rank_models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3ad382ea-6c22-484d-8923-b27051356f27",
   "metadata": {},
   "source": [
    "# Rank models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bac1b190-3a81-4774-8239-a284a2bce329",
   "metadata": {},
   "source": [
    "### Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "48c88b01",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import matplotlib.cm as cm\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scanpy as sc\n",
    "import sklearn\n",
    "from scipy.stats import ttest_ind\n",
    "import itertools as it\n",
    "import scipy\n",
    "import scipy.cluster.hierarchy as sch\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "26b01d31",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score, accuracy_score, recall_score, confusion_matrix, f1_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import preprocessing as pp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "119ee837-13f1-4287-81d3-c6f257dc8de9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import resample\n",
    "from imblearn.over_sampling import SMOTE,RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from imblearn.pipeline import Pipeline\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "407648db-207e-45a1-b452-543b366b8de0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "608ee588",
   "metadata": {},
   "source": [
    "### generate_seeds_samples\n",
    "\n",
    "This function generates a dictionary with the seeds that will be used troughout the classification. It is important to set the seeds as a means for reproducibility of the classification. Use this function if you want to set new seeds. In this notebook we will be using pre-computed seeds in our analysis.\n",
    "\n",
    "The dictionary keys are \"n\" seeds which is the number of iterations of the models. The default n is 100.\n",
    "The values are the list of indexes of the dataset containing the class 1 that are selected at each cycle.\n",
    "\n",
    "- n\n",
    "- sample_size (the size of the list generated)\n",
    "- max_index (the maximum index that can be selected from the data)\n",
    "\n",
    "Bellow we have the seeds used in the comparison between classificators."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bfab6b7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_seeds_samples(n, sample_size, max_index):\n",
    "    seeds = np.random.randint(1000, size=n)\n",
    "    dic_seeds = {}\n",
    "    for seed in seeds:\n",
    "        dic_seeds[seed] = np.random.randint(max_index, size=sample_size)\n",
    "    return dic_seeds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1c00e93b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24, 98)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seeds = pd.read_pickle(r\"./data/gene_lists/df_seeds.pkl\")\n",
    "seeds.shape\n",
    "#set of seeds used during the pre-computed ranking of the features lists. You may use it or not"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a457a0ae",
   "metadata": {},
   "source": [
    "### Split dataset into class0 and class1\n",
    "\n",
    "This function separates the adata object in two containing the elements from the two classes.\n",
    "- adata\n",
    "- classes (a dictionary containing in the keys 'class_0' and 'class_1'. In the values another dictionary with the key as column name and the value as the value the column assumes according to each class)\n",
    "\n",
    "The function returns separate dataframes for each class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8d420f3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#In this case we want class_0 to be defined as the cells where in column 'Extra' we have the 'D' values\n",
    "#while the class_1, the cells where in column 'labels' we have '1.0' values.\n",
    "def split_classes(adata, classes={'class_0':{'Extra':'D'}, 'class_1':{'labels':1.0}}):\n",
    "    target1 = list(classes['class_0'].keys())[0]\n",
    "    target2 = list(classes['class_1'].keys())[0]\n",
    "    print(target1)\n",
    "    df_class_0 = adata[(adata.obs[target1]==classes['class_0'][target1]),:].obs\n",
    "    df_class_1 = adata[(adata.obs[target2]==classes['class_1'][target2]),:].obs\n",
    "    return df_class_0, df_class_1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac73a70d",
   "metadata": {},
   "source": [
    "### do_logreg_n4\n",
    "\n",
    "The goal is to perform the model \"n\" times and average all the results as a means of statistical confirmation.\n",
    "The dataset has to be previously divided between class0 (dead) and class1 (alive)\n",
    "- df_class0\n",
    "- df_class1\n",
    "- target (the target feature, ideally a binary categorical)\n",
    "- seeds (obligatory for the reproducebility)\n",
    "- n (number of iterations)\n",
    "- max_degree (maximun polynomial degree to be tested with k-fold cross validation)\n",
    "- sampling_strategy (to overcome the imbalancing problem). Can be 'under' for undersampling the majority class, 'over' for oversampling the minority class or 'SMOTE' for the SMOTE synthetic algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "78f176af",
   "metadata": {},
   "outputs": [],
   "source": [
    "def do_logreg_n4(df_class0, df_class1, target, features, seeds, max_degree=2, max_knn=2,\n",
    "                 model_type=\"logistic\",\n",
    "                 sampling_strategy='SMOTE'):\n",
    "\n",
    "    #setting-up the listts to contain the results for each run\n",
    "    f1_train = []\n",
    "    f1_test = []\n",
    "    diff_f1 = []\n",
    "    acc_train = []\n",
    "    acc_test = []\n",
    "    diff_acc = []\n",
    "    pre_train = []\n",
    "    pre_test = []\n",
    "    diff_pre = []\n",
    "    rec_train = []\n",
    "    rec_test = []\n",
    "    diff_rec = []\n",
    "    cnf_mtx_test = []\n",
    "    deg = []\n",
    "    knn=[]\n",
    "\n",
    "    #setting-up split test size\n",
    "    split_test_size = 0.3\n",
    "    \n",
    "    #how many degrees will be tested in the the Polynomial regression\n",
    "    degrees = list(range(1, max_degree+1))\n",
    "    neighbors=list(range(1,max_knn))\n",
    "    #creating the f1 scorer\n",
    "    f1_scorer = make_scorer(f1_score, greater_is_better=True)\n",
    "    \n",
    "\n",
    "    #creo l'intero dsataset\n",
    "    data = pd.concat([df_class0, df_class1])\n",
    "    y = data[target].to_numpy().astype(np.float32)\n",
    "    if len(features) == 1:\n",
    "        X = data[features].to_numpy().astype(np.float32).reshape(-1,1)\n",
    "    else:\n",
    "        X = data[features].to_numpy().astype(np.float32)\n",
    "\n",
    "    for seed in seeds:\n",
    "        #creating train and test sets\n",
    "\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=split_test_size, \n",
    "                                                        random_state=int(seed), stratify=y)\n",
    "        #checking the sampling strategies\n",
    "\n",
    "        if sampling_strategy == 'under':\n",
    "            ros = RandomOverSampler(sampling_strategy='minority',random_state=seed)\n",
    "            X_train, y_train = ros.fit_resample(X_train, y_train)\n",
    "        elif sampling_strategy == 'over':\n",
    "            rus=RandomUnderSampler(sampling_strategy='majority',random_state=seed)\n",
    "            X_train, y_train = rus.fit_resample(X_train, y_train)\n",
    "        elif sampling_strategy == 'SMOTE':\n",
    "            simple_SMOTE = SMOTE(sampling_strategy='minority', random_state=seed)\n",
    "            X_train, y_train = simple_SMOTE.fit_resample(X_train, y_train)\n",
    "            \n",
    "        else:\n",
    "            print('sampling strategy not recognized. Using imbalanced classes (not recommended)')\n",
    "\n",
    "\n",
    "        #setting-up cross validation\n",
    "        cv = StratifiedKFold(n_splits=10, random_state=seed, shuffle=True)\n",
    "\n",
    "        if model_type==\"logistic\":\n",
    "            #testo solo il grado\n",
    "            val_scores = np.zeros(len(degrees))\n",
    "            # Validation for the selection of the degree hyperparameter\n",
    "            for i, d in enumerate(degrees):\n",
    "                \n",
    "                model = Pipeline([('poly', PolynomialFeatures(degree=d, include_bias=False)),\n",
    "                                ('scale', StandardScaler()),\n",
    "                                ('logistic', LogisticRegression(fit_intercept=True))])       \n",
    "                #cross-validation scores based on f1 score\n",
    "                scores = cross_val_score(model, X_train, y_train, scoring=f1_scorer, cv=cv)\n",
    "                #aggregation into a mean and addition to the list\n",
    "            val_scores[i] = np.mean(scores)\n",
    "\n",
    "            # Identifies which is the best degree\n",
    "            best_model_idx = np.argmax(val_scores)\n",
    "            best_degree = degrees[best_model_idx]\n",
    "\n",
    "            # Train again the Pipeline using the best parameter and the whole training set\n",
    "            model = Pipeline([('poly', PolynomialFeatures(degree=best_degree, include_bias=False)),\n",
    "                            ('scale', StandardScaler()),\n",
    "                            ('logistic', LogisticRegression(fit_intercept=True))])\n",
    "        else:\n",
    "            #testo solo il numero dei vicini\n",
    "            val_scores = np.zeros(len(neighbors))\n",
    "            # Validation for the selection of the degree hyperparameter\n",
    "            for i, d in enumerate(neighbors):\n",
    "                \n",
    "                model = Pipeline([\n",
    "                                ('scale', StandardScaler()),\n",
    "                                ('knn', KNeighborsClassifier(n_neighbors=d))\n",
    "                                ])       \n",
    "                #cross-validation scores based on f1 score\n",
    "                scores = cross_val_score(model, X_train, y_train, scoring=f1_scorer, cv=cv)\n",
    "                #aggregation into a mean and addition to the list\n",
    "            val_scores[i] = np.mean(scores)\n",
    "\n",
    "            # Identifies which is the best degree\n",
    "            best_model_idx = np.argmax(val_scores)\n",
    "            best_knn = neighbors[best_model_idx]\n",
    "\n",
    "        \n",
    "            # Train again the Pipeline using the best parameter and the whole training set\n",
    "            model = Pipeline([\n",
    "                            ('scale', StandardScaler()),\n",
    "                            ('knn', KNeighborsClassifier(n_neighbors=best_knn))\n",
    "                            ])\n",
    "\n",
    "        model = model.fit(X_train, y_train)\n",
    "        y_predict_train = model.predict(X_train)\n",
    "        y_predict_test = model.predict(X_test)\n",
    "\n",
    "        #computing all the metrics and adding them to their respective lists\n",
    "        f1tr = f1_score(y_train, y_predict_train,average=\"binary\")\n",
    "        f1te = f1_score(y_test, y_predict_test,average=\"binary\")\n",
    "        f1_train.append(f1tr)\n",
    "        f1_test.append(f1te)\n",
    "        diff_f1.append(f1tr - f1te)\n",
    "        \n",
    "        acctr = accuracy_score(y_train, y_predict_train)\n",
    "        accte = accuracy_score(y_test, y_predict_test)\n",
    "        acc_train.append(acctr)\n",
    "        acc_test.append(accte)\n",
    "        diff_acc.append(acctr - accte)\n",
    "        \n",
    "        pretr = precision_score(y_train, y_predict_train)\n",
    "        prete = precision_score(y_test, y_predict_test)\n",
    "        pre_train.append(pretr)\n",
    "        pre_test.append(prete)\n",
    "        diff_pre.append(pretr - prete)\n",
    "        \n",
    "        rectr = recall_score(y_train, y_predict_train)\n",
    "        recte = recall_score(y_test, y_predict_test)\n",
    "        rec_train.append(rectr)\n",
    "        rec_test.append(recte)\n",
    "        diff_rec.append(rectr - recte)\n",
    "        \n",
    "        cnf_mtx_test.append(confusion_matrix(y_test, y_predict_test))\n",
    "        if model_type==\"logistic\":\n",
    "            deg.append(best_degree)\n",
