Compare outcomes from differential analysis based on different imputation methods

• load scores based on 10_1_ald_diff_analysis

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import logging
from pathlib import Path

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

import pimmslearn
import pimmslearn.databases.diseases

logger = pimmslearn.logging.setup_nb_logger()

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

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

Parameters

Default and set parameters for the notebook.

folder_experiment = 'runs/appl_ald_data/plasma/proteinGroups'

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

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

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

Add set parameters to configuration

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

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

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

Excel file for exports

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

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

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

Load scores

Load baseline model scores

Show all statistics, later use selected statistics

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

	model	PI
	var	SS	DF	F	p-unc	np2	-Log10 pvalue	qvalue	rejected
protein groups	Source
A0A024QZX5;A0A087X1N8;P35237	AD	0.628	1	0.917	0.339	0.005	0.469	0.500	False
	age	0.142	1	0.208	0.649	0.001	0.188	0.769	False
	Kiel	2.676	1	3.910	0.049	0.020	1.306	0.121	False
	Magdeburg	5.703	1	8.331	0.004	0.042	2.362	0.017	True
	Sweden	9.257	1	13.523	0.000	0.066	3.514	0.002	True
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	0.135	1	0.141	0.707	0.001	0.150	0.812	False
	age	0.675	1	0.706	0.402	0.004	0.396	0.561	False
	Kiel	0.096	1	0.100	0.752	0.001	0.124	0.846	False
	Magdeburg	2.365	1	2.475	0.117	0.013	0.931	0.236	False
	Sweden	15.193	1	15.900	0.000	0.077	4.022	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	CF
	var	SS	DF	F	p-unc	np2	-Log10 pvalue	qvalue	rejected
protein groups	Source
A0A024QZX5;A0A087X1N8;P35237	AD	1.190	1	8.697	0.004	0.044	2.445	0.011	True
	age	0.009	1	0.063	0.801	0.000	0.096	0.869	False
	Kiel	0.266	1	1.945	0.165	0.010	0.783	0.271	False
	Magdeburg	0.389	1	2.843	0.093	0.015	1.030	0.173	False
	Sweden	1.535	1	11.219	0.001	0.055	3.011	0.004	True
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	1.660	1	3.260	0.073	0.017	1.139	0.141	False
	age	0.167	1	0.327	0.568	0.002	0.246	0.685	False
	Kiel	1.622	1	3.185	0.076	0.016	1.120	0.146	False
	Magdeburg	2.471	1	4.854	0.029	0.025	1.541	0.066	False
	Sweden	18.999	1	37.313	0.000	0.163	8.258	0.000	True

7105 rows × 8 columns

Combined scores

show only selected statistics for comparsion

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

	model	CF				PI
	var	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected
protein groups	Source
A0A024QZX5;A0A087X1N8;P35237	AD	0.004	2.445	0.011	True	0.339	0.469	0.500	False
	Kiel	0.165	0.783	0.271	False	0.049	1.306	0.121	False
	Magdeburg	0.093	1.030	0.173	False	0.004	2.362	0.017	True
	Sweden	0.001	3.011	0.004	True	0.000	3.514	0.002	True
	age	0.801	0.096	0.869	False	0.649	0.188	0.769	False
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	0.073	1.139	0.141	False	0.707	0.150	0.812	False
	Kiel	0.076	1.120	0.146	False	0.752	0.124	0.846	False
	Magdeburg	0.029	1.541	0.066	False	0.117	0.931	0.236	False
	Sweden	0.000	8.258	0.000	True	0.000	4.022	0.001	True
	age	0.568	0.246	0.685	False	0.402	0.396	0.561	False

7105 rows × 8 columns

Models in comparison (name mapping)

