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	0.261	1	0.513	0.475	0.003	0.324	0.628	False
	age	0.017	1	0.034	0.854	0.000	0.069	0.911	False
	Kiel	1.577	1	3.106	0.080	0.016	1.099	0.175	False
	Magdeburg	4.866	1	9.584	0.002	0.048	2.646	0.010	True
	Sweden	8.181	1	16.112	0.000	0.078	4.067	0.001	True
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	0.248	1	0.255	0.614	0.001	0.212	0.741	False
	age	0.492	1	0.505	0.478	0.003	0.320	0.631	False
	Kiel	0.000	1	0.000	0.984	0.000	0.007	0.991	False
	Magdeburg	1.226	1	1.259	0.263	0.007	0.580	0.421	False
	Sweden	14.711	1	15.099	0.000	0.073	3.852	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	1.066	1	7.727	0.006	0.039	2.223	0.017	True
	age	0.003	1	0.021	0.886	0.000	0.053	0.926	False
	Kiel	0.260	1	1.883	0.172	0.010	0.765	0.279	False
	Magdeburg	0.492	1	3.565	0.061	0.018	1.218	0.121	False
	Sweden	1.740	1	12.620	0.000	0.062	3.318	0.002	True
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	2.843	1	4.846	0.029	0.025	1.539	0.066	False
	age	0.601	1	1.025	0.313	0.005	0.505	0.442	False
	Kiel	3.475	1	5.924	0.016	0.030	1.800	0.039	True
	Magdeburg	3.375	1	5.754	0.017	0.029	1.759	0.043	True
	Sweden	15.553	1	26.514	0.000	0.122	6.188	0.000	True

7105 rows × 8 columns

Combined scores

show only selected statistics for comparsion

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

	model	DAE				PI
	var	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected
protein groups	Source
A0A024QZX5;A0A087X1N8;P35237	AD	0.006	2.223	0.017	True	0.475	0.324	0.628	False
	Kiel	0.172	0.765	0.279	False	0.080	1.099	0.175	False
	Magdeburg	0.061	1.218	0.121	False	0.002	2.646	0.010	True
	Sweden	0.000	3.318	0.002	True	0.000	4.067	0.001	True
	age	0.886	0.053	0.926	False	0.854	0.069	0.911	False
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	0.029	1.539	0.066	False	0.614	0.212	0.741	False
	Kiel	0.016	1.800	0.039	True	0.984	0.007	0.991	False
	Magdeburg	0.017	1.759	0.043	True	0.263	0.580	0.421	False
	Sweden	0.000	6.188	0.000	True	0.000	3.852	0.001	True
	age	0.313	0.505	0.442	False	0.478	0.320	0.631	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	3.317	0.278	0.259	2.482	0.335
std	0.293	6.333	0.319	0.301	5.314	0.329
min	0.000	0.000	0.000	0.000	0.001	0.000
25%	0.001	0.408	0.002	0.004	0.334	0.015
50%	0.061	1.216	0.122	0.119	0.925	0.238
75%	0.391	3.275	0.521	0.463	2.435	0.618
max	0.999	86.886	0.999	0.999	147.300	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_99922/3761369923.py:2: FutureWarning: Starting with pandas version 3.0 all arguments of to_excel except for the argument 'excel_writer' will be keyword-only.
  scores.to_excel(writer, 'scores', **writer_args)

	model	DAE				PI
	var	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected
protein groups	Source
A0A024QZX5;A0A087X1N8;P35237	AD	0.006	2.223	0.017	True	0.475	0.324	0.628	False
A0A024R0T9;K7ER74;P02655	AD	0.034	1.473	0.075	False	0.029	1.531	0.080	False
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	AD	0.225	0.648	0.344	False	0.089	1.050	0.191	False
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	AD	0.257	0.590	0.382	False	0.508	0.294	0.656	False
A0A075B6H7	AD	0.019	1.728	0.046	True	0.122	0.913	0.243	False
...	...	...	...	...	...	...	...	...	...
Q9Y6R7	AD	0.175	0.756	0.284	False	0.175	0.756	0.316	False
Q9Y6X5	AD	0.281	0.551	0.410	False	0.044	1.354	0.110	False
Q9Y6Y8;Q9Y6Y8-2	AD	0.083	1.079	0.157	False	0.083	1.079	0.182	False
Q9Y6Y9	AD	0.701	0.154	0.791	False	0.094	1.025	0.200	False
S4R3U6	AD	0.029	1.539	0.066	False	0.614	0.212	0.741	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	0.237	1.613	0.297	0.257	1.407	0.337
std	0.290	1.891	0.314	0.296	1.627	0.320
min	0.000	0.001	0.000	0.000	0.001	0.000
25%	0.006	0.383	0.018	0.012	0.342	0.040
50%	0.084	1.074	0.158	0.119	0.925	0.238
75%	0.414	2.209	0.544	0.455	1.907	0.610
max	0.997	24.290	0.997	0.997	21.214	0.998

and the boolean decision values

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

model	DAE	PI
var	rejected	rejected
count	1421	1421
unique	2	2
top	False	False
freq	933	1033

Load frequencies of observed features

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freq_feat = pd.read_csv(args.freq_features_observed, index_col=0)
freq_feat.columns = pd.MultiIndex.from_tuples([('data', 'frequency'),])
freq_feat

	data
	frequency
protein groups
A0A024QZX5;A0A087X1N8;P35237	186
A0A024R0T9;K7ER74;P02655	195
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	174
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	196
A0A075B6H7	91
...	...
Q9Y6R7	197
Q9Y6X5	173
Q9Y6Y8;Q9Y6Y8-2	197
Q9Y6Y9	119
S4R3U6	126