    "        else:\n",
    "            deg.append(best_knn)\n",
    "    #aggregating metrics into mean of the runs\n",
    "    mean_f1_train = np.mean(f1_train)\n",
    "    std_f1_train = np.std(f1_train)\n",
    "    mean_f1_test = np.mean(f1_test)\n",
    "    std_f1_test = np.std(f1_test)\n",
    "    mean_diff_f1 = np.mean(diff_f1)\n",
    "    std_diff_f1 = np.std(diff_f1)\n",
    "    \n",
    "    mean_acc_train = np.mean(acc_train)\n",
    "    std_acc_train = np.std(acc_train)\n",
    "    mean_acc_test = np.mean(acc_test)\n",
    "    std_acc_test = np.std(acc_test)\n",
    "    mean_diff_acc = np.mean(diff_acc)\n",
    "    std_diff_acc = np.std(diff_acc)\n",
    "    \n",
    "    mean_pre_train = np.mean(pre_train)\n",
    "    std_pre_train = np.std(pre_train)\n",
    "    mean_pre_test = np.mean(pre_test)\n",
    "    std_pre_test = np.std(pre_test)\n",
    "    mean_diff_pre = np.mean(diff_pre)\n",
    "    std_diff_pre = np.std(diff_pre) \n",
    "    \n",
    "    mean_rec_train = np.mean(rec_train)\n",
    "    std_rec_train = np.std(rec_train)\n",
    "    mean_rec_test = np.mean(rec_test)\n",
    "    std_rec_test = np.std(rec_test)\n",
    "    mean_diff_rec = np.mean(diff_rec)\n",
    "    std_diff_rec = np.std(diff_rec)\n",
    "    \n",
    "    mean_cnf_matrix_test = np.mean(cnf_mtx_test, axis = 0)\n",
    "    mean_degrees = np.mean(deg)\n",
    "    \n",
    "    #20 arguments returned\n",
    "    return mean_f1_train, std_f1_train, mean_f1_test, std_f1_test, mean_diff_f1, std_diff_f1, mean_acc_train, std_acc_train, mean_acc_test, std_acc_test, mean_diff_acc, std_diff_acc, mean_pre_train, std_pre_train, mean_pre_test, std_pre_test, mean_diff_pre, std_diff_pre, mean_rec_train, std_rec_train, mean_rec_test, std_rec_test, mean_diff_rec, std_diff_rec, mean_cnf_matrix_test, mean_degrees"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0ad7bae",
   "metadata": {},
   "source": [
    "### logreg_combinatorial4\n",
    "\n",
    "This function works like the \"do_logreg_n4\" but it sets up a combinatorial permutation of all the possible combinations of features from 1 to m elements, with m being the number of features passed. \n",
    "For example for three features: [feat1, feat2, feat3]\n",
    "the feature lists generated will be:\n",
    "[feat1]; [feat2]; [feat3]; [feat1, feat2]; [feat1, feat3]; [feat2, feat3]; [feat1, feat2, feat3]\n",
    "\n",
    "The model is then performed n times on each list of features using the previous function do_logreg_n4. In the end it returns a dataframe of the lists ranked by accuracy of the classificator.\n",
    "Warning: this function takes a long time to run for long lists of features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "05551ecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def logreg_combinatorial4(dataset1, dataset2, \n",
    "                          target, \n",
    "                          features, \n",
    "                          seeds,\n",
    "                          model_type=\"logistic\",\n",
    "                          sampling_strategy='SMOTE'):\n",
    "    \n",
    "    #setting-up the list of features lists\n",
    "    features_combinations = []\n",
    "    number = int(len(features))\n",
    "    \n",
    "    for i in range(0, number+1):\n",
    "        #generating the lists of features\n",
    "        [features_combinations.append(list(x)) for x in it.combinations(features, i)]\n",
    "\n",
    "    #df columns\n",
    "    features_combinations.pop(0) #removing first element because empty\n",
    "    features_index = []\n",
    "\n",
    "    #setting-up lists of metrics\n",
    "    mean_f1_train = []\n",
    "    std_f1_train = []\n",
    "    mean_f1_test = []\n",
    "    std_f1_test = []\n",
    "    mean_diff_f1 = []\n",
    "    std_diff_f1 = []\n",
    "    \n",
    "    mean_acc_train = []\n",
    "    std_acc_train = []\n",
    "    mean_acc_test = []\n",
    "    std_acc_test = []\n",
    "    mean_diff_acc = []\n",
    "    std_diff_acc = []\n",
    "    \n",
    "    mean_pre_train = []\n",
    "    std_pre_train = []\n",
    "    mean_pre_test = []\n",
    "    std_pre_test = []\n",
    "    mean_diff_pre = []\n",
    "    std_diff_pre = []\n",
    "    \n",
    "    mean_rec_train = []\n",
    "    std_rec_train = []\n",
    "    mean_rec_test = []\n",
    "    std_rec_test = []\n",
    "    mean_diff_rec = []\n",
    "    std_diff_rec = []\n",
    "    \n",
    "    mean_cnf_mtx_test = []\n",
    "    code = []\n",
    "    mean_degrees = []\n",
    "    \n",
    "    print('number of features: {x}'.format(x=number))\n",
    "    print('number of lists: {x}'.format(x=len(features_combinations)))\n",
    "    print('number of iterations to be performed for each list: {x}'.format(x=len(seeds)))\n",
    "    \n",
    "    for features in features_combinations:\n",
    "        print(features)\n",
    "        features_index.append('list_{x}'.format(x=features_combinations.index(features)))\n",
    "        #mean_f1_train, std_f1_train, mean_f1_test, std_f1_test, mean_diff_f1, std_diff_f1, mean_acc_train, std_acc_train, mean_acc_test, std_acc_test, mean_diff_acc, std_diff_acc, mean_pre_train, std_pre_train, mean_pre_test, std_pre_test, mean_diff_pre, std_diff_pre, mean_rec_train, std_rec_train, mean_rec_test, std_rec_test, mean_diff_rec, std_diff_rec, mean_cnf_matrix_test, mean_coeffs\n",
    "        #do_logreg_n4 returns a lot of values\n",
    "        #each time the model is trained and tested with a different list of features\n",
    "        resuls= do_logreg_n4(dataset1, dataset2, target, features, seeds=seeds, model_type=model_type,sampling_strategy=sampling_strategy)\n",
    "        [f1tr, stdf1tr, f1te, stdf1te, difff1, stddifff1, acctr, stdacctr, accte, stdaccte, diffacc, stddiffacc, pretr, stdpretr, prete, stdprete, diffpre, stddiffpre, rectr, stdrectr, recte, stdrecte, diffrec, stddiffrec, cnf_mtx, degree ]= resuls\n",
    "        \n",
    "        mean_f1_train.append(f1tr)\n",
    "        std_f1_train.append(stdf1tr)\n",
    "        mean_f1_test.append(f1te)\n",
    "        std_f1_test.append(stdf1te)\n",
    "        mean_diff_f1.append(difff1)\n",
    "        std_diff_f1.append(stddifff1)\n",
    "        \n",
    "        mean_acc_train.append(acctr)\n",
    "        std_acc_train.append(stdacctr)\n",
    "        mean_acc_test.append(accte)\n",
    "        std_acc_test.append(stdaccte)\n",
    "        mean_diff_acc.append(diffacc)\n",
    "        std_diff_acc.append(stddiffacc)\n",
    "        \n",
    "        mean_pre_train.append(pretr)\n",
    "        std_pre_train.append(stdpretr)\n",
    "        mean_pre_test.append(prete)\n",
    "        std_pre_test.append(stdprete)\n",
    "        mean_diff_pre.append(diffpre)\n",
    "        std_diff_pre.append(stddiffpre)\n",
    "        \n",
    "        mean_rec_train.append(rectr)\n",
    "        std_rec_train.append(stdrectr)\n",
    "        mean_rec_test.append(recte)\n",
    "        std_rec_test.append(stdrecte)\n",
    "        mean_diff_rec.append(diffrec)\n",
    "        std_diff_rec.append(stddiffrec)\n",
    "        \n",
    "        mean_cnf_mtx_test.append(cnf_mtx)\n",
    "        code.append(features)\n",
    "        mean_degrees.append(degree)\n",
    "        print('{x}/{tot}'.format(x=features_combinations.index(features)+1, tot=len(features_combinations)))\n",
    "\n",
    "    #transforming the results into a dataframe\n",
    "    features_df = pd.DataFrame(index=features_index, columns=['mean_f1_train', 'std_f1_train', 'mean_f1_test', 'std_f1_test',\n",
    "                                                              'mean_diff_f1', 'std_diff_f1', \n",
    "                                                              'mean_acc_train', 'std_acc_train', 'mean_acc_test', 'std_acc_test',\n",
    "                                                              'mean_diff_acc', 'std_diff_acc', 'mean_pre_train', 'std_pre_train',\n",
    "                                                              'mean_pre_test', 'std_pre_test', 'mean_diff_pre', 'std_diff_pre',\n",
    "                                                              'mean_rec_train', 'std_rec_train', 'mean_rec_test', 'std_rec_test',\n",
    "                                                              'mean_diff_rec', 'std_diff_rec',\n",
    "                                                             'mean_cnf_mtx_test', 'mean_degrees','code', \n",
    "                                                              'rank'])\n",
    "    features_df['mean_f1_train'] = mean_f1_train\n",
    "    features_df['std_f1_train'] = std_f1_train\n",
    "    features_df['mean_f1_test'] = mean_f1_test\n",
    "    features_df['std_f1_test'] = std_f1_test\n",
    "    features_df['mean_diff_f1'] = mean_diff_f1\n",
    "    features_df['std_diff_f1'] = std_diff_f1\n",
    "    \n",
    "    features_df['mean_acc_train'] = mean_acc_train\n",
    "    features_df['std_acc_train'] = std_acc_train\n",
    "    features_df['mean_acc_test'] = mean_acc_test\n",
    "    features_df['std_acc_test'] = std_acc_test\n",
    "    features_df['mean_diff_acc'] = mean_diff_acc\n",
    "    features_df['std_diff_acc'] = std_diff_acc\n",
    "    \n",
    "    features_df['mean_pre_train'] = mean_pre_train\n",
    "    features_df['std_pre_train'] = std_pre_train\n",
    "    features_df['mean_pre_test'] = mean_pre_test\n",
    "    features_df['std_pre_test'] = std_pre_test\n",
    "    features_df['mean_diff_pre'] = mean_diff_pre\n",
    "    features_df['std_diff_pre'] = std_diff_pre\n",
    "    \n",
    "    features_df['mean_rec_train'] = mean_rec_train\n",
    "    features_df['std_rec_train'] = std_rec_train\n",
    "    features_df['mean_rec_test'] = mean_rec_test\n",
    "    features_df['std_rec_test'] = std_rec_test\n",
    "    features_df['mean_diff_rec'] = mean_diff_rec\n",
    "    features_df['std_diff_rec'] = std_diff_rec\n",
    "    \n",
    "    features_df['mean_cnf_mtx_test'] = mean_cnf_mtx_test\n",
    "    features_df['code'] = code\n",
    "    features_df['mean_degrees'] = mean_degrees\n",
    "    #sorting values based onn mean f1 score in the test set\n",
    "    features_df.sort_values(by=['mean_f1_test'], ascending = False, inplace=True)\n",
    "    \n",
    "    for feature in features:\n",
    "        features_df[feature] = 0\n",
    "    \n",
    "    for index in features_df.index:\n",
    "        for feature in features_df.loc[index,'code']:\n",
    "            features_df.loc[index,feature] = 1\n",
    "\n",
    "    #creating a rank\n",
    "    features_df['rank'] = np.linspace(start=1, stop=features_df.shape[0], num=features_df.shape[0])\n",
    "    features_df.set_index('rank', drop=False, inplace=True)\n",
    "    \n",
    "    return features_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0e7f85c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_mouse_3_pp = sc.read_h5ad(r\"./outputs/adata_mouse_3_pp.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ebb3820",
   "metadata": {},
   "source": [
    "### Model ranking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "5dd1ad77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extra\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>labels</th>\n",
       "      <th>Extra</th>\n",
       "      <th>n_genes</th>\n",
       "      <th>n_genes_by_counts</th>\n",
       "      <th>total_counts</th>\n",
       "      <th>total_counts_mt</th>\n",
       "      <th>pct_counts_mt</th>\n",
       "      <th>total_counts_ribo</th>\n",
       "      <th>pct_counts_ribo</th>\n",
       "      <th>total_counts_apex_only</th>\n",
       "      <th>pct_counts_apex_only</th>\n",
       "      <th>total_counts_frac_seq_only</th>\n",
       "      <th>pct_counts_frac_seq_only</th>\n",
       "      <th>total_counts_lncrna</th>\n",
       "      <th>pct_counts_lncrna</th>\n",
       "      <th>total_counts_in_common_apex_frac_seq</th>\n",
       "      <th>pct_counts_in_common_apex_frac_seq</th>\n",
       "      <th>total_counts_control_agg</th>\n",
       "      <th>pct_counts_control_agg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2i_3_55</th>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>8708</td>\n",
       "      <td>8700</td>\n",
       "      <td>792644.68750</td>\n",
       "      <td>189997.312500</td>\n",