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

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

Describe scores

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scores.describe()

model	CF			PI
var	p-unc	-Log10 pvalue	qvalue	p-unc	-Log10 pvalue	qvalue
count	7,105.000	7,105.000	7,105.000	7,105.000	7,105.000	7,105.000
mean	0.226	3.267	0.280	0.259	2.483	0.335
std	0.294	6.231	0.321	0.301	5.337	0.329
min	0.000	0.001	0.000	0.000	0.000	0.000
25%	0.001	0.403	0.002	0.004	0.337	0.015
50%	0.062	1.207	0.124	0.121	0.918	0.242
75%	0.396	3.289	0.527	0.460	2.425	0.613
max	0.997	86.519	0.997	1.000	147.869	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_87417/3761369923.py:2: FutureWarning: Starting with pandas version 3.0 all arguments of to_excel except for the argument 'excel_writer' will be keyword-only.
  scores.to_excel(writer, 'scores', **writer_args)

	model	CF				PI
	var	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected
protein groups	Source
A0A024QZX5;A0A087X1N8;P35237	AD	0.004	2.445	0.011	True	0.339	0.469	0.500	False
A0A024R0T9;K7ER74;P02655	AD	0.031	1.506	0.071	False	0.061	1.213	0.143	False
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	AD	0.117	0.931	0.207	False	0.058	1.238	0.136	False
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	AD	0.259	0.587	0.385	False	0.562	0.250	0.700	False
A0A075B6H7	AD	0.002	2.758	0.006	True	0.172	0.764	0.311	False
...	...	...	...	...	...	...	...	...	...
Q9Y6R7	AD	0.175	0.756	0.285	False	0.175	0.756	0.315	False
Q9Y6X5	AD	0.203	0.692	0.320	False	0.047	1.325	0.117	False
Q9Y6Y8;Q9Y6Y8-2	AD	0.083	1.079	0.158	False	0.083	1.079	0.182	False
Q9Y6Y9	AD	0.477	0.322	0.602	False	0.570	0.244	0.707	False
S4R3U6	AD	0.073	1.139	0.141	False	0.707	0.150	0.812	False

1421 rows × 8 columns

And the descriptive statistics of the numeric values:

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scores.describe()

model	CF			PI
var	p-unc	-Log10 pvalue	qvalue	p-unc	-Log10 pvalue	qvalue
count	1,421.000	1,421.000	1,421.000	1,421.000	1,421.000	1,421.000
mean	0.236	1.606	0.297	0.253	1.396	0.335
std	0.291	1.879	0.314	0.291	1.589	0.316
min	0.000	0.001	0.000	0.000	0.003	0.000
25%	0.006	0.387	0.019	0.011	0.347	0.037
50%	0.086	1.065	0.162	0.122	0.913	0.243
75%	0.410	2.192	0.542	0.450	1.943	0.605
max	0.997	23.087	0.997	0.992	19.497	0.995

and the boolean decision values

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

model	CF	PI
var	rejected	rejected
count	1421	1421
unique	2	2
top	False	False
freq	950	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

	CF				PI				data
	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected	frequency
protein groups
A0A024QZX5;A0A087X1N8;P35237	0.004	2.445	0.011	True	0.339	0.469	0.500	False	186
A0A024R0T9;K7ER74;P02655	0.031	1.506	0.071	False	0.061	1.213	0.143	False	195
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	0.117	0.931	0.207	False	0.058	1.238	0.136	False	174
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	0.259	0.587	0.385	False	0.562	0.250	0.700	False	196
A0A075B6H7	0.002	2.758	0.006	True	0.172	0.764	0.311	False	91
...	...	...	...	...	...	...	...	...	...
Q9Y6R7	0.175	0.756	0.285	False	0.175	0.756	0.315	False	197
Q9Y6X5	0.203	0.692	0.320	False	0.047	1.325	0.117	False	173
Q9Y6Y8;Q9Y6Y8-2	0.083	1.079	0.158	False	0.083	1.079	0.182	False	197
Q9Y6Y9	0.477	0.322	0.602	False	0.570	0.244	0.707	False	119
S4R3U6	0.073	1.139	0.141	False	0.707	0.150	0.812	False	126

1421 rows × 9 columns

Annotate decisions in Confusion Table style:

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


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

Differential Analysis Comparison
CF (no)  - PI (no)    893
CF (yes) - PI (yes)   333
CF (yes) - PI (no)    138
CF (no)  - PI (yes)    57
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_87417/1417621106.py:6: FutureWarning: Starting with pandas version 3.0 all arguments of to_excel except for the argument 'excel_writer' will be keyword-only.
  _to_write.to_excel(writer, 'differences', **writer_args)
root - INFO     Writen to Excel file under sheet 'differences'.

