docs/python_api.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial we will detail the usage of the Python API with real-world data, showing how to integrate tspex with popular packages such as `pandas` and `seaborn`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial we are going to use data from the [Genotype-Tissue Expression (GTEx) project](https://gtexportal.org/), which provides a large catalogue of gene expression across 54 human tissues. In order to speed up the calculations and make the figures clearer, we are going to use only five tissues: Bladder, Liver, Lung, Pancreas and Stomach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tspex\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "gtex_link = 'https://storage.googleapis.com/gtex_analysis_v7/rna_seq_data/GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_median_tpm.gct.gz'\n",
    "expression_data = pd.read_csv(gtex_link, sep='\\t', index_col='gene_id', skiprows=2)\n",
    "expression_data = expression_data.loc[:, ['Bladder', 'Liver', 'Lung', 'Pancreas', 'Stomach']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll remove genes that are not expressed in any of the five tissues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "expression_data = expression_data.loc[(expression_data > 0).any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31872, 5)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expression_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bladder</th>\n",
       "      <th>Liver</th>\n",
       "      <th>Lung</th>\n",
       "      <th>Pancreas</th>\n",
       "      <th>Stomach</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gene_id</th>\n",
       "      <th></th>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "                    Bladder    Liver      Lung  Pancreas   Stomach\n",
       "gene_id                                                           \n",
       "ENSG00000223972.4   0.05878  0.06259   0.06655  0.027255  0.063895\n",
       "ENSG00000227232.4  14.24000  5.40600  13.68000  5.553000  9.342500\n",
       "ENSG00000243485.2   0.06097  0.08316   0.06216  0.034055  0.078575\n",
       "ENSG00000237613.2   0.04113  0.03354   0.03790  0.022915  0.043800\n",
       "ENSG00000268020.2   0.00000  0.02959   0.00000  0.000000  0.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expression_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The `TissueSpecificity` class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The usage of the tspex module is centered on the creation and analysis of `TissueSpecificity` objects. In order to create these objects, two parameters are required: (1) a `pandas` DataFrame containing gene expression data and (2) a tissue-specificity metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    Create an object of the TissueSpecificity class.\n",
      "\n",
      "    Parameters\n",
      "    ----------\n",
      "    expression_data : pandas.core.frame.DataFrame\n",
      "        Pandas DataFrame containing the expression matrix, with rows\n",
      "        corresponding to genes and columns to tissues/conditions.\n",
      "    method : str\n",
      "        A string representing which tissue-expression metric should be\n",
      "        calculated. One of: 'counts', 'tau', 'gini', 'simpson',\n",
      "        'shannon_specificity', 'roku_specificity', 'tsi', 'zscore', 'spm',\n",
      "        'spm_dpm', 'js_specificity', 'js_specificity_dpm'.\n",
      "    log : bool, default False\n",
      "        Log-transform the expression matrix before computing tissue-specificity\n",
