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	0.261	1	0.456	0.501	0.002	0.301	0.653	False
	age	0.036	1	0.063	0.802	0.000	0.096	0.878	False
	Kiel	1.631	1	2.848	0.093	0.015	1.031	0.198	False
	Magdeburg	4.653	1	8.127	0.005	0.041	2.315	0.018	True
	Sweden	7.129	1	12.451	0.001	0.061	3.281	0.003	True
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	0.832	1	0.895	0.345	0.005	0.462	0.508	False
	age	0.017	1	0.019	0.891	0.000	0.050	0.934	False
	Kiel	0.472	1	0.508	0.477	0.003	0.322	0.630	False
	Magdeburg	1.695	1	1.824	0.178	0.009	0.749	0.320	False
	Sweden	13.108	1	14.110	0.000	0.069	3.640	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	VAE
	var	SS	DF	F	p-unc	np2	-Log10 pvalue	qvalue	rejected
protein groups	Source
A0A024QZX5;A0A087X1N8;P35237	AD	1.005	1	7.427	0.007	0.037	2.153	0.020	True
	age	0.011	1	0.081	0.776	0.000	0.110	0.847	False
	Kiel	0.262	1	1.938	0.165	0.010	0.781	0.269	False
	Magdeburg	0.416	1	3.072	0.081	0.016	1.090	0.153	False
	Sweden	1.584	1	11.703	0.001	0.058	3.117	0.003	True
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	1.745	1	3.522	0.062	0.018	1.207	0.122	False
	age	0.489	1	0.988	0.322	0.005	0.493	0.453	False
	Kiel	2.470	1	4.986	0.027	0.025	1.573	0.061	False
	Magdeburg	2.088	1	4.216	0.041	0.022	1.383	0.088	False
	Sweden	15.708	1	31.715	0.000	0.142	7.200	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	0.501	0.301	0.653	False	0.007	2.153	0.020	True
	Kiel	0.093	1.031	0.198	False	0.165	0.781	0.269	False
	Magdeburg	0.005	2.315	0.018	True	0.081	1.090	0.153	False
	Sweden	0.001	3.281	0.003	True	0.001	3.117	0.003	True
	age	0.802	0.096	0.878	False	0.776	0.110	0.847	False
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	0.345	0.462	0.508	False	0.062	1.207	0.122	False
	Kiel	0.477	0.322	0.630	False	0.027	1.573	0.061	False
	Magdeburg	0.178	0.749	0.320	False	0.041	1.383	0.088	False
	Sweden	0.000	3.640	0.001	True	0.000	7.200	0.000	True
	age	0.891	0.050	0.934	False	0.322	0.493	0.453	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	0.260	2.484	0.337	0.223	3.313	0.276
std	0.301	5.376	0.329	0.293	6.191	0.319
min	0.000	0.000	0.000	0.000	0.000	0.000
25%	0.004	0.336	0.015	0.000	0.407	0.002
50%	0.123	0.909	0.247	0.059	1.232	0.117
75%	0.462	2.431	0.615	0.392	3.343	0.522
max	1.000	145.647	1.000	1.000	86.907	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_111367/3761369923.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	0.501	0.301	0.653	False	0.007	2.153	0.020	True
A0A024R0T9;K7ER74;P02655	AD	0.035	1.458	0.092	False	0.030	1.527	0.067	False
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	AD	0.102	0.990	0.214	False	0.298	0.526	0.428	False
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	AD	0.620	0.208	0.747	False	0.253	0.597	0.377	False
A0A075B6H7	AD	0.063	1.202	0.146	False	0.007	2.179	0.019	True
...	...	...	...	...	...	...	...	...	...
Q9Y6R7	AD	0.175	0.756	0.317	False	0.175	0.756	0.282	False
Q9Y6X5	AD	0.057	1.241	0.136	False	0.215	0.668	0.332	False
Q9Y6Y8;Q9Y6Y8-2	AD	0.083	1.079	0.183	False	0.083	1.079	0.155	False
Q9Y6Y9	AD	0.561	0.251	0.697	False	0.742	0.130	0.822	False
S4R3U6	AD	0.345	0.462	0.508	False	0.062	1.207	0.122	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	0.253	1.409	0.335	0.238	1.579	0.298
std	0.291	1.643	0.316	0.288	1.825	0.312
min	0.000	0.001	0.000	0.000	0.000	0.000
25%	0.012	0.363	0.040	0.007	0.380	0.020
50%	0.121	0.916	0.243	0.089	1.052	0.163
75%	0.434	1.909	0.591	0.417	2.141	0.546
max	0.997	23.415	0.998	1.000	20.499	1.000

