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	0.833	1	1.390	0.240	0.007	0.620	0.397	False
	age	0.147	1	0.246	0.620	0.001	0.207	0.750	False
	Kiel	2.439	1	4.072	0.045	0.021	1.347	0.112	False
	Magdeburg	4.762	1	7.949	0.005	0.040	2.274	0.020	True
	Sweden	8.268	1	13.800	0.000	0.067	3.574	0.002	True
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	0.531	1	0.525	0.469	0.003	0.328	0.623	False
	age	1.792	1	1.774	0.185	0.009	0.734	0.328	False
	Kiel	0.002	1	0.002	0.961	0.000	0.017	0.976	False
	Magdeburg	2.675	1	2.648	0.105	0.014	0.978	0.218	False
	Sweden	15.376	1	15.221	0.000	0.074	3.878	0.001	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	1.095	1	7.907	0.005	0.040	2.265	0.016	True
	age	0.005	1	0.036	0.850	0.000	0.071	0.903	False
	Kiel	0.260	1	1.878	0.172	0.010	0.764	0.279	False
	Magdeburg	0.448	1	3.234	0.074	0.017	1.132	0.142	False
	Sweden	1.622	1	11.717	0.001	0.058	3.120	0.003	True
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	2.408	1	4.245	0.041	0.022	1.390	0.087	False
	age	0.773	1	1.363	0.244	0.007	0.612	0.367	False
	Kiel	2.706	1	4.770	0.030	0.024	1.520	0.068	False
	Magdeburg	3.482	1	6.138	0.014	0.031	1.851	0.036	True
	Sweden	25.295	1	44.593	0.000	0.189	9.590	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	0.005	2.265	0.016	True	0.240	0.620	0.397	False
	Kiel	0.172	0.764	0.279	False	0.045	1.347	0.112	False
	Magdeburg	0.074	1.132	0.142	False	0.005	2.274	0.020	True
	Sweden	0.001	3.120	0.003	True	0.000	3.574	0.002	True
	age	0.850	0.071	0.903	False	0.620	0.207	0.750	False
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	0.041	1.390	0.087	False	0.469	0.328	0.623	False
	Kiel	0.030	1.520	0.068	False	0.961	0.017	0.976	False
	Magdeburg	0.014	1.851	0.036	True	0.105	0.978	0.218	False
	Sweden	0.000	9.590	0.000	True	0.000	3.878	0.001	True
	age	0.244	0.612	0.367	False	0.185	0.734	0.328	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	0.224	3.343	0.277	0.261	2.476	0.338
std	0.294	6.389	0.320	0.303	5.328	0.331
min	0.000	0.000	0.000	0.000	0.000	0.000
25%	0.000	0.409	0.002	0.004	0.335	0.016
50%	0.060	1.225	0.119	0.121	0.916	0.243
75%	0.390	3.312	0.520	0.463	2.411	0.617
max	0.999	86.396	0.999	0.999	146.241	0.999

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_89059/3761369923.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	0.005	2.265	0.016	True	0.240	0.620	0.397	False
A0A024R0T9;K7ER74;P02655	AD	0.033	1.478	0.074	False	0.059	1.228	0.139	False
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	AD	0.257	0.591	0.380	False	0.040	1.393	0.103	False
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	AD	0.253	0.598	0.376	False	0.420	0.377	0.580	False
A0A075B6H7	AD	0.014	1.852	0.036	True	0.027	1.567	0.075	False
...	...	...	...	...	...	...	...	...	...
Q9Y6R7	AD	0.175	0.756	0.283	False	0.175	0.756	0.316	False
Q9Y6X5	AD	0.143	0.846	0.240	False	0.070	1.155	0.159	False
Q9Y6Y8;Q9Y6Y8-2	AD	0.083	1.079	0.156	False	0.083	1.079	0.182	False
Q9Y6Y9	AD	0.723	0.141	0.808	False	0.348	0.459	0.512	False
S4R3U6	AD	0.041	1.390	0.087	False	0.469	0.328	0.623	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	0.238	1.599	0.298	0.252	1.411	0.334
std	0.290	1.867	0.314	0.291	1.654	0.316
min	0.000	0.000	0.000	0.000	0.001	0.000
25%	0.007	0.384	0.019	0.013	0.368	0.041
50%	0.088	1.057	0.162	0.121	0.917	0.242
75%	0.413	2.185	0.543	0.428	1.899	0.588
max	0.999	23.307	0.999	0.999	25.104	0.999