      "        by taking the base-2 logarithm of one plus the expression values. By\n",
      "        default, no transformation is performed.\n",
      "    transform : bool, default True\n",
      "        Transform the tissue-specificity values so that they range from 0\n",
      "        (perfectly ubiquitous) to 1 (perfectly tissue-specific). By default, the\n",
      "        value is transformed. The following metrics are affected by changes in\n",
      "        this parameter: 'gini', 'simpson', 'shannon_specificity',\n",
      "        'roku_specificity', 'zscore'.\n",
      "    threshold : int or float, default 0\n",
      "        Value above which the gene is considered to be expressed. By default,\n",
      "        any positive expression value is considered. Only the 'counts' metric\n",
      "        is affected by changes in this parameter.\n",
      "\n",
      "    Attributes\n",
      "    ----------\n",
      "    expression_data : pandas.DataFrame\n",
      "        Expression matrix used to compute the tissue-specificity values. If the\n",
      "        log parameter was set to True, the values will be log-transformed.\n",
      "    tissue_specificity : pandas.Series or pandas.DataFrame\n",
      "        Tissue-specificity values computed from the input expression matrix.\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "print(tspex.TissueSpecificity.__doc__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The nonobligatory parameters (`log`, `transform` and `threshold`) and the object attributes (`expression_data` and `tissue_specificity`) will be explained throughout the tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tissue-specificity metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "tspex provides twelve different tissue-specificity metrics that can be divided into two groups:\n",
    " - **General scoring metrics:** Describe in a single value how tissue-specific or ubiquitous is a gene across all tissues (`counts`, `tau`, `gini`, `simpson`, `shannon_specificity`, `roku_specificity`, `spm_dpm` and `js_specificity_dpm`).\n",
    " - **Individualized scoring metrics:** Quantify how specific is the expression of each gene to each tissue (`tsi`, `zscore`, `spm` and `js_specificity`)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1: General scoring metric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example we are going to quantify the tissue-specificity of each gene using the Tau metric, which is a general scoring metric. Therefore, we will get a single tissue-specificity score per gene."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When creating a `TissueSpecificity` object, you can choose to log-transform the expression values before quantifying tissue-specificity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "tso = tspex.TissueSpecificity(expression_data, 'tau', log=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The log-transformed expression matrix is stored in the `expression_data` attribute, which is a regular `pandas` DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bladder</th>\n",
       "      <th>Liver</th>\n",
       "      <th>Lung</th>\n",
       "      <th>Pancreas</th>\n",
       "      <th>Stomach</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gene_id</th>\n",
       "      <th></th>\n",
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      "text/plain": [
       "                    Bladder     Liver      Lung  Pancreas   Stomach\n",
       "gene_id                                                            \n",
       "ENSG00000223972.4  0.057117  0.060709  0.064429  0.026890  0.061937\n",
