3196 | 1.978210e-04 | 7 | 7 | 4.797158e-02 | 3276, 3692, 4015, 4343, 7057, 10398, 121504 |
| GO:0016579 | BP | e | protein deubiquitination | 28/727 | 64/3196 | 2.089647e-04 | 9 | 28 | 4.797158e-02 | 5684, 5689, 5700, 5702, 5704, 5705, 5706, 5707... |
| GO:0002181 | BP | e | cytoplasmic translation | 15/727 | 27/3196 | 2.110187e-04 | 8 | 15 | 4.797158e-02 | 3921, 6133, 6135, 6139, 6142, 6143, 6146, 6152... |
| GO:0005829 | CC | e | cytosol | 455/727 | 1334/3196 | 6.754852e-38 | 2 | 455 | 7.491131e-35 | 16, 48, 70, 120, 128, 143, 159, 160, 161, 162,... |
| GO:0022627 | CC | e | cytosolic small ribosomal subunit | 24/727 | 33/3196 | 1.227456e-09 | 5 | 24 | 2.722498e-07 | 1654, 3921, 6187, 6188, 6189, 6191, 6193, 6194... |
| GO:0016020 | CC | e | membrane | 266/727 | 884/3196 | 1.887482e-09 | 2 | 266 | 3.488696e-07 | 16, 70, 102, 120, 143, 160, 163, 164, 310, 311... |
| GO:0070062 | CC | e | extracellular exosome | 282/727 | 958/3196 | 7.712528e-09 | 6 | 282 | 1.221885e-06 | 16, 48, 52, 70, 102, 128, 143, 159, 203, 231, ... |
| GO:0005634 | CC | e | nucleus | 231/727 | 756/3196 | 1.019253e-08 | 5 | 231 | 1.412940e-06 | 102, 143, 207, 310, 403, 581, 790, 817, 824, 8... |
| GO:0022624 | CC | e | proteasome accessory complex | 15/727 | 17/3196 | 1.714408e-08 | 2 | 15 | 2.112532e-06 | 5700, 5702, 5704, 5705, 5706, 5707, 5708, 5709... |
| GO:0030666 | CC | e | endocytic vesicle membrane | 13/727 | 15/3196 | 2.643905e-07 | 6 | 13 | 2.932091e-05 | 160, 161, 163, 817, 857, 949, 1173, 1175, 1785... |
| GO:0022625 | CC | e | cytosolic large ribosomal subunit | 25/727 | 43/3196 | 4.994355e-07 | 5 | 25 | 5.035218e-05 | 6122, 6124, 6125, 6133, 6134, 6135, 6137, 6139... |
| GO:0005737 | CC | e | cytoplasm | 265/727 | 928/3196 | 8.023717e-07 | 2 | 265 | 7.415251e-05 | 16, 48, 52, 70, 102, 143, 159, 164, 203, 207, ... |
| GO:0000502 | CC | e | proteasome complex | 23/727 | 40/3196 | 1.895943e-06 | 5 | 23 | 1.617385e-04 | 3300, 3315, 5684, 5689, 5700, 5702, 5704, 5705... |
| GO:0005730 | CC | e | nucleolus | 60/727 | 161/3196 | 1.838891e-05 | 5 | 60 | 1.456664e-03 | 439, 1656, 1915, 2547, 3162, 3337, 3609, 3692,... |
| GO:0030126 | CC | e | COPI vesicle coat | 8/727 | 9/3196 | 5.006332e-05 | 5 | 8 | 3.402078e-03 | 372, 1314, 1315, 9276, 11316, 22818, 22820, 23423 |
| GO:0030904 | CC | e | retromer complex | 10/727 | 13/3196 | 5.095329e-05 | 3 | 10 | 3.402078e-03 | 1639, 6642, 6643, 7879, 8724, 9559, 51479, 516... |
| GO:0031901 | CC | e | early endosome membrane | 27/727 | 58/3196 | 5.215088e-05 | 6 | 27 | 3.402078e-03 | 857, 3107, 3133, 5867, 5868, 5869, 5878, 6642,... |
| GO:0010008 | CC | e | endosome membrane | 33/727 | 76/3196 | 7.128547e-05 | 5 | 33 | 3.952779e-03 | 392, 535, 967, 3916, 3949, 5868, 6233, 6642, 6... |