	CF				PI				data
	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected	frequency
protein groups
A0A024QZX5;A0A087X1N8;P35237	0.004	2.445	0.011	True	0.339	0.469	0.500	False	186
A0A075B6H7	0.002	2.758	0.006	True	0.172	0.764	0.311	False	91
A0A075B6J9	0.007	2.168	0.019	True	0.037	1.429	0.097	False	156
A0A075B6R2	0.002	2.793	0.006	True	0.373	0.428	0.534	False	164
A0A075B7B8	0.002	2.641	0.008	True	0.498	0.303	0.649	False	57
...	...	...	...	...	...	...	...	...	...
Q9ULZ9	0.036	1.445	0.079	False	0.002	2.682	0.009	True	171
Q9UNW1	0.916	0.038	0.947	False	0.011	1.942	0.037	True	171
Q9UP79	0.000	4.066	0.000	True	0.223	0.651	0.374	False	135
Q9UQ52	0.001	3.166	0.003	True	0.043	1.365	0.109	False	188
Q9Y6C2	0.019	1.711	0.048	True	0.702	0.153	0.808	False	119

195 rows × 9 columns

Plot qvalues of both models with annotated decisions

Prepare data for plotting (qvalues)

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

	CF	PI	frequency	Differential Analysis Comparison
protein groups
A0A024QZX5;A0A087X1N8;P35237	0.011	0.500	186	CF (yes) - PI (no)
A0A024R0T9;K7ER74;P02655	0.071	0.143	195	CF (no) - PI (no)
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	0.207	0.136	174	CF (no) - PI (no)
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	0.385	0.700	196	CF (no) - PI (no)
A0A075B6H7	0.006	0.311	91	CF (yes) - PI (no)
...	...	...	...	...
Q9Y6R7	0.285	0.315	197	CF (no) - PI (no)
Q9Y6X5	0.320	0.117	173	CF (no) - PI (no)
Q9Y6Y8;Q9Y6Y8-2	0.158	0.182	197	CF (no) - PI (no)
Q9Y6Y9	0.602	0.707	119	CF (no) - PI (no)
S4R3U6	0.141	0.812	126	CF (no) - PI (no)

1421 rows × 4 columns

List of features with the highest difference in qvalues

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

	CF	PI	frequency	Differential Analysis Comparison	diff_qvalue
protein groups
E5RJY1;E7ESM1;Q92597;Q92597-2;Q92597-3	0.009	0.995	60	CF (yes) - PI (no)	0.985
O94898	0.010	0.957	60	CF (yes) - PI (no)	0.947
Q9NPH3;Q9NPH3-2;Q9NPH3-5	0.024	0.966	186	CF (yes) - PI (no)	0.942
F5GWE5;I3L2X8;I3L3W1;I3L459;I3L471;I3L4C0;I3L4H1;I3L4U7;Q00169	0.011	0.938	78	CF (yes) - PI (no)	0.927
Q9UNW1	0.947	0.037	171	CF (no) - PI (yes)	0.910
...	...	...	...	...	...
F5GY80;F5H7G1;P07358	0.047	0.057	197	CF (yes) - PI (no)	0.010
Q9NX62	0.046	0.055	197	CF (yes) - PI (no)	0.010
P00740;P00740-2	0.043	0.052	197	CF (yes) - PI (no)	0.009
K7ERG9;P00746	0.042	0.051	197	CF (yes) - PI (no)	0.009
Q8N436	0.047	0.052	188	CF (yes) - PI (no)	0.005

195 rows × 5 columns

Differences plotted with created annotations

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

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

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

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

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

Only features contained in model

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

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

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

root - INFO     No features only in new comparision model.

DISEASES DB lookup

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

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

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

	ENSP	score
None
APP	ENSP00000284981	5.000
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