Mofa_Proteo_phospho_lipidomics.ipynb

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      "source": [
        "# **MOFA | Multi-Omics Factor Analysis**\n",
        "\n",
        "This MOFA analysis provides insights into the relationships between different omics\n",
        "data types and identifies key factors and features that explain the variance across the\n",
        "samples."
      ],
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      "metadata": {
        "id": "1w4roQTOZZe6"
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      "source": [
        "# !pip install muon AnnData mofax mofapy2"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from mofapy2 import *\n",
        "from muon import *\n",
        "import anndata as ad\n",
        "import muon as mu\n",
        "import mofax as mfx\n",
        "\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import StandardScaler\n"
      ],
      "metadata": {
        "id": "Bb_rZVd0Zixi"
      },
      "execution_count": null,
      "outputs": []
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      "source": [
        "## LIPIDOMICS\n",
        "\n",
        "lipid = pd.read_csv('/content/lipidomics.csv')\n",
        "lipid = lipid.set_index('LipidID') # set index\n",
        "lipid = lipid.fillna(0) # replace NaNs with 0"
      ],
      "metadata": {
        "id": "wX6W15O4ZySK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## PROTEOMICS\n",
        "\n",
        "prot = pd.read_csv('/content/proteomics.csv')\n",
        "prot.info()"
      ],
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          "base_uri": "https://localhost:8080/"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4878 entries, 0 to 4877\n",
            "Data columns (total 6 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   Protein.name  4878 non-null   object \n",
            " 1   HFpEF_11      4878 non-null   float64\n",
            " 2   HFpEF_8       4878 non-null   float64\n",
            " 3   HFpEF_1       4878 non-null   float64\n",
            " 4   HFpEF_3       4878 non-null   float64\n",
            " 5   HFpEF_6       4878 non-null   float64\n",
            "dtypes: float64(5), object(1)\n",
            "memory usage: 228.8+ KB\n"
          ]
        }
      ]
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      "source": [
        "# set index\n",
        "prot = prot.set_index('Protein.name')\n",
        "prot.head()"
      ],
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              "               HFpEF_11    HFpEF_8    HFpEF_1    HFpEF_3    HFpEF_6\n",
              "Protein.name                                                       \n",
              "A0A087WV05    18.113565  18.105642  18.315362  18.695195  18.353172\n",
              "A0A096LPI6    21.447813  21.562565  21.681084  21.648526  21.297472\n",
              "A0A0B4J203    16.153868  15.934126  15.065362  15.952924  16.174013\n",
              "A0A0G2JRQ6    17.887944  16.915221  17.161280  18.081226  16.308549\n",
              "A0A0J9YY99    18.511344  17.968177  18.276035  19.090701  17.627409"
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              "summary": "{\n  \"name\": \"prot\",\n  \"rows\": 4878,\n  \"fields\": [\n    {\n      \"column\": \"Protein.name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4878,\n        \"samples\": [\n          \"P62913\",\n          \"J3KRZ9\",\n          \"O14617-5\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HFpEF_11\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4037138931351545,\n        \"min\": 10.16078085,\n        \"max\": 29.67008848,\n        \"num_unique_values\": 4878,\n        \"samples\": [\n          17.80877401,\n          18.71311934,\n          17.62413347\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HFpEF_8\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4649403025713905,\n        \"min\": 8.869468744,\n        \"max\": 29.44996963,\n        \"num_unique_values\": 4878,\n        \"samples\": [\n          17.5118945,\n          18.77315199,\n          17.33063751\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HFpEF_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.541861011671965,\n        \"min\": 2.829280263,\n        \"max\": 30.32264103,\n        \"num_unique_values\": 4877,\n        \"samples\": [\n          17.76822411,\n          19.35121033,\n          17.7307278\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HFpEF_3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.451857412539122,\n        \"min\": 10.33368399,\n        \"max\": 30.46153603,\n        \"num_unique_values\": 4877,\n        \"samples\": [\n          16.94067608,\n          14.58766422,\n          17.27234089\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HFpEF_6\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4065651156172474,\n        \"min\": 9.372487556,\n        \"max\": 28.63294252,\n        \"num_unique_values\": 4878,\n        \"samples\": [\n          18.22395087,\n          18.34178184,\n          17.67492595\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
