Compare outcomes from differential analysis based on different imputation methods

• load scores based on 10_1_ald_diff_analysis

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import logging
from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from IPython.display import display

import pimmslearn
import pimmslearn.databases.diseases

logger = pimmslearn.logging.setup_nb_logger()

plt.rcParams['figure.figsize'] = (2, 2)
fontsize = 5
pimmslearn.plotting.make_large_descriptors(fontsize)
logging.getLogger('fontTools').setLevel(logging.ERROR)

# catch passed parameters
args = None
args = dict(globals()).keys()

Parameters

Default and set parameters for the notebook.

folder_experiment = 'runs/appl_ald_data/plasma/proteinGroups'

target = 'kleiner'
model_key = 'VAE'
baseline = 'RSN'
out_folder = 'diff_analysis'
selected_statistics = ['p-unc', '-Log10 pvalue', 'qvalue', 'rejected']

disease_ontology = 5082  # code from https://disease-ontology.org/
# split diseases notebook? Query gene names for proteins in file from uniprot?
annotaitons_gene_col = 'PG.Genes'

# Parameters
disease_ontology = 10652
folder_experiment = "runs/alzheimer_study"
target = "AD"
baseline = "PI"
model_key = "CF"
out_folder = "diff_analysis"
annotaitons_gene_col = "None"

Add set parameters to configuration

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params = pimmslearn.nb.get_params(args, globals=globals())
args = pimmslearn.nb.Config()
args.folder_experiment = Path(params["folder_experiment"])
args = pimmslearn.nb.add_default_paths(args,
                                 out_root=(
                                     args.folder_experiment
                                     / params["out_folder"]
                                     / params["target"]
                                     / f"{params['baseline']}_vs_{params['model_key']}"))
args.update_from_dict(params)
args.scores_folder = scores_folder = (args.folder_experiment
                                      / params["out_folder"]
                                      / params["target"]
                                      / 'scores')
args.freq_features_observed = args.folder_experiment / 'freq_features_observed.csv'
args

root - INFO     Removed from global namespace: folder_experiment
root - INFO     Removed from global namespace: target
root - INFO     Removed from global namespace: model_key
root - INFO     Removed from global namespace: baseline
root - INFO     Removed from global namespace: out_folder
root - INFO     Removed from global namespace: selected_statistics
root - INFO     Removed from global namespace: disease_ontology
root - INFO     Removed from global namespace: annotaitons_gene_col
root - INFO     Already set attribute: folder_experiment has value runs/alzheimer_study
root - INFO     Already set attribute: out_folder has value diff_analysis

{'annotaitons_gene_col': 'None',
 'baseline': 'PI',
 'data': PosixPath('runs/alzheimer_study/data'),
 'disease_ontology': 10652,
 'folder_experiment': PosixPath('runs/alzheimer_study'),
 'freq_features_observed': PosixPath('runs/alzheimer_study/freq_features_observed.csv'),
 'model_key': 'CF',
 'out_figures': PosixPath('runs/alzheimer_study/figures'),
 'out_folder': PosixPath('runs/alzheimer_study/diff_analysis/AD/PI_vs_CF'),
 'out_metrics': PosixPath('runs/alzheimer_study'),
 'out_models': PosixPath('runs/alzheimer_study'),
 'out_preds': PosixPath('runs/alzheimer_study/preds'),
 'scores_folder': PosixPath('runs/alzheimer_study/diff_analysis/AD/scores'),
 'selected_statistics': ['p-unc', '-Log10 pvalue', 'qvalue', 'rejected'],
 'target': 'AD'}

Excel file for exports

files_out = dict()
writer_args = dict(float_format='%.3f')

fname = args.out_folder / 'diff_analysis_compare_methods.xlsx'
files_out[fname.name] = fname
writer = pd.ExcelWriter(fname)
logger.info("Writing to excel file: %s", fname)

root - INFO     Writing to excel file: runs/alzheimer_study/diff_analysis/AD/PI_vs_CF/diff_analysis_compare_methods.xlsx

