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Transcriptomics/Figures/CD4_Correlation/ModuleConsistency_CD4.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,185 @@ | ||
| """ | ||
| Plots module consistence for the CD4 data between the train/test splits | ||
| See the _Monocyte.py version for a nearly identical script on the Monocyte data | ||
| """ | ||
| import os | ||
| import pandas as pd | ||
| from tqdm import tqdm | ||
| import matplotlib.pyplot as plt | ||
| import seaborn as sns | ||
|
|
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| plt.rcParams['svg.fonttype'] = 'none' | ||
|
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| base_dir = '../../CD4_w_protein' | ||
|
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| datasets = [ | ||
| { | ||
| 'Name': 'Hotspot', | ||
| 'Train': os.path.join(base_dir, 'train/hotspot_hs/modules.txt'), | ||
| 'Test': os.path.join(base_dir, 'test/hotspot_hs/modules.txt'), | ||
| }, | ||
| { | ||
| 'Name': 'WGCNA', | ||
| 'Train': os.path.join(base_dir, 'train/wgcna_hs/modules.txt'), | ||
| 'Test': os.path.join(base_dir, 'test/wgcna_hs/modules.txt'), | ||
| }, | ||
| { | ||
| 'Name': 'ICA5', | ||
| 'Train': os.path.join(base_dir, 'train/ica5/modules.txt'), | ||
| 'Test': os.path.join(base_dir, 'test/ica5/modules.txt'), | ||
| }, | ||
| # # Don't need so many ICA versions as this one isn't that different from ICA10 | ||
| # { | ||
| # 'Name': 'ICA8', | ||
| # 'Train': os.path.join(base_dir, 'train/ica8/modules.txt'), | ||
| # 'Test': os.path.join(base_dir, 'test/ica8/modules.txt'), | ||
| # }, | ||
| { | ||
| 'Name': 'ICA10', | ||
| 'Train': os.path.join(base_dir, 'train/ica10/modules.txt'), | ||
| 'Test': os.path.join(base_dir, 'test/ica10/modules.txt'), | ||
| }, | ||
| { | ||
| 'Name': 'Grnboost', | ||
| 'Train': os.path.join(base_dir, 'train/grnboost/modules.txt'), | ||
| 'Test': os.path.join(base_dir, 'test/grnboost/modules.txt'), | ||
| }, | ||
| ] | ||
|
|
||
| for data in datasets: | ||
| train = pd.read_table(data['Train'], index_col=0) | ||
| test = pd.read_table(data['Test'], index_col=0) | ||
|
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| train = train.Cluster.to_dict() | ||
| test = test.Cluster.to_dict() | ||
| data['TrainDict'] = train | ||
| data['TestDict'] = test | ||
|
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|
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| def eval_module_consistency(data): | ||
| consistency = ( | ||
| eval_module_consistency_inner(data['TrainDict'], data['TestDict']) + | ||
| eval_module_consistency_inner(data['TestDict'], data['TrainDict']) | ||
| ) / 2 | ||
|
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| data['Consistency'] = consistency | ||
|
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| def eval_module_consistency_inner(dict_a, dict_b): | ||
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| all_genes = set(dict_a.keys()) & set(dict_b.keys()) | ||
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| # For each pairs of genes that are in the same module in 'A', how many are in the same module in 'B'? | ||
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| denom = 0 | ||
| num = 0 | ||
| for ga in all_genes: | ||
| for gb in all_genes: | ||
|
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| if ga == gb: continue | ||
|
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| if dict_a[ga] == dict_a[gb] and dict_a[ga] != -1 and dict_a[gb] != -1: # Same module in A | ||
| denom += 1 | ||
|
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| if dict_b[ga] == dict_b[gb] and dict_b[ga] != -1 and dict_b[gb] != -1: # Same module in B | ||
| num += 1 | ||
|
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| num = num/2 | ||
| denom = denom/2 | ||
|
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| consistent_rate = num/denom | ||
|
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| return consistent_rate | ||
|
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|
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| def eval_num_modules(data): | ||
| num_modules = ( | ||
| pd.Series(data['TrainDict']).unique().size - 1 + | ||
| pd.Series(data['TestDict']).unique().size - 1 | ||
| ) / 2 | ||
|
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| data['NumModules'] = num_modules | ||
|
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|
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| def eval_num_assigned(data): | ||
| assigned = ( | ||
| (pd.Series(data['TrainDict']) != -1).sum()/2 + | ||
| (pd.Series(data['TestDict']) != -1).sum()/2 | ||
| ) | ||
|
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| data['NumAssigned'] = assigned | ||
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|
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| for data in tqdm(datasets): | ||
| train = pd.read_table(data['Train'], index_col=0) | ||
| test = pd.read_table(data['Test'], index_col=0) | ||
|
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| train = train.Cluster.to_dict() | ||
| test = test.Cluster.to_dict() | ||
| data['TrainDict'] = train | ||
| data['TestDict'] = test | ||
|
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| eval_module_consistency(data) | ||
| eval_num_modules(data) | ||
| eval_num_assigned(data) | ||
|
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|
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| # %% Consolidate into a nice dataframe | ||
| columns = [ | ||
| 'Name', | ||
| 'Consistency', | ||
| 'NumModules', | ||
| 'NumAssigned' | ||
| ] | ||
|
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| results = [] | ||
| for data in datasets: | ||
| results.append( | ||
| [data[x] for x in columns] | ||
| ) | ||
|
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| results = pd.DataFrame(results, columns=columns) | ||
|
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|
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| # %% Plot it | ||
| order = ['ICA5', 'ICA10', 'Grnboost', 'WGCNA', 'Hotspot'] | ||
| colors = sns.color_palette('deep')[:len(order)] | ||
| plot_data = results.set_index('Name').loc[order] | ||
|
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| fig, axs = plt.subplots(1, 3, figsize=(12, 4)) | ||
|
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| plt.sca(axs[0]) | ||
|
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| plt.bar( | ||
| plot_data.index, plot_data.Consistency, alpha=0.9, color=colors | ||
| ) | ||
| plt.xticks(rotation=45) | ||
| plt.ylabel('Proportion of Gene Pairs Which\nReplicate Across Data Split') | ||
| plt.title('Reproducibility') | ||
| plt.gca().set_axisbelow(True) | ||
| plt.grid(color='#CCCCCC', lw=0.5, axis='y', ls=(0, (5, 5))) | ||
|
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| plt.sca(axs[1]) | ||
|
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| plt.bar( | ||
| plot_data.index, plot_data.NumModules, alpha=0.9, color=colors | ||
| ) | ||
| plt.xticks(rotation=45) | ||
| plt.ylabel('Modules') | ||
| plt.title('# Modules') | ||
| plt.gca().set_axisbelow(True) | ||
| plt.grid(color='#CCCCCC', lw=0.5, axis='y', ls=(0, (5, 5))) | ||
|
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| plt.sca(axs[2]) | ||
|
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| plt.bar( | ||
| plot_data.index, plot_data.NumAssigned, alpha=0.9, color=colors | ||
| ) | ||
| plt.xticks(rotation=45) | ||
| plt.ylabel('Genes') | ||
| plt.title('# Genes Assigned') | ||
| plt.gca().set_axisbelow(True) | ||
| plt.grid(color='#CCCCCC', lw=0.5, axis='y', ls=(0, (5, 5))) | ||
|
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| plt.subplots_adjust(bottom=.25, wspace=0.4, left=0.1, right=0.9) | ||
| plt.savefig('CD4_Module_TrainTest.svg') |
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