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 = "QRILC"
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': 'QRILC',
 'out_figures': PosixPath('runs/alzheimer_study/figures'),
 'out_folder': PosixPath('runs/alzheimer_study/diff_analysis/AD/PI_vs_QRILC'),
 '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_QRILC/diff_analysis_compare_methods.xlsx

Load scores

Load baseline model scores

Show all statistics, later use selected statistics

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

	model	PI
	var	SS	DF	F	p-unc	np2	-Log10 pvalue	qvalue	rejected
protein groups	Source
A0A024QZX5;A0A087X1N8;P35237	AD	0.633	1	0.931	0.336	0.005	0.474	0.502	False
	age	0.035	1	0.052	0.820	0.000	0.086	0.894	False
	Kiel	2.253	1	3.315	0.070	0.017	1.154	0.159	False
	Magdeburg	5.871	1	8.639	0.004	0.043	2.432	0.015	True
	Sweden	10.533	1	15.498	0.000	0.075	3.937	0.001	True
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	0.795	1	0.798	0.373	0.004	0.428	0.538	False
	age	0.835	1	0.838	0.361	0.004	0.442	0.526	False
	Kiel	0.537	1	0.538	0.464	0.003	0.333	0.619	False
	Magdeburg	3.819	1	3.831	0.052	0.020	1.286	0.125	False
	Sweden	4.139	1	4.153	0.043	0.021	1.367	0.108	False

7105 rows × 8 columns

Load selected comparison model scores

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

	model	QRILC
	var	SS	DF	F	p-unc	np2	-Log10 pvalue	qvalue	rejected
protein groups	Source
A0A024QZX5;A0A087X1N8;P35237	AD	0.705	1	4.314	0.039	0.022	1.407	0.094	False
	age	0.017	1	0.101	0.750	0.001	0.125	0.835	False
	Kiel	0.471	1	2.882	0.091	0.015	1.040	0.183	False
	Magdeburg	1.017	1	6.222	0.013	0.032	1.871	0.039	True
	Sweden	2.631	1	16.098	0.000	0.078	4.064	0.001	True
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	0.852	1	0.417	0.519	0.002	0.285	0.654	False
	age	1.953	1	0.956	0.329	0.005	0.482	0.480	False
	Kiel	8.269	1	4.048	0.046	0.021	1.341	0.106	False
	Magdeburg	18.484	1	9.049	0.003	0.045	2.525	0.011	True
	Sweden	0.071	1	0.035	0.853	0.000	0.069	0.907	False

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				QRILC
	var	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected
protein groups	Source
A0A024QZX5;A0A087X1N8;P35237	AD	0.336	0.474	0.502	False	0.039	1.407	0.094	False
	Kiel	0.070	1.154	0.159	False	0.091	1.040	0.183	False
	Magdeburg	0.004	2.432	0.015	True	0.013	1.871	0.039	True
	Sweden	0.000	3.937	0.001	True	0.000	4.064	0.001	True
	age	0.820	0.086	0.894	False	0.750	0.125	0.835	False
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	0.373	0.428	0.538	False	0.519	0.285	0.654	False
	Kiel	0.464	0.333	0.619	False	0.046	1.341	0.106	False
	Magdeburg	0.052	1.286	0.125	False	0.003	2.525	0.011	True
	Sweden	0.043	1.367	0.108	False	0.853	0.069	0.907	False
	age	0.361	0.442	0.526	False	0.329	0.482	0.480	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', 'QRILC': 'QRILC'}

Describe scores

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scores.describe()

model	PI			QRILC
var	p-unc	-Log10 pvalue	qvalue	p-unc	-Log10 pvalue	qvalue
count	7,105.000	7,105.000	7,105.000	7,105.000	7,105.000	7,105.000
mean	0.262	2.481	0.339	0.245	2.748	0.311
std	0.304	5.331	0.332	0.298	5.182	0.325
min	0.000	0.000	0.000	0.000	0.000	0.000
25%	0.004	0.332	0.015	0.002	0.359	0.008
50%	0.119	0.923	0.239	0.093	1.029	0.187
75%	0.465	2.416	0.620	0.438	2.710	0.583
max	1.000	149.342	1.000	0.999	84.138	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_80365/3761369923.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				QRILC
	var	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected
protein groups	Source
A0A024QZX5;A0A087X1N8;P35237	AD	0.336	0.474	0.502	False	0.039	1.407	0.094	False
A0A024R0T9;K7ER74;P02655	AD	0.043	1.364	0.109	False	0.028	1.560	0.071	False
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	AD	0.072	1.144	0.161	False	0.311	0.508	0.460	False
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	AD	0.586	0.232	0.724	False	0.297	0.528	0.446	False
A0A075B6H7	AD	0.111	0.955	0.227	False	0.158	0.802	0.277	False
...	...	...	...	...	...	...	...	...	...
Q9Y6R7	AD	0.175	0.756	0.318	False	0.175	0.756	0.301	False
Q9Y6X5	AD	0.032	1.495	0.086	False	0.046	1.333	0.107	False
Q9Y6Y8;Q9Y6Y8-2	AD	0.083	1.079	0.182	False	0.083	1.079	0.171	False
Q9Y6Y9	AD	0.409	0.388	0.572	False	0.697	0.157	0.797	False
S4R3U6	AD	0.373	0.428	0.538	False	0.519	0.285	0.654	False

