notebooks/Case_study2_The_Tsignaling_pathway.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "41a84966a70395d6",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "# Case study 2: The T signaling pathway"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dda70f64-d371-40a3-b370-8f97f8547f25",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-25T17:50:43.215267Z",
     "start_time": "2024-01-25T17:50:43.144212Z"
    }
   },
   "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>key</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>eliater</td>\n",
       "      <td>0.0.3-dev-96cf1bf2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>y0</td>\n",
       "      <td>0.2.10-dev-8f27d998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Run at</td>\n",
       "      <td>2024-04-25 08:51:09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       key                value\n",
       "0  eliater   0.0.3-dev-96cf1bf2\n",
       "1       y0  0.2.10-dev-8f27d998\n",
       "2   Run at  2024-04-25 08:51:09"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image, set_matplotlib_formats\n",
    "\n",
    "import eliater\n",
    "from eliater.examples import t_cell_signaling_example as example\n",
    "from y0.dsl import P, Variable\n",
    "\n",
    "set_matplotlib_formats(\"svg\")\n",
    "\n",
    "eliater.version_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bf3ee538-6cd3-418c-80b1-a6c3492f3a0f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-25T17:51:18.623597Z",
     "start_time": "2024-01-25T17:51:18.570520Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "treatment = RAF = Variable(\"Raf\")\n",
    "outcome = ERK = Variable(\"Erk\")\n",
    "PKC = Variable(\"PKC\")\n",
    "MEK = Variable(\"Mek\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "231bb8431a64328e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-21T09:34:15.535225Z",
     "start_time": "2024-01-21T09:34:15.508813Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "This is case study 2 in this paper: Eliater: an open source software for causal query estimation from observational measurements of biomolecular networks. The Figure below is the protein signalling network (G) of the cell signaling pathway presented in (Sachs et al., 2005). It models the molecular\n",
    "mechanisms and regulatory processes involved in T cell activation, proliferation, and function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "faa4f8300eb220d7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-25T17:50:43.298456Z",
     "start_time": "2024-01-25T17:50:43.227934Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {
      "image/png": {
       "height": 500,
       "width": 500
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename=\"../img/Tsignaling.png\", width=500, height=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f2f48e7e-acb8-422c-b5a6-5f20bb845609",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-25T17:50:44.440304Z",
     "start_time": "2024-01-25T17:50:43.317716Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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       "   <cc:Work>\n",
       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
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       "    <dc:creator>\n",
       "     <cc:Agent>\n",
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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph = example.graph\n",
    "graph.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19295d3c869fc774",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-21T09:34:19.946275Z",
     "start_time": "2024-01-21T09:34:18.771941Z"
    },
    "collapsed": false,
    "jupyter": {
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   "source": [
    "The observational data consisted of quantitative multivariate flow cytometry measurements of phosphorylated proteins derived from thousands of individual primary immune system cells. The cells were subjected to general stimuli meant to activate the desired paths. The distributions of measurements of individual proteins were skewed, and pairs of proteins exhibited nonlinear relationships. To account for that, the data were binned into\n",
    "two levels corresponding to low, and high concentrations using Hartemink’s approach (Hartemink, 2001) to preserve\n",
    "the dependence structure of the original data."
