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 = "TRKNN"
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': 'TRKNN',
 'out_figures': PosixPath('runs/alzheimer_study/figures'),
 'out_folder': PosixPath('runs/alzheimer_study/diff_analysis/AD/PI_vs_TRKNN'),
 '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_TRKNN/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	0.180	1	0.291	0.590	0.002	0.229	0.728	False
	age	0.084	1	0.135	0.713	0.001	0.147	0.817	False
	Kiel	2.216	1	3.588	0.060	0.018	1.224	0.140	False
	Magdeburg	5.950	1	9.634	0.002	0.048	2.657	0.010	True
	Sweden	10.249	1	16.595	0.000	0.080	4.169	0.000	True
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	0.367	1	0.390	0.533	0.002	0.273	0.680	False
	age	1.453	1	1.542	0.216	0.008	0.666	0.365	False
	Kiel	0.099	1	0.105	0.746	0.001	0.127	0.840	False
	Magdeburg	3.254	1	3.453	0.065	0.018	1.189	0.148	False
	Sweden	8.790	1	9.329	0.003	0.047	2.589	0.011	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	TRKNN
	var	SS	DF	F	p-unc	np2	-Log10 pvalue	qvalue	rejected
protein groups	Source
A0A024QZX5;A0A087X1N8;P35237	AD	0.994	1	7.134	0.008	0.036	2.085	0.023	True
	age	0.004	1	0.029	0.864	0.000	0.063	0.913	False
	Kiel	0.269	1	1.933	0.166	0.010	0.780	0.277	False
	Magdeburg	0.519	1	3.727	0.055	0.019	1.259	0.114	False
	Sweden	1.796	1	12.893	0.000	0.063	3.378	0.002	True
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	2.295	1	4.480	0.036	0.023	1.449	0.080	False
	age	0.398	1	0.777	0.379	0.004	0.421	0.516	False
	Kiel	2.981	1	5.819	0.017	0.030	1.775	0.043	True
	Magdeburg	3.440	1	6.716	0.010	0.034	1.987	0.028	True
	Sweden	27.114	1	52.939	0.000	0.217	11.062	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				TRKNN
	var	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected
protein groups	Source
A0A024QZX5;A0A087X1N8;P35237	AD	0.590	0.229	0.728	False	0.008	2.085	0.023	True
	Kiel	0.060	1.224	0.140	False	0.166	0.780	0.277	False
	Magdeburg	0.002	2.657	0.010	True	0.055	1.259	0.114	False
	Sweden	0.000	4.169	0.000	True	0.000	3.378	0.002	True
	age	0.713	0.147	0.817	False	0.864	0.063	0.913	False
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	0.533	0.273	0.680	False	0.036	1.449	0.080	False
	Kiel	0.746	0.127	0.840	False	0.017	1.775	0.043	True
	Magdeburg	0.065	1.189	0.148	False	0.010	1.987	0.028	True
	Sweden	0.003	2.589	0.011	True	0.000	11.062	0.000	True
	age	0.216	0.666	0.365	False	0.379	0.421	0.516	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', 'TRKNN': 'TRKNN'}

Describe scores

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scores.describe()

model	PI			TRKNN
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	0.261	2.486	0.338	0.230	3.114	0.287
std	0.304	5.378	0.332	0.296	5.792	0.323
min	0.000	0.000	0.000	0.000	0.000	0.000
25%	0.004	0.327	0.015	0.001	0.392	0.003
50%	0.119	0.925	0.238	0.066	1.182	0.131
75%	0.471	2.440	0.628	0.405	3.156	0.540
max	1.000	143.804	1.000	1.000	82.663	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_102992/3761369923.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				TRKNN
	var	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected
protein groups	Source
A0A024QZX5;A0A087X1N8;P35237	AD	0.590	0.229	0.728	False	0.008	2.085	0.023	True
A0A024R0T9;K7ER74;P02655	AD	0.059	1.228	0.139	False	0.031	1.512	0.071	False
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	AD	0.114	0.944	0.230	False	0.264	0.578	0.394	False
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	AD	0.524	0.281	0.672	False	0.266	0.575	0.396	False
A0A075B6H7	AD	0.109	0.964	0.223	False	0.020	1.707	0.048	True
...	...	...	...	...	...	...	...	...	...
Q9Y6R7	AD	0.175	0.756	0.315	False	0.175	0.756	0.289	False
Q9Y6X5	AD	0.050	1.305	0.121	False	0.113	0.946	0.205	False
Q9Y6Y8;Q9Y6Y8-2	AD	0.083	1.079	0.181	False	0.083	1.079	0.160	False
Q9Y6Y9	AD	0.302	0.520	0.464	False	0.334	0.476	0.472	False
S4R3U6	AD	0.533	0.273	0.680	False	0.036	1.449	0.080	False

1421 rows × 8 columns

And the descriptive statistics of the numeric values:

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scores.describe()

model	PI			TRKNN
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	0.254	1.405	0.336	0.239	1.579	0.301
std	0.292	1.623	0.319	0.294	1.800	0.317
min	0.000	0.001	0.000	0.000	0.002	0.000
25%	0.011	0.352	0.036	0.007	0.373	0.021
50%	0.125	0.904	0.247	0.086	1.063	0.164
75%	0.444	1.960	0.605	0.423	2.151	0.559
max	0.997	23.616	0.998	0.996	19.900	0.997

and the boolean decision values

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

model	PI	TRKNN
var	rejected	rejected
count	1421	1421
unique	2	2
top	False	False
freq	1027	936