       "      <td>23.970049</td>\n",
       "      <td>105398.281250</td>\n",
       "      <td>13.297039</td>\n",
       "      <td>83.802460</td>\n",
       "      <td>0.010573</td>\n",
       "      <td>537.119629</td>\n",
       "      <td>0.067763</td>\n",
       "      <td>27.634245</td>\n",
       "      <td>0.003486</td>\n",
       "      <td>47.250729</td>\n",
       "      <td>0.005961</td>\n",
       "      <td>24468.230469</td>\n",
       "      <td>3.086911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2i_3_74</th>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>8685</td>\n",
       "      <td>8684</td>\n",
       "      <td>743908.68750</td>\n",
       "      <td>382920.593750</td>\n",
       "      <td>51.474144</td>\n",
       "      <td>50091.273438</td>\n",
       "      <td>6.733525</td>\n",
       "      <td>162.016312</td>\n",
       "      <td>0.021779</td>\n",
       "      <td>612.440308</td>\n",
       "      <td>0.082327</td>\n",
       "      <td>4.240090</td>\n",
       "      <td>0.000570</td>\n",
       "      <td>1.736489</td>\n",
       "      <td>0.000233</td>\n",
       "      <td>22577.589844</td>\n",
       "      <td>3.034994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2i_3_75</th>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>9927</td>\n",
       "      <td>9925</td>\n",
       "      <td>744331.87500</td>\n",
       "      <td>330827.375000</td>\n",
       "      <td>44.446217</td>\n",
       "      <td>65233.710938</td>\n",
       "      <td>8.764063</td>\n",
       "      <td>186.372559</td>\n",
       "      <td>0.025039</td>\n",
       "      <td>441.818756</td>\n",
       "      <td>0.059358</td>\n",
       "      <td>4.008549</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>16.335190</td>\n",
       "      <td>0.002195</td>\n",
       "      <td>22546.660156</td>\n",
       "      <td>3.029114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2i_3_81</th>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>6861</td>\n",
       "      <td>6860</td>\n",
       "      <td>776147.25000</td>\n",
       "      <td>264396.812500</td>\n",
       "      <td>34.065292</td>\n",
       "      <td>66048.601562</td>\n",
       "      <td>8.509803</td>\n",
       "      <td>293.842346</td>\n",
       "      <td>0.037859</td>\n",
       "      <td>367.483826</td>\n",
       "      <td>0.047347</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>84.941345</td>\n",
       "      <td>0.010944</td>\n",
       "      <td>23560.894531</td>\n",
       "      <td>3.035622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2i_3_93</th>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>8990</td>\n",
       "      <td>8987</td>\n",
       "      <td>711284.75000</td>\n",
       "      <td>375009.125000</td>\n",
       "      <td>52.722782</td>\n",
       "      <td>40869.398438</td>\n",
       "      <td>5.745856</td>\n",
       "      <td>112.814774</td>\n",
       "      <td>0.015861</td>\n",
       "      <td>382.169586</td>\n",
       "      <td>0.053729</td>\n",
       "      <td>4.197289</td>\n",
       "      <td>0.000590</td>\n",
       "      <td>3.735358</td>\n",
       "      <td>0.000525</td>\n",
       "      <td>21566.332031</td>\n",
       "      <td>3.032025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a2i_3_5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>5860</td>\n",
       "      <td>5858</td>\n",
       "      <td>749046.62500</td>\n",
       "      <td>711002.875000</td>\n",
       "      <td>94.921043</td>\n",
       "      <td>4764.084961</td>\n",
       "      <td>0.636020</td>\n",
       "      <td>4.287537</td>\n",
       "      <td>0.000572</td>\n",
       "      <td>4.739263</td>\n",
       "      <td>0.000633</td>\n",
       "      <td>1.124792</td>\n",
       "      <td>0.000150</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25075.009766</td>\n",
       "      <td>3.347589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a2i_3_32</th>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>8124</td>\n",
       "      <td>8124</td>\n",
       "      <td>746048.87500</td>\n",
       "      <td>602355.625000</td>\n",
       "      <td>80.739433</td>\n",
       "      <td>29408.515625</td>\n",
       "      <td>3.941902</td>\n",
       "      <td>62.311474</td>\n",
       "      <td>0.008352</td>\n",
       "      <td>83.760109</td>\n",
       "      <td>0.011227</td>\n",
       "      <td>2.666718</td>\n",
       "      <td>0.000357</td>\n",
       "      <td>1.417979</td>\n",
       "      <td>0.000190</td>\n",
       "      <td>23803.949219</td>\n",
       "      <td>3.190669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a2i_3_46</th>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>8263</td>\n",
       "      <td>8263</td>\n",
       "      <td>804032.50000</td>\n",
       "      <td>252442.484375</td>\n",
       "      <td>31.397051</td>\n",
       "      <td>114524.617188</td>\n",
       "      <td>14.243780</td>\n",
       "      <td>80.988571</td>\n",
       "      <td>0.010073</td>\n",
       "      <td>410.440674</td>\n",
       "      <td>0.051048</td>\n",
       "      <td>3.643741</td>\n",
       "      <td>0.000453</td>\n",
       "      <td>32.947151</td>\n",
       "      <td>0.004098</td>\n",
       "      <td>24331.027344</td>\n",
       "      <td>3.026125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a2i_3_55</th>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>8306</td>\n",
       "      <td>8305</td>\n",
       "      <td>828516.56250</td>\n",
       "      <td>203855.140625</td>\n",
       "      <td>24.604836</td>\n",
       "      <td>123159.734375</td>\n",
       "      <td>14.865090</td>\n",
       "      <td>171.696579</td>\n",
       "      <td>0.020723</td>\n",
       "      <td>693.507385</td>\n",
       "      <td>0.083705</td>\n",
       "      <td>0.351162</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25515.185547</td>\n",
       "      <td>3.079623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a2i_3_63</th>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>7884</td>\n",
       "      <td>7881</td>\n",
       "      <td>843804.31250</td>\n",
       "      <td>157987.687500</td>\n",
       "      <td>18.723261</td>\n",
       "      <td>123146.875000</td>\n",
       "      <td>14.594247</td>\n",
       "      <td>526.306519</td>\n",
       "      <td>0.062373</td>\n",
       "      <td>359.781311</td>\n",
       "      <td>0.042638</td>\n",
       "      <td>2.239357</td>\n",
       "      <td>0.000265</td>\n",
       "      <td>0.728967</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>25703.755859</td>\n",
       "      <td>3.046175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a2i_3_72</th>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>7671</td>\n",
       "      <td>7668</td>\n",
       "      <td>787311.12500</td>\n",
       "      <td>456362.406250</td>\n",
       "      <td>57.964684</td>\n",
       "      <td>59957.636719</td>\n",
       "      <td>7.615495</td>\n",
       "      <td>112.261520</td>\n",
       "      <td>0.014259</td>\n",
       "      <td>175.273880</td>\n",
       "      <td>0.022262</td>\n",
       "      <td>62.040287</td>\n",
       "      <td>0.007880</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24935.724609</td>\n",
       "      <td>3.167202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a2i_3_87</th>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>10077</td>\n",
       "      <td>10065</td>\n",
       "      <td>474731.21875</td>\n",
       "      <td>368075.093750</td>\n",
       "      <td>77.533363</td>\n",
       "      <td>16680.082031</td>\n",
       "      <td>3.513584</td>\n",
       "      <td>44.284534</td>\n",
       "      <td>0.009328</td>\n",
       "      <td>181.457748</td>\n",
       "      <td>0.038223</td>\n",
       "      <td>7.474108</td>\n",
       "      <td>0.001574</td>\n",
       "      <td>36.832901</td>\n",
       "      <td>0.007759</td>\n",
       "      <td>14538.697266</td>\n",
       "      <td>3.062511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a2i_3_93</th>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>8299</td>\n",
       "      <td>8298</td>\n",
       "      <td>719890.18750</td>\n",
       "      <td>423875.031250</td>\n",
       "      <td>58.880512</td>\n",
       "      <td>68115.789062</td>\n",
       "      <td>9.461969</td>\n",
       "      <td>338.297607</td>\n",
       "      <td>0.046993</td>\n",
       "      <td>186.869171</td>\n",
       "      <td>0.025958</td>\n",
       "      <td>0.572617</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>41.102837</td>\n",
       "      <td>0.005710</td>\n",
       "      <td>24175.425781</td>\n",
       "      <td>3.358211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2i_4_4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>9832</td>\n",
       "      <td>9829</td>\n",
       "      <td>831754.56250</td>\n",
       "      <td>175490.750000</td>\n",
       "      <td>21.098862</td>\n",
       "      <td>102565.726562</td>\n",
       "      <td>12.331249</td>\n",
       "      <td>279.326721</td>\n",
       "      <td>0.033583</td>\n",
       "      <td>395.872894</td>\n",
       "      <td>0.047595</td>\n",
       "      <td>79.852882</td>\n",
       "      <td>0.009601</td>\n",
       "      <td>7.711544</td>\n",
       "      <td>0.000927</td>\n",
       "      <td>24854.005859</td>\n",
       "      <td>2.988142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2i_4_16</th>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>10006</td>\n",
       "      <td>9999</td>\n",
       "      <td>761709.87500</td>\n",
       "      <td>343880.125000</td>\n",
       "      <td>45.145817</td>\n",
       "      <td>95954.726562</td>\n",
       "      <td>12.597281</td>\n",
       "      <td>93.942848</td>\n",
       "      <td>0.012333</td>\n",
       "      <td>248.022873</td>\n",
       "      <td>0.032561</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43.008869</td>\n",
       "      <td>0.005646</td>\n",
       "      <td>22185.865234</td>\n",
       "      <td>2.912641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2i_4_48</th>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>10153</td>\n",
       "      <td>10152</td>\n",
       "      <td>802477.37500</td>\n",
       "      <td>256888.140625</td>\n",
       "      <td>32.011883</td>\n",
       "      <td>108317.921875</td>\n",
       "      <td>13.497941</td>\n",
       "      <td>117.491463</td>\n",
       "      <td>0.014641</td>\n",
       "      <td>273.724945</td>\n",
       "      <td>0.034110</td>\n",
       "      <td>6.408801</td>\n",
       "      <td>0.000799</td>\n",
       "      <td>12.259122</td>\n",
       "      <td>0.001528</td>\n",
       "      <td>23138.398438</td>\n",
       "      <td>2.883370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2i_4_79</th>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>9787</td>\n",
       "      <td>9783</td>\n",
       "      <td>746822.62500</td>\n",
       "      <td>376834.593750</td>\n",
       "      <td>50.458378</td>\n",
       "      <td>80459.750000</td>\n",
       "      <td>10.773609</td>\n",
       "      <td>67.432686</td>\n",
       "      <td>0.009029</td>\n",
       "      <td>324.303314</td>\n",
       "      <td>0.043424</td>\n",
       "      <td>3.566941</td>\n",
       "      <td>0.000478</td>\n",
       "      <td>7.115561</td>\n",
       "      <td>0.000953</td>\n",
       "      <td>22590.970703</td>\n",
       "      <td>3.024945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2i_4_82</th>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>8956</td>\n",
       "      <td>8952</td>\n",
       "      <td>812332.18750</td>\n",
       "      <td>290371.468750</td>\n",
       "      <td>35.745411</td>\n",
       "      <td>72001.890625</td>\n",
       "      <td>8.863602</td>\n",
       "      <td>82.092339</td>\n",
       "      <td>0.010106</td>\n",
       "      <td>423.929718</td>\n",
       "      <td>0.052187</td>\n",
       "      <td>0.518429</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>3.828822</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>24432.716797</td>\n",
