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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"\n",
"import pandas as pd\n",
"scores = {'major_class':{},'sub_class':{}}\n",
"models = ['Baseline','Seq','Seq-Seq','Seq-Struct','Seq-Rev']\n",
"models_path = '/nfs/home/yat_ldap/VS_Projects/TransfoRNA-Framework/models/tcga/TransfoRNA_ID'\n",
"for model1 in models:\n",
" summary_pd = pd.read_csv(models_path+'/major_class/'+model1+'/summary_pd.tsv',sep='\\t')\n",
" scores['major_class'][model1] = str(summary_pd['B. Acc'].mean()*100)+'+/-'+' ('+str(summary_pd['B. Acc'].std()*100)+')'\n",
" summary_pd = pd.read_csv(models_path+'/sub_class/'+model1+'/summary_pd.tsv',sep='\\t')\n",
" scores['sub_class'][model1] = str(summary_pd['B. Acc'].mean()*100)+'+/-'+' ('+str(summary_pd['B. Acc'].std()*100) +')'"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Baseline': '52.83789870060305+/- (1.0961119898709506)',\n",
" 'Seq': '97.70018230805728+/- (0.3819207447704567)',\n",
" 'Seq-Seq': '95.65091330992355+/- (0.4963151975035616)',\n",
" 'Seq-Struct': '97.71071590680333+/- (0.6173598637101496)',\n",
" 'Seq-Rev': '97.51224133899979+/- (0.3418133671042992)'}"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scores['sub_class']"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"\n",
"import json\n",
"import pandas as pd\n",
"with open('/media/ftp_share/hbdx/data_for_upload/TransfoRNA//data/subclass_to_annotation.json') as f:\n",
" mapping_dict = json.load(f)\n",
"\n",
"b_acc_sc_to_mc = {}\n",
"for model1 in models:\n",
" b_acc = []\n",
" for idx in range(5):\n",
" confusion_matrix = pd.read_csv(models_path+'/sub_class/'+model1+f'/embedds/confusion_matrix_{idx}.csv',sep=',',index_col=0)\n",
" confusion_matrix.index = confusion_matrix.index.map(mapping_dict)\n",
" confusion_matrix.columns = confusion_matrix.columns.map(mapping_dict)\n",
" confusion_matrix = confusion_matrix.groupby(confusion_matrix.index).sum().groupby(confusion_matrix.columns,axis=1).sum()\n",
" b_acc.append(confusion_matrix.values.diagonal().sum()/confusion_matrix.values.sum())\n",
" b_acc_sc_to_mc[model1] = str(pd.Series(b_acc).mean()*100)+'+/-'+' ('+str(pd.Series(b_acc).std()*100)+')'\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Baseline': '89.6182558114013+/- (0.6372156071358975)',\n",
" 'Seq': '99.66714304286457+/- (0.1404591049684126)',\n",
" 'Seq-Seq': '99.40702944026852+/- (0.18268320317601783)',\n",
" 'Seq-Struct': '99.77114728744993+/- (0.06976258667467564)',\n",
" 'Seq-Rev': '99.70878801385821+/- (0.11954774341354062)'}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b_acc_sc_to_mc"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"\n",
"import plotly.express as px\n",
"no_annotation_predictions = {}\n",
"for model1 in models:\n",
" #multiindex\n",
" no_annotation_predictions[model1] = pd.read_csv(models_path+'/sub_class/'+model1+'/embedds/no_annotation_embedds.tsv',sep='\\t',header=[0,1],index_col=[0])\n",
" no_annotation_predictions[model1].set_index([('RNA Sequences','0')] ,inplace=True)\n",
" no_annotation_predictions[model1].index.name = 'RNA Sequences'\n",
" no_annotation_predictions[model1] = no_annotation_predictions[model1]['Logits'].idxmax(axis=1)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from transforna.src.utils.tcga_post_analysis_utils import correct_labels\n",
"import pandas as pd\n",
"correlation = pd.DataFrame(index=models,columns=models)\n",
"for model1 in models:\n",
" for model2 in models:\n",
" model1_predictions = correct_labels(no_annotation_predictions[model1],no_annotation_predictions[model2],mapping_dict)\n",
" is_equal = model1_predictions == no_annotation_predictions[model2].values\n",
" correlation.loc[model1,model2] = is_equal.sum()/len(is_equal)\n",
"font_size = 20\n",
"fig = px.imshow(correlation, color_continuous_scale='Blues')\n",
"#annotate\n",
"for i in range(len(models)):\n",
" for j in range(len(models)):\n",
" if i != j:\n",
" font = dict(color='black', size=font_size)\n",
" else:\n",
" font = dict(color='white', size=font_size) \n",
" \n",
" fig.add_annotation(\n",
" x=j, y=i,\n",
" text=str(round(correlation.iloc[i,j],2)),\n",