Load scores

Load baseline model scores

Show all statistics, later use selected statistics

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fname = args.scores_folder / f'diff_analysis_scores_{args.baseline}.pkl'
scores_baseline = pd.read_pickle(fname)
scores_baseline

	model	PI
	var	SS	DF	F	p-unc	np2	-Log10 pvalue	qvalue	rejected
protein groups	Source
A0A024QZX5;A0A087X1N8;P35237	AD	0.274	1	0.399	0.528	0.002	0.277	0.670	False
	age	0.212	1	0.309	0.579	0.002	0.237	0.712	False
	Kiel	2.676	1	3.897	0.050	0.020	1.303	0.121	False
	Magdeburg	5.765	1	8.393	0.004	0.042	2.376	0.016	True
	Sweden	8.898	1	12.955	0.000	0.064	3.391	0.002	True
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	0.074	1	0.070	0.791	0.000	0.102	0.872	False
	age	0.829	1	0.785	0.377	0.004	0.424	0.538	False
	Kiel	0.040	1	0.038	0.846	0.000	0.073	0.909	False
	Magdeburg	3.442	1	3.263	0.072	0.017	1.140	0.163	False
	Sweden	6.688	1	6.340	0.013	0.032	1.899	0.041	True

7105 rows × 8 columns

Load selected comparison model scores

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fname = args.scores_folder / f'diff_analysis_scores_{args.model_key}.pkl'
scores_model = pd.read_pickle(fname)
scores_model

	model	CF
	var	SS	DF	F	p-unc	np2	-Log10 pvalue	qvalue	rejected
protein groups	Source
A0A024QZX5;A0A087X1N8;P35237	AD	1.029	1	7.615	0.006	0.038	2.197	0.018	True
	age	0.007	1	0.050	0.824	0.000	0.084	0.885	False
	Kiel	0.256	1	1.893	0.171	0.010	0.768	0.279	False
	Magdeburg	0.436	1	3.229	0.074	0.017	1.131	0.144	False
	Sweden	1.613	1	11.929	0.001	0.059	3.167	0.003	True
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	1.620	1	3.245	0.073	0.017	1.135	0.143	False
	age	0.943	1	1.889	0.171	0.010	0.767	0.280	False
	Kiel	1.784	1	3.572	0.060	0.018	1.220	0.122	False
	Magdeburg	1.542	1	3.088	0.080	0.016	1.094	0.154	False
	Sweden	20.833	1	41.727	0.000	0.179	9.071	0.000	True

7105 rows × 8 columns

Combined scores

show only selected statistics for comparsion

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scores = scores_model.join(scores_baseline, how='outer')[[args.baseline, args.model_key]]
scores = scores.loc[:, pd.IndexSlice[scores.columns.levels[0].to_list(),
                                     args.selected_statistics]]
scores

	model	CF				PI
	var	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected
protein groups	Source
A0A024QZX5;A0A087X1N8;P35237	AD	0.006	2.197	0.018	True	0.528	0.277	0.670	False
	Kiel	0.171	0.768	0.279	False	0.050	1.303	0.121	False
	Magdeburg	0.074	1.131	0.144	False	0.004	2.376	0.016	True
	Sweden	0.001	3.167	0.003	True	0.000	3.391	0.002	True
	age	0.824	0.084	0.885	False	0.579	0.237	0.712	False
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	0.073	1.135	0.143	False	0.791	0.102	0.872	False
	Kiel	0.060	1.220	0.122	False	0.846	0.073	0.909	False
	Magdeburg	0.080	1.094	0.154	False	0.072	1.140	0.163	False
	Sweden	0.000	9.071	0.000	True	0.013	1.899	0.041	True
	age	0.171	0.767	0.280	False	0.377	0.424	0.538	False

7105 rows × 8 columns

Models in comparison (name mapping)

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models = pimmslearn.nb.Config.from_dict(
    pimmslearn.pandas.index_to_dict(scores.columns.get_level_values(0)))
vars(models)

{'CF': 'CF', 'PI': 'PI'}

Describe scores

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scores.describe()

model	CF			PI
var	p-unc	-Log10 pvalue	qvalue	p-unc	-Log10 pvalue	qvalue
count	7,105.000	7,105.000	7,105.000	7,105.000	7,105.000	7,105.000
mean	0.227	3.251	0.282	0.259	2.472	0.336
std	0.295	6.180	0.321	0.301	5.305	0.328
min	0.000	0.000	0.000	0.000	0.000	0.000
25%	0.001	0.403	0.002	0.004	0.343	0.016
50%	0.064	1.194	0.128	0.123	0.910	0.246
75%	0.396	3.279	0.527	0.454	2.409	0.605
max	1.000	85.803	1.000	1.000	144.895	1.000

One to one comparison of by feature:

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scores = scores.loc[pd.IndexSlice[:, args.target], :]
scores.to_excel(writer, 'scores', **writer_args)
scores

/tmp/ipykernel_77268/3761369923.py:2: FutureWarning: Starting with pandas version 3.0 all arguments of to_excel except for the argument 'excel_writer' will be keyword-only.
  scores.to_excel(writer, 'scores', **writer_args)

	model	CF				PI
	var	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected
protein groups	Source
A0A024QZX5;A0A087X1N8;P35237	AD	0.006	2.197	0.018	True	0.528	0.277	0.670	False
A0A024R0T9;K7ER74;P02655	AD	0.032	1.494	0.073	False	0.045	1.351	0.111	False
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	AD	0.238	0.623	0.362	False	0.078	1.106	0.174	False
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	AD	0.268	0.573	0.395	False	0.499	0.302	0.644	False
A0A075B6H7	AD	0.003	2.481	0.011	True	0.091	1.040	0.195	False
...	...	...	...	...	...	...	...	...	...
Q9Y6R7	AD	0.175	0.756	0.285	False	0.175	0.756	0.318	False
Q9Y6X5	AD	0.289	0.540	0.418	False	0.096	1.019	0.203	False
Q9Y6Y8;Q9Y6Y8-2	AD	0.083	1.079	0.159	False	0.083	1.079	0.182	False
Q9Y6Y9	AD	0.748	0.126	0.828	False	0.452	0.345	0.604	False
S4R3U6	AD	0.073	1.135	0.143	False	0.791	0.102	0.872	False

1421 rows × 8 columns

And the descriptive statistics of the numeric values:

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scores.describe()

model	CF			PI
var	p-unc	-Log10 pvalue	qvalue	p-unc	-Log10 pvalue	qvalue
count	1,421.000	1,421.000	1,421.000	1,421.000	1,421.000	1,421.000
mean	0.246	1.565	0.307	0.251	1.400	0.333
std	0.297	1.840	0.319	0.290	1.600	0.314
min	0.000	0.003	0.000	0.000	0.000	0.000
25%	0.007	0.379	0.021	0.012	0.371	0.038
50%	0.094	1.027	0.174	0.124	0.907	0.247
75%	0.418	2.134	0.548	0.426	1.934	0.583
max	0.994	21.374	0.996	0.999	21.057	1.000

and the boolean decision values

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scores.describe(include=['bool', 'O'])

model	CF	PI
var	rejected	rejected
count	1421	1421
unique	2	2
top	False	False
freq	958	1023

Load frequencies of observed features

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freq_feat = pd.read_csv(args.freq_features_observed, index_col=0)
freq_feat.columns = pd.MultiIndex.from_tuples([('data', 'frequency'),])
freq_feat

	data
	frequency
protein groups
A0A024QZX5;A0A087X1N8;P35237	186
A0A024R0T9;K7ER74;P02655	195
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	174
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	196
A0A075B6H7	91
...	...
Q9Y6R7	197
Q9Y6X5	173
Q9Y6Y8;Q9Y6Y8-2	197
Q9Y6Y9	119
S4R3U6	126