       "      <td>3.007724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2i_4_85</th>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>7091</td>\n",
       "      <td>7086</td>\n",
       "      <td>785840.12500</td>\n",
       "      <td>421821.343750</td>\n",
       "      <td>53.677757</td>\n",
       "      <td>40904.648438</td>\n",
       "      <td>5.205213</td>\n",
       "      <td>22.872942</td>\n",
       "      <td>0.002911</td>\n",
       "      <td>180.551407</td>\n",
       "      <td>0.022976</td>\n",
       "      <td>0.317411</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>10.269088</td>\n",
       "      <td>0.001307</td>\n",
       "      <td>23118.773438</td>\n",
       "      <td>2.941917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2i_4_92</th>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>9794</td>\n",
       "      <td>9793</td>\n",
       "      <td>820137.43750</td>\n",
       "      <td>136833.734375</td>\n",
       "      <td>16.684244</td>\n",
       "      <td>153392.203125</td>\n",
       "      <td>18.703232</td>\n",
       "      <td>120.888809</td>\n",
       "      <td>0.014740</td>\n",
       "      <td>276.456207</td>\n",
       "      <td>0.033709</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.205444</td>\n",
       "      <td>0.001488</td>\n",
       "      <td>24717.154297</td>\n",
       "      <td>3.013783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2i_5_33</th>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>9401</td>\n",
       "      <td>9400</td>\n",
       "      <td>795376.62500</td>\n",
       "      <td>194060.296875</td>\n",
       "      <td>24.398540</td>\n",
       "      <td>154526.781250</td>\n",
       "      <td>19.428127</td>\n",
       "      <td>153.568527</td>\n",
       "      <td>0.019308</td>\n",
       "      <td>275.548950</td>\n",
       "      <td>0.034644</td>\n",
       "      <td>3.315303</td>\n",
       "      <td>0.000417</td>\n",
       "      <td>12.705378</td>\n",
       "      <td>0.001597</td>\n",
       "      <td>23751.740234</td>\n",
       "      <td>2.986225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2i_5_50</th>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>9600</td>\n",
       "      <td>9598</td>\n",
       "      <td>766126.56250</td>\n",
       "      <td>227695.656250</td>\n",
       "      <td>29.720371</td>\n",
       "      <td>143280.218750</td>\n",
       "      <td>18.701899</td>\n",
       "      <td>111.732964</td>\n",
       "      <td>0.014584</td>\n",
       "      <td>256.135193</td>\n",
       "      <td>0.033432</td>\n",
       "      <td>7.759818</td>\n",
       "      <td>0.001013</td>\n",
       "      <td>24.153620</td>\n",
       "      <td>0.003153</td>\n",
       "      <td>22487.376953</td>\n",
       "      <td>2.935204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2i_5_64</th>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>9056</td>\n",
       "      <td>9054</td>\n",
       "      <td>817969.25000</td>\n",
       "      <td>165540.140625</td>\n",
       "      <td>20.237942</td>\n",
       "      <td>121157.351562</td>\n",
       "      <td>14.811969</td>\n",
       "      <td>74.150421</td>\n",
       "      <td>0.009065</td>\n",
       "      <td>295.427185</td>\n",
       "      <td>0.036117</td>\n",
       "      <td>0.430013</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>5.752658</td>\n",
       "      <td>0.000703</td>\n",
       "      <td>24379.693359</td>\n",
       "      <td>2.980515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2i_5_84</th>\n",
       "      <td>0.0</td>\n",
       "      <td>D</td>\n",
       "      <td>5324</td>\n",
       "      <td>5323</td>\n",
       "      <td>801493.31250</td>\n",
       "      <td>238006.218750</td>\n",
       "      <td>29.695347</td>\n",
       "      <td>82264.625000</td>\n",
       "      <td>10.263919</td>\n",
       "      <td>23.875624</td>\n",
       "      <td>0.002979</td>\n",
       "      <td>7.590167</td>\n",
       "      <td>0.000947</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24496.783203</td>\n",
       "      <td>3.056392</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          labels Extra  n_genes  n_genes_by_counts  total_counts  \\\n",
       "2i_3_55      0.0     D     8708               8700  792644.68750   \n",
       "2i_3_74      0.0     D     8685               8684  743908.68750   \n",
       "2i_3_75      0.0     D     9927               9925  744331.87500   \n",
       "2i_3_81      0.0     D     6861               6860  776147.25000   \n",
       "2i_3_93      0.0     D     8990               8987  711284.75000   \n",
       "a2i_3_5      0.0     D     5860               5858  749046.62500   \n",
       "a2i_3_32     0.0     D     8124               8124  746048.87500   \n",
       "a2i_3_46     0.0     D     8263               8263  804032.50000   \n",
       "a2i_3_55     0.0     D     8306               8305  828516.56250   \n",
       "a2i_3_63     0.0     D     7884               7881  843804.31250   \n",
       "a2i_3_72     0.0     D     7671               7668  787311.12500   \n",
       "a2i_3_87     0.0     D    10077              10065  474731.21875   \n",
       "a2i_3_93     0.0     D     8299               8298  719890.18750   \n",
       "2i_4_4       0.0     D     9832               9829  831754.56250   \n",
       "2i_4_16      0.0     D    10006               9999  761709.87500   \n",
       "2i_4_48      0.0     D    10153              10152  802477.37500   \n",
       "2i_4_79      0.0     D     9787               9783  746822.62500   \n",
       "2i_4_82      0.0     D     8956               8952  812332.18750   \n",
       "2i_4_85      0.0     D     7091               7086  785840.12500   \n",
       "2i_4_92      0.0     D     9794               9793  820137.43750   \n",
       "2i_5_33      0.0     D     9401               9400  795376.62500   \n",
       "2i_5_50      0.0     D     9600               9598  766126.56250   \n",
       "2i_5_64      0.0     D     9056               9054  817969.25000   \n",
       "2i_5_84      0.0     D     5324               5323  801493.31250   \n",
       "\n",
       "          total_counts_mt  pct_counts_mt  total_counts_ribo  pct_counts_ribo  \\\n",
       "2i_3_55     189997.312500      23.970049      105398.281250        13.297039   \n",
       "2i_3_74     382920.593750      51.474144       50091.273438         6.733525   \n",
       "2i_3_75     330827.375000      44.446217       65233.710938         8.764063   \n",
       "2i_3_81     264396.812500      34.065292       66048.601562         8.509803   \n",
       "2i_3_93     375009.125000      52.722782       40869.398438         5.745856   \n",
       "a2i_3_5     711002.875000      94.921043        4764.084961         0.636020   \n",
       "a2i_3_32    602355.625000      80.739433       29408.515625         3.941902   \n",
       "a2i_3_46    252442.484375      31.397051      114524.617188        14.243780   \n",
       "a2i_3_55    203855.140625      24.604836      123159.734375        14.865090   \n",
       "a2i_3_63    157987.687500      18.723261      123146.875000        14.594247   \n",
       "a2i_3_72    456362.406250      57.964684       59957.636719         7.615495   \n",
       "a2i_3_87    368075.093750      77.533363       16680.082031         3.513584   \n",
       "a2i_3_93    423875.031250      58.880512       68115.789062         9.461969   \n",
       "2i_4_4      175490.750000      21.098862      102565.726562        12.331249   \n",
       "2i_4_16     343880.125000      45.145817       95954.726562        12.597281   \n",
       "2i_4_48     256888.140625      32.011883      108317.921875        13.497941   \n",
       "2i_4_79     376834.593750      50.458378       80459.750000        10.773609   \n",
       "2i_4_82     290371.468750      35.745411       72001.890625         8.863602   \n",
       "2i_4_85     421821.343750      53.677757       40904.648438         5.205213   \n",
       "2i_4_92     136833.734375      16.684244      153392.203125        18.703232   \n",
       "2i_5_33     194060.296875      24.398540      154526.781250        19.428127   \n",
       "2i_5_50     227695.656250      29.720371      143280.218750        18.701899   \n",
       "2i_5_64     165540.140625      20.237942      121157.351562        14.811969   \n",
       "2i_5_84     238006.218750      29.695347       82264.625000        10.263919   \n",
       "\n",
       "          total_counts_apex_only  pct_counts_apex_only  \\\n",
       "2i_3_55                83.802460              0.010573   \n",
       "2i_3_74               162.016312              0.021779   \n",
       "2i_3_75               186.372559              0.025039   \n",
       "2i_3_81               293.842346              0.037859   \n",
       "2i_3_93               112.814774              0.015861   \n",
       "a2i_3_5                 4.287537              0.000572   \n",
       "a2i_3_32               62.311474              0.008352   \n",
       "a2i_3_46               80.988571              0.010073   \n",
       "a2i_3_55              171.696579              0.020723   \n",
       "a2i_3_63              526.306519              0.062373   \n",
       "a2i_3_72              112.261520              0.014259   \n",
       "a2i_3_87               44.284534              0.009328   \n",
       "a2i_3_93              338.297607              0.046993   \n",
       "2i_4_4                279.326721              0.033583   \n",
       "2i_4_16                93.942848              0.012333   \n",
       "2i_4_48               117.491463              0.014641   \n",
       "2i_4_79                67.432686              0.009029   \n",
       "2i_4_82                82.092339              0.010106   \n",
       "2i_4_85                22.872942              0.002911   \n",
       "2i_4_92               120.888809              0.014740   \n",
       "2i_5_33               153.568527              0.019308   \n",
       "2i_5_50               111.732964              0.014584   \n",
       "2i_5_64                74.150421              0.009065   \n",
       "2i_5_84                23.875624              0.002979   \n",
       "\n",
       "          total_counts_frac_seq_only  pct_counts_frac_seq_only  \\\n",
       "2i_3_55                   537.119629                  0.067763   \n",
       "2i_3_74                   612.440308                  0.082327   \n",
       "2i_3_75                   441.818756                  0.059358   \n",
       "2i_3_81                   367.483826                  0.047347   \n",
       "2i_3_93                   382.169586                  0.053729   \n",
       "a2i_3_5                     4.739263                  0.000633   \n",
       "a2i_3_32                   83.760109                  0.011227   \n",
       "a2i_3_46                  410.440674                  0.051048   \n",
       "a2i_3_55                  693.507385                  0.083705   \n",
       "a2i_3_63                  359.781311                  0.042638   \n",
       "a2i_3_72                  175.273880                  0.022262   \n",
       "a2i_3_87                  181.457748                  0.038223   \n",
       "a2i_3_93                  186.869171                  0.025958   \n",
       "2i_4_4                    395.872894                  0.047595   \n",
       "2i_4_16                   248.022873                  0.032561   \n",
       "2i_4_48                   273.724945                  0.034110   \n",
       "2i_4_79                   324.303314                  0.043424   \n",
       "2i_4_82                   423.929718                  0.052187   \n",
       "2i_4_85                   180.551407                  0.022976   \n",
       "2i_4_92                   276.456207                  0.033709   \n",
       "2i_5_33                   275.548950                  0.034644   \n",
       "2i_5_50                   256.135193                  0.033432   \n",
       "2i_5_64                   295.427185                  0.036117   \n",
       "2i_5_84                     7.590167                  0.000947   \n",
       "\n",
       "          total_counts_lncrna  pct_counts_lncrna  \\\n",
       "2i_3_55             27.634245           0.003486   \n",
       "2i_3_74              4.240090           0.000570   \n",