   ]
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  {
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   "execution_count": 5,
   "id": "64c167d7feaf9c75",
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    "collapsed": false,
    "jupyter": {
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   },
   "outputs": [
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       "</table>\n",
       "<p>853 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Raf  Mek  Plcg  PIP2  PIP3  Erk  Akt  PKA  PKC  P38  Jnk\n",
       "0      0    0     0     1     1    1    0    1    1    1    1\n",
       "1      0    1     1     1     1    1    1    1    1    1    1\n",
       "2      1    0     0     1     1    1    1    1    1    0    0\n",
       "3      1    0     0     1     1    1    1    1    1    0    0\n",
       "4      0    0     1     1     0    1    1    1    1    1    1\n",
       "..   ...  ...   ...   ...   ...  ...  ...  ...  ...  ...  ...\n",
       "848    1    0     0     0     1    1    1    0    1    0    0\n",
       "849    0    0     0     1     1    1    1    1    1    1    0\n",
       "850    1    1     0     1     1    1    0    1    1    0    0\n",
       "851    1    0     0     1     1    1    1    0    1    1    0\n",
       "852    1    0     0     1     1    1    1    0    1    1    0\n",
       "\n",
       "[853 rows x 11 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = example.data\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "93ec48fb3ab155ce",
   "metadata": {
    "ExecuteTime": {
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     "start_time": "2024-01-25T17:50:44.551693Z"
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   "outputs": [
    {
     "data": {
      "text/markdown": [
       "## Step 1: Checking the ADMG Structure"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Of the 35 d-separations implied by the ADMG's structure, only 6 (17.14%) rejected the null hypothesis for the cressie_read test at p<0.01.\n",
       "\n",
       "Since this is less than 30%, Eliater considers this minor and leaves the ADMG unmodified. Finished in 1.22 seconds.\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/markdown": [
       "| left   | right   | given        |    stats |           p |   dof |      p_adj | p_adj_significant   |\n",
       "|:-------|:--------|:-------------|---------:|------------:|------:|-----------:|:--------------------|\n",
       "| Erk    | PIP3    | PKC          | 447.612  | 0           |     2 | 0          | True                |\n",
       "| Plcg   | Raf     | PKC          | 245.416  | 0           |     2 | 0          | True                |\n",
       "| Jnk    | P38     | PKA;PKC      | 174.846  | 0           |     4 | 0          | True                |\n",
       "| Erk    | PIP2    | PKC          |  86.6709 | 0           |     2 | 0          | True                |\n",
       "| Mek    | Plcg    | PKC          | 206.312  | 0           |     2 | 0          | True                |\n",
       "| Akt    | PKC     | Erk;PIP3;PKA |  36.8695 | 4.96534e-06 |     7 | 0.00014896 | True                |"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eliater.step_1_notebook(graph=graph, data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a974b5b8-6140-49bc-8681-106026ba0a0f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-25T17:50:54.222128Z",
     "start_time": "2024-01-25T17:50:54.170917Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "## Step 2: Check Query Identifiability\n",
       "\n",
       "The causal query of interest is the average treatment effect of $Raf$ on $Erk$, defined as: \n",
       "$\\mathbb{E}[Erk \\mid do(Raf=1)] - \\mathbb{E}[Erk \\mid do(Raf=0)]$.\n",
       "\n",
       "\n",
       "Running the ID algorithm defined by [Identification of joint interventional distributions in recursive\n",
       "semi-Markovian causal models](https://dl.acm.org/doi/10.5555/1597348.1597382) (Shpitser and Pearl, 2006)\n",
       "and implemented in the $Y_0$ Causal Reasoning Engine gives the following estimand:\n",
       "\n",
       "$\\sum\\limits_{Mek, PIP_2, PIP_3, PKA, PKC, Plcg} P(Erk | Mek, PIP_2, PIP_3, PKA, PKC, Plcg, Raf) P(Mek | PIP_2, PIP_3, PKA, PKC, Plcg, Raf) P(PIP_2 | PIP_3, Plcg) P(PIP_3 | Plcg) P(PKA | PIP_2, PIP_3, PKC, Plcg) P(PKC | PIP_2, PIP_3, Plcg) \\sum\\limits_{Erk, Mek, PIP_2, PIP_3, PKA, PKC, Raf} \\sum\\limits_{Akt, Jnk, P_{38}} P(Akt, Erk, Jnk, Mek, P_{38}, PIP_2, PIP_3, PKA, PKC, Plcg, Raf)$\n",
       "\n",
       "Because the query is identifiable, we can proceed to Step 3.\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eliater.step_2_notebook(graph=graph, treatment=treatment, outcome=outcome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d4dd9120fe7b258",
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    "ExecuteTime": {