| GO:0008541 | CC | e | proteasome regulatory particle, lid subcomplex | 6/727 | 6/3196 | 1.363396e-04 | 2 | 6 | 7.200028e-03 | 5709, 5714, 5717, 5718, 5719, 10213 |
| GO:0033290 | CC | e | eukaryotic 48S preinitiation complex | 9/727 | 12/3196 | 1.752318e-04 | 4 | 9 | 8.833274e-03 | 1965, 3646, 8661, 8662, 8663, 8667, 8668, 2733... |
| GO:0005838 | CC | e | proteasome regulatory particle | 7/727 | 8/3196 | 1.978210e-04 | 2 | 7 | 9.538410e-03 | 5707, 5708, 5713, 5714, 5718, 5719, 9861 |
| GO:0005840 | CC | e | ribosome | 19/727 | 38/3196 | 2.496730e-04 | 5 | 19 | 1.153547e-02 | 5610, 6133, 6173, 6188, 6191, 6193, 6201, 6203... |
| GO:0005852 | CC | e | eukaryotic translation initiation factor 3 com... | 9/727 | 13/3196 | 4.548583e-04 | 2 | 9 | 1.868288e-02 | 1654, 3646, 8661, 8662, 8663, 8667, 8668, 2733... |
| GO:0008540 | CC | e | proteasome regulatory particle, base subcomplex | 8/727 | 11/3196 | 5.879953e-04 | 2 | 8 | 2.227521e-02 | 5700, 5702, 5704, 5705, 5706, 5707, 5708, 5711 |
| GO:0031597 | CC | e | cytosolic proteasome complex | 5/727 | 5/3196 | 6.025756e-04 | 6 | 5 | 2.227521e-02 | 3416, 5704, 5705, 5706, 10213 |
| GO:0030131 | CC | e | clathrin adaptor complex | 5/727 | 5/3196 | 6.025756e-04 | 4 | 5 | 2.227521e-02 | 162, 163, 8546, 8907, 26985 |
| GO:0005874 | CC | e | microtubule | 28/727 | 68/3196 | 6.231870e-04 | 6 | 28 | 2.229401e-02 | 1155, 1639, 1778, 1781, 1785, 1808, 2010, 2288... |
| GO:1990904 | CC | e | ribonucleoprotein complex | 28/727 | 69/3196 | 7.439872e-04 | 2 | 28 | 2.521062e-02 | 143, 1653, 1938, 3609, 4691, 4869, 6124, 6125,... |
| GO:0005765 | CC | e | lysosomal membrane | 50/727 | 144/3196 | 7.501806e-04 | 7 | 50 | 2.521062e-02 | 162, 164, 523, 526, 528, 529, 535, 949, 967, 1... |
| GO:0012505 | CC | e | endomembrane system | 28/727 | 70/3196 | 1.256637e-03 | 2 | 28 | 4.098854e-02 | 526, 5861, 5864, 5865, 5867, 5868, 5869, 5873,... |
| GO:0016282 | CC | e | eukaryotic 43S preinitiation complex | 8/727 | 12/3196 | 1.413522e-03 | 4 | 8 | 4.478847e-02 | 3646, 8661, 8662, 8663, 8667, 8668, 27335, 51386 |
| GO:0005515 | MF | e | protein binding | 559/727 | 2190/3196 | 2.110386e-08 | 2 | 559 | 4.625966e-05 | 48, 52, 102, 143, 159, 160, 161, 162, 163, 164... |
| GO:0003723 | MF | e | RNA binding | 158/727 | 483/3196 | 4.995147e-08 | 4 | 158 | 5.474682e-05 | 48, 310, 311, 372, 1615, 1634, 1653, 1654, 165... |
| GO:0003743 | MF | e | translation initiation factor activity | 18/727 | 30/3196 | 1.140057e-05 | 6 | 18 | 7.057986e-03 | 1964, 1965, 1968, 1973, 1981, 1982, 1983, 3646... |
| GO:0048027 | MF | e | mRNA 5'-UTR binding | 12/727 | 16/3196 | 1.287954e-05 | 6 | 12 | 7.057986e-03 | 1654, 4628, 4691, 6125, 6189, 6201, 6207, 6208... |
