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	0.180	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	QRILC
	var	SS	DF	F	p-unc	np2	-Log10 pvalue	qvalue	rejected
protein groups	Source
A0A024QZX5;A0A087X1N8;P35237	AD	0.836	1	4.919	0.028	0.025	1.557	0.071	False
	age	0.008	1	0.048	0.826	0.000	0.083	0.891	False
	Kiel	0.444	1	2.612	0.108	0.013	0.968	0.209	False
	Magdeburg	0.981	1	5.771	0.017	0.029	1.763	0.048	True
	Sweden	2.458	1	14.455	0.000	0.070	3.714	0.001	True
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	0.881	1	0.462	0.498	0.002	0.303	0.637	False
	age	0.714	1	0.374	0.541	0.002	0.266	0.672	False
	Kiel	7.750	1	4.059	0.045	0.021	1.344	0.105	False
	Magdeburg	19.996	1	10.474	0.001	0.052	2.846	0.006	True
	Sweden	0.678	1	0.355	0.552	0.002	0.258	0.681	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	0.590	0.229	0.728	False	0.028	1.557	0.071	False
	Kiel	0.060	1.224	0.140	False	0.108	0.968	0.209	False
	Magdeburg	0.002	2.657	0.010	True	0.017	1.763	0.048	True
	Sweden	0.000	4.169	0.000	True	0.000	3.714	0.001	True
	age	0.713	0.147	0.817	False	0.826	0.083	0.891	False
...	...	...	...	...	...	...	...	...	...
S4R3U6	AD	0.533	0.273	0.680	False	0.498	0.303	0.637	False
	Kiel	0.746	0.127	0.840	False	0.045	1.344	0.105	False
	Magdeburg	0.065	1.189	0.148	False	0.001	2.846	0.006	True
	Sweden	0.003	2.589	0.011	True	0.552	0.258	0.681	False
	age	0.216	0.666	0.365	False	0.541	0.266	0.672	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	0.261	2.486	0.338	0.246	2.735	0.313
std	0.304	5.378	0.332	0.299	5.167	0.327
min	0.000	0.000	0.000	0.000	0.000	0.000
25%	0.004	0.327	0.015	0.002	0.358	0.008
50%	0.119	0.925	0.238	0.093	1.031	0.186
75%	0.471	2.440	0.628	0.439	2.698	0.585
max	1.000	143.804	1.000	0.999	83.512	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_102245/3761369923.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	0.590	0.229	0.728	False	0.028	1.557	0.071	False
A0A024R0T9;K7ER74;P02655	AD	0.059	1.228	0.139	False	0.030	1.517	0.076	False
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	AD	0.114	0.944	0.230	False	0.190	0.721	0.321	False
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	AD	0.524	0.281	0.672	False	0.314	0.503	0.466	False
A0A075B6H7	AD	0.109	0.964	0.223	False	0.112	0.952	0.214	False
...	...	...	...	...	...	...	...	...	...
Q9Y6R7	AD	0.175	0.756	0.315	False	0.175	0.756	0.302	False
Q9Y6X5	AD	0.050	1.305	0.121	False	0.076	1.120	0.159	False
Q9Y6Y8;Q9Y6Y8-2	AD	0.083	1.079	0.181	False	0.083	1.079	0.171	False
Q9Y6Y9	AD	0.302	0.520	0.464	False	0.495	0.305	0.635	False
S4R3U6	AD	0.533	0.273	0.680	False	0.498	0.303	0.637	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	0.254	1.405	0.336	0.251	1.486	0.324
std	0.292	1.623	0.319	0.291	1.765	0.317
min	0.000	0.001	0.000	0.000	0.002	0.000
25%	0.011	0.352	0.036	0.009	0.353	0.030
50%	0.125	0.904	0.247	0.109	0.961	0.211
75%	0.444	1.960	0.605	0.443	2.023	0.589
max	0.997	23.616	0.998	0.995	23.394	0.996

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	1027	996

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	0.590	0.229	0.728	False	0.028	1.557	0.071	False	186
A0A024R0T9;K7ER74;P02655	0.059	1.228	0.139	False	0.030	1.517	0.076	False	195
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	0.114	0.944	0.230	False	0.190	0.721	0.321	False	174
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	0.524	0.281	0.672	False	0.314	0.503	0.466	False	196
A0A075B6H7	0.109	0.964	0.223	False	0.112	0.952	0.214	False	91
...	...	...	...	...	...	...	...	...	...