       "2i_3_75              4.008549           0.000539   \n",
       "2i_3_81              0.000000           0.000000   \n",
       "2i_3_93              4.197289           0.000590   \n",
       "a2i_3_5              1.124792           0.000150   \n",
       "a2i_3_32             2.666718           0.000357   \n",
       "a2i_3_46             3.643741           0.000453   \n",
       "a2i_3_55             0.351162           0.000042   \n",
       "a2i_3_63             2.239357           0.000265   \n",
       "a2i_3_72            62.040287           0.007880   \n",
       "a2i_3_87             7.474108           0.001574   \n",
       "a2i_3_93             0.572617           0.000080   \n",
       "2i_4_4              79.852882           0.009601   \n",
       "2i_4_16              0.000000           0.000000   \n",
       "2i_4_48              6.408801           0.000799   \n",
       "2i_4_79              3.566941           0.000478   \n",
       "2i_4_82              0.518429           0.000064   \n",
       "2i_4_85              0.317411           0.000040   \n",
       "2i_4_92              0.000000           0.000000   \n",
       "2i_5_33              3.315303           0.000417   \n",
       "2i_5_50              7.759818           0.001013   \n",
       "2i_5_64              0.430013           0.000053   \n",
       "2i_5_84              0.000000           0.000000   \n",
       "\n",
       "          total_counts_in_common_apex_frac_seq  \\\n",
       "2i_3_55                              47.250729   \n",
       "2i_3_74                               1.736489   \n",
       "2i_3_75                              16.335190   \n",
       "2i_3_81                              84.941345   \n",
       "2i_3_93                               3.735358   \n",
       "a2i_3_5                               0.000000   \n",
       "a2i_3_32                              1.417979   \n",
       "a2i_3_46                             32.947151   \n",
       "a2i_3_55                              0.000000   \n",
       "a2i_3_63                              0.728967   \n",
       "a2i_3_72                              0.000000   \n",
       "a2i_3_87                             36.832901   \n",
       "a2i_3_93                             41.102837   \n",
       "2i_4_4                                7.711544   \n",
       "2i_4_16                              43.008869   \n",
       "2i_4_48                              12.259122   \n",
       "2i_4_79                               7.115561   \n",
       "2i_4_82                               3.828822   \n",
       "2i_4_85                              10.269088   \n",
       "2i_4_92                              12.205444   \n",
       "2i_5_33                              12.705378   \n",
       "2i_5_50                              24.153620   \n",
       "2i_5_64                               5.752658   \n",
       "2i_5_84                               0.000000   \n",
       "\n",
       "          pct_counts_in_common_apex_frac_seq  total_counts_control_agg  \\\n",
       "2i_3_55                             0.005961              24468.230469   \n",
       "2i_3_74                             0.000233              22577.589844   \n",
       "2i_3_75                             0.002195              22546.660156   \n",
       "2i_3_81                             0.010944              23560.894531   \n",
       "2i_3_93                             0.000525              21566.332031   \n",
       "a2i_3_5                             0.000000              25075.009766   \n",
       "a2i_3_32                            0.000190              23803.949219   \n",
       "a2i_3_46                            0.004098              24331.027344   \n",
       "a2i_3_55                            0.000000              25515.185547   \n",
       "a2i_3_63                            0.000086              25703.755859   \n",
       "a2i_3_72                            0.000000              24935.724609   \n",
       "a2i_3_87                            0.007759              14538.697266   \n",
       "a2i_3_93                            0.005710              24175.425781   \n",
       "2i_4_4                              0.000927              24854.005859   \n",
       "2i_4_16                             0.005646              22185.865234   \n",
       "2i_4_48                             0.001528              23138.398438   \n",
       "2i_4_79                             0.000953              22590.970703   \n",
       "2i_4_82                             0.000471              24432.716797   \n",
       "2i_4_85                             0.001307              23118.773438   \n",
       "2i_4_92                             0.001488              24717.154297   \n",
       "2i_5_33                             0.001597              23751.740234   \n",
       "2i_5_50                             0.003153              22487.376953   \n",
       "2i_5_64                             0.000703              24379.693359   \n",
       "2i_5_84                             0.000000              24496.783203   \n",
       "\n",
       "          pct_counts_control_agg  \n",
       "2i_3_55                 3.086911  \n",
       "2i_3_74                 3.034994  \n",
       "2i_3_75                 3.029114  \n",
       "2i_3_81                 3.035622  \n",
       "2i_3_93                 3.032025  \n",
       "a2i_3_5                 3.347589  \n",
       "a2i_3_32                3.190669  \n",
       "a2i_3_46                3.026125  \n",
       "a2i_3_55                3.079623  \n",
       "a2i_3_63                3.046175  \n",
       "a2i_3_72                3.167202  \n",
       "a2i_3_87                3.062511  \n",
       "a2i_3_93                3.358211  \n",
       "2i_4_4                  2.988142  \n",
       "2i_4_16                 2.912641  \n",
       "2i_4_48                 2.883370  \n",
       "2i_4_79                 3.024945  \n",
       "2i_4_82                 3.007724  \n",
       "2i_4_85                 2.941917  \n",
       "2i_4_92                 3.013783  \n",
       "2i_5_33                 2.986225  \n",
       "2i_5_50                 2.935204  \n",
       "2i_5_64                 2.980515  \n",
       "2i_5_84                 3.056392  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#splitting dataframe into dead and alive cells\n",
    "dead, alive = split_classes(adata_mouse_3_pp)\n",
    "dead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "cc1985e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>labels</th>\n",
       "      <th>Extra</th>\n",
       "      <th>n_genes</th>\n",
       "      <th>n_genes_by_counts</th>\n",
       "      <th>total_counts</th>\n",
       "      <th>total_counts_mt</th>\n",
       "      <th>pct_counts_mt</th>\n",
       "      <th>total_counts_ribo</th>\n",
       "      <th>pct_counts_ribo</th>\n",
       "      <th>total_counts_apex_only</th>\n",
       "      <th>pct_counts_apex_only</th>\n",
       "      <th>total_counts_frac_seq_only</th>\n",
       "      <th>pct_counts_frac_seq_only</th>\n",
       "      <th>total_counts_lncrna</th>\n",
       "      <th>pct_counts_lncrna</th>\n",
       "      <th>total_counts_in_common_apex_frac_seq</th>\n",
       "      <th>pct_counts_in_common_apex_frac_seq</th>\n",
       "      <th>total_counts_control_agg</th>\n",
       "      <th>pct_counts_control_agg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2i_3_1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>G</td>\n",
       "      <td>7343</td>\n",
       "      <td>7342</td>\n",
       "      <td>751806.1875</td>\n",
       "      <td>344606.500000</td>\n",
       "      <td>45.837147</td>\n",
       "      <td>76945.210938</td>\n",
       "      <td>10.234714</td>\n",
       "      <td>71.897903</td>\n",
       "      <td>0.009563</td>\n",
       "      <td>324.269226</td>\n",
       "      <td>0.043132</td>\n",
       "      <td>0.686075</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>48.745010</td>\n",
       "      <td>0.006484</td>\n",
       "      <td>23582.796875</td>\n",
       "      <td>3.136819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2i_3_2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>G</td>\n",
       "      <td>8198</td>\n",
       "      <td>8197</td>\n",
       "      <td>828963.0625</td>\n",
       "      <td>119525.609375</td>\n",
       "      <td>14.418690</td>\n",
       "      <td>138415.640625</td>\n",
       "      <td>16.697445</td>\n",
       "      <td>510.377960</td>\n",
       "      <td>0.061568</td>\n",
       "      <td>676.499695</td>\n",
       "      <td>0.081608</td>\n",
       "      <td>111.282761</td>\n",
       "      <td>0.013424</td>\n",
       "      <td>0.288049</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>25309.443359</td>\n",
       "      <td>3.053144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2i_3_4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>G</td>\n",
       "      <td>7879</td>\n",
       "      <td>7878</td>\n",
       "      <td>836599.5000</td>\n",
       "      <td>129189.671875</td>\n",
       "      <td>15.442236</td>\n",
       "      <td>130173.429688</td>\n",
       "      <td>15.559827</td>\n",
       "      <td>89.635078</td>\n",
       "      <td>0.010714</td>\n",
       "      <td>390.982147</td>\n",
       "      <td>0.046735</td>\n",
       "      <td>21.949064</td>\n",
       "      <td>0.002624</td>\n",
       "      <td>25.886753</td>\n",
       "      <td>0.003094</td>\n",
       "      <td>25964.273438</td>\n",
       "      <td>3.103549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2i_3_5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>G</td>\n",
       "      <td>8057</td>\n",
       "      <td>8057</td>\n",
       "      <td>865648.9375</td>\n",
       "      <td>58412.328125</td>\n",
       "      <td>6.747808</td>\n",
       "      <td>123120.632812</td>\n",
       "      <td>14.222929</td>\n",
       "      <td>438.315155</td>\n",
       "      <td>0.050634</td>\n",
       "      <td>607.157227</td>\n",
       "      <td>0.070139</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.335428</td>\n",
       "      <td>0.000616</td>\n",
       "      <td>26313.613281</td>\n",
       "      <td>3.039756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2i_3_6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>G</td>\n",
       "      <td>8742</td>\n",
       "      <td>8741</td>\n",
       "      <td>843173.7500</td>\n",
       "      <td>97892.023438</td>\n",
       "      <td>11.609947</td>\n",
       "      <td>139215.859375</td>\n",
       "      <td>16.510933</td>\n",
       "      <td>153.715912</td>\n",
       "      <td>0.018231</td>\n",
       "      <td>363.107330</td>\n",
       "      <td>0.043064</td>\n",
       "      <td>1.287474</td>\n",
       "      <td>0.000153</td>\n",
       "      <td>7.993662</td>\n",
       "      <td>0.000948</td>\n",
       "      <td>25298.707031</td>\n",
       "      <td>3.000414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2i_5_90</th>\n",
       "      <td>1.0</td>\n",
       "      <td>G</td>\n",
       "      <td>8112</td>\n",
       "      <td>8111</td>\n",
       "      <td>861520.5000</td>\n",
       "      <td>79435.890625</td>\n",
       "      <td>9.220429</td>\n",
       "      <td>115582.406250</td>\n",
       "      <td>13.416095</td>\n",
       "      <td>125.629311</td>\n",
       "      <td>0.014582</td>\n",
       "      <td>489.145630</td>\n",
       "      <td>0.056777</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.673828</td>\n",
       "      <td>0.004373</td>\n",
       "      <td>25971.064453</td>\n",
       "      <td>3.014561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2i_5_92</th>\n",
       "      <td>1.0</td>\n",
       "      <td>G</td>\n",
       "      <td>8205</td>\n",
       "      <td>8202</td>\n",
       "      <td>848197.7500</td>\n",
       "      <td>146478.890625</td>\n",
       "      <td>17.269426</td>\n",
       "      <td>103697.273438</td>\n",
       "      <td>12.225601</td>\n",
       "      <td>413.465729</td>\n",
       "      <td>0.048746</td>\n",
       "      <td>769.047485</td>\n",
       "      <td>0.090668</td>\n",
       "      <td>25.306046</td>\n",
       "      <td>0.002984</td>\n",
       "      <td>2.620146</td>\n",