          "execution_count": 14
        }
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    {
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      "source": [
        "## PHOSPHOPROTEOMICS\n",
        "\n",
        "phospho = phospho[['HFpEF_11', 'HFpEF_8', 'HFpEF_1', 'HFpEF_3', 'HFpEF_6']].copy()\n",
        "phospho"
      ],
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              "                 HFpEF_11    HFpEF_8    HFpEF_1    HFpEF_3    HFpEF_6\n",
              "Site                                                                 \n",
              "A2M_S603_M1     15.462205  17.985899  15.961170  17.037105  18.985370\n",
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              "AAK1_S1093_M1   11.560831  12.320832  11.710129  13.020071  12.658149\n",
              "AAK1_S1095_M1   12.902770  12.158988  12.426941  14.725998  11.771403\n",
              "AAK1_S1102_M1   11.140863  13.420912  13.461715  13.505646  12.832785\n",
              "...                   ...        ...        ...        ...        ...\n",
              "ZZEF1_S1518_M1  14.342968  15.014197  13.967869  14.233461  16.693463\n",
              "ZZEF1_S2444_M1  13.111120  12.803479  13.166611  12.913501  12.967847\n",
              "ZZEF1_T1477_M1  17.935690  17.682227  16.853652  17.473071  17.167205\n",
              "ZZEF1_T1484_M1  10.699889  13.234353  12.595155  12.276209  12.743388\n",
              "ZZEF1_T1512_M1  14.109113  14.468113  12.095961  13.961840  16.564143\n",
              "\n",
              "[19382 rows x 5 columns]"
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              "        vertical-align: top;\n",
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              "\n",
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              "        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>HFpEF_11</th>\n",
              "      <th>HFpEF_8</th>\n",
              "      <th>HFpEF_1</th>\n",
              "      <th>HFpEF_3</th>\n",
              "      <th>HFpEF_6</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Site</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>A2M_S603_M1</th>\n",
              "      <td>15.462205</td>\n",
              "      <td>17.985899</td>\n",
              "      <td>15.961170</td>\n",
              "      <td>17.037105</td>\n",
              "      <td>18.985370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AAAS_S371_M1</th>\n",
              "      <td>14.008024</td>\n",
              "      <td>14.008239</td>\n",
              "      <td>14.104418</td>\n",
              "      <td>14.153989</td>\n",
              "      <td>14.329930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AAK1_S1093_M1</th>\n",
              "      <td>11.560831</td>\n",
              "      <td>12.320832</td>\n",
              "      <td>11.710129</td>\n",
              "      <td>13.020071</td>\n",
              "      <td>12.658149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AAK1_S1095_M1</th>\n",
              "      <td>12.902770</td>\n",
              "      <td>12.158988</td>\n",
              "      <td>12.426941</td>\n",
              "      <td>14.725998</td>\n",
              "      <td>11.771403</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AAK1_S1102_M1</th>\n",
              "      <td>11.140863</td>\n",
              "      <td>13.420912</td>\n",
              "      <td>13.461715</td>\n",
              "      <td>13.505646</td>\n",
              "      <td>12.832785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ZZEF1_S1518_M1</th>\n",
              "      <td>14.342968</td>\n",
              "      <td>15.014197</td>\n",
              "      <td>13.967869</td>\n",
              "      <td>14.233461</td>\n",
              "      <td>16.693463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ZZEF1_S2444_M1</th>\n",
              "      <td>13.111120</td>\n",
              "      <td>12.803479</td>\n",
              "      <td>13.166611</td>\n",
              "      <td>12.913501</td>\n",
              "      <td>12.967847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ZZEF1_T1477_M1</th>\n",
              "      <td>17.935690</td>\n",
              "      <td>17.682227</td>\n",
              "      <td>16.853652</td>\n",
              "      <td>17.473071</td>\n",
              "      <td>17.167205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ZZEF1_T1484_M1</th>\n",
              "      <td>10.699889</td>\n",
              "      <td>13.234353</td>\n",
              "      <td>12.595155</td>\n",
              "      <td>12.276209</td>\n",
              "      <td>12.743388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ZZEF1_T1512_M1</th>\n",
              "      <td>14.109113</td>\n",
              "      <td>14.468113</td>\n",
              "      <td>12.095961</td>\n",
              "      <td>13.961840</td>\n",
              "      <td>16.564143</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>19382 rows × 5 columns</p>\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('phospho')\"\n",