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	938	1031

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	0.005	2.265	0.016	True	0.240	0.620	0.397	False	186
A0A024R0T9;K7ER74;P02655	0.033	1.478	0.074	False	0.059	1.228	0.139	False	195
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	0.257	0.591	0.380	False	0.040	1.393	0.103	False	174
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	0.253	0.598	0.376	False	0.420	0.377	0.580	False	196
A0A075B6H7	0.014	1.852	0.036	True	0.027	1.567	0.075	False	91
...	...	...	...	...	...	...	...	...	...
Q9Y6R7	0.175	0.756	0.283	False	0.175	0.756	0.316	False	197
Q9Y6X5	0.143	0.846	0.240	False	0.070	1.155	0.159	False	173
Q9Y6Y8;Q9Y6Y8-2	0.083	1.079	0.156	False	0.083	1.079	0.182	False	197
Q9Y6Y9	0.723	0.141	0.808	False	0.348	0.459	0.512	False	119
S4R3U6	0.041	1.390	0.087	False	0.469	0.328	0.623	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)    882
DAE (yes) - PI (yes)   334
DAE (yes) - PI (no)    149
DAE (no)  - PI (yes)    56
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_89059/1417621106.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	0.005	2.265	0.016	True	0.240	0.620	0.397	False	186
A0A075B6H7	0.014	1.852	0.036	True	0.027	1.567	0.075	False	91
A0A075B6I0	0.001	3.218	0.002	True	0.023	1.641	0.066	False	194
A0A075B6Q5	0.004	2.398	0.012	True	0.891	0.050	0.936	False	104
A0A075B6R2	0.000	3.353	0.002	True	0.259	0.587	0.419	False	164
...	...	...	...	...	...	...	...	...	...
Q9UKB5	0.093	1.030	0.171	False	0.014	1.861	0.044	True	148
Q9UNW1	0.960	0.018	0.976	False	0.016	1.803	0.049	True	171
Q9UP79	0.000	4.724	0.000	True	0.316	0.501	0.480	False	135
Q9UQ52	0.000	3.374	0.002	True	0.034	1.473	0.089	False	188
Q9Y6C2	0.015	1.820	0.038	True	0.828	0.082	0.896	False	119

205 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	0.016	0.397	186	DAE (yes) - PI (no)
A0A024R0T9;K7ER74;P02655	0.074	0.139	195	DAE (no) - PI (no)
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	0.380	0.103	174	DAE (no) - PI (no)
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	0.376	0.580	196	DAE (no) - PI (no)
A0A075B6H7	0.036	0.075	91	DAE (yes) - PI (no)
...	...	...	...	...
Q9Y6R7	0.283	0.316	197	DAE (no) - PI (no)
Q9Y6X5	0.240	0.159	173	DAE (no) - PI (no)
Q9Y6Y8;Q9Y6Y8-2	0.156	0.182	197	DAE (no) - PI (no)
Q9Y6Y9	0.808	0.512	119	DAE (no) - PI (no)
S4R3U6	0.087	0.623	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
P22692;P22692-2	0.043	0.988	170	DAE (yes) - PI (no)	0.946
P17302	0.001	0.942	135	DAE (yes) - PI (no)	0.940
A0A087WU43;A0A087WX17;A0A087WXI5;P12830;P12830-2	0.000	0.931	134	DAE (yes) - PI (no)	0.930
Q9UNW1	0.976	0.049	171	DAE (no) - PI (yes)	0.927
P22748	0.030	0.955	159	DAE (yes) - PI (no)	0.925
...	...	...	...	...	...
Q9NX62	0.045	0.056	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)	0.009
P09211	0.047	0.053	169	DAE (yes) - PI (no)	0.007

205 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
APP	ENSP00000284981	5.000
PSEN2	ENSP00000355747	5.000
PSEN1	ENSP00000326366	5.000
APOE	ENSP00000252486	5.000
TREM2	ENSP00000362205	4.825
...	...	...
PTTG1	ENSP00000377536	0.682
ISL2	ENSP00000290759	0.682
hsa-miR-4433b-3p	hsa-miR-4433b-3p	0.682
NEURL1B	ENSP00000358815	0.681
SLC26A4	ENSP00000494017	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/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