| GO:0035615 | MF | e | clathrin adaptor activity | 9/727 | 11/3196 | 5.488424e-05 | 5 | 9 | 2.406125e-02 | 160, 161, 163, 164, 1173, 1175, 1601, 3092, 8907 |
Plot the enriched terms and their values
[27]:
import matplotlib.pyplot as plt
TRANSPIRE.visualization.result_visualization.plot_GO_enrichment_results(translocation_enrichment, orient='horizontal')
plt.show()
_13_0.png)
4. Determine co-translocating proteins¶
Compute Mahalanobis distances between all proteins
[5]:
possible_translocation_profiles =TRANSPIRE.data.generate_translocations.make_translocations(df, comparisons, synthetic=False).reset_index().set_index(predictions.index.names)
# get rid of index levels that aren't informative
possible_translocation_profiles = possible_translocation_profiles.reset_index(['localization_A', 'localization_B', 'accession_B', 'gene name_B'], drop=True)
possible_translocation_profiles.index.names = ['condition_A', 'condition_B', 'accession', 'gene name']
dists = possible_translocation_profiles.groupby(['condition_A', 'condition_B']).apply(lambda x: TRANSPIRE.cotranslocation.compute_distance(x.loc[x.name, :]))
dists = dists.reset_index().melt(dists.index.names).dropna().set_index(['condition_A', 'condition_B', 'accession_A', 'accession_B', 'gene name_A', 'gene name_B']).squeeze()
[20]:
dists.sort_values()
[20]:
condition_A condition_B accession_A accession_B gene name_A gene name_B
uninfected infected P13667 Q15084 PDIA4 PDIA6 0.487088
P51398 Q5JTZ9 DAP3 AARS2 0.521965
P04083 P08758 ANXA1 ANXA5 0.546352
P30040 P30101 ERP29 PDIA3 0.548835
P34897 Q99797 SHMT2 MIPEP 0.641325
...
P35527 Q6P1Q0 KRT9 LETMD1 111.279417
Q9NSI6 KRT9 BRWD1 111.435963
Q9NYJ1 KRT9 COA4 111.567667
Q8TAA5 KRT9 GRPEL2 112.014439
Q13057 KRT9 COASY 112.063816
Name: value, Length: 2573046, dtype: float64
Load CORUM complexes
[21]:
corum, prot_to_complex, complex_to_prot = TRANSPIRE.data.import_data.load_CORUM()
Extract true postive (members of CORUM complexes) and true negative (marker proteins of distinct subcellular organelles) populations from the data
[62]:
tn = TRANSPIRE.cotranslocation.extract_true_neg(dists, df)
tp = TRANSPIRE.cotranslocation.extract_true_pos(dists, complex_to_prot, prot_to_complex)
Compute a false-positive rate based on true negative and true positive populations
[67]:
fpr = TRANSPIRE.cotranslocation.compute_fpr(tp, tn)
Determine a distance cutoff based on your desired fpr
[92]:
fpr_cutoff = 0.05 # 5% FPR cutoff
dist_cutoff = fpr.groupby(['condition_A', 'condition_B']).apply(lambda x: x[x<=fpr_cutoff].idxmax()[0])
Identify cotranslocation based on distance cutoff
[99]:
transloc_accs = predictions.index.get_level_values('accession_A')[predictions['passes cutoff?']].unique()
transloc_dists = dists[dists.index.get_level_values('accession_A').isin(transloc_accs)&dists.index.get_level_values('accession_B').isin(transloc_accs)]