       "      <td>0.000309</td>\n",
       "      <td>25564.757812</td>\n",
       "      <td>3.014009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2i_5_94</th>\n",
       "      <td>1.0</td>\n",
       "      <td>G</td>\n",
       "      <td>8608</td>\n",
       "      <td>8607</td>\n",
       "      <td>864421.0000</td>\n",
       "      <td>88516.359375</td>\n",
       "      <td>10.239960</td>\n",
       "      <td>125820.531250</td>\n",
       "      <td>14.555470</td>\n",
       "      <td>107.637352</td>\n",
       "      <td>0.012452</td>\n",
       "      <td>360.102844</td>\n",
       "      <td>0.041658</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.234550</td>\n",
       "      <td>0.000259</td>\n",
       "      <td>25874.119141</td>\n",
       "      <td>2.993232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2i_5_95</th>\n",
       "      <td>1.0</td>\n",
       "      <td>G</td>\n",
       "      <td>7509</td>\n",
       "      <td>7507</td>\n",
       "      <td>853671.1875</td>\n",
       "      <td>119482.375000</td>\n",
       "      <td>13.996300</td>\n",
       "      <td>136082.343750</td>\n",
       "      <td>15.940839</td>\n",
       "      <td>286.629578</td>\n",
       "      <td>0.033576</td>\n",
       "      <td>200.535263</td>\n",
       "      <td>0.023491</td>\n",
       "      <td>3.170100</td>\n",
       "      <td>0.000371</td>\n",
       "      <td>0.037798</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>25080.562500</td>\n",
       "      <td>2.937965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2i_5_96</th>\n",
       "      <td>1.0</td>\n",
       "      <td>G</td>\n",
       "      <td>9122</td>\n",
       "      <td>9122</td>\n",
       "      <td>857636.5000</td>\n",
       "      <td>93204.804688</td>\n",
       "      <td>10.867635</td>\n",
       "      <td>126638.781250</td>\n",
       "      <td>14.766021</td>\n",
       "      <td>105.253273</td>\n",
       "      <td>0.012272</td>\n",
       "      <td>383.837982</td>\n",
       "      <td>0.044755</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.364111</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>25646.662109</td>\n",
       "      <td>2.990388</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>338 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         labels Extra  n_genes  n_genes_by_counts  total_counts  \\\n",
       "2i_3_1      1.0     G     7343               7342   751806.1875   \n",
       "2i_3_2      1.0     G     8198               8197   828963.0625   \n",
       "2i_3_4      1.0     G     7879               7878   836599.5000   \n",
       "2i_3_5      1.0     G     8057               8057   865648.9375   \n",
       "2i_3_6      1.0     G     8742               8741   843173.7500   \n",
       "...         ...   ...      ...                ...           ...   \n",
       "2i_5_90     1.0     G     8112               8111   861520.5000   \n",
       "2i_5_92     1.0     G     8205               8202   848197.7500   \n",
       "2i_5_94     1.0     G     8608               8607   864421.0000   \n",
       "2i_5_95     1.0     G     7509               7507   853671.1875   \n",
       "2i_5_96     1.0     G     9122               9122   857636.5000   \n",
       "\n",
       "         total_counts_mt  pct_counts_mt  total_counts_ribo  pct_counts_ribo  \\\n",
       "2i_3_1     344606.500000      45.837147       76945.210938        10.234714   \n",
       "2i_3_2     119525.609375      14.418690      138415.640625        16.697445   \n",
       "2i_3_4     129189.671875      15.442236      130173.429688        15.559827   \n",
       "2i_3_5      58412.328125       6.747808      123120.632812        14.222929   \n",
       "2i_3_6      97892.023438      11.609947      139215.859375        16.510933   \n",
       "...                  ...            ...                ...              ...   \n",
       "2i_5_90     79435.890625       9.220429      115582.406250        13.416095   \n",
       "2i_5_92    146478.890625      17.269426      103697.273438        12.225601   \n",
       "2i_5_94     88516.359375      10.239960      125820.531250        14.555470   \n",
       "2i_5_95    119482.375000      13.996300      136082.343750        15.940839   \n",
       "2i_5_96     93204.804688      10.867635      126638.781250        14.766021   \n",
       "\n",
       "         total_counts_apex_only  pct_counts_apex_only  \\\n",
       "2i_3_1                71.897903              0.009563   \n",
       "2i_3_2               510.377960              0.061568   \n",
       "2i_3_4                89.635078              0.010714   \n",
       "2i_3_5               438.315155              0.050634   \n",
       "2i_3_6               153.715912              0.018231   \n",
       "...                         ...                   ...   \n",
       "2i_5_90              125.629311              0.014582   \n",
       "2i_5_92              413.465729              0.048746   \n",
       "2i_5_94              107.637352              0.012452   \n",
       "2i_5_95              286.629578              0.033576   \n",
       "2i_5_96              105.253273              0.012272   \n",
       "\n",
       "         total_counts_frac_seq_only  pct_counts_frac_seq_only  \\\n",
       "2i_3_1                   324.269226                  0.043132   \n",
       "2i_3_2                   676.499695                  0.081608   \n",
       "2i_3_4                   390.982147                  0.046735   \n",
       "2i_3_5                   607.157227                  0.070139   \n",
       "2i_3_6                   363.107330                  0.043064   \n",
       "...                             ...                       ...   \n",
       "2i_5_90                  489.145630                  0.056777   \n",
       "2i_5_92                  769.047485                  0.090668   \n",
       "2i_5_94                  360.102844                  0.041658   \n",
       "2i_5_95                  200.535263                  0.023491   \n",
       "2i_5_96                  383.837982                  0.044755   \n",
       "\n",
       "         total_counts_lncrna  pct_counts_lncrna  \\\n",
       "2i_3_1              0.686075           0.000091   \n",
       "2i_3_2            111.282761           0.013424   \n",
       "2i_3_4             21.949064           0.002624   \n",
       "2i_3_5              0.000000           0.000000   \n",
       "2i_3_6              1.287474           0.000153   \n",
       "...                      ...                ...   \n",
       "2i_5_90             0.000000           0.000000   \n",
       "2i_5_92            25.306046           0.002984   \n",
       "2i_5_94             0.000000           0.000000   \n",
       "2i_5_95             3.170100           0.000371   \n",
       "2i_5_96             0.000000           0.000000   \n",
       "\n",
       "         total_counts_in_common_apex_frac_seq  \\\n",
       "2i_3_1                              48.745010   \n",
       "2i_3_2                               0.288049   \n",
       "2i_3_4                              25.886753   \n",
       "2i_3_5                               5.335428   \n",
       "2i_3_6                               7.993662   \n",
       "...                                       ...   \n",
       "2i_5_90                             37.673828   \n",
       "2i_5_92                              2.620146   \n",
       "2i_5_94                              2.234550   \n",
       "2i_5_95                              0.037798   \n",
       "2i_5_96                              0.364111   \n",
       "\n",
       "         pct_counts_in_common_apex_frac_seq  total_counts_control_agg  \\\n",
       "2i_3_1                             0.006484              23582.796875   \n",
       "2i_3_2                             0.000035              25309.443359   \n",
       "2i_3_4                             0.003094              25964.273438   \n",
       "2i_3_5                             0.000616              26313.613281   \n",
       "2i_3_6                             0.000948              25298.707031   \n",
       "...                                     ...                       ...   \n",
       "2i_5_90                            0.004373              25971.064453   \n",
       "2i_5_92                            0.000309              25564.757812   \n",
       "2i_5_94                            0.000259              25874.119141   \n",
       "2i_5_95                            0.000004              25080.562500   \n",
       "2i_5_96                            0.000042              25646.662109   \n",
       "\n",
       "         pct_counts_control_agg  \n",
       "2i_3_1                 3.136819  \n",
       "2i_3_2                 3.053144  \n",
       "2i_3_4                 3.103549  \n",
       "2i_3_5                 3.039756  \n",
       "2i_3_6                 3.000414  \n",
       "...                         ...  \n",
       "2i_5_90                3.014561  \n",
       "2i_5_92                3.014009  \n",
       "2i_5_94                2.993232  \n",
       "2i_5_95                2.937965  \n",
       "2i_5_96                2.990388  \n",
       "\n",
       "[338 rows x 19 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2494421a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(338, 19)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alive.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4c61e57e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#list of all the features\n",
    "all_features_total = ['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only',\n",
    "                      'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
    "len(all_features_total)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb5c14c2",
   "metadata": {},
   "source": [
    "Perform some statistical tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d1058ec2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_genes 0.0509\n",
      "pct_counts_mt 0.0\n",
      "pct_counts_ribo 0.0105\n",
      "pct_counts_apex_only 0.0124\n",
      "pct_counts_frac_seq_only 0.0014\n",
      "pct_counts_in_common_apex_frac_seq 0.165\n",
      "pct_counts_control_agg 0.3724\n",
      "total_counts 0.0001\n"
     ]
    }
   ],
   "source": [
    "for any_column in all_features_total:\n",
    "    column_dead=dead[any_column]\n",
    "    column_alive=alive[any_column]\n",
    "    zscore,pvalue=ttest_ind(column_alive.values,column_dead.values,equal_var=False)\n",
    "    print(any_column,np.round(pvalue,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e02d2f87",
   "metadata": {},
   "outputs": [],
   "source": [
    "seeds=range(0,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "cb9a1dbf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "modello: logistic\n",
      "strategy sampling: SMOTE\n",
      "number of features: 8\n",
      "number of lists: 255\n",
      "number of iterations to be performed for each list: 100\n",
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      "['pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "155/255\n",
      "['pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "156/255\n",
      "['pct_counts_ribo', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "157/255\n",
      "['pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "158/255\n",
      "['pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "159/255\n",
      "['pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "160/255\n",
      "['pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "161/255\n",
      "['pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "162/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only']\n",
      "163/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq']\n",
      "164/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_control_agg']\n",
      "165/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'total_counts']\n",
      "166/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq']\n",
      "167/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_control_agg']\n",
      "168/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'total_counts']\n",
      "169/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "170/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "171/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_control_agg', 'total_counts']\n",