1421 rows × 1 columns

Compare shared features

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scores_common = (scores
                 .dropna()
                 .reset_index(-1, drop=True)
                 ).join(
    freq_feat, how='left'
)
scores_common

	DAE				PI				data
	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected	frequency
protein groups
A0A024QZX5;A0A087X1N8;P35237	0.006	2.223	0.017	True	0.475	0.324	0.628	False	186
A0A024R0T9;K7ER74;P02655	0.034	1.473	0.075	False	0.029	1.531	0.080	False	195
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	0.225	0.648	0.344	False	0.089	1.050	0.191	False	174
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	0.257	0.590	0.382	False	0.508	0.294	0.656	False	196
A0A075B6H7	0.019	1.728	0.046	True	0.122	0.913	0.243	False	91
...	...	...	...	...	...	...	...	...	...
Q9Y6R7	0.175	0.756	0.284	False	0.175	0.756	0.316	False	197
Q9Y6X5	0.281	0.551	0.410	False	0.044	1.354	0.110	False	173
Q9Y6Y8;Q9Y6Y8-2	0.083	1.079	0.157	False	0.083	1.079	0.182	False	197
Q9Y6Y9	0.701	0.154	0.791	False	0.094	1.025	0.200	False	119
S4R3U6	0.029	1.539	0.066	False	0.614	0.212	0.741	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)    880
DAE (yes) - PI (yes)   335
DAE (yes) - PI (no)    153
DAE (no)  - PI (yes)    53
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_99922/1417621106.py:6: FutureWarning: Starting with pandas version 3.0 all arguments of to_excel except for the argument 'excel_writer' will be keyword-only.
  _to_write.to_excel(writer, 'differences', **writer_args)
root - INFO     Writen to Excel file under sheet 'differences'.

	DAE				PI				data
	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected	frequency
protein groups
A0A024QZX5;A0A087X1N8;P35237	0.006	2.223	0.017	True	0.475	0.324	0.628	False	186
A0A075B6H7	0.019	1.728	0.046	True	0.122	0.913	0.243	False	91
A0A075B6I0	0.001	3.174	0.003	True	0.018	1.738	0.055	False	194
A0A075B6J9	0.012	1.937	0.030	True	0.097	1.015	0.204	False	156
A0A075B6Q5	0.017	1.769	0.042	True	0.584	0.233	0.717	False	104
...	...	...	...	...	...	...	...	...	...
Q9UMX0;Q9UMX0-2;Q9UMX0-4	0.018	1.751	0.043	True	0.123	0.911	0.244	False	57
Q9UNW1	0.762	0.118	0.836	False	0.012	1.925	0.039	True	171
Q9UP79	0.000	4.621	0.000	True	0.198	0.704	0.343	False	135
Q9UQ52	0.000	3.387	0.002	True	0.040	1.397	0.102	False	188
Q9Y6C2	0.010	1.985	0.028	True	0.990	0.004	0.995	False	119

206 rows × 9 columns

Plot qvalues of both models with annotated decisions

Prepare data for plotting (qvalues)

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

	DAE	PI	frequency	Differential Analysis Comparison
protein groups
A0A024QZX5;A0A087X1N8;P35237	0.017	0.628	186	DAE (yes) - PI (no)
A0A024R0T9;K7ER74;P02655	0.075	0.080	195	DAE (no) - PI (no)
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	0.344	0.191	174	DAE (no) - PI (no)
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	0.382	0.656	196	DAE (no) - PI (no)
A0A075B6H7	0.046	0.243	91	DAE (yes) - PI (no)
...	...	...	...	...
Q9Y6R7	0.284	0.316	197	DAE (no) - PI (no)
Q9Y6X5	0.410	0.110	173	DAE (no) - PI (no)
Q9Y6Y8;Q9Y6Y8-2	0.157	0.182	197	DAE (no) - PI (no)
Q9Y6Y9	0.791	0.200	119	DAE (no) - PI (no)
S4R3U6	0.066	0.741	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
Q9Y6C2	0.028	0.995	119	DAE (yes) - PI (no)	0.967
A0A087WXE9;E9PQ70;Q6UXH9;Q6UXH9-2;Q6UXH9-3	0.011	0.976	108	DAE (yes) - PI (no)	0.965
P22692;P22692-2	0.035	0.987	170	DAE (yes) - PI (no)	0.952
P48745	0.012	0.955	180	DAE (yes) - PI (no)	0.943
P22748	0.033	0.976	159	DAE (yes) - PI (no)	0.943
...	...	...	...	...	...
A0A0J9YXX1	0.047	0.057	197	DAE (yes) - PI (no)	0.010
F5GY80;F5H7G1;P07358	0.046	0.057	197	DAE (yes) - PI (no)	0.010
Q9NX62	0.045	0.055	197	DAE (yes) - PI (no)	0.010
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

206 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
PSEN1	ENSP00000326366	5.000
APP	ENSP00000284981	5.000
PSEN2	ENSP00000355747	5.000
APOE	ENSP00000252486	5.000
TREM2	ENSP00000362205	4.825
...	...	...
CEP170B	ENSP00000404151	0.683
SMPDL3A	ENSP00000357425	0.683
ADAMTS10	ENSP00000471851	0.683
PPP3R2	ENSP00000498330	0.683
VAT1	ENSP00000347872	0.683

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:3755: 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