cotransloc_dists = transloc_dists[transloc_dists <= dist_cutoff.loc[list(zip(transloc_dists.index.get_level_values('condition_A'), transloc_dists.index.get_level_values('condition_B')))].values]
cotransloc_accs = np.unique(cotransloc_dists.index.get_level_values('accession_A').values.tolist()+cotransloc_dists.index.get_level_values('accession_B').values.tolist())
Query STRINGdb for known interactions
[102]:
gsi = TRANSPIRE.cotranslocation.GetSTRINGInteractions()
known_interactions = gsi.query(cotransloc_accs, '9606')
[103]:
known_interactions.head()
[103]:
| stringId_A | stringId_B | preferredName_A | preferredName_B | ncbiTaxonId | score | nscore | fscore | pscore | ascore | escore | dscore | tscore | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Accession_A | Accession_B | |||||||||||||
| Q16401 | P43686 | ENSP00000210313 | ENSP00000157812 | PSMD5 | PSMC4 | 9606 | 0.989 | 0.000 | 0.0 | 0.000 | 0.063 | 0.827 | 0.9 | 0.410 |
| P48556 | P43686 | ENSP00000215071 | ENSP00000157812 | PSMD8 | PSMC4 | 9606 | 0.999 | 0.000 | 0.0 | 0.000 | 0.880 | 0.994 | 0.9 | 0.917 |
| Q16401 | ENSP00000215071 | ENSP00000210313 | PSMD8 | PSMD5 | 9606 | 0.990 | 0.000 | 0.0 | 0.000 | 0.081 | 0.869 | 0.9 | 0.290 | |
| Q9Y5K8 | O94804 | ENSP00000216442 | ENSP00000176763 | ATP6V1D | STK10 | 9606 | 0.900 | 0.000 | 0.0 | 0.000 | 0.000 | 0.000 | 0.9 | 0.000 |
| P25788 | P43686 | ENSP00000216455 | ENSP00000157812 | PSMA3 | PSMC4 | 9606 | 0.999 | 0.059 | 0.0 | 0.201 | 0.798 | 0.994 | 0.9 | 0.429 |
Combine known interaction information with cotranslocation information
[110]:
isknown = pd.Series(cotransloc_dists.reset_index([n for n in cotransloc_dists.index.names if not 'accession' in n]).index.isin(known_interactions.index), index = cotransloc_dists.index)
pd.concat([cotransloc_dists, isknown], axis=1, keys = ['Mahalanobis distance', 'known interaction?'])
[110]:
| Mahalanobis distance | known interaction? | ||||||
|---|---|---|---|---|---|---|---|
| condition_A | condition_B | accession_A | accession_B | gene name_A | gene name_B | ||
| uninfected | infected | A0AVT1 | O00231 | UBA6 | PSMD11 | 2.482505 | True |
| O00154 | O00231 | ACOT7 | PSMD11 | 3.157640 | False | ||
| O00203 | O00231 | AP3B1 | PSMD11 | 3.091579 | False | ||
| O00231 | O00232 | PSMD11 | PSMD12 | 2.637260 | True | ||
| A0AVT1 | O00425 | UBA6 | IGF2BP3 | 2.502198 | False | ||
| ... | ... | ... | ... | ... | ... | ||
| Q9UNM6 | Q9Y6M1 | PSMD13 | IGF2BP2 | 2.395706 | False | ||
| Q9Y230 | Q9Y6M1 | RUVBL2 | IGF2BP2 | 2.972433 | False | ||
| Q9Y265 | Q9Y6M1 | RUVBL1 | IGF2BP2 | 3.339705 | False | ||
| Q9Y3I0 | Q9Y6M1 | RTCB | IGF2BP2 | 2.039269 | False | ||
| Q9Y678 | Q9Y6M1 | COPG1 | IGF2BP2 | 2.863510 | False |
15265 rows × 2 columns