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              "            style=\"display:none;\">\n",
              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "phospho",
              "summary": "{\n  \"name\": \"phospho\",\n  \"rows\": 19382,\n  \"fields\": [\n    {\n      \"column\": \"Site\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 19340,\n        \"samples\": [\n          \"WASF2_S308_M1\",\n          \"HIVEP2_S1062_M1\",\n          \"CAVIN3_S62_M1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HFpEF_11\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.040995860139211,\n        \"min\": 6.48142721475396,\n        \"max\": 29.73733400275475,\n        \"num_unique_values\": 19043,\n        \"samples\": [\n          11.0512547520863,\n          12.70888307079088,\n          12.70763843778269\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HFpEF_8\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.9283211057780605,\n        \"min\": 6.655940751703021,\n        \"max\": 29.49492746691946,\n        \"num_unique_values\": 19030,\n        \"samples\": [\n          11.41381151613552,\n          13.18510000213187,\n          14.21012611643405\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HFpEF_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.8923163869623925,\n        \"min\": 6.424960718855893,\n        \"max\": 29.44135522598869,\n        \"num_unique_values\": 19041,\n        \"samples\": [\n          11.90177654677624,\n          11.82514920661014,\n          17.02413359177461\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HFpEF_3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.9567333746282647,\n        \"min\": 6.338263047045421,\n        \"max\": 29.49105401948362,\n        \"num_unique_values\": 19036,\n        \"samples\": [\n          14.97919324151331,\n          13.64502090883557,\n          14.3445558502964\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HFpEF_6\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.9151484328369106,\n        \"min\": 6.647488290225499,\n        \"max\": 29.12711287105989,\n        \"num_unique_values\": 19047,\n        \"samples\": [\n          15.16045019842282,\n          14.00709332811277,\n          18.02033586562718\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# creating 3 anndata obj and integrating them into one mudata object\n",
        "\n",
        "proteomics_anndata = ad.AnnData(prot.T)\n",
        "lipidomics_anndata = ad.AnnData(lipid.T)\n",
        "phosphoproteomics_anndata = ad.AnnData(phospho.T)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9jfUUV4tfvXA",
        "outputId": "e4b9d2b6-3ca5-46a5-c42f-b731b8cbeb13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/anndata/_core/anndata.py:1820: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
            "  utils.warn_names_duplicates(\"var\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Create a dictionary of AnnData objects\n",
        "mdata_dict = {\n",
        "    \"proteomics\": proteomics_anndata,\n",
        "    \"lipidomics\": lipidomics_anndata,\n",
        "    \"phosphoproteomics\": phosphoproteomics_anndata\n",
        "}\n",
        "\n",
        "# Initialize MuData with the dictionary\n",
        "mdata = MuData(mdata_dict)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jIug58XMhG6o",
        "outputId": "12603cdf-8b28-4997-8032-e1b57a00d918"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/mudata/_core/mudata.py:1454: FutureWarning: From 0.4 .update() will not pull obs/var columns from individual modalities by default anymore. Set mudata.set_options(pull_on_update=False) to adopt the new behaviour, which will become the default. Use new pull_obs/pull_var and push_obs/push_var methods for more flexibility.\n",
            "  self._update_attr(\"var\", axis=0, join_common=join_common)\n",
            "/usr/local/lib/python3.10/dist-packages/mudata/_core/mudata.py:856: UserWarning: var_names are not unique. To make them unique, call `.var_names_make_unique`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/mudata/_core/mudata.py:1352: FutureWarning: From 0.4 .update() will not pull obs/var columns from individual modalities by default anymore. Set mudata.set_options(pull_on_update=False) to adopt the new behaviour, which will become the default. Use new pull_obs/pull_var and push_obs/push_var methods for more flexibility.\n",
            "  self._update_attr(\"obs\", axis=1, join_common=join_common)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "mdata"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "id": "8BYzK883hIm0",
        "outputId": "fa1e0b36-b761-4bba-d51f-210b3e803b27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MuData object with n_obs × n_vars = 5 × 27432\n",
              "  3 modalities\n",
              "    proteomics:\t5 x 4878\n",
              "    lipidomics:\t5 x 3172\n",
              "    phosphoproteomics:\t5 x 19382"
            ],
            "text/html": [
              "<pre>MuData object with n_obs × n_vars = 5 × 27432\n",
              "  3 modalities\n",
              "    proteomics:\t5 x 4878\n",