      "172/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq']\n",
      "173/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg']\n",
      "174/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'total_counts']\n",
      "175/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "176/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "177/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_control_agg', 'total_counts']\n",
      "178/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "179/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "180/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "181/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "182/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq']\n",
      "183/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg']\n",
      "184/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'total_counts']\n",
      "185/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "186/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "187/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_control_agg', 'total_counts']\n",
      "188/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "189/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "190/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "191/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "192/255\n",
      "['n_genes', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "193/255\n",
      "['n_genes', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "194/255\n",
      "['n_genes', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "195/255\n",
      "['n_genes', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "196/255\n",
      "['n_genes', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "197/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq']\n",
      "198/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg']\n",
      "199/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'total_counts']\n",
      "200/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "201/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "202/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_control_agg', 'total_counts']\n",
      "203/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "204/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "205/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "206/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "207/255\n",
      "['pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "208/255\n",
      "['pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "209/255\n",
      "['pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "210/255\n",
      "['pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "211/255\n",
      "['pct_counts_mt', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "212/255\n",
      "['pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "213/255\n",
      "['pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "214/255\n",
      "['pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "215/255\n",
      "['pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "216/255\n",
      "['pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "217/255\n",
      "['pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "218/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq']\n",
      "219/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg']\n",
      "220/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'total_counts']\n",
      "221/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "222/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "223/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_control_agg', 'total_counts']\n",
      "224/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "225/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "226/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "227/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "228/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "229/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "230/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "231/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "232/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "233/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "234/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "235/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "236/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "237/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "238/255\n",
      "['n_genes', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "239/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "240/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "241/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "242/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "243/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "244/255\n",
      "['pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "245/255\n",
      "['pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "246/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "247/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "248/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "249/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "250/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "251/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "252/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "253/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "254/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "255/255\n",
      "modello: logistic\n",
      "strategy sampling: under\n",
      "number of features: 8\n",
      "number of lists: 255\n",
      "number of iterations to be performed for each list: 100\n",
      "['n_genes']\n",
      "1/255\n",
      "['pct_counts_mt']\n",
      "2/255\n",
      "['pct_counts_ribo']\n",
      "3/255\n",
      "['pct_counts_apex_only']\n",
      "4/255\n",
      "['pct_counts_frac_seq_only']\n",
      "5/255\n",
      "['pct_counts_in_common_apex_frac_seq']\n",
      "6/255\n",
      "['pct_counts_control_agg']\n",
      "7/255\n",
      "['total_counts']\n",
      "8/255\n",
      "['n_genes', 'pct_counts_mt']\n",
      "9/255\n",
      "['n_genes', 'pct_counts_ribo']\n",
      "10/255\n",
      "['n_genes', 'pct_counts_apex_only']\n",
      "11/255\n",
      "['n_genes', 'pct_counts_frac_seq_only']\n",
      "12/255\n",
      "['n_genes', 'pct_counts_in_common_apex_frac_seq']\n",
      "13/255\n",
      "['n_genes', 'pct_counts_control_agg']\n",
      "14/255\n",
      "['n_genes', 'total_counts']\n",
      "15/255\n",
      "['pct_counts_mt', 'pct_counts_ribo']\n",
      "16/255\n",
      "['pct_counts_mt', 'pct_counts_apex_only']\n",
      "17/255\n",
      "['pct_counts_mt', 'pct_counts_frac_seq_only']\n",
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      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "171/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_control_agg', 'total_counts']\n",
      "172/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq']\n",
      "173/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg']\n",
      "174/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'total_counts']\n",
      "175/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "176/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "177/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_control_agg', 'total_counts']\n",
      "178/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "179/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "180/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "181/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "182/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq']\n",
      "183/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg']\n",
      "184/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'total_counts']\n",
      "185/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "186/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "187/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_control_agg', 'total_counts']\n",
      "188/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "189/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "190/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "191/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "192/255\n",
      "['n_genes', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "193/255\n",
      "['n_genes', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "194/255\n",
      "['n_genes', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "195/255\n",
      "['n_genes', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "196/255\n",
      "['n_genes', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "197/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq']\n",
      "198/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg']\n",
      "199/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'total_counts']\n",
      "200/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "201/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "202/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_control_agg', 'total_counts']\n",
      "203/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "204/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "205/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "206/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "207/255\n",
      "['pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "208/255\n",
      "['pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "209/255\n",
      "['pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "210/255\n",
      "['pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "211/255\n",
      "['pct_counts_mt', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "212/255\n",
      "['pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "213/255\n",
      "['pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "214/255\n",
      "['pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "215/255\n",
      "['pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "216/255\n",
      "['pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "217/255\n",
      "['pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "218/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq']\n",
      "219/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg']\n",
      "220/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'total_counts']\n",
      "221/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "222/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "223/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_control_agg', 'total_counts']\n",
      "224/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "225/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "226/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "227/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "228/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "229/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "230/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "231/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "232/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "233/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "234/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "235/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "236/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "237/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "238/255\n",
      "['n_genes', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "239/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "240/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "241/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "242/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "243/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "244/255\n",
      "['pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "245/255\n",