1421 rows × 1 columns

Compare shared features

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scores_common = (scores
                 .dropna()
                 .reset_index(-1, drop=True)
                 ).join(
    freq_feat, how='left'
)
scores_common

	CF				PI				data
	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected	frequency
protein groups
A0A024QZX5;A0A087X1N8;P35237	0.006	2.197	0.018	True	0.528	0.277	0.670	False	186
A0A024R0T9;K7ER74;P02655	0.032	1.494	0.073	False	0.045	1.351	0.111	False	195
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	0.238	0.623	0.362	False	0.078	1.106	0.174	False	174
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	0.268	0.573	0.395	False	0.499	0.302	0.644	False	196
A0A075B6H7	0.003	2.481	0.011	True	0.091	1.040	0.195	False	91
...	...	...	...	...	...	...	...	...	...
Q9Y6R7	0.175	0.756	0.285	False	0.175	0.756	0.318	False	197
Q9Y6X5	0.289	0.540	0.418	False	0.096	1.019	0.203	False	173
Q9Y6Y8;Q9Y6Y8-2	0.083	1.079	0.159	False	0.083	1.079	0.182	False	197
Q9Y6Y9	0.748	0.126	0.828	False	0.452	0.345	0.604	False	119
S4R3U6	0.073	1.135	0.143	False	0.791	0.102	0.872	False	126

1421 rows × 9 columns

Annotate decisions in Confusion Table style:

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def annotate_decision(scores, model, model_column):
    return scores[(model_column, 'rejected')].replace({False: f'{model} (no) ', True: f'{model} (yes)'})


annotations = None
for model, model_column in models.items():
    if annotations is not None:
        annotations += ' - '
        annotations += annotate_decision(scores_common,
                                         model=model, model_column=model_column)
    else:
        annotations = annotate_decision(
            scores_common, model=model, model_column=model_column)
annotations.name = 'Differential Analysis Comparison'
annotations.value_counts()

Differential Analysis Comparison
CF (no)  - PI (no)    893
CF (yes) - PI (yes)   333
CF (yes) - PI (no)    130
CF (no)  - PI (yes)    65
Name: count, dtype: int64

List different decisions between models

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mask_different = (
    (scores_common.loc[:, pd.IndexSlice[:, 'rejected']].any(axis=1))
    & ~(scores_common.loc[:, pd.IndexSlice[:, 'rejected']].all(axis=1))
)
_to_write = scores_common.loc[mask_different]
_to_write.to_excel(writer, 'differences', **writer_args)
logger.info("Writen to Excel file under sheet 'differences'.")
_to_write

/tmp/ipykernel_77268/1417621106.py:6: FutureWarning: Starting with pandas version 3.0 all arguments of to_excel except for the argument 'excel_writer' will be keyword-only.
  _to_write.to_excel(writer, 'differences', **writer_args)
root - INFO     Writen to Excel file under sheet 'differences'.

	CF				PI				data
	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected	frequency
protein groups
A0A024QZX5;A0A087X1N8;P35237	0.006	2.197	0.018	True	0.528	0.277	0.670	False	186
A0A075B6H7	0.003	2.481	0.011	True	0.091	1.040	0.195	False	91
A0A075B6J9	0.013	1.892	0.033	True	0.038	1.415	0.099	False	156
A0A075B6R2	0.001	2.879	0.005	True	0.189	0.723	0.335	False	164
A0A075B7B8	0.000	4.185	0.000	True	0.406	0.391	0.565	False	57
...	...	...	...	...	...	...	...	...	...
Q9UIB8;Q9UIB8-2;Q9UIB8-3;Q9UIB8-4;Q9UIB8-5;Q9UIB8-6	0.093	1.033	0.172	False	0.001	3.040	0.005	True	115
Q9UP79	0.001	3.109	0.003	True	0.420	0.376	0.578	False	135
Q9UQ52	0.001	3.258	0.002	True	0.050	1.302	0.121	False	188
Q9Y653;Q9Y653-2;Q9Y653-3	0.930	0.032	0.957	False	0.013	1.879	0.042	True	177
Q9Y6C2	0.003	2.494	0.010	True	0.442	0.355	0.595	False	119