1421 rows × 8 columns

And the descriptive statistics of the numeric values:

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scores.describe()

model	PI			QRILC
var	p-unc	-Log10 pvalue	qvalue	p-unc	-Log10 pvalue	qvalue
count	1,421.000	1,421.000	1,421.000	1,421.000	1,421.000	1,421.000
mean	0.255	1.414	0.336	0.244	1.505	0.317
std	0.296	1.648	0.321	0.285	1.794	0.311
min	0.000	0.001	0.000	0.000	0.001	0.000
25%	0.011	0.365	0.038	0.009	0.360	0.029
50%	0.116	0.934	0.234	0.110	0.959	0.212
75%	0.432	1.941	0.592	0.436	2.031	0.582
max	0.998	23.733	0.999	0.999	25.796	0.999

and the boolean decision values

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

model	PI	QRILC
var	rejected	rejected
count	1421	1421
unique	2	2
top	False	False
freq	1025	992

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				QRILC				data
	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected	frequency
protein groups
A0A024QZX5;A0A087X1N8;P35237	0.336	0.474	0.502	False	0.039	1.407	0.094	False	186
A0A024R0T9;K7ER74;P02655	0.043	1.364	0.109	False	0.028	1.560	0.071	False	195
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	0.072	1.144	0.161	False	0.311	0.508	0.460	False	174
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	0.586	0.232	0.724	False	0.297	0.528	0.446	False	196
A0A075B6H7	0.111	0.955	0.227	False	0.158	0.802	0.277	False	91
...	...	...	...	...	...	...	...	...	...
Q9Y6R7	0.175	0.756	0.318	False	0.175	0.756	0.301	False	197
Q9Y6X5	0.032	1.495	0.086	False	0.046	1.333	0.107	False	173
Q9Y6Y8;Q9Y6Y8-2	0.083	1.079	0.182	False	0.083	1.079	0.171	False	197
Q9Y6Y9	0.409	0.388	0.572	False	0.697	0.157	0.797	False	119
S4R3U6	0.373	0.428	0.538	False	0.519	0.285	0.654	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)  - QRILC (no)    954
PI (yes) - QRILC (yes)   358
PI (no)  - QRILC (yes)    71
PI (yes) - QRILC (no)     38
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_80365/1417621106.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				QRILC				data
	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected	frequency
protein groups
A0A075B6I0	0.027	1.575	0.074	False	0.002	2.743	0.007	True	194
A0A075B6J9	0.027	1.563	0.076	False	0.006	2.244	0.019	True	156
A0A087WUM0;A0A087WX56;A0A087WYV9;A0A087X1F5;Q9P0S2	0.014	1.840	0.045	True	0.124	0.907	0.231	False	165
A0A087WWT2;Q9NPD7	0.049	1.310	0.120	False	0.007	2.183	0.022	True	193
A0A087X0M8	0.042	1.375	0.106	False	0.005	2.280	0.018	True	189
...	...	...	...	...	...	...	...	...	...
Q9UJ14	0.026	1.593	0.072	False	0.010	1.985	0.032	True	169
Q9UKB5	0.006	2.234	0.022	True	0.069	1.160	0.147	False	148
Q9ULP0-3;Q9ULP0-6	0.023	1.635	0.067	False	0.005	2.324	0.017	True	136
Q9UQ52	0.073	1.134	0.164	False	0.005	2.285	0.018	True	188
Q9Y281;Q9Y281-3	0.000	3.368	0.002	True	0.405	0.393	0.552	False	51

109 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	QRILC	frequency	Differential Analysis Comparison
protein groups
A0A024QZX5;A0A087X1N8;P35237	0.502	0.094	186	PI (no) - QRILC (no)
A0A024R0T9;K7ER74;P02655	0.109	0.071	195	PI (no) - QRILC (no)
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	0.161	0.460	174	PI (no) - QRILC (no)
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	0.724	0.446	196	PI (no) - QRILC (no)
A0A075B6H7	0.227	0.277	91	PI (no) - QRILC (no)
...	...	...	...	...
Q9Y6R7	0.318	0.301	197	PI (no) - QRILC (no)
Q9Y6X5	0.086	0.107	173	PI (no) - QRILC (no)
Q9Y6Y8;Q9Y6Y8-2	0.182	0.171	197	PI (no) - QRILC (no)
Q9Y6Y9	0.572	0.797	119	PI (no) - QRILC (no)
S4R3U6	0.538	0.654	126	PI (no) - QRILC (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	QRILC	frequency	Differential Analysis Comparison	diff_qvalue
protein groups
Q14517	0.006	0.990	53	PI (yes) - QRILC (no)	0.984
J3KSJ8;Q9UD71;Q9UD71-2	0.892	0.013	51	PI (no) - QRILC (yes)	0.879
P06702	0.764	0.039	97	PI (no) - QRILC (yes)	0.725
Q9Y281;Q9Y281-3	0.002	0.552	51	PI (yes) - QRILC (no)	0.550
A0A1W2PQ94;B4DS77;B4DS77-2;B4DS77-3	0.534	0.012	69	PI (no) - QRILC (yes)	0.522
...	...	...	...	...	...
P02743	0.066	0.041	195	PI (no) - QRILC (yes)	0.024
Q6UWH4;Q6UWH4-2	0.057	0.038	190	PI (no) - QRILC (yes)	0.019
Q9P0K9	0.052	0.035	192	PI (no) - QRILC (yes)	0.017
K7ERG9;P00746	0.052	0.048	197	PI (no) - QRILC (yes)	0.004
P00740;P00740-2	0.053	0.049	197	PI (no) - QRILC (yes)	0.004

