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 = "VAE"
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': 'VAE',
 'out_figures': PosixPath('runs/alzheimer_study/figures'),
 'out_folder': PosixPath('runs/alzheimer_study/diff_analysis/AD/PI_vs_VAE'),
 '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_VAE/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	0.633	1	0.931	0.336	0.005	0.474	0.502	False
	age	0.035	1	0.052	0.820	0.000	0.086	0.894	False
	Kiel	2.253	1	3.315	0.070	0.017	1.154	0.159	False
	Magdeburg	5.871	1	8.639	0.004	0.043	2.432	0.015	True
	Sweden	10.533	1	15.498	0.000	0.075	3.937	0.001	True
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	0.795	1	0.798	0.373	0.004	0.428	0.538	False
	age	0.835	1	0.838	0.361	0.004	0.442	0.526	False
	Kiel	0.537	1	0.538	0.464	0.003	0.333	0.619	False
	Magdeburg	3.819	1	3.831	0.052	0.020	1.286	0.125	False
	Sweden	4.139	1	4.153	0.043	0.021	1.367	0.108	False

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	VAE
	var	SS	DF	F	p-unc	np2	-Log10 pvalue	qvalue	rejected
protein groups	Source
A0A024QZX5;A0A087X1N8;P35237	AD	1.029	1	7.334	0.007	0.037	2.132	0.021	True
	age	0.011	1	0.080	0.777	0.000	0.109	0.851	False
	Kiel	0.318	1	2.267	0.134	0.012	0.873	0.229	False
	Magdeburg	0.534	1	3.806	0.053	0.020	1.280	0.108	False
	Sweden	1.814	1	12.934	0.000	0.063	3.386	0.002	True
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	1.769	1	3.573	0.060	0.018	1.220	0.121	False
	age	0.624	1	1.260	0.263	0.007	0.580	0.389	False
	Kiel	2.754	1	5.562	0.019	0.028	1.713	0.047	True
	Magdeburg	2.388	1	4.821	0.029	0.025	1.533	0.066	False
	Sweden	19.067	1	38.503	0.000	0.168	8.479	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	PI				VAE
	var	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected
protein groups	Source
A0A024QZX5;A0A087X1N8;P35237	AD	0.336	0.474	0.502	False	0.007	2.132	0.021	True
	Kiel	0.070	1.154	0.159	False	0.134	0.873	0.229	False
	Magdeburg	0.004	2.432	0.015	True	0.053	1.280	0.108	False
	Sweden	0.000	3.937	0.001	True	0.000	3.386	0.002	True
	age	0.820	0.086	0.894	False	0.777	0.109	0.851	False
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	0.373	0.428	0.538	False	0.060	1.220	0.121	False
	Kiel	0.464	0.333	0.619	False	0.019	1.713	0.047	True
	Magdeburg	0.052	1.286	0.125	False	0.029	1.533	0.066	False
	Sweden	0.043	1.367	0.108	False	0.000	8.479	0.000	True
	age	0.361	0.442	0.526	False	0.263	0.580	0.389	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)

{'PI': 'PI', 'VAE': 'VAE'}

Describe scores

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scores.describe()

model	PI			VAE
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	0.262	2.481	0.339	0.224	3.301	0.278
std	0.304	5.331	0.332	0.294	6.177	0.321
min	0.000	0.000	0.000	0.000	0.000	0.000
25%	0.004	0.332	0.015	0.000	0.408	0.002
50%	0.119	0.923	0.239	0.061	1.217	0.121
75%	0.465	2.416	0.620	0.391	3.329	0.522
max	1.000	149.342	1.000	0.999	86.369	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_80050/3761369923.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	PI				VAE
	var	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected
protein groups	Source
A0A024QZX5;A0A087X1N8;P35237	AD	0.336	0.474	0.502	False	0.007	2.132	0.021	True
A0A024R0T9;K7ER74;P02655	AD	0.043	1.364	0.109	False	0.032	1.491	0.072	False
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	AD	0.072	1.144	0.161	False	0.277	0.558	0.403	False
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	AD	0.586	0.232	0.724	False	0.258	0.589	0.383	False
A0A075B6H7	AD	0.111	0.955	0.227	False	0.004	2.404	0.012	True
...	...	...	...	...	...	...	...	...	...
Q9Y6R7	AD	0.175	0.756	0.318	False	0.175	0.756	0.283	False
Q9Y6X5	AD	0.032	1.495	0.086	False	0.239	0.622	0.361	False
Q9Y6Y8;Q9Y6Y8-2	AD	0.083	1.079	0.182	False	0.083	1.079	0.157	False
Q9Y6Y9	AD	0.409	0.388	0.572	False	0.896	0.048	0.936	False
S4R3U6	AD	0.373	0.428	0.538	False	0.060	1.220	0.121	False

1421 rows × 8 columns

And the descriptive statistics of the numeric values:

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scores.describe()

model	PI			VAE
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	0.255	1.414	0.336	0.239	1.601	0.299
std	0.296	1.648	0.321	0.294	1.848	0.317
min	0.000	0.001	0.000	0.000	0.000	0.000
25%	0.011	0.365	0.038	0.007	0.376	0.019
50%	0.116	0.934	0.234	0.085	1.069	0.160
75%	0.432	1.941	0.592	0.421	2.168	0.550
max	0.998	23.733	0.999	0.999	20.448	0.999

and the boolean decision values

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

model	PI	VAE
var	rejected	rejected
count	1421	1421
unique	2	2
top	False	False
freq	1025	935