195 rows × 9 columns

Plot qvalues of both models with annotated decisions

Prepare data for plotting (qvalues)

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var = 'qvalue'
to_plot = [scores_common[v][var] for v in models.values()]
for s, k in zip(to_plot, models.keys()):
    s.name = k.replace('_', ' ')
to_plot.append(scores_common['data'])
to_plot.append(annotations)
to_plot = pd.concat(to_plot, axis=1)
to_plot

	CF	PI	frequency	Differential Analysis Comparison
protein groups
A0A024QZX5;A0A087X1N8;P35237	0.018	0.670	186	CF (yes) - PI (no)
A0A024R0T9;K7ER74;P02655	0.073	0.111	195	CF (no) - PI (no)
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	0.362	0.174	174	CF (no) - PI (no)
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	0.395	0.644	196	CF (no) - PI (no)
A0A075B6H7	0.011	0.195	91	CF (yes) - PI (no)
...	...	...	...	...
Q9Y6R7	0.285	0.318	197	CF (no) - PI (no)
Q9Y6X5	0.418	0.203	173	CF (no) - PI (no)
Q9Y6Y8;Q9Y6Y8-2	0.159	0.182	197	CF (no) - PI (no)
Q9Y6Y9	0.828	0.604	119	CF (no) - PI (no)
S4R3U6	0.143	0.872	126	CF (no) - PI (no)

1421 rows × 4 columns

List of features with the highest difference in qvalues

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# should it be possible to run not only RSN?
to_plot['diff_qvalue'] = (to_plot[str(args.baseline)] - to_plot[str(args.model_key)]).abs()
to_plot.loc[mask_different].sort_values('diff_qvalue', ascending=False)

	CF	PI	frequency	Differential Analysis Comparison	diff_qvalue
protein groups
F5GWE5;I3L2X8;I3L3W1;I3L459;I3L471;I3L4C0;I3L4H1;I3L4U7;Q00169	0.012	0.991	78	CF (yes) - PI (no)	0.979
P22748	0.021	0.999	159	CF (yes) - PI (no)	0.978
O94898	0.011	0.979	60	CF (yes) - PI (no)	0.968
P22692;P22692-2	0.047	0.999	170	CF (yes) - PI (no)	0.952
Q9BX67	0.974	0.035	161	CF (no) - PI (yes)	0.939
...	...	...	...	...	...
Q9NX62	0.046	0.056	197	CF (yes) - PI (no)	0.010
F5GY80;F5H7G1;P07358	0.047	0.057	197	CF (yes) - PI (no)	0.010
P00740;P00740-2	0.043	0.053	197	CF (yes) - PI (no)	0.010
K7ERG9;P00746	0.042	0.052	197	CF (yes) - PI (no)	0.009
Q16706	0.051	0.050	195	CF (no) - PI (yes)	0.001