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     "start_time": "2024-01-25T17:50:54.229933Z"
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    {
     "data": {
      "text/markdown": [
       "## Step 3/4: Identify Nuisance Variables and Simplify the ADMG"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "The following 1 variables were identified as _nuisance_ variables,\n",
       "meaning that they appear as descendants of nodes appearing in paths between\n",
       "the treatment and outcome, but are not themselves ancestors of the outcome variable:\n",
       "\n",
       "$Akt$\n",
       "\n",
       "These variables are marked as \"latent\", then\n",
       "the algorithm proposed in [Graphs for margins of Bayesian\n",
       "networks](https://arxiv.org/abs/1408.1809) (Evans, 2016) and implemented in\n",
       "the $Y_0$ Causal Reasoning Engine is applied to the ADMG to\n",
       "simplify the graph. This minimally removes the latent variables and makes\n",
       "further simplifications if the latent variables are connected by bidirected\n",
       "edges to other nodes.\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "The simplification did not modify the graph."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reduced_graph = eliater.step_3_notebook(graph=graph, treatment=treatment, outcome=outcome)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e387c5a179bac8c8",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "## Step 5: Estimate the query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3b9657cb-7af2-4f5f-abf4-d87de17808f2",
   "metadata": {},
   "outputs": [
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       "Subsampling:   0%|          | 0/500 [00:00<?, ?it/s]"
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       "Estimating:   0%|          | 0/500 [00:00<?, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eliater.step_5_notebook_real(\n",
    "    graph=graph, example=example, treatment=treatment, outcome=outcome, subsample_size=400\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d59724cde170ff98",
   "metadata": {
    "ExecuteTime": {
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     "start_time": "2024-01-25T17:55:47.365866Z"
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   "source": [
    "# What do we need to say about this alternate query?\n",
    "# estimate_ace(new_graph, treatments=PKC, outcomes=ERK, data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2908894523de226",
   "metadata": {
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    "jupyter": {
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    }
   },
   "source": [
    "# Test the data that Karen gave me\n",
    "\n",
    "TODO: what is the context for these? what are these datasets, where do they come from? Why are they here?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5ec4866c17ae5b52",
   "metadata": {
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   "outputs": [
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       "      <th>praf</th>\n",
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       "      <th>PIP2</th>\n",
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       "      <th>PKA</th>\n",
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      "text/plain": [
       "    praf   pmek   plcg   PIP2   PIP3  p44/42  pakts473    PKA     PKC     P38  \\\n",
       "0  176.0  294.0  337.0  644.0   9.73    18.4     101.0   5.99    93.1   505.0   \n",
       "1  126.0  204.0  164.0  308.0   2.53    99.1     835.0  16.80  1358.0  6436.0   \n",
       "2  433.0  710.0  233.0  422.0   8.66    23.9     150.0   3.11    91.4   487.0   \n",
       "3  161.0  248.0  362.0  661.0   6.38    69.8     505.0   8.98  1358.0  6916.0   \n",
       "4  151.0  257.0   93.1  172.0  14.30    47.0     392.0  26.40    69.8   422.0   \n",
       "\n",
       "     pjnk  \n",
       "0   142.0  \n",
       "1  2110.0  \n",
       "2   202.0  \n",
       "3  4740.0  \n",
       "4   418.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "from eliater.data import load_sachs_continuous, load_sachs_test1, load_sachs_test2\n",
    "\n",
    "data1 = load_sachs_test1()\n",
    "data1.head()"
   ]
  },
  {
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       "      <td>44.9</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.9</td>\n",
       "      <td>16.5</td>\n",
       "      <td>12.30</td>\n",
       "      <td>16.80</td>\n",
       "      <td>8.13</td>\n",
       "      <td>18.60</td>\n",
       "      <td>32.5</td>\n",
       "      <td>352.0</td>\n",
       "      <td>3.37</td>\n",
       "      <td>16.5</td>\n",
       "      <td>61.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>59.4</td>\n",
       "      <td>44.1</td>\n",
       "      <td>14.60</td>\n",