" showarrow=False,\n",
" font=font\n",
" )\n",
"\n",
"#set figure size: width and height\n",
"fig.update_layout(width=800, height=800)\n",
"\n",
"fig.update_layout(title='Correlation between models for each sub_class model')\n",
"#set x and y axis to Models\n",
"fig.update_xaxes(title_text='Models', tickfont=dict(size=font_size))\n",
"fig.update_yaxes(title_text='Models', tickfont=dict(size=font_size))\n",
"fig.show()\n",
"#save\n",
"fig.write_image('/nfs/home/yat_ldap/VS_Projects/TransfoRNA-Framework/transforna/bin/figures/correlation_id_models_sub_class.png')\n",
"fig.write_image('/nfs/home/yat_ldap/VS_Projects/TransfoRNA-Framework/transforna/bin/figures/correlation_id_models_sub_class.svg')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"#create umap for every model from embedds folder\n",
"models_path = '/nfs/home/yat_ldap/VS_Projects/TransfoRNA-Framework/models/tcga/TransfoRNA_ID'\n",
"\n",
"#read\n",
"sc_embedds = {}\n",
"mc_embedds = {}\n",
"sc_to_mc_labels = {}\n",
"sc_labels = {}\n",
"mc_labels = {}\n",
"for model in models:\n",
" df = pd.read_csv(models_path+'/sub_class/'+model+'/embedds/train_embedds.tsv',sep='\\t',header=[0,1],index_col=[0])\n",
" sc_embedds[model] = df['RNA Embedds'].values\n",
" sc_labels[model] = df['Labels']['0']\n",
" sc_to_mc_labels[model] = sc_labels[model].map(mapping_dict).values\n",
"\n",
" #major class\n",
" df = pd.read_csv(models_path+'/major_class/'+model+'/embedds/train_embedds.tsv',sep='\\t',header=[0,1],index_col=[0])\n",
" mc_embedds[model] = df['RNA Embedds'].values\n",
" mc_labels[model] = df['Labels']['0']"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"import umap\n",
"#compute umap coordinates\n",
"sc_umap_coords = {}\n",
"mc_umap_coords = {}\n",
"for model in models:\n",
" sc_umap_coords[model] = umap.UMAP(n_neighbors=5, min_dist=0.3, n_components=2, metric='euclidean').fit_transform(sc_embedds[model])\n",
" mc_umap_coords[model] = umap.UMAP(n_neighbors=5, min_dist=0.3, n_components=2, metric='euclidean').fit_transform(mc_embedds[model])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#plot umap\n",
"import plotly.express as px\n",
"import numpy as np\n",
"\n",
"mcs = np.unique(sc_to_mc_labels[models[0]])\n",
"colors = px.colors.qualitative.Plotly\n",
"color_mapping = dict(zip(mcs,colors))\n",
"for model in models:\n",
" fig = px.scatter(x=sc_umap_coords[model][:,0],y=sc_umap_coords[model][:,1],color=sc_to_mc_labels[model],labels={'color':'Major Class'},title=model, width=800, height=800,\\\n",
"\n",
" hover_data={'Major Class':sc_labels[model],'Sub Class':sc_to_mc_labels[model]},color_discrete_map=color_mapping)\n",
"\n",
" fig.update_traces(marker=dict(size=1))\n",
" #white background\n",
" fig.update_layout(plot_bgcolor='rgba(0,0,0,0)')\n",
"\n",
" fig.write_image(models_path+'/sub_class/'+model+'/figures/sc_umap.svg')\n",
" fig.write_image(models_path+'/sub_class/'+model+'/figures/sc_umap.png')\n",
" fig.show()\n",
"\n",
" #plot umap for major class\n",
" fig = px.scatter(x=mc_umap_coords[model][:,0],y=mc_umap_coords[model][:,1],color=mc_labels[model],labels={'color':'Major Class'},title=model, width=800, height=800,\\\n",
"\n",
" hover_data={'Major Class':mc_labels[model]},color_discrete_map=color_mapping)\n",
" fig.update_traces(marker=dict(size=1))\n",
" #white background\n",
" fig.update_layout(plot_bgcolor='rgba(0,0,0,0)')\n",
"\n",
" fig.write_image(models_path+'/major_class/'+model+'/figures/mc_umap.svg')\n",
" fig.write_image(models_path+'/major_class/'+model+'/figures/mc_umap.png')\n",
" fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from transforna import fold_sequences\n",
"df = pd.read_csv(models_path+'/major_class/Seq-Struct/embedds/train_embedds.tsv',sep='\\t',header=[0,1],index_col=[0])\n",
"sec_struct = fold_sequences(df['RNA Sequences']['0'])['structure_37']\n",
"#sec struct ratio is calculated as the number of non '.' characters divided by the length of the sequence\n",
"sec_struct_ratio = sec_struct.apply(lambda x: (len(x)-x.count('.'))/len(x))\n",
"fig = px.scatter(x=mc_umap_coords['Seq-Struct'][:,0],y=mc_umap_coords['Seq-Struct'][:,1],color=sec_struct_ratio,labels={'color':'Base Pairing'},title='Seq-Struct', width=800, height=800,\\\n",