      "['pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "246/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "247/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "248/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "249/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "250/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "251/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "252/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "253/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "254/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "255/255\n",
      "modello: logistic\n",
      "strategy sampling: over\n",
      "number of features: 8\n",
      "number of lists: 255\n",
      "number of iterations to be performed for each list: 100\n",
      "['n_genes']\n",
      "1/255\n",
      "['pct_counts_mt']\n",
      "2/255\n",
      "['pct_counts_ribo']\n",
      "3/255\n",
      "['pct_counts_apex_only']\n",
      "4/255\n",
      "['pct_counts_frac_seq_only']\n",
      "5/255\n",
      "['pct_counts_in_common_apex_frac_seq']\n",
      "6/255\n",
      "['pct_counts_control_agg']\n",
      "7/255\n",
      "['total_counts']\n",
      "8/255\n",
      "['n_genes', 'pct_counts_mt']\n",
      "9/255\n",
      "['n_genes', 'pct_counts_ribo']\n",
      "10/255\n",
      "['n_genes', 'pct_counts_apex_only']\n",
      "11/255\n",
      "['n_genes', 'pct_counts_frac_seq_only']\n",
      "12/255\n",
      "['n_genes', 'pct_counts_in_common_apex_frac_seq']\n",
      "13/255\n",
      "['n_genes', 'pct_counts_control_agg']\n",
      "14/255\n",
      "['n_genes', 'total_counts']\n",
      "15/255\n",
      "['pct_counts_mt', 'pct_counts_ribo']\n",
      "16/255\n",
      "['pct_counts_mt', 'pct_counts_apex_only']\n",
      "17/255\n",
      "['pct_counts_mt', 'pct_counts_frac_seq_only']\n",
      "18/255\n",
      "['pct_counts_mt', 'pct_counts_in_common_apex_frac_seq']\n",
      "19/255\n",
      "['pct_counts_mt', 'pct_counts_control_agg']\n",
      "20/255\n",
      "['pct_counts_mt', 'total_counts']\n",
      "21/255\n",
      "['pct_counts_ribo', 'pct_counts_apex_only']\n",
      "22/255\n",
      "['pct_counts_ribo', 'pct_counts_frac_seq_only']\n",
      "23/255\n",
      "['pct_counts_ribo', 'pct_counts_in_common_apex_frac_seq']\n",
      "24/255\n",
      "['pct_counts_ribo', 'pct_counts_control_agg']\n",
      "25/255\n",
      "['pct_counts_ribo', 'total_counts']\n",
      "26/255\n",
      "['pct_counts_apex_only', 'pct_counts_frac_seq_only']\n",
      "27/255\n",
      "['pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq']\n",
      "28/255\n",
      "['pct_counts_apex_only', 'pct_counts_control_agg']\n",
      "29/255\n",
      "['pct_counts_apex_only', 'total_counts']\n",
      "30/255\n",
      "['pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq']\n",
      "31/255\n",
      "['pct_counts_frac_seq_only', 'pct_counts_control_agg']\n",
      "32/255\n",
      "['pct_counts_frac_seq_only', 'total_counts']\n",
      "33/255\n",
      "['pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "34/255\n",
      "['pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "35/255\n",
      "['pct_counts_control_agg', 'total_counts']\n",
      "36/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo']\n",
      "37/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_apex_only']\n",
      "38/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_frac_seq_only']\n",
      "39/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_in_common_apex_frac_seq']\n",
      "40/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_control_agg']\n",
      "41/255\n",
      "['n_genes', 'pct_counts_mt', 'total_counts']\n",
      "42/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only']\n",
      "43/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_frac_seq_only']\n",
      "44/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_in_common_apex_frac_seq']\n",
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      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_control_agg', 'total_counts']\n",
      "188/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "189/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "190/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "191/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "192/255\n",
      "['n_genes', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "193/255\n",
      "['n_genes', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "194/255\n",
      "['n_genes', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "195/255\n",
      "['n_genes', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "196/255\n",
      "['n_genes', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "197/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq']\n",
      "198/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg']\n",
      "199/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'total_counts']\n",
      "200/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "201/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "202/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_control_agg', 'total_counts']\n",
      "203/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "204/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "205/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "206/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "207/255\n",
      "['pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "208/255\n",
      "['pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "209/255\n",
      "['pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "210/255\n",
      "['pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "211/255\n",
      "['pct_counts_mt', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "212/255\n",
      "['pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "213/255\n",
      "['pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "214/255\n",
      "['pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "215/255\n",
      "['pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "216/255\n",
      "['pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "217/255\n",
      "['pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "218/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq']\n",
      "219/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg']\n",
      "220/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'total_counts']\n",
      "221/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "222/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "223/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_control_agg', 'total_counts']\n",
      "224/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "225/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "226/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "227/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "228/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "229/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "230/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "231/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "232/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "233/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "234/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "235/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "236/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "237/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "238/255\n",
      "['n_genes', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "239/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "240/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "241/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "242/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "243/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "244/255\n",
      "['pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "245/255\n",
      "['pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "246/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg']\n",
      "247/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'total_counts']\n",
      "248/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_control_agg', 'total_counts']\n",
      "249/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "250/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "251/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "252/255\n",
      "['n_genes', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "253/255\n",
      "['pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "254/255\n",
      "['n_genes', 'pct_counts_mt', 'pct_counts_ribo', 'pct_counts_apex_only', 'pct_counts_frac_seq_only', 'pct_counts_in_common_apex_frac_seq', 'pct_counts_control_agg', 'total_counts']\n",
      "255/255\n"
     ]
    }
   ],
   "source": [
    "for sampling_strategy in [\"SMOTE\",\"under\",\"over\"]:\n",
    "    for model_type in [\"logistic\"]:\n",
    "        #testing and ranking the feature lists\n",
    "        print(\"modello:\",model_type)\n",
    "        print(\"strategy sampling:\",sampling_strategy)\n",
    "        df = logreg_combinatorial4(alive, dead, ['labels'], \n",
    "                                all_features_total, \n",
    "                                seeds=seeds, \n",
    "                                model_type=model_type,\n",
    "                                sampling_strategy=sampling_strategy  #tipo di strategia\n",
    "                                )\n",
    "        #saving the results\n",
    "        df.to_pickle('outputs/ranks_'+model_type+'_strategy_'+sampling_strategy+'_binary2.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "21edb09f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean f1 test 1st rank features:  0.9441807346984981\n",
      "mean f1 test traditional features 0.9407222527762312\n",
      "mean f1 test 1st rank features:  0.935445616907291\n",
      "mean f1 test traditional features 0.935445616907291\n",
      "mean f1 test 1st rank features:  0.9353912728531851\n",
      "mean f1 test traditional features 0.9317389180310559\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=4, ncols=1, sharex=True, figsize=(12, 8))\n",
    "names=[\"SMOTE\",\"undersampling\",\"oversampling\"]\n",
    "colors=[\"red\",\"blue\",\"green\"]\n",
    "i=0\n",
    "for el in [\"SMOTE\",\"over\",\"under\"]:\n",
    "    df = pd.read_pickle('outputs/ranks_logistic_strategy_'+el+'_binary.pkl')\n",
    "    #plotting the results in a nice visualization\n",
    "    fig.suptitle('Best performing features and f1 scores')\n",
    "    sns.lineplot(data=df, x=df.index.values, y='mean_f1_test', ax=axes[0],label=names[i],color=colors[i])\n",
    "    sns.heatmap(df.iloc[:, 29:37].T, cbar=False, cmap=cm.binary, ax=axes[i+1])\n",
    "    fig.subplots_adjust(hspace=0.0)\n",
    "    o = list(df.code.values).index(['n_genes', 'pct_counts_mt'])\n",
    "    print('mean f1 test 1st rank features: ', df.loc[1,'mean_f1_test'])\n",
    "    print('mean f1 test traditional features', df.loc[o+1,'mean_f1_test'])\n",
    "    axes[0].set_xlim(0, max(df['rank']))\n",
    "    axes[0].set_ylim(0.75, 1)\n",
    "    #min(df_SMOTE_total_corrected_simple['mean_f1_test'])\n",
    "    #max(df_SMOTE_total_corrected_simple['mean_f1_test'])\n",
    "    axes[0].set_ylabel('mean f1 in test')\n",
    "    axes[0].axvline(o, linewidth=2, color=colors[i])\n",
    "    axes[0].fill_between([i for i in range(1,df.shape[0]+1)],\n",
    "                    df['std_f1_test'] + df['mean_f1_test'],\n",
    "                    df['mean_f1_test'] - df['std_f1_test'],\n",
    "                    facecolor=\"blue\", # The fill color\n",
    "                    color=colors[i],       # The outline color\n",
    "                    alpha=0.2)\n",
    "    axes[i+1].set_yticklabels(['n genes', '% mt', 'total counts','% ribo', '% APEX', '% frac-seq', '% APEX/frac-seq', '% control'])\n",
    "    axes[i+1].set_ylabel('')\n",
    "    axes[i+1].tick_params(left=False)\n",
    "    axes[i+1].axvline(o, linewidth=2, color=colors[i])\n",
    "    plt.tight_layout()\n",
    "    #fig.subplots_adjust(hspace=0.5)\n",
    "    i=i+1\n",
    "#\n",
    "labels = ['A', 'B', 'C', 'D']\n",
    "#\n",
    "for idx, ax in enumerate(axes):\n",
    "    ax.text(-0.1, 1.1, labels[idx], transform=ax.transAxes,\n",
    "            fontsize=16, fontweight='bold', va='top', ha='right')\n",
    "fig.savefig('./figures/best_performing_features_macro.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}