109 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_QRILC/diff_analysis_comparision_1_QRILC

• 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_QRILC/diff_analysis_comparision_2_QRILC

Only features contained in model

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

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

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

root - INFO     No features only in new comparision model.

DISEASES DB lookup

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

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

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

	ENSP	score
None
APOE	ENSP00000252486	5.000
PSEN2	ENSP00000355747	5.000
PSEN1	ENSP00000326366	5.000
APP	ENSP00000284981	5.000
TREM2	ENSP00000362205	4.825
...	...	...
ERP27	ENSP00000266397	0.681
ZNF585B	ENSP00000433773	0.681
KIR3DL2	ENSP00000325525	0.681
C12orf66	ENSP00000311486	0.681
ELP2	ENSP00000414851	0.681

10000 rows × 2 columns

Shared features

ToDo: new script -> DISEASES DB lookup

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feat_name = scores.index.names[0]  # first index level is feature name
if args.annotaitons_gene_col in scores.index.names:
    logger.info(f"Found gene annotation in scores index:  {scores.index.names}")
else:
    logger.info(f"No gene annotation in scores index:  {scores.index.names}"
                " Exiting.")
    import sys
    sys.exit(0)

root - INFO     No gene annotation in scores index:  ['protein groups', 'Source'] Exiting.
/home/runner/work/pimms/pimms/project/.snakemake/conda/924ec7e362d761ecf0807b9074d79999_/lib/python3.12/site-packages/IPython/core/interactiveshell.py:3707: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.
  warn("To exit: use 'exit', 'quit', or Ctrl-D.", stacklevel=1)

An exception has occurred, use %tb to see the full traceback.

SystemExit: 0

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gene_to_PG = (scores.droplevel(
    list(set(scores.index.names) - {feat_name, args.annotaitons_gene_col})
)
    .index
    .to_frame()
    .reset_index(drop=True)
    .set_index(args.annotaitons_gene_col)
)
gene_to_PG.head()

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disease_associations_all = data.join(
    gene_to_PG).dropna().reset_index().set_index(feat_name).join(annotations)
disease_associations_all

only by model

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idx = disease_associations_all.index.intersection(scores_model_only.index)
disease_assocications_new = disease_associations_all.loc[idx].sort_values(
    'score', ascending=False)
disease_assocications_new.head(20)

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mask = disease_assocications_new.loc[idx, 'score'] >= 2.0
disease_assocications_new.loc[idx].loc[mask]

Only by model which were significant

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idx = disease_associations_all.index.intersection(
    scores_model_only_rejected.index)
disease_assocications_new_rejected = disease_associations_all.loc[idx].sort_values(
    'score', ascending=False)
disease_assocications_new_rejected.head(20)

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mask = disease_assocications_new_rejected.loc[idx, 'score'] >= 2.0
disease_assocications_new_rejected.loc[idx].loc[mask]

Shared which are only significant for by model

mask = (scores_common[(str(args.model_key), 'rejected')] & mask_different)
mask.sum()

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idx = disease_associations_all.index.intersection(mask.index[mask])
disease_assocications_shared_rejected_by_model = (disease_associations_all.loc[idx].sort_values(
    'score', ascending=False))
disease_assocications_shared_rejected_by_model.head(20)

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mask = disease_assocications_shared_rejected_by_model.loc[idx, 'score'] >= 2.0
disease_assocications_shared_rejected_by_model.loc[idx].loc[mask]

Only significant by RSN

mask = (scores_common[(str(args.baseline), 'rejected')] & mask_different)
mask.sum()

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idx = disease_associations_all.index.intersection(mask.index[mask])
disease_assocications_shared_rejected_by_RSN = (
    disease_associations_all
    .loc[idx]
    .sort_values('score', ascending=False))
disease_assocications_shared_rejected_by_RSN.head(20)

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mask = disease_assocications_shared_rejected_by_RSN.loc[idx, 'score'] >= 2.0
disease_assocications_shared_rejected_by_RSN.loc[idx].loc[mask]

Write to excel

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disease_associations_all.to_excel(
    writer, sheet_name='disease_assoc_all', **writer_args)
disease_assocications_new.to_excel(
    writer, sheet_name='disease_assoc_new', **writer_args)
disease_assocications_new_rejected.to_excel(
    writer, sheet_name='disease_assoc_new_rejected', **writer_args)

Outputs

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writer.close()
files_out