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 = "RF"
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': 'RF',
 'out_figures': PosixPath('runs/alzheimer_study/figures'),
 'out_folder': PosixPath('runs/alzheimer_study/diff_analysis/AD/PI_vs_RF'),
 '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_RF/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	RF
	var	SS	DF	F	p-unc	np2	-Log10 pvalue	qvalue	rejected
protein groups	Source
A0A024QZX5;A0A087X1N8;P35237	AD	0.943	1	7.097	0.008	0.036	2.077	0.024	True
	age	0.001	1	0.008	0.929	0.000	0.032	0.959	False
	Kiel	0.200	1	1.506	0.221	0.008	0.655	0.350	False
	Magdeburg	0.423	1	3.180	0.076	0.016	1.118	0.151	False
	Sweden	1.576	1	11.860	0.001	0.058	3.152	0.003	True
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	1.116	1	2.443	0.120	0.013	0.922	0.216	False
	age	0.983	1	2.152	0.144	0.011	0.842	0.250	False
	Kiel	1.931	1	4.226	0.041	0.022	1.385	0.091	False
	Magdeburg	1.500	1	3.282	0.072	0.017	1.145	0.143	False
	Sweden	12.159	1	26.609	0.000	0.122	6.207	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				RF
	var	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected
protein groups	Source
A0A024QZX5;A0A087X1N8;P35237	AD	0.528	0.277	0.670	False	0.008	2.077	0.024	True
	Kiel	0.050	1.303	0.121	False	0.221	0.655	0.350	False
	Magdeburg	0.004	2.376	0.016	True	0.076	1.118	0.151	False
	Sweden	0.000	3.391	0.002	True	0.001	3.152	0.003	True
	age	0.579	0.237	0.712	False	0.929	0.032	0.959	False
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	0.791	0.102	0.872	False	0.120	0.922	0.216	False
	Kiel	0.846	0.073	0.909	False	0.041	1.385	0.091	False
	Magdeburg	0.072	1.140	0.163	False	0.072	1.145	0.143	False
	Sweden	0.013	1.899	0.041	True	0.000	6.207	0.000	True
	age	0.377	0.424	0.538	False	0.144	0.842	0.250	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', 'RF': 'RF'}

Describe scores

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scores.describe()

model	PI			RF
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	0.259	2.472	0.336	0.234	3.078	0.292
std	0.301	5.305	0.328	0.297	5.787	0.324
min	0.000	0.000	0.000	0.000	0.000	0.000
25%	0.004	0.343	0.016	0.001	0.383	0.003
50%	0.123	0.910	0.246	0.071	1.146	0.143
75%	0.454	2.409	0.605	0.414	3.101	0.552
max	1.000	144.895	1.000	0.999	84.938	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_77384/3761369923.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				RF
	var	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected
protein groups	Source
A0A024QZX5;A0A087X1N8;P35237	AD	0.528	0.277	0.670	False	0.008	2.077	0.024	True
A0A024R0T9;K7ER74;P02655	AD	0.045	1.351	0.111	False	0.032	1.496	0.074	False
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	AD	0.078	1.106	0.174	False	0.382	0.418	0.520	False
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	AD	0.499	0.302	0.644	False	0.253	0.596	0.386	False
A0A075B6H7	AD	0.091	1.040	0.195	False	0.005	2.281	0.016	True
...	...	...	...	...	...	...	...	...	...
Q9Y6R7	AD	0.175	0.756	0.318	False	0.175	0.756	0.292	False
Q9Y6X5	AD	0.096	1.019	0.203	False	0.173	0.761	0.289	False
Q9Y6Y8;Q9Y6Y8-2	AD	0.083	1.079	0.182	False	0.083	1.079	0.162	False
Q9Y6Y9	AD	0.452	0.345	0.604	False	0.256	0.592	0.389	False
S4R3U6	AD	0.791	0.102	0.872	False	0.120	0.922	0.216	False

1421 rows × 8 columns

And the descriptive statistics of the numeric values:

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scores.describe()

model	PI			RF
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	0.251	1.400	0.333	0.248	1.517	0.313
std	0.290	1.600	0.314	0.296	1.759	0.319
min	0.000	0.000	0.000	0.000	0.001	0.000
25%	0.012	0.371	0.038	0.009	0.362	0.025
50%	0.124	0.907	0.247	0.100	1.002	0.187
75%	0.426	1.934	0.583	0.435	2.055	0.572
max	0.999	21.057	1.000	0.997	18.918	0.998

and the boolean decision values

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

model	PI	RF
var	rejected	rejected
count	1421	1421
unique	2	2
top	False	False
freq	1023	967

