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 = "DAE"
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': 'DAE',
 'out_figures': PosixPath('runs/alzheimer_study/figures'),
 'out_folder': PosixPath('runs/alzheimer_study/diff_analysis/AD/PI_vs_DAE'),
 '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_DAE/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	0.180	1	0.291	0.590	0.002	0.229	0.728	False
	age	0.084	1	0.135	0.713	0.001	0.147	0.817	False
	Kiel	2.216	1	3.588	0.060	0.018	1.224	0.140	False
	Magdeburg	5.950	1	9.634	0.002	0.048	2.657	0.010	True
	Sweden	10.249	1	16.595	0.000	0.080	4.169	0.000	True
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	0.367	1	0.390	0.533	0.002	0.273	0.680	False
	age	1.453	1	1.542	0.216	0.008	0.666	0.365	False
	Kiel	0.099	1	0.105	0.746	0.001	0.127	0.840	False
	Magdeburg	3.254	1	3.453	0.065	0.018	1.189	0.148	False
	Sweden	8.790	1	9.329	0.003	0.047	2.589	0.011	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	DAE
	var	SS	DF	F	p-unc	np2	-Log10 pvalue	qvalue	rejected
protein groups	Source
A0A024QZX5;A0A087X1N8;P35237	AD	1.047	1	7.682	0.006	0.039	2.213	0.017	True
	age	0.005	1	0.038	0.846	0.000	0.072	0.899	False
	Kiel	0.253	1	1.857	0.175	0.010	0.758	0.281	False
	Magdeburg	0.445	1	3.263	0.072	0.017	1.140	0.139	False
	Sweden	1.633	1	11.980	0.001	0.059	3.178	0.003	True
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	1.592	1	2.985	0.086	0.015	1.067	0.159	False
	age	1.234	1	2.314	0.130	0.012	0.887	0.223	False
	Kiel	2.762	1	5.180	0.024	0.026	1.621	0.056	False
	Magdeburg	2.279	1	4.274	0.040	0.022	1.397	0.085	False
	Sweden	23.946	1	44.911	0.000	0.190	9.647	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	DAE				PI
	var	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected
protein groups	Source
A0A024QZX5;A0A087X1N8;P35237	AD	0.006	2.213	0.017	True	0.590	0.229	0.728	False
	Kiel	0.175	0.758	0.281	False	0.060	1.224	0.140	False
	Magdeburg	0.072	1.140	0.139	False	0.002	2.657	0.010	True
	Sweden	0.001	3.178	0.003	True	0.000	4.169	0.000	True
	age	0.846	0.072	0.899	False	0.713	0.147	0.817	False
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	0.086	1.067	0.159	False	0.533	0.273	0.680	False
	Kiel	0.024	1.621	0.056	False	0.746	0.127	0.840	False
	Magdeburg	0.040	1.397	0.085	False	0.065	1.189	0.148	False
	Sweden	0.000	9.647	0.000	True	0.003	2.589	0.011	True
	age	0.130	0.887	0.223	False	0.216	0.666	0.365	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)

{'DAE': 'DAE', 'PI': 'PI'}

Describe scores

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scores.describe()

model	DAE			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	0.223	3.350	0.275	0.261	2.486	0.338
std	0.293	6.411	0.320	0.304	5.378	0.332
min	0.000	0.000	0.000	0.000	0.000	0.000
25%	0.000	0.411	0.002	0.004	0.327	0.015
50%	0.057	1.243	0.114	0.119	0.925	0.238
75%	0.388	3.353	0.517	0.471	2.440	0.628
max	1.000	86.822	1.000	1.000	143.804	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_102502/3761369923.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	DAE				PI
	var	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected
protein groups	Source
A0A024QZX5;A0A087X1N8;P35237	AD	0.006	2.213	0.017	True	0.590	0.229	0.728	False
A0A024R0T9;K7ER74;P02655	AD	0.035	1.457	0.076	False	0.059	1.228	0.139	False
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	AD	0.204	0.690	0.319	False	0.114	0.944	0.230	False
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	AD	0.250	0.602	0.372	False	0.524	0.281	0.672	False
A0A075B6H7	AD	0.017	1.761	0.043	True	0.109	0.964	0.223	False
...	...	...	...	...	...	...	...	...	...
Q9Y6R7	AD	0.175	0.756	0.282	False	0.175	0.756	0.315	False
Q9Y6X5	AD	0.247	0.607	0.369	False	0.050	1.305	0.121	False
Q9Y6Y8;Q9Y6Y8-2	AD	0.083	1.079	0.156	False	0.083	1.079	0.181	False
Q9Y6Y9	AD	0.783	0.106	0.855	False	0.302	0.520	0.464	False
S4R3U6	AD	0.086	1.067	0.159	False	0.533	0.273	0.680	False

1421 rows × 8 columns

And the descriptive statistics of the numeric values:

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scores.describe()

model	DAE			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	0.239	1.616	0.298	0.254	1.405	0.336
std	0.292	1.891	0.316	0.292	1.623	0.319
min	0.000	0.002	0.000	0.000	0.001	0.000
25%	0.006	0.374	0.018	0.011	0.352	0.036
50%	0.087	1.059	0.162	0.125	0.904	0.247
75%	0.423	2.192	0.551	0.444	1.960	0.605
max	0.995	24.453	0.997	0.997	23.616	0.998

and the boolean decision values

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

model	DAE	PI
var	rejected	rejected
count	1421	1421
unique	2	2
top	False	False
freq	926	1027