Q9Y6R7	0.175	0.756	0.315	False	0.175	0.756	0.302	False	197
Q9Y6X5	0.050	1.305	0.121	False	0.076	1.120	0.159	False	173
Q9Y6Y8;Q9Y6Y8-2	0.083	1.079	0.181	False	0.083	1.079	0.171	False	197
Q9Y6Y9	0.302	0.520	0.464	False	0.495	0.305	0.635	False	119
S4R3U6	0.533	0.273	0.680	False	0.498	0.303	0.637	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)    957
PI (yes) - QRILC (yes)   355
PI (no)  - QRILC (yes)    70
PI (yes) - QRILC (no)     39
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_102245/1417621106.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
A0A087WTT8;A0A0A0MQX5;O94779;O94779-2	0.001	3.056	0.004	True	0.352	0.453	0.504	False	114
A0A087WWT2;Q9NPD7	0.030	1.518	0.082	False	0.005	2.275	0.018	True	193
A0A087X0M8	0.051	1.289	0.124	False	0.003	2.502	0.012	True	189
A0A087X152;D6RE16;E0CX15;O95185;O95185-2	0.009	2.037	0.031	True	0.088	1.055	0.178	False	176
A0A087X1G7;A0A0B4J1S4;O60613	0.061	1.213	0.142	False	0.010	2.005	0.030	True	184
...	...	...	...	...	...	...	...	...	...
Q9P0K9	0.034	1.464	0.091	False	0.012	1.923	0.036	True	192
Q9UKB5	0.010	1.984	0.034	True	0.020	1.706	0.054	False	148
Q9UNW1	0.010	1.999	0.033	True	0.112	0.951	0.215	False	171
Q9UQ52	0.058	1.233	0.138	False	0.004	2.348	0.016	True	188
Q9Y281;Q9Y281-3	0.002	2.821	0.007	True	0.343	0.465	0.495	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	0.728	0.071	186	PI (no) - QRILC (no)
A0A024R0T9;K7ER74;P02655	0.139	0.076	195	PI (no) - QRILC (no)
A0A024R3W6;A0A024R412;O60462;O60462-2;O60462-3;O60462-4;O60462-5;Q7LBX6;X5D2Q8	0.230	0.321	174	PI (no) - QRILC (no)
A0A024R644;A0A0A0MRU5;A0A1B0GWI2;O75503	0.672	0.466	196	PI (no) - QRILC (no)
A0A075B6H7	0.223	0.214	91	PI (no) - QRILC (no)
...	...	...	...	...
Q9Y6R7	0.315	0.302	197	PI (no) - QRILC (no)
Q9Y6X5	0.121	0.159	173	PI (no) - QRILC (no)
Q9Y6Y8;Q9Y6Y8-2	0.181	0.171	197	PI (no) - QRILC (no)
Q9Y6Y9	0.464	0.635	119	PI (no) - QRILC (no)
S4R3U6	0.680	0.637	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
E7EN89;E9PP67;E9PQ25;F2Z2Y8;Q9H0E2;Q9H0E2-2	0.940	0.003	86	PI (no) - QRILC (yes)	0.936
Q8TEA8	0.016	0.917	56	PI (yes) - QRILC (no)	0.901
P43004;P43004-2;P43004-3	0.839	0.016	89	PI (no) - QRILC (yes)	0.823
P35754	0.036	0.807	143	PI (yes) - QRILC (no)	0.772
A0A087WTT8;A0A0A0MQX5;O94779;O94779-2	0.004	0.504	114	PI (yes) - QRILC (no)	0.500
...	...	...	...	...	...
P26572	0.056	0.048	194	PI (no) - QRILC (yes)	0.007
D6RCE0;E9PD25;O43897;O43897-2	0.050	0.044	180	PI (no) - QRILC (yes)	0.006
Q16706	0.052	0.046	195	PI (no) - QRILC (yes)	0.006
P00740;P00740-2	0.053	0.048	197	PI (no) - QRILC (yes)	0.004
K7ERG9;P00746	0.052	0.048	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
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