       "ENSG00000227232.4  2.723924  1.857235  2.686486  1.879923  2.336262\n",
       "ENSG00000243485.2  0.059184  0.079883  0.060305  0.033488  0.075641\n",
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       "ENSG00000268020.2  0.000000  0.029161  0.000000  0.000000  0.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tso.expression_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The computed tissue-specificity values are stored in the `tissue_specificity` attribute that, in this case, is an one-dimensional `pandas` Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gene_id\n",
       "ENSG00000223972.4    0.1981\n",
       "ENSG00000227232.4    0.1960\n",
       "ENSG00000243485.2    0.2845\n",
       "ENSG00000237613.2    0.2235\n",
       "ENSG00000268020.2    1.0000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tso.tissue_specificity.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Any of the Series' methods can be used to inspect `tissue_specificity`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.50    0.502950\n",
       "0.75    0.761925\n",
       "0.95    1.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tso.tissue_specificity.quantile([0.5, 0.75, 0.95])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two visualization methods are available in tspex: `plot_histogram` and `plot_heatmap`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`plot_histogram` can be used to verify the distribution of the tissue-specificity values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 450x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tso.plot_histogram()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whereas `plot_heatmap` may be used to visualize the amount of genes that are specific for each tissue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x300 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tso.plot_heatmap(threshold=0.8, sort_genes=True, use_zscore=True, gene_names=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2: Individualized scoring metric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In some cases we may be interested in discovering genes that are specifically expressed in one or more tissues of interest. In this situation, general scoring metrics are not be the best choice, as they would only tell us how tissue-specific is each gene, regardless of the tissue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accordingly, in this example we are going to quantify the tissue-specificity of each gene using the SPM metric, an individualized scoring metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "tso = tspex.TissueSpecificity(expression_data, 'spm', log=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the SPM metric computes how specific each gene is in each tissue, the `tissue_specificity` attribute is a two-dimensional `pandas` DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Bladder</th>\n",
       "      <th>Liver</th>\n",
       "      <th>Lung</th>\n",
       "      <th>Pancreas</th>\n",
       "      <th>Stomach</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gene_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000223972.4</th>\n",
       "      <td>0.4565</td>\n",
       "      <td>0.4852</td>\n",
       "      <td>0.5149</td>\n",
       "      <td>0.2149</td>\n",
       "      <td>0.4950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000227232.4</th>\n",
       "      <td>0.5235</td>\n",
       "      <td>0.3569</td>\n",
       "      <td>0.5163</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.4490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000243485.2</th>\n",
       "      <td>0.4147</td>\n",