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				TRKNN				data
	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected	frequency
protein groups
A0A024QZX5;A0A087X1N8;P35237	0.590	0.229	0.728	False	0.008	2.085	0.023	True	186
A0A024R0T9;K7ER74;P02655	0.059	1.228	0.139	False	0.031	1.512	0.071	False	195
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	0.114	0.944	0.230	False	0.264	0.578	0.394	False	174
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	0.524	0.281	0.672	False	0.266	0.575	0.396	False	196
A0A075B6H7	0.109	0.964	0.223	False	0.020	1.707	0.048	True	91
...	...	...	...	...	...	...	...	...	...
Q9Y6R7	0.175	0.756	0.315	False	0.175	0.756	0.289	False	197
Q9Y6X5	0.050	1.305	0.121	False	0.113	0.946	0.205	False	173
Q9Y6Y8;Q9Y6Y8-2	0.083	1.079	0.181	False	0.083	1.079	0.160	False	197
Q9Y6Y9	0.302	0.520	0.464	False	0.334	0.476	0.472	False	119
S4R3U6	0.533	0.273	0.680	False	0.036	1.449	0.080	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)  - TRKNN (no)    879
PI (yes) - TRKNN (yes)   337
PI (no)  - TRKNN (yes)   148
PI (yes) - TRKNN (no)     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_102992/1417621106.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				TRKNN				data
	p-unc	-Log10 pvalue	qvalue	rejected	p-unc	-Log10 pvalue	qvalue	rejected	frequency
protein groups
A0A024QZX5;A0A087X1N8;P35237	0.590	0.229	0.728	False	0.008	2.085	0.023	True	186
A0A075B6H7	0.109	0.964	0.223	False	0.020	1.707	0.048	True	91
A0A075B6J9	0.052	1.285	0.125	False	0.009	2.040	0.026	True	156
A0A075B6Q5	0.510	0.292	0.662	False	0.002	2.639	0.008	True	104
A0A075B6R2	0.282	0.550	0.442	False	0.001	2.926	0.004	True	164
...	...	...	...	...	...	...	...	...	...
Q9UNW1	0.010	1.999	0.033	True	0.912	0.040	0.944	False	171
Q9UP79	0.330	0.481	0.494	False	0.000	4.258	0.000	True	135
Q9UQ52	0.058	1.233	0.138	False	0.001	3.136	0.003	True	188
Q9Y281;Q9Y281-3	0.002	2.821	0.007	True	0.395	0.403	0.531	False	51
Q9Y6C2	0.751	0.125	0.843	False	0.002	2.626	0.008	True	119

205 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	TRKNN	frequency	Differential Analysis Comparison
protein groups
A0A024QZX5;A0A087X1N8;P35237	0.728	0.023	186	PI (no) - TRKNN (yes)
A0A024R0T9;K7ER74;P02655	0.139	0.071	195	PI (no) - TRKNN (no)
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	0.230	0.394	174	PI (no) - TRKNN (no)
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	0.672	0.396	196	PI (no) - TRKNN (no)
A0A075B6H7	0.223	0.048	91	PI (no) - TRKNN (yes)
...	...	...	...	...
Q9Y6R7	0.315	0.289	197	PI (no) - TRKNN (no)
Q9Y6X5	0.121	0.205	173	PI (no) - TRKNN (no)
Q9Y6Y8;Q9Y6Y8-2	0.181	0.160	197	PI (no) - TRKNN (no)
Q9Y6Y9	0.464	0.472	119	PI (no) - TRKNN (no)
S4R3U6	0.680	0.080	126	PI (no) - TRKNN (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	TRKNN	frequency	Differential Analysis Comparison	diff_qvalue
protein groups
A0A087WU43;A0A087WX17;A0A087WXI5;P12830;P12830-2	0.989	0.000	134	PI (no) - TRKNN (yes)	0.989
Q504Y2	0.970	0.008	96	PI (no) - TRKNN (yes)	0.962
O15204;O15204-2	0.982	0.024	156	PI (no) - TRKNN (yes)	0.957
P22748	0.994	0.048	159	PI (no) - TRKNN (yes)	0.946
Q9GZT8;Q9GZT8-2	0.948	0.002	86	PI (no) - TRKNN (yes)	0.945
...	...	...	...	...	...
Q9NX62	0.055	0.046	197	PI (no) - TRKNN (yes)	0.009
P00740;P00740-2	0.053	0.044	197	PI (no) - TRKNN (yes)	0.009
K7ERG9;P00746	0.052	0.043	197	PI (no) - TRKNN (yes)	0.009
P26572	0.056	0.048	194	PI (no) - TRKNN (yes)	0.008
Q16706	0.052	0.047	195	PI (no) - TRKNN (yes)	0.005

205 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_TRKNN/diff_analysis_comparision_1_TRKNN

• 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_TRKNN/diff_analysis_comparision_2_TRKNN

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
PSEN1	ENSP00000326366	5.000
PSEN2	ENSP00000355747	5.000
APP	ENSP00000284981	5.000
TREM2	ENSP00000362205	4.825
...	...	...
CARMIL1	ENSP00000331983	0.681
CENPJ	ENSP00000371308	0.681
ERP27	ENSP00000266397	0.681
ZNF585B	ENSP00000433773	0.681
KIR3DL2	ENSP00000325525	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