195 rows × 5 columns

Differences plotted with created annotations

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figsize = (4, 4)
size = 5
fig, ax = plt.subplots(figsize=figsize)
x_col = to_plot.columns[0]
y_col = to_plot.columns[1]
ax = sns.scatterplot(data=to_plot,
                     x=x_col,
                     y=y_col,
                     s=size,
                     hue='Differential Analysis Comparison',
                     ax=ax)
_ = ax.legend(fontsize=fontsize,
              title_fontsize=fontsize,
              markerscale=0.4,
              title='',
              )
ax.set_xlabel(f"qvalue for {x_col}")
ax.set_ylabel(f"qvalue for {y_col}")
ax.hlines(0.05, 0, 1, color='grey', linestyles='dotted')
ax.vlines(0.05, 0, 1, color='grey', linestyles='dotted')
sns.move_legend(ax, "upper right")
files_out[f'diff_analysis_comparision_1_{args.model_key}'] = (
    args.out_folder /
    f'diff_analysis_comparision_1_{args.model_key}')
fname = files_out[f'diff_analysis_comparision_1_{args.model_key}']
pimmslearn.savefig(fig, name=fname)

pimmslearn.plotting - INFO     Saved Figures to runs/alzheimer_study/diff_analysis/AD/PI_vs_CF/diff_analysis_comparision_1_CF

• also showing how many features were measured (“observed”) by size of circle

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fig, ax = plt.subplots(figsize=figsize)
ax = sns.scatterplot(data=to_plot,
                     x=to_plot.columns[0],
                     y=to_plot.columns[1],
                     size='frequency',
                     s=size,
                     sizes=(5, 20),
                     hue='Differential Analysis Comparison')
_ = ax.legend(fontsize=fontsize,
              title_fontsize=fontsize,
              markerscale=0.6,
              title='',
              )
ax.set_xlabel(f"qvalue for {x_col}")
ax.set_ylabel(f"qvalue for {y_col}")
ax.hlines(0.05, 0, 1, color='grey', linestyles='dotted')
ax.vlines(0.05, 0, 1, color='grey', linestyles='dotted')
sns.move_legend(ax, "upper right")
files_out[f'diff_analysis_comparision_2_{args.model_key}'] = (
    args.out_folder / f'diff_analysis_comparision_2_{args.model_key}')
pimmslearn.savefig(
    fig, name=files_out[f'diff_analysis_comparision_2_{args.model_key}'])

pimmslearn.plotting - INFO     Saved Figures to runs/alzheimer_study/diff_analysis/AD/PI_vs_CF/diff_analysis_comparision_2_CF

Only features contained in model

• this block exist due to a specific part in the ALD analysis of the paper

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scores_model_only = scores.reset_index(level=-1, drop=True)
_diff = scores_model_only.index.difference(scores_common.index)
if not _diff.empty:
    scores_model_only = (scores_model_only
                         .loc[
                             _diff,
                             args.model_key]
                         .sort_values(by='qvalue', ascending=True)
                         .join(freq_feat.squeeze().rename(freq_feat.columns.droplevel()[0])
                               )
                         )
    display(scores_model_only)
else:
    scores_model_only = None
    logger.info("No features only in new comparision model.")

if not _diff.empty:
    scores_model_only.to_excel(writer, 'only_model', **writer_args)
    display(scores_model_only.rejected.value_counts())
    scores_model_only_rejected = scores_model_only.loc[scores_model_only.rejected]
    scores_model_only_rejected.to_excel(
        writer, 'only_model_rejected', **writer_args)

root - INFO     No features only in new comparision model.

DISEASES DB lookup

Query diseases database for gene associations with specified disease ontology id.

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data = pimmslearn.databases.diseases.get_disease_association(
    doid=args.disease_ontology, limit=10000)
data = pd.DataFrame.from_dict(data, orient='index').rename_axis('ENSP', axis=0)
data = data.rename(columns={'name': args.annotaitons_gene_col}).reset_index(
).set_index(args.annotaitons_gene_col)
data

pimmslearn.databases.diseases - WARNING  There are more associations available

	ENSP	score
None
APP	ENSP00000284981	5.000
PSEN1	ENSP00000326366	5.000
APOE	ENSP00000252486	5.000
PSEN2	ENSP00000355747	5.000
TREM2	ENSP00000362205	4.825
...	...	...
hsa-miR-760	hsa-miR-760	0.682
PCDH11Y	ENSP00000355419	0.682
JPH1	ENSP00000344488	0.682
RCN1	ENSP00000054950	0.682
RNF157	ENSP00000269391	0.682