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

	DAE				PI				data
	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected	frequency
protein groups
A0A024QZX5;A0A087X1N8;P35237	0.006	2.213	0.017	True	0.590	0.229	0.728	False	186
A0A024R0T9;K7ER74;P02655	0.035	1.457	0.076	False	0.059	1.228	0.139	False	195
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	0.204	0.690	0.319	False	0.114	0.944	0.230	False	174
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	0.250	0.602	0.372	False	0.524	0.281	0.672	False	196
A0A075B6H7	0.017	1.761	0.043	True	0.109	0.964	0.223	False	91
...	...	...	...	...	...	...	...	...	...
Q9Y6R7	0.175	0.756	0.282	False	0.175	0.756	0.315	False	197
Q9Y6X5	0.247	0.607	0.369	False	0.050	1.305	0.121	False	173
Q9Y6Y8;Q9Y6Y8-2	0.083	1.079	0.156	False	0.083	1.079	0.181	False	197
Q9Y6Y9	0.783	0.106	0.855	False	0.302	0.520	0.464	False	119
S4R3U6	0.086	1.067	0.159	False	0.533	0.273	0.680	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
DAE (no)  - PI (no)    871
DAE (yes) - PI (yes)   339
DAE (yes) - PI (no)    156
DAE (no)  - PI (yes)    55
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_102502/1417621106.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'.

	DAE				PI				data
	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected	frequency
protein groups
A0A024QZX5;A0A087X1N8;P35237	0.006	2.213	0.017	True	0.590	0.229	0.728	False	186
A0A075B6H7	0.017	1.761	0.043	True	0.109	0.964	0.223	False	91
A0A075B6J9	0.018	1.738	0.044	True	0.052	1.285	0.125	False	156
A0A075B6Q5	0.006	2.223	0.017	True	0.510	0.292	0.662	False	104
A0A075B6R2	0.000	3.351	0.002	True	0.282	0.550	0.442	False	164
...	...	...	...	...	...	...	...	...	...
Q9UNW1	0.813	0.090	0.876	False	0.010	1.999	0.033	True	171
Q9UP79	0.000	4.566	0.000	True	0.330	0.481	0.494	False	135
Q9UQ52	0.000	3.421	0.002	True	0.058	1.233	0.138	False	188
Q9Y281;Q9Y281-3	0.047	1.327	0.097	False	0.002	2.821	0.007	True	51
Q9Y6C2	0.003	2.499	0.010	True	0.751	0.125	0.843	False	119

211 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

	DAE	PI	frequency	Differential Analysis Comparison
protein groups
A0A024QZX5;A0A087X1N8;P35237	0.017	0.728	186	DAE (yes) - PI (no)
A0A024R0T9;K7ER74;P02655	0.076	0.139	195	DAE (no) - PI (no)
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	0.319	0.230	174	DAE (no) - PI (no)
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	0.372	0.672	196	DAE (no) - PI (no)
A0A075B6H7	0.043	0.223	91	DAE (yes) - PI (no)
...	...	...	...	...
Q9Y6R7	0.282	0.315	197	DAE (no) - PI (no)
Q9Y6X5	0.369	0.121	173	DAE (no) - PI (no)
Q9Y6Y8;Q9Y6Y8-2	0.156	0.181	197	DAE (no) - PI (no)
Q9Y6Y9	0.855	0.464	119	DAE (no) - PI (no)
S4R3U6	0.159	0.680	126	DAE (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)

	DAE	PI	frequency	Differential Analysis Comparison	diff_qvalue
protein groups
A0A087WU43;A0A087WX17;A0A087WXI5;P12830;P12830-2	0.000	0.989	134	DAE (yes) - PI (no)	0.989
O15204;O15204-2	0.014	0.982	156	DAE (yes) - PI (no)	0.968
P22692;P22692-2	0.032	0.998	170	DAE (yes) - PI (no)	0.966
P22748	0.031	0.994	159	DAE (yes) - PI (no)	0.963
O14745	0.018	0.981	62	DAE (yes) - PI (no)	0.962
...	...	...	...	...	...
Q9NX62	0.045	0.055	197	DAE (yes) - PI (no)	0.011
P00740;P00740-2	0.043	0.053	197	DAE (yes) - PI (no)	0.010
K7ERG9;P00746	0.042	0.052	197	DAE (yes) - PI (no)	0.010
K7ERI9;P02654	0.051	0.042	196	DAE (no) - PI (yes)	0.008
Q16706	0.044	0.052	195	DAE (yes) - PI (no)	0.008

211 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_DAE/diff_analysis_comparision_1_DAE

• 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_DAE/diff_analysis_comparision_2_DAE

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
APOE	ENSP00000252486	5.000
PSEN1	ENSP00000326366	5.000
PSEN2	ENSP00000355747	5.000
APP	ENSP00000284981	5.000
TREM2	ENSP00000362205	4.825
...	...	...
CARMIL1	ENSP00000331983	0.681
CENPJ	ENSP00000371308	0.681
ERP27	ENSP00000266397	0.681
ZNF585B	ENSP00000433773	0.681
KIR3DL2	ENSP00000325525	0.681

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/924ec7e362d761ecf0807b9074d79999_/lib/python3.12/site-packages/IPython/core/interactiveshell.py:3707: 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