              "    lipidomics:\t5 x 3172\n",
              "    phosphoproteomics:\t5 x 19382</pre>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "mu.tl.mofa(mdata, outfile=\"mofapy2.h5ad\", n_factors=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M5W5QQQqhNdR",
        "outputId": "ed86bd01-7dc5-4464-d749-768e039f5678"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "        #########################################################\n",
            "        ###           __  __  ____  ______                    ### \n",
            "        ###          |  \\/  |/ __ \\|  ____/\\    _             ### \n",
            "        ###          | \\  / | |  | | |__ /  \\ _| |_           ### \n",
            "        ###          | |\\/| | |  | |  __/ /\\ \\_   _|          ###\n",
            "        ###          | |  | | |__| | | / ____ \\|_|            ###\n",
            "        ###          |_|  |_|\\____/|_|/_/    \\_\\              ###\n",
            "        ###                                                   ### \n",
            "        ######################################################### \n",
            "       \n",
            " \n",
            "        \n",
            "Loaded view='proteomics' group='group1' with N=5 samples and D=4878 features...\n",
            "Loaded view='lipidomics' group='group1' with N=5 samples and D=3172 features...\n",
            "Loaded view='phosphoproteomics' group='group1' with N=5 samples and D=19382 features...\n",
            "\n",
            "\n",
            "Warning: 1393 features(s) in view 1 have zero variance, consider removing them before training the model...\n",
            "\n",
            "Model options:\n",
            "- Automatic Relevance Determination prior on the factors: True\n",
            "- Automatic Relevance Determination prior on the weights: True\n",
            "- Spike-and-slab prior on the factors: False\n",
            "- Spike-and-slab prior on the weights: True\n",
            "Likelihoods:\n",
            "- View 0 (proteomics): gaussian\n",
            "- View 1 (lipidomics): gaussian\n",
            "- View 2 (phosphoproteomics): gaussian\n",
            "\n",
            "\n",
            "\n",
            "Warning: some group(s) have less than 15 samples, MOFA won't be able to learn meaningful factors for these group(s)...\n",
            "\n",
            "\n",
            "\n",
            "######################################\n",
            "## Training the model with seed 1 ##\n",
            "######################################\n",
            "\n",
            "\n",
            "\n",
            "Converged!\n",
            "\n",
            "\n",
            "\n",
            "#######################\n",
            "## Training finished ##\n",
            "#######################\n",
            "\n",
            "\n",
            "Saving model in mofapy2.h5ad...\n",
            "Saved MOFA embeddings in .obsm['X_mofa'] slot and their loadings in .varm['LFs'].\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = mfx.mofa_model(\"/content/mofapy2.h5ad\")"
      ],
      "metadata": {
        "id": "pk0KhtFSxD4P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "ax = mfx.plot_weights(model, factor=2, n_features=10,\n",
        "                      y_repel_coef=0.04, x_rank_offset=-150)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "d5iEvFPPhi2K",
        "outputId": "ffcf50d3-18ec-41d9-b005-0a92b867d797"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1920x480 with 3 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mfx.plot_weights_heatmap(model, n_features=5,\n",
        "                         factors=range(0, 5),\n",
        "                         xticklabels_size=6, w_abs=True,\n",
        "                         cmap=\"viridis\", cluster_factors=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "id": "_9jOq0R4hoIY",
        "outputId": "fb62d8df-0282-49ad-9c34-b14c5edb2a5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.matrix.ClusterGrid at 0x7d7aedb37190>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mfx.plot_weights_dotplot(model)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "id": "FrYOKISWhvem",
        "outputId": "4d11aca1-76e1-44c7-9d92-7ae7bef6951c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'proteomics'}, xlabel='factor', ylabel='feature'>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.get_r2(3).sort_values(\"R2\", ascending=False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "VZ57uuydh-ol",
        "outputId": "bf5873f9-7367-4c17-cc06-124da41c38dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Factor               View   Group         R2\n",
              "10  Factor4         lipidomics  group1  18.938944\n",
              "11  Factor4  phosphoproteomics  group1  14.679820\n",
              "9   Factor4         proteomics  group1   8.080041"
            ],