       "      <td>0.5598</td>\n",
       "      <td>0.4226</td>\n",
       "      <td>0.2347</td>\n",
       "      <td>0.5301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000237613.2</th>\n",
       "      <td>0.5020</td>\n",
       "      <td>0.4108</td>\n",
       "      <td>0.4633</td>\n",
       "      <td>0.2822</td>\n",
       "      <td>0.5339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000268020.2</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Bladder   Liver    Lung  Pancreas  Stomach\n",
       "gene_id                                                      \n",
       "ENSG00000223972.4   0.4565  0.4852  0.5149    0.2149   0.4950\n",
       "ENSG00000227232.4   0.5235  0.3569  0.5163    0.3613   0.4490\n",
       "ENSG00000243485.2   0.4147  0.5598  0.4226    0.2347   0.5301\n",
       "ENSG00000237613.2   0.5020  0.4108  0.4633    0.2822   0.5339\n",
       "ENSG00000268020.2   0.0000  1.0000  0.0000    0.0000   0.0000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tso.tissue_specificity.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To inspect the distribution of the tissue-specificity values separately for each tissue, we can use the `violinplot` function from the `seaborn` package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x=\"variable\", y=\"value\", inner=None, scale=\"count\", color='C0',\n",
    "               data=tso.tissue_specificity.melt());"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `query` method can be used to select genes that satisfy certain criteria. In this case, we are selecting genes that are preferentially expressed in the bladder and lung."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1282, 5)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_genes_specificity = tso.tissue_specificity.query('Bladder >= 0.6 & Lung >= 0.6')\n",
    "selected_genes_specificity.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Bladder</th>\n",
       "      <th>Liver</th>\n",
       "      <th>Lung</th>\n",
       "      <th>Pancreas</th>\n",
       "      <th>Stomach</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gene_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000228526.2</th>\n",
       "      <td>0.6410</td>\n",
       "      <td>0.1192</td>\n",
       "      <td>0.6566</td>\n",
       "      <td>0.2270</td>\n",
       "      <td>0.3038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000233623.2</th>\n",
       "      <td>0.6775</td>\n",
       "      <td>0.1907</td>\n",
       "      <td>0.6832</td>\n",
       "      <td>0.1948</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000171819.4</th>\n",
       "      <td>0.7653</td>\n",
       "      <td>0.0433</td>\n",
       "      <td>0.6072</td>\n",
       "      <td>0.0858</td>\n",
       "      <td>0.1908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000202461.1</th>\n",
       "      <td>0.6564</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.6383</td>\n",
       "      <td>0.2459</td>\n",
       "      <td>0.3181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000162493.12</th>\n",
       "      <td>0.6019</td>\n",
       "      <td>0.0214</td>\n",
       "      <td>0.6080</td>\n",
       "      <td>0.2072</td>\n",
       "      <td>0.4740</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Bladder   Liver    Lung  Pancreas  Stomach\n",
       "gene_id                                                       \n",
       "ENSG00000228526.2    0.6410  0.1192  0.6566    0.2270   0.3038\n",
       "ENSG00000233623.2    0.6775  0.1907  0.6832    0.1948   0.0000\n",
       "ENSG00000171819.4    0.7653  0.0433  0.6072    0.0858   0.1908\n",