       "      <td>10.20</td>\n",
       "      <td>13.00</td>\n",
       "      <td>14.90</td>\n",
       "      <td>32.5</td>\n",
       "      <td>403.0</td>\n",
       "      <td>11.40</td>\n",
       "      <td>31.9</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>73.0</td>\n",
       "      <td>82.8</td>\n",
       "      <td>23.10</td>\n",
       "      <td>13.50</td>\n",
       "      <td>1.29</td>\n",
       "      <td>5.83</td>\n",
       "      <td>11.8</td>\n",
       "      <td>528.0</td>\n",
       "      <td>13.70</td>\n",
       "      <td>28.6</td>\n",
       "      <td>23.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.7</td>\n",
       "      <td>19.8</td>\n",
       "      <td>5.19</td>\n",
       "      <td>9.73</td>\n",
       "      <td>24.80</td>\n",
       "      <td>21.10</td>\n",
       "      <td>46.1</td>\n",
       "      <td>305.0</td>\n",
       "      <td>4.66</td>\n",
       "      <td>25.7</td>\n",
       "      <td>81.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   praf  pmek   plcg   PIP2   PIP3  p44/42  pakts473    PKA    PKC   P38  pjnk\n",
       "0  26.4  13.2   8.82  18.30  58.80    6.61      17.0  414.0  17.00  44.9  40.0\n",
       "1  35.9  16.5  12.30  16.80   8.13   18.60      32.5  352.0   3.37  16.5  61.5\n",
       "2  59.4  44.1  14.60  10.20  13.00   14.90      32.5  403.0  11.40  31.9  19.5\n",
       "3  73.0  82.8  23.10  13.50   1.29    5.83      11.8  528.0  13.70  28.6  23.1\n",
       "4  33.7  19.8   5.19   9.73  24.80   21.10      46.1  305.0   4.66  25.7  81.3"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = load_sachs_test2()\n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c674ce2242fa4810",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-26T03:49:54.562118400Z",
     "start_time": "2024-01-26T03:49:54.474769100Z"
    },
    "collapsed": false,
    "jupyter": {
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    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Raf</th>\n",
       "      <th>Mek</th>\n",
       "      <th>Plcg</th>\n",
       "      <th>PIP2</th>\n",
       "      <th>PIP3</th>\n",
       "      <th>Erk</th>\n",
       "      <th>Akt</th>\n",
       "      <th>PKA</th>\n",
       "      <th>PKC</th>\n",
       "      <th>P38</th>\n",
       "      <th>Jnk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26.4</td>\n",
       "      <td>13.2</td>\n",
       "      <td>8.82</td>\n",
       "      <td>18.30</td>\n",
       "      <td>58.80</td>\n",
       "      <td>6.61</td>\n",
       "      <td>17.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>17.00</td>\n",
       "      <td>44.9</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.9</td>\n",
       "      <td>16.5</td>\n",
       "      <td>12.30</td>\n",
       "      <td>16.80</td>\n",
       "      <td>8.13</td>\n",
       "      <td>18.60</td>\n",
       "      <td>32.5</td>\n",
       "      <td>352.0</td>\n",
       "      <td>3.37</td>\n",
       "      <td>16.5</td>\n",
       "      <td>61.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>59.4</td>\n",
       "      <td>44.1</td>\n",
       "      <td>14.60</td>\n",
       "      <td>10.20</td>\n",
       "      <td>13.00</td>\n",
       "      <td>14.90</td>\n",
       "      <td>32.5</td>\n",
       "      <td>403.0</td>\n",
       "      <td>11.40</td>\n",
       "      <td>31.9</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>73.0</td>\n",
       "      <td>82.8</td>\n",
       "      <td>23.10</td>\n",
       "      <td>13.50</td>\n",
       "      <td>1.29</td>\n",
       "      <td>5.83</td>\n",
       "      <td>11.8</td>\n",
       "      <td>528.0</td>\n",
       "      <td>13.70</td>\n",
       "      <td>28.6</td>\n",
       "      <td>23.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>33.7</td>\n",
       "      <td>19.8</td>\n",
       "      <td>5.19</td>\n",
       "      <td>9.73</td>\n",
       "      <td>24.80</td>\n",
       "      <td>21.10</td>\n",
       "      <td>46.1</td>\n",
       "      <td>305.0</td>\n",
       "      <td>4.66</td>\n",
       "      <td>25.7</td>\n",
       "      <td>81.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Raf   Mek   Plcg   PIP2   PIP3    Erk   Akt    PKA    PKC   P38   Jnk\n",
       "1  26.4  13.2   8.82  18.30  58.80   6.61  17.0  414.0  17.00  44.9  40.0\n",
       "2  35.9  16.5  12.30  16.80   8.13  18.60  32.5  352.0   3.37  16.5  61.5\n",
       "3  59.4  44.1  14.60  10.20  13.00  14.90  32.5  403.0  11.40  31.9  19.5\n",
       "4  73.0  82.8  23.10  13.50   1.29   5.83  11.8  528.0  13.70  28.6  23.1\n",
       "5  33.7  19.8   5.19   9.73  24.80  21.10  46.1  305.0   4.66  25.7  81.3"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_continuous = load_sachs_continuous()\n",
    "data_continuous.head()"
   ]
  }
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