" hover_data={'Major Class':mc_labels['Seq-Struct']}, color_continuous_scale='Viridis',range_color=[0,1])\n",
"\n",
"fig.update_traces(marker=dict(size=3))\n",
"#white background\n",
"fig.update_layout(plot_bgcolor='rgba(0,0,0,0)')\n",
"fig.show()\n",
"fig.write_image(models_path+'/major_class/Seq-Struct/figures/mc_umap_sec_struct.svg')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"from transforna import fold_sequences\n",
"df = pd.read_csv(models_path+'/sub_class/Seq-Struct/embedds/train_embedds.tsv',sep='\\t',header=[0,1],index_col=[0])\n",
"sec_struct = fold_sequences(df['RNA Sequences']['0'])['structure_37']\n",
"#sec struct ratio is calculated as the number of non '.' characters divided by the length of the sequence\n",
"sec_struct_ratio = sec_struct.apply(lambda x: (len(x)-x.count('.'))/len(x))\n",
"fig = px.scatter(x=sc_umap_coords['Seq-Struct'][:,0],y=sc_umap_coords['Seq-Struct'][:,1],color=sec_struct_ratio,labels={'color':'Base Pairing'},title='Seq-Struct', width=800, height=800,\\\n",
" hover_data={'Major Class':mc_labels['Seq-Struct']}, color_continuous_scale='Viridis',range_color=[0,1])\n",
"\n",
"fig.update_traces(marker=dict(size=3))\n",
"#white background\n",
"fig.update_layout(plot_bgcolor='rgba(0,0,0,0)')\n",
"fig.show()\n",
"fig.write_image(models_path+'/sub_class/Seq-Struct/figures/sc_umap_sec_struct.svg')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"from transforna import Results_Handler,get_closest_ngbr_per_split\n",
"\n",
"splits = ['train','valid','test','ood','artificial','no_annotation']\n",
"splits_to_plot = ['test','ood','random','recombined','artificial_affix']\n",
"renaming_dict= {'test':'ID (test)','ood':'Rare sub-classes','random':'Random','artificial_affix':'Putative 5\\'-adapter prefixes','recombined':'Recombined'}\n",
"\n",
"lev_dist_df = pd.DataFrame()\n",
"for model in models:\n",
" results = Results_Handler(models_path+f'/sub_class/{model}/embedds',splits=splits,read_dataset=True)\n",
" results.append_loco_variants()\n",
" results.get_knn_model()\n",
" \n",
" #compute levenstein distance per split\n",
" for split in splits_to_plot:\n",
" split_seqs,split_labels,top_n_seqs,top_n_labels,distances,lev_dist = get_closest_ngbr_per_split(results,split)\n",
" #create df from split and levenstein distance\n",
" lev_dist_split_df = pd.DataFrame({'split':split,'lev_dist':lev_dist,'seqs':split_seqs,'labels':split_labels,'top_n_seqs':top_n_seqs,'top_n_labels':top_n_labels})\n",
" #rename \n",
" lev_dist_split_df['split'] = lev_dist_split_df['split'].map(renaming_dict)\n",
" lev_dist_split_df['model'] = model\n",
" #append \n",
" lev_dist_df = pd.concat([lev_dist_df,lev_dist_split_df],axis=0)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#plot the distribution of lev_dist for each split for each model\n",
"model_thresholds = {'Baseline':0.267,'Seq':0.246,'Seq-Seq':0.272,'Seq-Struct': 0.242,'Seq-Rev':0.237}\n",
"model_aucs = {'Baseline':0.76,'Seq':0.97,'Seq-Seq':0.96,'Seq-Struct': 0.97,'Seq-Rev':0.97}\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"sns.set_theme(style=\"whitegrid\")\n",
"sns.set(rc={'figure.figsize':(15,10)})\n",
"sns.set(font_scale=1.5)\n",
"ax = sns.boxplot(x=\"model\", y=\"lev_dist\", hue=\"split\", data=lev_dist_df, palette=\"Set3\",order=models,showfliers = True)\n",
"#add title\n",
"ax.set_facecolor('None')\n",
"plt.title('Levenshtein Distance Distribution per Model on ID')\n",
"ax.set(xlabel='Model', ylabel='Normalized Levenshtein Distance')\n",
"#legend background should transparent\n",
"ax.legend(loc='upper left', bbox_to_anchor=(1.05, 1), borderaxespad=0.,facecolor=None,framealpha=0.0)\n",
"# add horizontal lines for thresholds for each model while making sure the line is within the boxplot\n",
"min_val = 0 \n",
"for model in models:\n",
" thresh = model_thresholds[model]\n",
" plt.axhline(y=thresh, color='g', linestyle='--',xmin=min_val,xmax=min_val+0.2)\n",
" min_val+=0.2\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "transforna",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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