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	947

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	0.501	0.301	0.653	False	0.007	2.153	0.020	True	186
A0A024R0T9;K7ER74;P02655	0.035	1.458	0.092	False	0.030	1.527	0.067	False	195
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	0.102	0.990	0.214	False	0.298	0.526	0.428	False	174
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	0.620	0.208	0.747	False	0.253	0.597	0.377	False	196
A0A075B6H7	0.063	1.202	0.146	False	0.007	2.179	0.019	True	91
...	...	...	...	...	...	...	...	...	...
Q9Y6R7	0.175	0.756	0.317	False	0.175	0.756	0.282	False	197
Q9Y6X5	0.057	1.241	0.136	False	0.215	0.668	0.332	False	173
Q9Y6Y8;Q9Y6Y8-2	0.083	1.079	0.183	False	0.083	1.079	0.155	False	197
Q9Y6Y9	0.561	0.251	0.697	False	0.742	0.130	0.822	False	119
S4R3U6	0.345	0.462	0.508	False	0.062	1.207	0.122	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)    883
PI (yes) - VAE (yes)   332
PI (no)  - VAE (yes)   142
PI (yes) - VAE (no)     64
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_111367/1417621106.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	0.501	0.301	0.653	False	0.007	2.153	0.020	True	186
A0A075B6H7	0.063	1.202	0.146	False	0.007	2.179	0.019	True	91
A0A075B6I0	0.032	1.488	0.088	False	0.001	3.165	0.003	True	194
A0A075B6J9	0.069	1.158	0.158	False	0.015	1.812	0.038	True	156
A0A075B6Q5	0.797	0.099	0.876	False	0.010	2.018	0.026	True	104
...	...	...	...	...	...	...	...	...	...
Q9UIB8;Q9UIB8-2;Q9UIB8-3;Q9UIB8-4;Q9UIB8-5;Q9UIB8-6	0.001	2.922	0.006	True	0.085	1.072	0.157	False	115
Q9ULP0-3;Q9ULP0-6	0.025	1.596	0.072	False	0.000	3.620	0.001	True	136
Q9UP79	0.346	0.460	0.509	False	0.000	4.816	0.000	True	135
Q9UQ52	0.061	1.212	0.144	False	0.001	3.229	0.002	True	188
Q9Y6C2	0.588	0.230	0.721	False	0.009	2.028	0.025	True	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

	PI	VAE	frequency	Differential Analysis Comparison
protein groups
A0A024QZX5;A0A087X1N8;P35237	0.653	0.020	186	PI (no) - VAE (yes)
A0A024R0T9;K7ER74;P02655	0.092	0.067	195	PI (no) - VAE (no)
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	0.214	0.428	174	PI (no) - VAE (no)
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	0.747	0.377	196	PI (no) - VAE (no)
A0A075B6H7	0.146	0.019	91	PI (no) - VAE (yes)
...	...	...	...	...
Q9Y6R7	0.317	0.282	197	PI (no) - VAE (no)
Q9Y6X5	0.136	0.332	173	PI (no) - VAE (no)
Q9Y6Y8;Q9Y6Y8-2	0.183	0.155	197	PI (no) - VAE (no)
Q9Y6Y9	0.697	0.822	119	PI (no) - VAE (no)
S4R3U6	0.508	0.122	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
A0A075B7B8	0.982	0.000	57	PI (no) - VAE (yes)	0.982
Q8N9I0	0.981	0.014	141	PI (no) - VAE (yes)	0.967
O15197;O15197-3	0.969	0.012	104	PI (no) - VAE (yes)	0.957
F6VDH7;P50502;Q3KNR6	0.006	0.951	175	PI (yes) - VAE (no)	0.946
A0A087WU43;A0A087WX17;A0A087WXI5;P12830;P12830-2	0.942	0.000	134	PI (no) - VAE (yes)	0.942
...	...	...	...	...	...
Q9NX62	0.056	0.045	197	PI (no) - VAE (yes)	0.011
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
K7ERI9;P02654	0.042	0.051	196	PI (yes) - VAE (no)	0.009
P26572	0.058	0.049	194	PI (no) - VAE (yes)	0.009

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_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
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