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				RF				data
	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected	frequency
protein groups
A0A024QZX5;A0A087X1N8;P35237	0.528	0.277	0.670	False	0.008	2.077	0.024	True	186
A0A024R0T9;K7ER74;P02655	0.045	1.351	0.111	False	0.032	1.496	0.074	False	195
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	0.078	1.106	0.174	False	0.382	0.418	0.520	False	174
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	0.499	0.302	0.644	False	0.253	0.596	0.386	False	196
A0A075B6H7	0.091	1.040	0.195	False	0.005	2.281	0.016	True	91
...	...	...	...	...	...	...	...	...	...
Q9Y6R7	0.175	0.756	0.318	False	0.175	0.756	0.292	False	197
Q9Y6X5	0.096	1.019	0.203	False	0.173	0.761	0.289	False	173
Q9Y6Y8;Q9Y6Y8-2	0.083	1.079	0.182	False	0.083	1.079	0.162	False	197
Q9Y6Y9	0.452	0.345	0.604	False	0.256	0.592	0.389	False	119
S4R3U6	0.791	0.102	0.872	False	0.120	0.922	0.216	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)  - RF (no)    896
PI (yes) - RF (yes)   327
PI (no)  - RF (yes)   127
PI (yes) - RF (no)     71
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_77384/1417621106.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				RF				data
	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected	frequency
protein groups
A0A024QZX5;A0A087X1N8;P35237	0.528	0.277	0.670	False	0.008	2.077	0.024	True	186
A0A075B6H7	0.091	1.040	0.195	False	0.005	2.281	0.016	True	91
A0A075B6J9	0.038	1.415	0.099	False	0.018	1.737	0.047	True	156
A0A075B6R2	0.189	0.723	0.335	False	0.001	2.883	0.005	True	164
A0A075B6S5	0.098	1.007	0.207	False	0.018	1.738	0.047	True	129
...	...	...	...	...	...	...	...	...	...
Q9UJ14	0.016	1.788	0.050	False	0.018	1.740	0.047	True	169
Q9ULZ9	0.004	2.394	0.016	True	0.055	1.259	0.116	False	171
Q9UP79	0.420	0.376	0.578	False	0.000	4.141	0.000	True	135
Q9UQ52	0.050	1.302	0.121	False	0.001	3.028	0.004	True	188
Q9Y653;Q9Y653-2;Q9Y653-3	0.013	1.879	0.042	True	0.808	0.093	0.876	False	177

198 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	RF	frequency	Differential Analysis Comparison
protein groups
A0A024QZX5;A0A087X1N8;P35237	0.670	0.024	186	PI (no) - RF (yes)
A0A024R0T9;K7ER74;P02655	0.111	0.074	195	PI (no) - RF (no)
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	0.174	0.520	174	PI (no) - RF (no)
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	0.644	0.386	196	PI (no) - RF (no)
A0A075B6H7	0.195	0.016	91	PI (no) - RF (yes)
...	...	...	...	...
Q9Y6R7	0.318	0.292	197	PI (no) - RF (no)
Q9Y6X5	0.203	0.289	173	PI (no) - RF (no)
Q9Y6Y8;Q9Y6Y8-2	0.182	0.162	197	PI (no) - RF (no)
Q9Y6Y9	0.604	0.389	119	PI (no) - RF (no)
S4R3U6	0.872	0.216	126	PI (no) - RF (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	RF	frequency	Differential Analysis Comparison	diff_qvalue
protein groups
Q96PQ0	0.005	0.995	177	PI (yes) - RF (no)	0.990
P52758	0.000	0.974	119	PI (yes) - RF (no)	0.974
F5GWE5;I3L2X8;I3L3W1;I3L459;I3L471;I3L4C0;I3L4H1;I3L4U7;Q00169	0.991	0.024	78	PI (no) - RF (yes)	0.968
P22748	0.999	0.038	159	PI (no) - RF (yes)	0.961
A0A087X1Z2;C9JTV4;H0Y4Y4;Q8WYH2;Q96C19;Q9BUP0;Q9BUP0-2	0.000	0.954	66	PI (yes) - RF (no)	0.954
...	...	...	...	...	...
F5GY80;F5H7G1;P07358	0.057	0.049	197	PI (no) - RF (yes)	0.008
P00740;P00740-2	0.053	0.045	197	PI (no) - RF (yes)	0.008
K7ERG9;P00746	0.052	0.044	197	PI (no) - RF (yes)	0.008
Q9UJ14	0.050	0.047	169	PI (no) - RF (yes)	0.004
Q16706	0.050	0.052	195	PI (yes) - RF (no)	0.002

198 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_RF/diff_analysis_comparision_1_RF

• 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_RF/diff_analysis_comparision_2_RF

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