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

	PI				VAE				data
	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected	frequency
protein groups
A0A024QZX5;A0A087X1N8;P35237	0.336	0.474	0.502	False	0.007	2.132	0.021	True	186
A0A024R0T9;K7ER74;P02655	0.043	1.364	0.109	False	0.032	1.491	0.072	False	195
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	0.072	1.144	0.161	False	0.277	0.558	0.403	False	174
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	0.586	0.232	0.724	False	0.258	0.589	0.383	False	196
A0A075B6H7	0.111	0.955	0.227	False	0.004	2.404	0.012	True	91
...	...	...	...	...	...	...	...	...	...
Q9Y6R7	0.175	0.756	0.318	False	0.175	0.756	0.283	False	197
Q9Y6X5	0.032	1.495	0.086	False	0.239	0.622	0.361	False	173
Q9Y6Y8;Q9Y6Y8-2	0.083	1.079	0.182	False	0.083	1.079	0.157	False	197
Q9Y6Y9	0.409	0.388	0.572	False	0.896	0.048	0.936	False	119
S4R3U6	0.373	0.428	0.538	False	0.060	1.220	0.121	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
PI (no)  - VAE (no)    873
PI (yes) - VAE (yes)   334
PI (no)  - VAE (yes)   152
PI (yes) - VAE (no)     62
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_80050/1417621106.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'.

	PI				VAE				data
	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected	frequency
protein groups
A0A024QZX5;A0A087X1N8;P35237	0.336	0.474	0.502	False	0.007	2.132	0.021	True	186
A0A075B6H7	0.111	0.955	0.227	False	0.004	2.404	0.012	True	91
A0A075B6H9	0.493	0.307	0.646	False	0.019	1.732	0.045	True	189
A0A075B6I0	0.027	1.575	0.074	False	0.001	3.194	0.002	True	194
A0A075B6J9	0.027	1.563	0.076	False	0.014	1.846	0.036	True	156
...	...	...	...	...	...	...	...	...	...
Q9UKB5	0.006	2.234	0.022	True	0.118	0.930	0.207	False	148
Q9ULP0-3;Q9ULP0-6	0.023	1.635	0.067	False	0.000	3.579	0.001	True	136
Q9UP79	0.372	0.429	0.537	False	0.000	4.605	0.000	True	135
Q9UQ52	0.073	1.134	0.164	False	0.001	3.296	0.002	True	188
Q9Y6C2	0.775	0.110	0.864	False	0.005	2.292	0.015	True	119

214 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

	PI	VAE	frequency	Differential Analysis Comparison
protein groups
A0A024QZX5;A0A087X1N8;P35237	0.502	0.021	186	PI (no) - VAE (yes)
A0A024R0T9;K7ER74;P02655	0.109	0.072	195	PI (no) - VAE (no)
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	0.161	0.403	174	PI (no) - VAE (no)
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	0.724	0.383	196	PI (no) - VAE (no)
A0A075B6H7	0.227	0.012	91	PI (no) - VAE (yes)
...	...	...	...	...
Q9Y6R7	0.318	0.283	197	PI (no) - VAE (no)
Q9Y6X5	0.086	0.361	173	PI (no) - VAE (no)
Q9Y6Y8;Q9Y6Y8-2	0.182	0.157	197	PI (no) - VAE (no)
Q9Y6Y9	0.572	0.936	119	PI (no) - VAE (no)
S4R3U6	0.538	0.121	126	PI (no) - VAE (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)

	PI	VAE	frequency	Differential Analysis Comparison	diff_qvalue
protein groups
O15197;O15197-3	0.980	0.009	104	PI (no) - VAE (yes)	0.971
O00187;O00187-2	0.999	0.030	119	PI (no) - VAE (yes)	0.969
Q8N9I0	0.989	0.030	141	PI (no) - VAE (yes)	0.960
Q9NPH3;Q9NPH3-2;Q9NPH3-5	0.976	0.035	186	PI (no) - VAE (yes)	0.941
H3BRQ4;K4DIB9;P50238	0.941	0.007	72	PI (no) - VAE (yes)	0.934
...	...	...	...	...	...
K7ERI9;P02654	0.043	0.054	196	PI (yes) - VAE (no)	0.011
F5GY80;F5H7G1;P07358	0.057	0.046	197	PI (no) - VAE (yes)	0.011
Q9NX62	0.055	0.045	197	PI (no) - VAE (yes)	0.010
P00740;P00740-2	0.053	0.043	197	PI (no) - VAE (yes)	0.010
K7ERG9;P00746	0.052	0.042	197	PI (no) - VAE (yes)	0.010

214 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_VAE/diff_analysis_comparision_1_VAE

• 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_VAE/diff_analysis_comparision_2_VAE

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
PSEN2	ENSP00000355747	5.000
PSEN1	ENSP00000326366	5.000
APP	ENSP00000284981	5.000
TREM2	ENSP00000362205	4.825
...	...	...
ERP27	ENSP00000266397	0.681
ZNF585B	ENSP00000433773	0.681
KIR3DL2	ENSP00000325525	0.681
C12orf66	ENSP00000311486	0.681
ELP2	ENSP00000414851	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