10000 rows × 2 columns

Shared features

ToDo: new script -> DISEASES DB lookup

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feat_name = scores.index.names[0]  # first index level is feature name
if args.annotaitons_gene_col in scores.index.names:
    logger.info(f"Found gene annotation in scores index:  {scores.index.names}")
else:
    logger.info(f"No gene annotation in scores index:  {scores.index.names}"
                " Exiting.")
    import sys
    sys.exit(0)

root - INFO     No gene annotation in scores index:  ['protein groups', 'Source'] Exiting.
/home/runner/work/pimms/pimms/project/.snakemake/conda/43fbe714d68d8fe6f9b0c93f5652adb3_/lib/python3.12/site-packages/IPython/core/interactiveshell.py:3756: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.
  warn("To exit: use 'exit', 'quit', or Ctrl-D.", stacklevel=1)

An exception has occurred, use %tb to see the full traceback.

SystemExit: 0

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gene_to_PG = (scores.droplevel(
    list(set(scores.index.names) - {feat_name, args.annotaitons_gene_col})
)
    .index
    .to_frame()
    .reset_index(drop=True)
    .set_index(args.annotaitons_gene_col)
)
gene_to_PG.head()

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disease_associations_all = data.join(
    gene_to_PG).dropna().reset_index().set_index(feat_name).join(annotations)
disease_associations_all

only by model

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idx = disease_associations_all.index.intersection(scores_model_only.index)
disease_assocications_new = disease_associations_all.loc[idx].sort_values(
    'score', ascending=False)
disease_assocications_new.head(20)

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mask = disease_assocications_new.loc[idx, 'score'] >= 2.0
disease_assocications_new.loc[idx].loc[mask]

Only by model which were significant

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idx = disease_associations_all.index.intersection(
    scores_model_only_rejected.index)
disease_assocications_new_rejected = disease_associations_all.loc[idx].sort_values(
    'score', ascending=False)
disease_assocications_new_rejected.head(20)

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mask = disease_assocications_new_rejected.loc[idx, 'score'] >= 2.0
disease_assocications_new_rejected.loc[idx].loc[mask]

Shared which are only significant for by model

mask = (scores_common[(str(args.model_key), 'rejected')] & mask_different)
mask.sum()

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idx = disease_associations_all.index.intersection(mask.index[mask])
disease_assocications_shared_rejected_by_model = (disease_associations_all.loc[idx].sort_values(
    'score', ascending=False))
disease_assocications_shared_rejected_by_model.head(20)

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mask = disease_assocications_shared_rejected_by_model.loc[idx, 'score'] >= 2.0
disease_assocications_shared_rejected_by_model.loc[idx].loc[mask]

Only significant by RSN

mask = (scores_common[(str(args.baseline), 'rejected')] & mask_different)
mask.sum()

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idx = disease_associations_all.index.intersection(mask.index[mask])
disease_assocications_shared_rejected_by_RSN = (
    disease_associations_all
    .loc[idx]
    .sort_values('score', ascending=False))
disease_assocications_shared_rejected_by_RSN.head(20)

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mask = disease_assocications_shared_rejected_by_RSN.loc[idx, 'score'] >= 2.0
disease_assocications_shared_rejected_by_RSN.loc[idx].loc[mask]

Write to excel

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disease_associations_all.to_excel(
    writer, sheet_name='disease_assoc_all', **writer_args)
disease_assocications_new.to_excel(
    writer, sheet_name='disease_assoc_new', **writer_args)
disease_assocications_new_rejected.to_excel(
    writer, sheet_name='disease_assoc_new_rejected', **writer_args)

Outputs

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writer.close()
files_out