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d9e9eb7a-59ea-4786-9751-34574aa97170 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"model\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Factor\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Factor4\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"View\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"lipidomics\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Group\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"group1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.47133428081705,\n        \"min\": 8.080040726230086,\n        \"max\": 18.938943584689294,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          18.938943584689294\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mfx.plot_r2(model, factors=list(range(5)), cmap=\"Blues\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "HIGbRMc0iBN9",
        "outputId": "a4e06233-caff-4bfa-f12b-62d51de8cae8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 4 Axes>"
            ],
            "image/png": 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x49Wr8JgZM2bo8ccf13fffVftrivA23zwwQcaNmyYli1bpl//+teebg4aUVP+rEtKStIbb7yhsrKyRq+7KR8XNA9cQwgAAGBxBEIAAACLIxACAABYHNcQAgAAWBxnCAEAACyOQAgAAGBxBEIAAACL45tKLpCtX/1Y+0JALS7pGtTodZYer2r0OuF9gv0b/3zD14dONHqd8D6d29g93QSP4AwhAACAxREIAQAALI5ACAAAYHEEQgAAAIsjEAIAAFgcgRAAAMDiCIQAAAAWRyAEAACwOAIhAACAxREIAQAALI5ACAAAYHEEQgAAAIsjEAIAAFgcgRAAAMDiCIQAAAAWRyAEAACwOAIhAACAxREIAQAALI5ACAAAYHEEQgAAAIsjEAIAAFgcgRAAAMDiCIQAAAAWRyAEAACwOAIhAACAxREIAQAALI5ACAAAYHEEQgAAAIsjEAIAAFgcgRAAAMDiPBoIk5KSZLPZqk27d+8+r+0mJCQoJSXFPY38/7d3ZhsnTZrktu0DAAB4UgtPN2DUqFFavHixS1loaKiHWuPqxIkTstvtkqSJEyfqiSeecL4WGBjoqWYBAAC4lceHjP38/BQWFuYyZWRkaMCAAXI4HIqIiFBycrLKyspc1svJyVFCQoICAwPVpk0bJSYm6tChQ0pKStKGDRuUkZHhPJtXVFQkSdqwYYNiY2Pl5+en8PBwTZs2TSdPnnRuMyEhQZMnT1ZKSorat2+vxMRE52uBgYEubQwODm6U4wMAAHCheTwQ1sTHx0fz5s3Tjh07tGTJEq1bt05Tp051vp6fn68RI0YoKipKubm52rhxo8aOHavKykplZGQoLi5OEydO1P79+7V//35FRETom2++0ZgxY3TZZZepoKBA2dnZWrRokWbOnOlS95IlS2S325WTk6OFCxc6y5cuXar27durf//+SktL07FjxxrteAAAAFxIHh8yfvfdd9WqVSvn/OjRo7Vs2TLnfLdu3TRz5kxNmjRJWVlZkqTZs2dr8ODBznlJ6tevn/Nnu93uPKN3WlZWliIiIrRgwQLZbDb16dNHxcXFeuihh/TYY4/Jx+dUNu7Zs6dmz57t0sbbb79dXbt21UUXXaRt27bpoYce0q5du/Tmm2+692AAAAB4gMcD4bBhw5Sdne2cdzgcWrNmjdLT01VYWKjS0lKdPHlSx48f17FjxxQYGKj8/HzdfPPN9apn586diouLk81mc5bFx8errKxMX3/9tbp06SJJGjRoULV17777bufPAwYMUHh4uEaMGKEvvvhCF198cX13GQAAoEnx+JCxw+FQZGSkcyovL9d1112n6OhoLV++XHl5ecrMzJR06iYPSQoICLig7anN5ZdfLknnfTc0AABAU+DxQHimvLw8VVVVac6cORoyZIh69eql4uJil2Wio6O1du3as27DbrersrLSpaxv377Kzc2VMcZZlpOTo6CgIHXu3LlebczPz5ckhYeH12s9AACApqjJBcLIyEhVVFRo/vz52rNnj1555RWXmzskKS0tTVu2bFFycrK2bdumwsJCZWdn6+DBg5JOXXe4efNmFRUV6eDBg6qqqlJycrL27dunKVOmqLCwUCtWrND06dOVmprqvH6wJl988YWefPJJ5eXlqaioSO+8847uuusuDR06VNHR0Rf0WAAAADSGJhcIY2JiNHfuXM2aNUv9+/fX0qVLlZ6e7rJMr169tHr1ahUUFCg2NlZxcXFasWKFWrQ4dUnkAw88IF9fX0VFRSk0NFR79+5Vp06dtGrVKn344YeKiYnRpEmTNGHCBD3yyCPnbI/dbteaNWt0zTXXqE+fPrr//vt10003aeXKlRfsGAAAADQmm/n5GCrcZutXP3q6CfACl3QNavQ6S49XNXqd8D7B/o1/vuHrQycavU54n85t7J5ugkc0uTOEAAAAaFwEQgAAAIsjEAIAAFgcgRAAAMDiCIQAAAAWRyAEAACwOAIhAACAxREIAQAALI5ACAAAYHEEQgAAAIsjEAIAAFgcgRAAAMDiCIQAAAAWRyAEAACwOAIhAACAxREIAQAALI5ACAAAYHEEQgAAAIsjEAIAAFgcgRAAAMDiCIQAAAAWRyAEAACwOAIhAACAxREIAQAALI5ACAAAYHEEQgAAAIsjEAIAAFgcgRAAAMDiCIQAAAAWRyAEAACwOAIhAACAxREIAQAALK6FpxvgrfxakrXRPL300VeebgK8wB+u6N7odW79+lCj1wnv07lNR083wSNILQAAABZHIAQAALA4AiEAAIDFEQgBAAAsjkAIAABgcQRCAAAAiyMQAgAAWByBEAAAwOIIhAAAABZHIAQAALA4AiEAAIDF8V3GAAAAtQi4ZLJbt/fT1gVu3d75IhACAADUxubdg6oEQgAAgNrYbJ5uwQVFIAQAAKiNj6+nW3BBEQgBAABqw5AxAACAxTFkDAAAYHEMGQMAAFgcQ8YAAAAWx5AxAACAxTFkDAAAYHEMGQMAAFgcgRAAAMDifBkyBgAAsDZuKgEAALA4howBAAAsjruMAQAALI4hYwAAAItjyBgAAMDivHzI2KNxNykpSTabrdq0e/fu89puQkKCUlJS3NPInzHGaPTo0bLZbHr77bfdvn0AANBE2WzunZoYj58hHDVqlBYvXuxSFhoa6qHWuDpx4oTsdrtz/vnnn5etCf4SAQDABeblQ8Ye3zs/Pz+FhYW5TBkZGRowYIAcDociIiKUnJyssrIyl/VycnKUkJCgwMBAtWnTRomJiTp06JCSkpK0YcMGZWRkOM84FhUVSZI2bNig2NhY+fn5KTw8XNOmTdPJkyed20xISNDkyZOVkpKi9u3bKzEx0flafn6+5syZoxdffLFRjgsAAGhCfHzdOzUxHg+ENfHx8dG8efO0Y8cOLVmyROvWrdPUqVOdr+fn52vEiBGKiopSbm6uNm7cqLFjx6qyslIZGRmKi4vTxIkTtX//fu3fv18RERH65ptvNGbMGF122WUqKChQdna2Fi1apJkzZ7rUvWTJEtntduXk5GjhwoWSpGPHjun2229XZmamwsLCGvVYAACAJsDm496pifH4kPG7776rVq1aOedHjx6tZcuWOee7deummTNnatKkScrKypIkzZ49W4MHD3bOS1K/fv2cP9vtdgUGBrqEt6ysLEVERGjBggWy2Wzq06ePiouL9dBDD+mxxx6Tj8+pX07Pnj01e/Zslzbed999+sUvfqEbbrjBvTsPAACahyZyydjTTz+ttLQ03XvvvXr++eclScePH9f999+v1157TeXl5UpMTFRWVpY6duxY5+16PBAOGzZM2dnZznmHw6E1a9YoPT1dhYWFKi0t1cmTJ3X8+HEdO3ZMgYGBys/P180331yvenbu3Km4uDiXawDj4+NVVlamr7/+Wl26dJEkDRo0yGW9d955R+vWrdPWrVvPYy8BAECz1gSGebds2aIXXnhB0dHRLuX33Xef/vGPf2jZsmUKCQnR5MmT9atf/Uo5OTl13rbHz1k6HA5FRkY6p/Lycl133XWKjo7W8uXLlZeXp8zMTEmnbvKQpICAgAvanp9bt26dvvjiC7Vu3VotWrRQixanMvRNN92khISEC9YOAADQdNT0VJTzmeqrrKxMv/3tb/WXv/xFbdq0cZYfOXJEixYt0ty5czV8+HANGjRIixcv1n//+19t2rSpztv3eCA8U15enqqqqjRnzhwNGTJEvXr1UnFxscsy0dHRWrt27Vm3YbfbVVlZ6VLWt29f5ebmyhjjLMvJyVFQUJA6d+581m1NmzZN27ZtU35+vnOSpOeee67a3dEAAMA7uTsQlpeXq7S01GUqLy8/a/333HOPrr32Wo0cOdKlPC8vTxUVFS7lffr0UZcuXZSbm1vn/WtygTAyMlIVFRWaP3++9uzZo1deecV5c8dpaWlp2rJli5KTk7Vt2zYVFhYqOztbBw8elHTqusPNmzerqKhIBw8eVFVVlZKTk7Vv3z5NmTJFhYWFWrFihaZPn67U1FTn9YM1CQsLU//+/V0mSerSpYu6d+9+4Q4EAABoMmw+NrdO6enpCgkJcZnS09NrrPu1117Txx9/XOPrJSUlstvtat26tUt5x44dVVJSUuf9a3KBMCYmRnPnztWsWbPUv39/LV26tNoB6NWrl1avXq2CggLFxsYqLi5OK1ascA7nPvDAA/L19VVUVJRCQ0O1d+9ederUSatWrdKHH36omJgYTZo0SRMmTNAjjzziid0EAADNiLvPEKalpenIkSMuU1paWrV69+3bp3vvvVdLly6Vv7//hds/8/MxVLjNp8VHPd0EeIGoixy1L+Rm8zZ+2eh1wvv84YrGH0FZuf1Ao9cJ7zN2QM135gbf9rJb6yl97a46Lff222/rl7/8pXx9/++mlsrKStlsNvn4+Ohf//qXRo4cqUOHDrmcJezatatSUlJ033331akej99lDAAA0NTZfDzz2JkRI0Zo+/btLmXjx49Xnz599NBDDykiIkItW7bU2rVrddNNN0mSdu3apb179youLq7O9RAIAQAAauGpr64NCgpy3r9wmsPhULt27ZzlEyZMUGpqqtq2bavg4GBNmTJFcXFxGjJkSJ3rIRACAADUwlOBsC6ee+45+fj46KabbnJ5MHV9EAgBAABqca4nkjS2Dz74wGXe399fmZmZzuc2NwSBEAAAoDZN9wShWxAIAQAAatGUh4zdgUAIAABQi6Y0ZHwhEAgBAABq490nCAmEAAAAtWHIGAAAwOIYMgYAALA4zhACAABYnXfnQQIhAABAbRgyBgAAsDiGjAEAAKzOu/MggRAAAKA2DBkDAABYHEPGAAAAFkcgBAAAsDibD4EQAADA0jhDCAAAYHFengcJhAAAALXxYcgYAADA2hgyBgAAsDgvz4MEQgAAgNr4+np3IiQQAgAA1IIhYwAAAIvz8jxIIAQAAKgN32UMAABgcZwhBAAAsDiuIQQAALA4HkwNAABgcV5+gpBAeKEc+PG4p5sALxAlR6PXmdA1tNHrBNwhd98RTzcBXmDsgI41ljNkDAAAYHEMGQMAAFicl58glHc/VAcAAMANbDabW6e6ys7OVnR0tIKDgxUcHKy4uDj985//dL5+/Phx3XPPPWrXrp1atWqlm266SQcOHKj3/hEIAQAAauHjY3PrVFedO3fW008/rby8PH300UcaPny4brjhBu3YsUOSdN9992nlypVatmyZNmzYoOLiYv3qV7+q9/4xZAwAAFALTw0Zjx071mX+qaeeUnZ2tjZt2qTOnTtr0aJFevXVVzV8+HBJ0uLFi9W3b19t2rRJQ4YMqXM9BEIAAIBauPsu4/LycpWXl7uU+fn5yc/P76zrVFZWatmyZTp69Kji4uKUl5eniooKjRw50rlMnz591KVLF+Xm5tYrEDJkDAAAUAt3Dxmnp6crJCTEZUpPT6+x7u3bt6tVq1by8/PTpEmT9NZbbykqKkolJSWy2+1q3bq1y/IdO3ZUSUlJvfaPM4QAAAC1cPcZwrS0NKWmprqUne3sYO/evZWfn68jR47ojTfe0Lhx47Rhwwa3todACAAAUAt3X0NY2/Dwz9ntdkVGRkqSBg0apC1btigjI0O33nqrTpw4ocOHD7ucJTxw4IDCwsLq1R6GjAEAAGrhqbuMa1JVVaXy8nINGjRILVu21Nq1a52v7dq1S3v37lVcXFy9tskZQgAAgFr4eOg247S0NI0ePVpdunTRjz/+qFdffVUffPCB/vWvfykkJEQTJkxQamqq2rZtq+DgYE2ZMkVxcXH1uqFEIhACAADUylOPnfn222911113af/+/QoJCVF0dLT+9a9/6eqrr5YkPffcc/Lx8dFNN92k8vJyJSYmKisrq971EAgBAABq4euh7zJetGjROV/39/dXZmamMjMzz6seAiEAAEAt3H2XcVNDIAQAAKiFl+dBAiEAAEBtfL08ERIIAQAAasGQMQAAgMV5eR6s/4OpKyoqNGLECH3++ecXoj0AAABNjq+Pza1TU1PvM4QtW7bUtm3bLkRbAAAAmiRvHzJu0FfX3XHHHbU+FwcAAMBb2GzunZqaBl1DePLkSb344otas2aNBg0aJIfD4fL63Llz3dI4AACApoC7jGvwySef6NJLL5UkffbZZy6vefspVQAAYD3enm8aFAjXr1/v7nYAAAA0WU3wPhC3Ou/Hznz99deSpM6dO593YwAAAJoiHy9PhA26qaSqqkpPPPGEQkJC1LVrV3Xt2lWtW7fWk08+qaqqKne3EQAAwKNsNptbp6amQWcI//jHP2rRokV6+umnFR8fL0nauHGjZsyYoePHj+upp55yayMBAAA8yctPEDYsEC5ZskR//etfdf311zvLoqOj1alTJyUnJxMIAQCAV/Fpgmf13KlBgfCHH35Qnz59qpX36dNHP/zww3k3CgAAoCnx9kDYoGsIY2JitGDBgmrlCxYsUExMzHk3CgAAoCnx9gdTNygQzp49Wy+++KKioqI0YcIETZgwQVFRUXrppZf0zDPP1Hk7SUlJNV5ouXv37oY0yykhIUEpKSnntY2f+3//7//p4osvVkBAgEJDQ3XDDTeosLDQbdsHAABNm4+Pza1TU9OgQHjVVVfps88+0y9/+UsdPnxYhw8f1q9+9Svt2rVLV155Zb22NWrUKO3fv99l6t69e0Oa5XYnTpyQJA0aNEiLFy/Wzp079a9//UvGGF1zzTWqrKz0cAsBAEBj8LHZ3Do1NQ0KhHv37lV4eLieeuopLV++XMuXL9fMmTN10UUXae/evfXalp+fn8LCwlymjIwMDRgwQA6HQxEREUpOTlZZWZnLejk5OUpISFBgYKDatGmjxMREHTp0SElJSdqwYYMyMjKcZxyLiookSRs2bFBsbKz8/PwUHh6uadOm6eTJk85tJiQkaPLkyUpJSVH79u2VmJgoSbr77rs1dOhQdevWTZdeeqlmzpypffv2ObcLAAC8G0PGNejevbu+++67auXff/+9W87u+fj4aN68edqxY4eWLFmidevWaerUqc7X8/PzNWLECEVFRSk3N1cbN27U2LFjVVlZqYyMDMXFxWnixInOM44RERH65ptvNGbMGF122WUqKChQdna2Fi1apJkzZ7rUvWTJEtntduXk5GjhwoXV2nb06FEtXrxY3bt3V0RExHnvKwAAaPp8bTa3Tk1Ng+4yNsbU+FDFsrIy+fv712tb7777rlq1auWcHz16tJYtW+ac79atm2bOnKlJkyYpKytL0qlrGAcPHuycl6R+/fo5f7bb7QoMDFRYWJizLCsrSxEREVqwYIFsNpv69Omj4uJiPfTQQ3rsscfk43MqG/fs2VOzZ8+u1s6srCxNnTpVR48eVe/evfX+++/LbrfXa18BAEDz1BQfJu1O9QqEqampkk4dlEcffVSBgYHO1yorK7V582YNHDiwXg0YNmyYsrOznfMOh0Nr1qxRenq6CgsLVVpaqpMnT+r48eM6duyYAgMDlZ+fr5tvvrle9ezcuVNxcXEuv9D4+HiVlZXp66+/VpcuXSSdul6wJr/97W919dVXa//+/Xr22Wd1yy23KCcnp94BGAAAND9N8D4Qt6pXINy6daukU2cIt2/f7nKGzG63KyYmRg888EC9GuBwOBQZGemcLyoq0nXXXaff//73euqpp9S2bVtt3LhREyZM0IkTJxQYGKiAgIB61VHf9tQkJCREISEh6tmzp4YMGaI2bdrorbfe0m9+85sL1hYAANA0+Hp5IqxXIFy/fr0kafz48crIyFBwcLDbG5SXl6eqqirNmTPHOYz7+uuvuywTHR2ttWvX6vHHH69xG3a7vdodwH379tXy5ctdhrtzcnIUFBSkzp0716uNxhgZY1ReXl6v9QAAQPPk5XmwYTeVPP/88y535572ww8/qLS09LwaFBkZqYqKCs2fP1979uzRK6+8Uu3mjrS0NG3ZskXJycnatm2bCgsLlZ2drYMHD0o6dd3h5s2bVVRUpIMHD6qqqkrJycnat2+fpkyZosLCQq1YsULTp09XamqqM3jWZM+ePUpPT1deXp727t2r//73v7r55psVEBCgMWPGnNe+AgCA5oG7jGtw22236bXXXqtW/vrrr+u22247rwbFxMRo7ty5mjVrlvr376+lS5cqPT3dZZlevXpp9erVKigoUGxsrOLi4rRixQq1aHHqhOcDDzwgX19fRUVFKTQ0VHv37lWnTp20atUqffjhh4qJidGkSZM0YcIEPfLII+dsj7+/v/7zn/9ozJgxioyM1K233qqgoCD997//VYcOHc5rXwEAQPPQwmZz69TU2Iwxpr4rtW3bVjk5Oerbt69LeWFhoeLj4/X999+7rYHN1fpdHAOcv2G92zV6ndv2ldW+EFCL6IhWtS/kZg+v+qzR64T3+dOYXjWW//Gf7u1fT42uuR5PadBjZ8rLy2scMq6oqNBPP/103o0CAABoSprit4u4U4OGjGNjY/XnP/+5WvnChQvP+tgWAACA5srXx71TU9OgM4QzZ87UyJEjVVBQoBEjRkiS1q5dqy1btmj16tVubSAAAICneeoMYXp6ut58800VFhYqICBAv/jFLzRr1iz17t3buczx48d1//3367XXXlN5ebkSExOVlZWljh071rmeBmXU+Ph45ebmKiIiQq+//rpWrlypyMhIbdu2TVdeeWVDNgkAANBkeeou4w0bNuiee+7Rpk2b9P7776uiokLXXHONjh496lzmvvvu08qVK7Vs2TJt2LBBxcXF+tWvflWv/WvQGUJJGjhwoJYuXdrQ1QEAAJoNT33/8Hvvvecy/9JLL6lDhw7Ky8vT0KFDdeTIES1atEivvvqqhg8fLklavHix+vbtq02bNmnIkCF1qqfBgfC048eP68SJEy5lF+KB1QAAAJ7i7gdTl5eXV/uCCz8/P/n5+Z1zvSNHjkg69cQX6dQXelRUVGjkyJHOZfr06aMuXbooNze3zoGwQUPGx44d0+TJk9WhQwc5HA61adPGZQIAAPAmPjb3Tunp6c6vxT09nfnc5TNVVVUpJSVF8fHx6t+/vySppKREdrtdrVu3dlm2Y8eOKikpqfP+NegM4YMPPqj169crOztbd955pzIzM/XNN9/ohRde0NNPP92QTQIAADRZ7v4u47S0NKWmprqU1XZ28J577tEnn3yijRs3urUtUgMD4cqVK/Xyyy8rISFB48eP15VXXqnIyEh17dpVS5cu1W9/+1t3txMAAMBj3H0JYV2Gh39u8uTJevfdd/Xvf/9bnTt3dpaHhYXpxIkTOnz4sMtZwgMHDigsLKzO22/QkPEPP/ygHj16SDp1veAPP/wgSbriiiv073//uyGbBAAAaLJ8bDa3TnVljNHkyZP11ltvad26derevbvL64MGDVLLli21du1aZ9muXbu0d+9excXF1bmeBp0h7NGjh7788kt16dJFffr00euvv67Y2FitXLmy2hg2AABAc+eph0nfc889evXVV7VixQoFBQU5rwsMCQlRQECAQkJCNGHCBKWmpqpt27YKDg7WlClTFBcXV+cbSqR6BsI9e/aoW7duGj9+vAoKCnTVVVdp2rRpGjt2rBYsWKCKigrNnTu3fnsKAADQxPnIM4+dyc7OliQlJCS4lC9evFhJSUmSpOeee04+Pj666aabXB5MXR/1CoQ9e/bU/v37dd9990mSbr31Vs2bN0+FhYXKy8tTZGSkoqOj69UAAACAps5TX2VsjKl1GX9/f2VmZiozM7PB9dQrEJ7ZqFWrVik9PV09evRQ165dG9wIAACApqyFux9E2MSc94OpAQAAvJ2nzhA2lnoFQpvNJtsZR+TMeQAAAG9TnzuDm6N6DxknJSU5n5tz/PhxTZo0SQ6Hw2W5N998030tBAAA8DBf786D9QuE48aNc5m/44473NoYAACApsjbR0TrFQgXL158odoBAADQZHl3HOSmEgAAgFr5coYQAADA2rw8DxIIAQAAasM1hAAAABbHkDEAAIDFeXccJBACAADUiiFjAAAAi2PIGAAAwOK8Ow4SCAEAAGrl5ScICYQAAAC1YcgYAADA4mxePmhMIAQAAKiFl58gJBACAADUhiFjNMjrnxzwdBPgBYb1btfodfrbfRq9TsAdRvZo/PcLrMPL8yCBEAAAoDZcQwgAAGBxDBkDAABYnJfnQQIhAABAbRgyBgAAsDiGjAEAACzOy/MggRAAAKA2Xp4HCYQAAAC1YcgYAADA6rw7DxIIAQAAauPtdxnzHVUAAAC18LG5d6qPf//73xo7dqwuuugi2Ww2vf322y6vG2P02GOPKTw8XAEBARo5cqQ+//zz+u1f/ZoEAABgQTY3T/Vw9OhRxcTEKDMzs8bXZ8+erXnz5mnhwoXavHmzHA6HEhMTdfz48TrXwZAxAABALTw5ZDx69GiNHj26xteMMXr++ef1yCOP6IYbbpAkvfzyy+rYsaPefvtt3XbbbXWqgzOEAAAAtXD3kHF5eblKS0tdpvLy8nq368svv1RJSYlGjhzpLAsJCdHll1+u3Nzcuu9fvWsGAACwGjcPGaenpyskJMRlSk9Pr3ezSkpKJEkdO3Z0Ke/YsaPztbpgyBgAAKAW7h4yTktLU2pqqkuZn5+fW+uoDwIhAABALep7Z3Bt/Pz83BIAw8LCJEkHDhxQeHi4s/zAgQMaOHBgnbfDkDEAAEBtPHiX8bl0795dYWFhWrt2rbOstLRUmzdvVlxcXJ23wxlCAACAWnjyLuOysjLt3r3bOf/ll18qPz9fbdu2VZcuXZSSkqKZM2eqZ8+e6t69ux599FFddNFFuvHGG+tcB4EQAACgFu4eMq6Pjz76SMOGDXPOn772cNy4cXrppZc0depUHT16VHfffbcOHz6sK664Qu+99578/f3rXAeBEAAAoDYeDIQJCQkyxpz1dZvNpieeeEJPPPFEg+sgEAIAANTC27/LmEAIAABQC08OGTcGAiEAAEBtCIQAAADWxpAxAACAxTFkDAAAYHUEQgAAAGtjyBgAAMDiGDIGAACwOi8PhD6erDwpKUk2m63a9PPv62uIhIQEpaSkuKWNP/zwg6ZMmaLevXsrICBAXbp00R/+8AcdOXLELdsHAABNn83N/5oaj58hHDVqlBYvXuxSFhoa6qHWuDpx4oSKi4tVXFysZ599VlFRUfrqq680adIkFRcX64033vB0EwEAQCPw9iFjj54hlCQ/Pz+FhYW5TBkZGRowYIAcDociIiKUnJyssrIyl/VycnKUkJCgwMBAtWnTRomJiTp06JCSkpK0YcMGZWRkOM84FhUVSZI2bNig2NhY+fn5KTw8XNOmTdPJkyed20xISNDkyZOVkpKi9u3bKzExUf3799fy5cs1duxYXXzxxRo+fLieeuoprVy50mVdAADgxWxunpoYjwfCmvj4+GjevHnasWOHlixZonXr1mnq1KnO1/Pz8zVixAhFRUUpNzdXGzdu1NixY1VZWamMjAzFxcVp4sSJ2r9/v/bv36+IiAh98803GjNmjC677DIVFBQoOztbixYt0syZM13qXrJkiex2u3JycrRw4cIa23fkyBEFBwerRQuPn2AFAACNgCHjC+zdd99Vq1atnPOjR4/WsmXLnPPdunXTzJkzNWnSJGVlZUmSZs+ercGDBzvnJalfv37On+12uwIDAxUWFuYsy8rKUkREhBYsWCCbzaY+ffqouLhYDz30kB577DH5+JzKxj179tTs2bPP2t6DBw/qySef1N13333+Ow8AAJoFbx8y9nggHDZsmLKzs53zDodDa9asUXp6ugoLC1VaWqqTJ0/q+PHjOnbsmAIDA5Wfn6+bb765XvXs3LlTcXFxstn+7zcaHx+vsrIyff311+rSpYskadCgQWfdRmlpqa699lpFRUVpxowZ9dtRAADQbNm8PBB6fMjY4XAoMjLSOZWXl+u6665TdHS0li9frry8PGVmZko6dZOHJAUEBFzQ9tTkxx9/1KhRoxQUFKS33npLLVu2vGBtAAAATY13X0To8UB4pry8PFVVVWnOnDkaMmSIevXqpeLiYpdloqOjtXbt2rNuw263q7Ky0qWsb9++ys3NlTHGWZaTk6OgoCB17tz5nG0qLS3VNddcI7vdrnfeeUf+/v4N2DMAANBc+djcOzU1TS4QRkZGqqKiQvPnz9eePXv0yiuvVLu5Iy0tTVu2bFFycrK2bdumwsJCZWdn6+DBg5JOXXe4efNmFRUV6eDBg6qqqlJycrL27dunKVOmqLCwUCtWrND06dOVmprqvH6wJqfD4NGjR7Vo0SKVlpaqpKREJSUl1UInAADwTjabe6empskFwpiYGM2dO1ezZs1S//79tXTpUqWnp7ss06tXL61evVoFBQWKjY1VXFycVqxY4bzr94EHHpCvr6+ioqIUGhqqvXv3qlOnTlq1apU+/PBDxcTEaNKkSZowYYIeeeSRc7bn448/1ubNm7V9+3ZFRkYqPDzcOe3bt++CHQcAANB0ePtdxjbz8zFUuM3vl3/q6SbAC2TfFNXodX524Fij1wnv06tjYKPXua7w+0avE95neJ92NZYfKK1waz0dg5vWvQgev8sYAACgqWuKw7zuRCAEAACoRVMc5nUnAiEAAEAtOEMIAABgcQRCAAAAi2PIGAAAwOI4QwgAAGBxBEIAAACLY8gYAADA4pri9w+7E4EQAACgNl4eCJvcdxkDAAA0NZ7+LuPMzEx169ZN/v7+uvzyy/Xhhx+6df8IhAAAALXwsbl3qo+///3vSk1N1fTp0/Xxxx8rJiZGiYmJ+vbbb923f27bEgAAgLeyuXmqh7lz52rixIkaP368oqKitHDhQgUGBurFF190w46dwjWEAAAAtXD3Xcbl5eUqLy93KfPz85Ofn59L2YkTJ5SXl6e0tDRnmY+Pj0aOHKnc3Fy3tYczhAAAALUIaOneKT09XSEhIS5Tenp6tXoPHjyoyspKdezY0aW8Y8eOKikpcdv+cYYQAACgkaWlpSk1NdWl7Myzg42JQAgAANDIahoerkn79u3l6+urAwcOuJQfOHBAYWFhbmsPQ8YAAABNlN1u16BBg7R27VpnWVVVldauXau4uDi31cMZQgAAgCYsNTVV48aN0+DBgxUbG6vnn39eR48e1fjx491WB4EQAACgCbv11lv13Xff6bHHHlNJSYkGDhyo9957r9qNJueDQAgAANDETZ48WZMnT75g2+caQgAAAIsjEAIAAFgcgRAAAMDiCIQAAAAWRyAEAACwOAIhAACAxREIAQAALI5ACAAAYHEEQgAAAIsjEAIAAFgcgRAAAMDi+C7jC2TioM6ebgLQIDGjpnq6CfACP21d0Oh1Fh/9qdHrBLwFZwgBAAAsjkAIAABgcQRCAAAAiyMQAgAAWByBEAAAwOIIhAAAABZHIAQAALA4AiEAAIDFEQgBAAAsjkAIAABgcQRCAAAAiyMQAgAAWByBEAAAwOIIhAAAABZHIAQAALA4AiEAAIDFEQgBAAAsjkAIAABgcQRCAAAAiyMQAgAAWByBEAAAwOIIhAAAABZHIAQAALA4AiEAAIDFEQgBAAAsjkAIAABgcQRCAAAAiyMQAgAAWByBEAAAwOIIhAAAABbn0UCYlJQkm81Wbdq9e/d5bTchIUEpKSnuaaSkP//5z0pISFBwcLBsNpsOHz7stm0DAAB4msfPEI4aNUr79+93mbp37+7pZkmSTpw4IUk6duyYRo0apYcfftjDLQIAAHA/jwdCPz8/hYWFuUwZGRkaMGCAHA6HIiIilJycrLKyMpf1cnJylJCQoMDAQLVp00aJiYk6dOiQkpKStGHDBmVkZDjPOBYVFUmSNmzYoNjYWPn5+Sk8PFzTpk3TyZMnndtMSEjQ5MmTlZKSovbt2ysxMVGSlJKSomnTpmnIkCGNdlwAAAAai8cDYU18fHw0b9487dixQ0uWLNG6des0depU5+v5+fkaMWKEoqKilJubq40bN2rs2LGqrKxURkaG4uLiNHHiROcZx4iICH3zzTcaM2aMLrvsMhUUFCg7O1uLFi3SzJkzXepesmSJ7Ha7cnJytHDhwsbedQAAgEbXwtMNePfdd9WqVSvn/OjRo7Vs2TLnfLdu3TRz5kxNmjRJWVlZkqTZs2dr8ODBznlJ6tevn/Nnu92uwMBAhYWFOcuysrIUERGhBQsWyGazqU+fPiouLtZDDz2kxx57TD4+p7Jxz549NXv27Au2vwAAAE2NxwPhsGHDlJ2d7Zx3OBxas2aN0tPTVVhYqNLSUp08eVLHjx/XsWPHFBgYqPz8fN188831qmfnzp2Ki4uTzWZzlsXHx6usrExff/21unTpIkkaNGiQe3YMAACgmfD4kLHD4VBkZKRzKi8v13XXXafo6GgtX75ceXl5yszMlPR/N3kEBARc0PYAAABYiccD4Zny8vJUVVWlOXPmaMiQIerVq5eKi4tdlomOjtbatWvPug273a7KykqXsr59+yo3N1fGGGdZTk6OgoKC1LlzZ/fuBAAAQDPS5AJhZGSkKioqNH