       "ENSG00000202461.1    0.6564  0.0000  0.6383    0.2459   0.3181\n",
       "ENSG00000162493.12   0.6019  0.0214  0.6080    0.2072   0.4740"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_genes_expression = tso.expression_data.reindex(selected_genes_specificity.index)\n",
    "selected_genes_specificity.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(selected_genes_expression, figsize=(6,6), z_score=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 3: The `transform` parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When a `TissueSpecificity` object is created, the computed tissue-specificity values are transformed so that they fall within 0 (perfectly ubiquitous expression) and 1 (perfectly tissue-specific expression). If the user wants to obtain the untransformed values given by the chosen tissue-specificity metric, he can set the `transform` argument to `False`. The following metrics are affected by changes in this parameter: `gini`, `simpson`, `shannon_specificity`, `roku_specificity` and `zscore`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For instance, untransformed values obtained with the `roku_specificity` method fall within 0 and log2(number of tissues)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tso = tspex.TissueSpecificity(expression_data, 'roku_specificity', log=True)\n",
    "tso.tissue_specificity.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tso.plot_histogram()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.3219"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tso = tspex.TissueSpecificity(expression_data, 'roku_specificity', log=True, transform=False)\n",
    "tso.tissue_specificity.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9AcDy5cthY2ODUaNGwdXVFePGjcPPP/8M4MntRTExMfDw8ICvry+aNWv20veYEtUm9XLCgdomPj4eer2+zC0EVLXq2w30fn5+mDt3Lnx9fWu6FKIGjyNKEzh16hTy8vKg1+tx9OhRfPPNN9K9ikQV2b9/P3Q6HQYMGFDTpRAReDGPSdy+fRvR0dH4448/YGtri+joaHh7e9d0WXXCqVOn8D//8z/lrvvss8/q/fykQ4YMwf379/H+++/zPB9RLcFDr0RERDJ46JWIiEhGvTr0+uyMMC96wzcRETUszx5Yffb2tqfqVVACeKEpuoiIiAxNOFLvghJ48mE5oqxYVlYWnJ2da7qMeoP9WbXYn1WL/WmYEEJ2kFWvgvJpOCoUCgalEdhPVYv9WbXYn1WL/VkxQ/3Di3mIiIhkMCiJiIhkMCiJiIhkMCiJiIhkmDwofXx8MGzYMAQFBSEoKEh6gHFCQgIGDRqEgIAAHDhwQNr+ypUrCAkJgb+/PyIiIlBUVCStM9SGiIjIVKrlqtcvvviizEOCjxw5gvPnzyM1NRWFhYUICwuDl5cXrK2tsWjRIsycORNeXl5ISEjAhg0bEBUVJduGiIjIVGrk0GtaWhpCQkJgYWEBW1tbuLm5ITMzEwUFBVCpVPDy8gLw5AG2Tx8Oa6gNERGRKVXLiDIiIgJ6vR4DBw7E9OnToVarYWdnJ623s7ODSqWCSqUqM/K0t7eHSqUCAINtypOVlcX7hYyg0WiQlZVV02XUG+zPqsX+rFrsT8MqejaIyYMyOTkZSqUSRUVFmDVrFjZs2ACg7I2dzxYpF3CG2vyZs7Mzg9IInKmjarE/qxb7s2qxPw0TQuDMmTMG15s8KJ+OEK2trREWFoaUlBQolUrk5uZK26jVavTo0QNKpbLMKDEvL09qb6iNqQQEBFRq+4MHD5qoEiIiqkkmPUf58OFD6arV0tJSpKWlwdHREf7+/tizZw90Oh3UajVOnz4NT09PtG7dGra2ttK5x5SUFAwaNAgADLYhIiIyJZOOKAsLCxEZGQm9Xg+9Xg9XV1dMmTIFr7zyCjIzMzF48GAoFArMmTNHunp18eLFmDt3LpYsWQIHBwckJiYCADw9PQ22ISIiMhWFqOgsZh3y9Dizq6vrS5+jbAiHXnnOomqxP6sW+7NqsT8Nqyg7ODMPERGRDAYlERGRDAYlERGRDAYlERGRDAYlERGRDAYlERGRDAYlERGRjGqZFL0haAj3XRIRNUQcURIREclgUBIREclgUBIREclgUBIREclgUBIREclgUBIREclgUBIREclgUBIREclgUBIREclgUBIREclgUBIREclgUBIREclgUBIREclgUBIREclgUBIREclgUBIREclgUBIREclgUBIREclgUBIREclgUBIREclgUBIREclgUBIREclgUBIREclgUBIREclgUBIREclgUBIREclgUBIREclgUBIREclgUBIREclgUBIREcmolqBcvHgxunTpIr1OSEjAoEGDEBAQgAMHDkjLr1y5gpCQEPj7+yMiIgJFRUUVtiEiIjIlkwflqVOn8OjRI+n1kSNHcP78eaSmpmLLli1Yvny5FIiLFi3CzJkzkZaWBgcHB2zYsKHCNkRERKZk0qDUarVITEzEnDlzpGVpaWkICQmBhYUFbG1t4ebmhszMTBQUFEClUsHLywsAMGrUKKSnp8u2ISIiMjWTBuW6deswatQotGjRQlqmVqthZ2cnvbazs4NKpYJKpYJSqZSW29vbQ6VSybYhIiIyNQtT7fjy5cs4f/48ZsyY8dw6hUIh/SyEKHe5sW3Kk5WVJbsvY2i12pdqX5GsrCyT7t8YGo2mVtRRX7A/qxb7s2qxPw2rKFNMFpRnzpzBtWvX4OvrCwDQ6XTw8fGBt7c3cnNzpe3UajV69OgBpVJZZpSYl5cnjTCVSmW5bQxxdnZ+6aC0tLR8qfYVcXZ2Nun+jZGVlVUr6qgv2J9Vi/1ZtdifhgkhcObMGYPrTXbo9a233sKRI0dw6NAhHDp0CObm5jh06BD8/f2xZ88e6HQ6qNVqnD59Gp6enmjdujVsbW2lc48pKSkYNGgQABhsQ0REZGomG1Ea4unpiczMTAwePBgKhQJz5syBtbU1gCe3kcydOxdLliyBg4MDEhMTK2xDRERkStUWlBcvXpR+nj17NmbPnv3cNk5OTti9e3e57Q21ISIiMiXOzENERCSDQUlERCSDQUlERCSDQUlERCSDQUlERCSDQUlERCSDQUlERCSDQUlERCSDQUlERCSDQUlERCSDQUlERCSDQUlERCSDQUlERCSDQUlERCSDQUlERCSDQUlERCSDQUlERCSDQUlERCTDqKDMy8uDVqsFAJw9exbJyckoKioyaWFERES1gVFBOXXqVACASqXCzJkzcfnyZcyZM8ekhREREdUGRgWlQqGApaUlvv/+e4wdOxZLly5FTk6OqWsjIiKqcUYFpU6ng1qtRlpaGry9vaVlRERE9Z2FMRtFRUVhypQp8PDwgJOTE3799Ve0b9/e1LXVawEBAZXa/uDBgyaqhIiI5BgVlH5+fvDz85Ne//Wvf8W6detMVhQREVFtYdSh16tXryIsLAw+Pj4AgMuXL2P16tUmLYyIiKg2MCooFy1ahAULFsDGxgYA4OTkhPT0dJMWRkREVBsYFZQajQbdu3cvs8zCwqijtkRERHWaUUFpbW2N/Px8KBQKAMDRo0el0SUREVF9ZtSwcN68eZg+fTpu3bqFkJAQFBcX82IeIiJqEIwKSkdHR2zbtg03b96EEAIODg489EpERA2C0WlXWFiIBw8eQKfT4ezZswAADw8PkxVGRERUGxgVlB9++CF27twJBwcHmJubA3gyrR2DkoiI6jujgjI1NRXp6elo3LixqeshIiKqVYy66rVt27bSSJKIiKghMWpEqVQqMWbMGAwYMACWlpbS8oiICJMVRkREVBsYFZRt27ZF27ZtAQClpaUmLYiIiKg2MSooIyMjTV0HERFRrcRJ0YmIiGRwUnQiIiIZJp0UPTw8HIGBgRgxYgSioqJQVFQEANi0aRP8/f3h5+eHL7/8Uto+Ly8Pb731FgICAjB+/Hio1WppnaE2REREpmTUOcoXnRR9zZo1aNasGQBg+fLl2LRpE4YOHYrt27dj9+7dEEJg5MiR6N+/P9q3b4/ExEQEBQVh9OjR2LZtGz744AOsWLECN27cMNiGiIjqj4CAgEptf/DgQRNV8h9GjSj/PCn6okWLEB8fX2G7pyGp1+vx6NEjAEBGRgaGDh2Kpk2bwtraGgEBAcjIyIAQAj/88AOCg4MBACEhITh06JBsGyIiIlMz+aToEREROHfuHF577TXMmTMHq1atgpOTk7Tezs4Ov/76K+7du4fGjRvDysoKANCkSRM0atQIDx48gEqlKreNIVlZWdLo90VptdqXal/VsrKyqnyfGo3GJPttqNifVYv9WbXqSn9W9m9vVXwmIYTseqPS7syZM2VeX7hwAdbW1ujQoYMUbIYkJSVBp9Phgw8+QHJyMgCUCbFnC/xzuBlaV9GHcnZ2fumgfHZihdrA2dm5yveZlZVlkv02VOzPqsX+rFp1pT8r+7e3Kj6TEOK5nHuWUUH5/vvv4+LFi+jcuTOEEPjll1/g5OSEwsJCLF26FP369ZNtb25ujtDQUERFRSEwMBC5ubnSOrVaDaVSiebNm+Phw4fQaDSwsrLCo0ePUFpaimbNmkGpVJbbhoiIyNSMOkdpZ2eHlJQU7Nq1C7t378bOnTvRuXNnbN68GatWrSq3TVFREfLz86XXaWlpeP311zFo0CCkpqbi4cOHKCoqwsGDBzFo0CAoFAr0798fe/fuBQDs3r1bum/TUBsiIiJTM2pEeePGjTLnCJ2cnHDp0iV06NDB4GHQBw8eIDIyUjre7ODggPj4eLRq1QpjxoxBUFAQhBD429/+hnbt2gEAYmNjMXPmTHzxxRdo1aoVPvjgAwBAx44dDbYhIiIyJaMnRf/oo48wYsQIKBQK7N27F61atUJpaanBc4F2dnbYuXNnuesmTZqESZMmldvm6XlMY9sQERGZklGHXleuXImioiLMnDkTMTExKC4uxsqVKyGEwKeffmrqGomIiGqMUSPK//qv/8KCBQvKXWdvb1+lBREREdUmRo0oiYiIGioGJRERkQzZoExLSwOAMvcwEhERNSSyQZmUlAQAePvtt6ulGCIiotpG9mKeRo0aISYmBnl5eeVOgr5s2TKTFUZERFQbyAZlUlISjh49irNnz8LV1bW6aiIiIqo1ZIOyefPmGDp0KF599dXnHtxMRETUEBh1H2Xbtm0RExOD48ePQ6FQoFevXpg3bx5atmxp6vqIiIhqlFG3h8yfPx/dunXDgQMHkJqaCmdnZ8ybN8/UtREREdU4o4IyLy8P4eHhaNasGZo1a4a///3vUKlUpq6NiIioxhkVlBYWFrh8+bL0+tKlS7CwMOqoLRERUZ1mVNrNnTsXERERaN26NYQQKCwsRGJioqlrIyIiqnFGBaW7uzvS0tJw8+ZNCCHg4OAAS0tLU9dGRERU44w+fmppaQlHR0dT1kJERFTrcFJ0IiIiGQxKIiIiGRUGpV6vx8SJE6ujFiIiolqnwqA0MzODlZUVHj16VB31EBER1SpGXczTpk0bhIWFYeDAgWjSpIm0PCIiwmSFERER1QZGBaWdnR3s7OwAAKWlpSYtiIiIqDYxKigjIyNNXQcREVGtZNRVr1evXkVYWBh8fHwAAJcvX8bq1atNWhgREVFtYFRQLlq0CAsWLICNjQ0AwMnJCenp6SYtjIiIqDYwKig1Gs1zD27mpOhERNQQGBWU1tbWyM/Ph0KhAAAcPXpUGl0SERHVZ0YNC+fNm4fp06fj1q1bCAkJQXFxMdatW2fq2ugZAQEBldr+4MGDJqqEiKhhMSooHR0dsW3btjJPD+GhVyIiagiMTrvMzEycOHECCoUCHh4e6N+/vynrIiIiqhWMOkeZkJCATz/9FLa2tmjTpg0+/fRTrFy50tS1ERER1TijRpSHDh3C119/jUaNGgEAxowZg6CgIMyaNcukxREREdU0o0aUrVq1QklJifS6pKREmtKOiIioPpMdUSYlJQEA7O3tMXLkSPj5+QEAMjIy4OHhYfrqiIiIaphsUD6dAL19+/Zo3769tHz48OGmrYqIiKiWkA1KToZOREQNnVEX8xQWFmLHjh3Izs6GTqeTlickJJisMCIiotrAqKCMiIhAz5494enpCTMzo67/ISIiqheMCsrS0lLeCkJERA2SUUHp7++P1NRU+Pn5SfdSGiMvLw9xcXHIz8+Hubk5fH19ER0dDeDJYdv09HSYmZkhOjoagwcPBgBcuXIFcXFxKC4uhoODAxITE2FtbS3bhp5nzNywWq0WlpaWADg3LBGRIUYFZYcOHTBv3jzExsZCoVBACAGFQoGff/5Ztp25uTliY2PRrVs3aLVa/P3vf8d3330HKysrnD9/HqmpqSgsLERYWBi8vLxgbW2NRYsWYebMmfDy8kJCQgI2bNiAqKgoHDlyxGAbIiIiUzHqhGNiYiJ27NiBrKws/Pzzz9J/K9KmTRt069YNAGBpaQlHR0fk5uYiLS0NISEhsLCwgK2tLdzc3JCZmYmCggKoVCp4eXkBAEaNGiU9INpQGyIiIlMyekTp6Oj4Um907949ZGRkYMOGDThy5EiZmX3s7OygUqmgUqmgVCql5fb29lCpVAAAtVpdbpvyZGVlSc/OfFFarfal2tcFQgjpc2ZlZdVwNXWfRqNhP1Yh9mfVqiv9Wdm/vVXxmYQQsuuNDspp06bB19dXOqcFACNGjDCqCK1Wi6ioKISHh6NTp04AUCbIni1SLuAMtfkzZ2fnlw7KZz9nffXsOUpnZ+carqbuy8rKYj9WIfZn1aor/VnZv71V8ZmEEDhz5ozB9UYFpUajwV/+8hecOnWqzHJjglKn0yE2NhZdu3bFpEmTAABKpRK5ubnSNmq1Gj169IBSqSwzSszLy5NGmIbaEBERmZJRQbl8+fIXfoP4+Hg0bdoUs2fPlpb5+/vj008/RUhICO7cuYPTp09jyZIlsLa2hq2tLTIzM+Hp6YmUlBQMGjRItg1PtPLhAAAO3klEQVQREZEpGRWUe/bsKXd5cHCwbLvTp09j586d6Ny5s7RtaGgoJk6ciMzMTAwePBgKhQJz5syRrl5dvHgx5s6diyVLlki3hwCAp6enwTZERESmYlRQPnvIVavV4sSJE3B2dq4wKN3c3HDlypVy182ePbvMKPMpJycn7N69u1JtiIiITMWooHz33XfLvP7999+xcOFCkxRERERUm7zQxK02Nja4fv16VddCRERU6xg1olywYIF0u4Ver8fly5eliQSIiIjqM6OC0s3NTfrZ3NwcoaGhcHV1NVlRREREtYVRQRkSEmLqOoiIiGol2aCcMGGCwRluFAoFNm/ebJKiiIiIagvZoJwxY8Zzy65du4b169dX6nFbREREdZVsUD57bvLmzZv4+OOP8dNPP+Htt9+u8B5KIiKi+qDCc5S//fYb1q5dizNnzmDKlClYsWIFLCyMOrVJRERU58km3ty5c3HixAlMnjwZy5cvZ0ASEVGDI5t8u3fvhpmZGd59912899570nIhBBQKhVEPbyYiIqrLZIPy8uXL1VUHERFRrfRCU9gRERE1FAxKIiIiGQxKIiIiGbyMlQAAAQEBldr+4MGDJqqEiKh24YiSiIhIBoOSiIhIBoOSiIhIBoOSiIhIBoOSiIhIBoOSiIhIBoOSiIhIBoOSiIhIBoOSiIhIBoOSiIhIBoOSiIhIBoOSiIhIBoOSiIhIBoOSiIhIBoOSiIhIBp9HSS+kss+vBPgMSyKqmziiJCIiksGgJCIiksGgJCIiksGgJCIiksGgJCIiksGgJCIiksGgJCIikmHS+yiXLVuG9PR03LlzBxcvXpSWJyQkID09HWZmZoiOjsbgwYMBAFeuXEFcXByKi4vh4OCAxMREWFtby7YhItOp7P2yvFeW6iOTBuWQIUMwdepU9OvXT1p25MgRnD9/HqmpqSgsLERYWBi8vLxgbW2NRYsWYebMmfDy8kJCQgI2bNiAqKgo2TZUd/CPLhHVRSY99Oru7o5WrVqVWZaWloaQkBBYWFjA1tYWbm5uyMzMREFBAVQqFby8vAAAo0aNQnp6umwbIiIiU6v2KezUajXs7Oyk13Z2dlCpVFCpVFAqldJye3t7qFQq2TaGZGVlQaFQvFSdWq32pdrXBUKIWv05s7KyarqEStFoNHWu5opU9vtRlZ+/PvZnTaor/VkT3zkhhOz6Gpnr9dkQe7ZAuXAz1KY8zs7OLx2UlpaWL9W+LtBqtbX6czo7O9d0CZWSlZVV52quSGW/H1X5+etjf9akutKfNfGdE0LgzJkzBtdX+1WvSqUSubm50mu1Wg2lUgmlUllmlJiXlyeNMA21ISIiMrVqD0p/f3/s2bMHOp0OarUap0+fhqenJ1q3bg1bW1vp3GNKSgoGDRok24aIiMjUTHrodeHChTh8+DB0Oh369euHAQMGYOnSpcjMzMTgwYOhUCgwZ84c6erVxYsXY+7cuViyZIl0ewgAeHp6GmxDRERkSiYNyqVLl5a7fPbs2Zg9e/Zzy52cnLB79+5KtSEiIjIlzsxDREQko0aueiUyRmUnKKgsTmhARMbgiJKIiEgGR5TUYHFKPSIyBkeUREREMhiUREREMhiUREREMhiUREREMhiUREREMhiUREREMhiUREREMhiUREREMjjhAJGRKpqg4M8PwuYEBUT1A0eUREREMhiUREREMhiUREREMhiUREREMhiUREREMhiUREREMnh7CJGJ8HmXRPUDR5REREQyOKIkoirDUTTVRwxKolqCIUNUO/HQKxERkQwGJRERkQwGJRERkQyeoySqo3hOk6h6cERJREQkgyNKIqoxcqPiPz/fE+ComGoGR5REREQyOKIkaiAqe06zNuJ5WaoJHFESERHJYFASERHJYFASERHJ4DlKIqq3TH1eludAGwaOKImIiGRwRElE9IJ4FW7DwKAkIqomDNa6iUFJRFSPGArj8mY6ehENMbzrVFAeO3YMS5cuRUlJCdzd3bFs2TJYWNSpj0BEZLSGOElEbVRnUkan02H+/PlISkrC66+/jnfeeQd79+5FaGhoTZdGRNRg1Ifgq6w6E5Q//fQTlEolXn/9dQDAqFGjsG3btjJBKYQo89+XYWZW/y8INjMzaxCfs7qwP6sW+7Nq1df+rIq/989mh0KheG59nQlKlUoFpVIpvba3t4dKpSp327Nnz770+7377rsvvQ8iIjKtM2fOmPw96kxQAiiT9Ib+FeHi4vLctkRERIZUNCqtM0FpZ2eHvLw86fWfR5hAwzhcSkREVauigVWdSZauXbtCrVbj2rVrAICdO3di0KBBNVwVERHVdwpRFWdCq8nRo0exbNky3h5SCcbcUuPo6AgnJyfp9ZdffgkbG5vqLrVOWLZsGdLT03Hnzh1cvHix3G14G5PxjOlPHx8fNG7cWOrDVatW4bXXXqvOMuuEvLw8xMXFIT8/H+bm5vD19UV0dPRz2/H7+QIE1VulpaXCx8dH/PLLL0IIIaKiokRKSspz273xxhvVXVqddfLkSVFQUGCwz4ztc3qiov4UQoiBAweKvLy8aqyqblKr1eLChQtCCCE0Go0YN26cyMjIKLMNv58vps4ceqXKK++WmvT09Bquqm5zd3dHq1atDK5nn1dORf1JxmvTpg26desGALC0tISjoyNyc3PLbMPv54thUNZjxt5So9frERoaipEjR2Ljxo3VWWK9U5nbmMh4ERERCAwMxIcffojS0tKaLqfWu3fvHjIyMtC3b98yy/n9fDE8MF3PGXNLzeHDh6FUKlFYWIipU6fC1tYWQ4cOra4S6x1j+pyMl5ycDKVSiaKiIsyaNQsbNmzAlClTarqsWkur1SIqKgrh4eHo1KnTc+v5/aw8jijrMWNuqQEgLWvZsiUCAwNx7ty5aquxvjG2z8l4T/vP2toaYWFhOH/+fA1XVHvpdDrExsaia9eumDRp0nPr+f18MQzKesyYW2ru378PjUYDAHj8+DEOHToER0fHaq+1vuBtTFXr4cOHKCoqAgCUlpYiLS2N308Z8fHxaNq0KWbPnl3uen4/X0yduj2EKq+8W2p++OEHHDp0CO+99x7Onj2LhQsXQqFQQKfTwcfHB9HR0Zy8wYCFCxfi8OHDUKvVsLW1xYABA/Dmm29izZo1+OyzzwDwNqbKqKg/b9++jcjISOj1euj1eri6umL+/Pl45ZVXarr0Wuf06dN466230LlzZ+n/v6GhoXBxceH38yUxKImIiGRw2EBERCSDQUlERCSDQUlERCSDQUlERCSDQUlERCSDQUlERCSDQUlUA44fP17uzCm10ciRI6Wft27diiFDhmDSpEn46aefsHDhQtm227dvx1dffQXgyWc+efKkSWslMgXeZUpkIjqdDubm5jVdxkvbtWuX9PO2bdvw8ccfS3OIPn1ahSFjx46Vfj5x4gTMzc3h4eFhmkKJTIQjSqIq1KVLF6xfvx5jxozBt99+C5VKhX/84x8YMWIEQkNDy51H97fffkNwcDBOnTr13Ejzk08+wdq1aw2+340bNzB69GgEBwdj2LBhyMjIAABMmDAB77//PkaOHImAgADs379fapOamoo333wTwcHBmD59Oh48eAAAyM7OxuTJkzFixAgEBgbi6NGj0mcCgLlz5+L27duIiopCYmLic7Vu2bJFahsVFQUAWLt2LT755BPcuHEDO3bsQHJyMoKCgpCamoqAgABkZ2dL7SdPnowffvihkj1OZHocURJVIZ1OhxYtWmDHjh0AgMjISPTv3x8TJ07EhQsXMGPGDBw8eFDa/vLly5g1axZWrFiBLl264Pjx45V6v+TkZIwdOxbBwcEQQkjzogJAfn4+du7cifz8fISGhqJPnz74/fff8c9//hPbtm2DpaUl/vd//xdJSUmYNWsWYmNjMWbMGAQHB0Ov15fZFwAsX74cx48fxxdffAGlUlmm1szMTOzevRvJyclo1qwZ7t27V6atg4MDxowZA3Nzc0ybNg0AkJOTg507d+Kdd95BXl4ebty4AW9v70p9fqLqwKAkqmKBgYHSzydOnMCKFSsAAN27d0ezZs1w69YtAE9GklFRUUhKSoKDg8MLvZerqys++eQTqFQq9OvXTxr9AUBQUBAUCgVsbW3Ro0cPXLhwAdnZ2bh69SrefPNNAE8mGu/cuTOKiopw8+ZNBAcHAwDMzMxgY2NjdB1HjhzByJEj0axZMwBA8+bNK2wzcuRIhIWFITIyEikpKRg5ciTnGKZaiUFJVIXMzc1hZWUlu83T5wG2bNkS5ubmOH36tBSU5ubm0Ov10rZarVb2POfQoUPx3//93/jxxx8RHx+PIUOGYPLkyQbfVwiBIUOGYP78+WXW/Xn0WFkvMmV0ixYt0L17dxw+fBh79uzB1q1bX6oGIlPhP9+ITKhnz55ISUkBAFy4cAEPHjxA+/btAQBNmzbFZ599hq+++go7d+4EALz66qu4ceMGHj9+jMePH+Nf//qX7P5v3boFOzs7jB49GpMmTcKFCxekdXv37oUQAmq1GufPn0e3bt3Qt29fZGRkSE+1f/ToEa5fvw5ra2t07NgRe/bsAQDo9Xr88ccfRn/Ofv36YdeuXdL5zj8fegWePE+yuLi4zLLRo0dj6dKl6NSpE+zs7Ix+P6LqxBElkQktWLAA8+fPR0pKCiwtLbFq1So0atRIWt+sWTN8/vnnCA8Ph5mZGUJCQjBy5EgEBgaiQ4cOeP3112X3n5qaiq+//hqNGjWClZUVlixZIq1r164d3nrrLdy9exdxcXFo0aIFWrRogbi4OEydOhU6nQ5CCERGRqJTp05YuXIlFi9ejC+++AJmZmaIi4tDnz59jPqcffv2RXBwMMaMGQMLCwv89a9/xerVq8ts4+fnh+nTp+Po0aOIiIiAv78/evXqBTMzM4SFhVWiV4mqFx+zRVQPTZgwAe+88w7c3d1ruhRZubm5mDhxIg4cOMBnIlKtxUOvRFQjNm/ejHHjxiE2NpYhSbUaR5REdcDHH3+M9PT0Mss6duyIjz76qIYqImo4GJREREQyeOiViIhIBoOSiIhIBoOSiIhIBoOSiIhIBoOSiIhIxv8DBYNmU9fh1QwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 450x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tso.plot_histogram()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 4: The `threshold` parameter of the `counts` metric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `counts` metric quantifies tissue-specificity as the proportion of tissues above a given expression threshold. Consequently, the `threshold` parameter directly influences the results obtained when using this method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example we will first set `threshold` to 5. Therefore, genes will be considered expressed in any given tissue if their expression is above this value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2400"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tso = tspex.TissueSpecificity(expression_data, 'counts', threshold=5)\n",
    "sum(tso.tissue_specificity >= 0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x300 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tso.plot_heatmap(0.8, sort_genes=True, use_zscore=True, gene_names=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we increase `threshold` to 50 fewer genes will be selected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1557"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tso = tspex.TissueSpecificity(expression_data, 'counts', threshold=50)\n",
    "sum(tso.tissue_specificity >= 0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x300 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tso.plot_heatmap(0.8, sort_genes=True, use_zscore=True, gene_names=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}