/+fO3Zs0evvPJKtZs70tLStGXLFiUnJ2vbtm0qLCxUdna2Dh48KOnUdYebN29WUVGRDh48qKqqKiUnJ2vfvn2aMmWKCgsLtWLFCk2fPl2pqanO6wfPpqSkRPn5+c7nI27fvl35+fn64YcfLsxBAAAAaERNLhDGxMRo7ty5mjVrlvr376+lS5cqPT3dZZlevXpp9erVKigoUGxsrOLi4rRixQq1aHHqksgHHnhAvr6+ioqKUmhoqPbu3atOnTpp1apV+vDDDxUTE6NJkyZpwoQJeuSRR2pt08KFC3XJJZdo4sSJkqShQ4fqkksu0TvvvOP+AwAAANDIbObnY6hwm4+LSj3dBHiBS7sFN3qdAZdMbvQ64X1+2rqg0ev8W97XjV4nvM8dg6x5GVmTO0MIAACAxkUgBAAAsDgCIQAAgMURCAEAACyOQAgAAGBxBEIAAACLIxACAABYHIEQAADA4giEAAAAFkcgBAAAsDgCIQAAgMURCAEAACyOQAgAAGBxBEIAAACLIxACAABYHIEQAADA4giEAAAAFkcgBAAAsDgCIQAAgMURCAEAACyOQAgAAGBxBEIAAACLIxACAABYHIEQAADA4giEAAAAFkcgBAAAsDgCIQAAgMURCAEAACyOQAgAAGBxBEIAAACLIxACAABYHIEQAADA4mzGGOPpRgAAAMBzOEMIAABgcQRCAAAAiyMQAgAAWByBEAAAwOIIhAAAABZHIESz9oc//EGDBg2Sn5+fBg4c6OnmAHVG30VzRd/1TgRCuNWJEycavc7f/e53uvXWWxu9XngX+i6aK/ou3IFAiHP68ccf9dvf/lYOh0Ph4eF67rnnlJCQoJSUFElSt27d9OSTT+quu+5ScHCw7r77bknS8uXL1a9fP/n5+albt26aM2eOy3ZtNpvefvttl7LWrVvrpZdekiQVFRXJZrPptdde0y9+8Qv5+/urf//+2rBhg8s68+bN0z333KMePXpckP1H80XfRXNF34UnEAhxTqmpqcrJydE777yj999/X//5z3/08ccfuyzz7LPPKiYmRlu3btWjjz6qvLw83XLLLbrtttu0fft2zZgxQ48++qjzQ6c+HnzwQd1///3aunWr4uLiNHbsWH3//fdu2jt4M/oumiv6LjzCAGdRWlpqWrZsaZYtW+YsO3z4sAkMDDT33nuvMcaYrl27mhtvvNFlvdtvv91cffXVLmUPPvigiYqKcs5LMm+99ZbLMiEhIWbx4sXGGGO+/PJLI8k8/fTTztcrKipM586dzaxZs6q1dfr06SYmJqYBewlvRN9Fc0XfhadwhhBntWfPHlVUVCg2NtZZFhISot69e7ssN3jwYJf5nTt3Kj4+3qUsPj5en3/+uSorK+vVhri4OOfPLVq00ODBg7Vz5856bQPWQ99Fc0XfhacQCHHeHA5Hvdex2WwyZ3yNdkVFhbuaBNQJfRfNFX0X7kYgxFn16NFDLVu21JYtW5xlR44c0WeffXbO9fr27aucnByXspycHPXq1Uu+vr6SpNDQUO3fv9/5+ueff65jx45V29amTZucP588eVJ5eXnq27dvg/YH1kHfRXNF34WntPB0A9B0BQUFady4cXrwwQfVtm1bdejQQdOnT5ePj49sNttZ17v//vt12WWX6cknn9Stt96q3NxcLViwQFlZWc5lhg8frgULFiguLk6VlZV66KGH1LJly2rbyszMVM+ePdW3b18999xzOnTokH73u985X9+9e7fKyspUUlKin376Sfn5+ZKkqKgo2e129x0MNCv0XTRX9F14jIevYUQTV1paam6//XYTGBhowsLCzNy5c01sbKyZNm2aMebUxc3PPfdctfXeeOMNExUVZVq2bGm6dOlinnnmGZfXv/nmG3PNNdcYh8NhevbsaVatWlXjxc2vvvqqiY2NNXa73URFRZl169a5bOeqq64ykqpNX3755YU4HGhG6Ltorui78ASbMWdcUACcw9GjR9WpUyfNmTNHEyZMuGD1FBUVqXv37tq6dStPwodb0HfRXNF30RgYMsY5bd26VYWFhYqNjdWRI0f0xBNPSJJuuOEGD7cMODf6Lpor+i48gUCIWj377LPatWuX7Ha7Bg0apP/85z9q3769p5sF1Iq+i+aKvovGxpAxAACAxfHYGQAAAIsjEAIAAFgcgRAAAMDiCIQAAAAWRyAEAACwOAIh6q2kpET33nuvIiMj5e/vr44dOyo+Pl7Z2dk1fi8m0FTQd9Fc0XdxofEcQtTLnj17FB8fr9atW+tPf/qTBgwYID8/P23fvl1//vOf1alTJ11//fXV1quoqKjxOzOBxkLfRXNF30Wj8Ow356G5SUxMNJ07dzZlZWU1vl5VVWWMMUaSycrKMmPHjjWBgYFm+vTpxhhjsrKyTI8ePUzLli1Nr169zMsvv+xc9/T3aG7dutVZdujQISPJrF+/3hhjzPr1640k8+6775oBAwYYPz8/c/nll5vt27dfkP2F96Dvormi76IxEAhRZwcPHjQ2m82kp6fXuqwk06FDB/Piiy+aL774wnz11VfmzTffNC1btjSZmZlm165dZs6cOcbX19f5xen1+WDq27evWb16tdm2bZu57rrrTLdu3cyJEycuxG7DC9B30VzRd9FYCISos02bNhlJ5s0333Qpb9eunXE4HMbhcJipU6caY059MKWkpLgs94tf/MJMnDjRpezmm282Y8aMMcbU74Pptddecy7z/fffm4CAAPP3v//dXbsKL0PfRXNF30Vj4aYSnLcPP/xQ+fn56tevn8rLy53lgwcPdllu586dio+PdymLj4/Xzp07611nXFyc8+e2bduqd+/eDdoOrI2+i+aKvgt346YS1FlkZKRsNpt27drlUt6jRw9JUkBAgEu5w+Go1/Z9fE79/8T87Ou1KyoqGtJUwAV9F80VfReNhTOEqLN27drp6quv1oIFC3T06NF6r9+3b1/l5OS4lOXk5CgqKkqSFBoaKknav3+/8/X8/Pwat7Vp0ybnz4cOHdJnn32mvn371rtNsAb6Lpor+i4aC2cIUS9ZWVmKj4/X4MGDNWPGDEVHR8vHx0dbtmxRYWGhBg0adNZ1H3zwQd1yyy265JJLNHLkSK1cuVJvvvmm1qxZI+nU/3SHDBmip59+Wt27d9e3336rRx55pMZtPfHEE2rXrp06duyoP/7xj2rfvr1uvPHGC7HL8BL0XTRX9F00Cg9fw4hmqLi42EyePNl0797dtGzZ0rRq1crExsaaZ555xhw9etQYc+ri5rfeeqvauud6/IExxnz66acmLi7OBAQEmIEDB5rVq1fXeHHzypUrTb9+/YzdbjexsbGmoKDgQu82vAB9F80VfRcXms2Yn104ADRxH3zwgYYNG6ZDhw6pdevWnm4OUGf0XTRX9F1r4BpCAAAAiyMQAgAAWBxDxgAAABbHGUIAAACLIxACAABYHIEQAADA4giEAAAAFkcgBAAAsDgCIQAAgMURCAEAACyOQAgAAGBxBEIAAACL+/8Ap7b6HERXTMYAAAAASUVORK5CYII=\n"
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "mfx.plot_weights_correlation(model, factors=list(range(5)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "RJdBfMxpiHhP",
        "outputId": "7ebcc9c4-64e6-40f3-ca0d-f37b05f3b9ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1100x900 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "variance_explained_df = pd.DataFrame(mfx.mofa_model.get_variance_explained(model))"
      ],
      "metadata": {
        "id": "bE3teiAciM1K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mofa_features_df = pd.DataFrame(mfx.mofa_model.get_features(model))"
      ],
      "metadata": {
        "id": "KARUQ798icsD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "variance_explained_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "jE6ei1MxiP6S",
        "outputId": "bc96d96d-1fc6-4f39-cacd-8fb17ac45190"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Factor               View   Group         R2\n",
              "0   Factor1         proteomics  group1  10.185313\n",
              "1   Factor1         lipidomics  group1  46.564318\n",
              "2   Factor1  phosphoproteomics  group1  16.784940\n",
              "3   Factor2         proteomics  group1  23.776308\n",
              "4   Factor2         lipidomics  group1   7.065490\n",
              "5   Factor2  phosphoproteomics  group1  12.936879\n",
              "6   Factor3         proteomics  group1  12.739802\n",
              "7   Factor3         lipidomics  group1   8.921560\n",
              "8   Factor3  phosphoproteomics  group1  21.683354\n",
              "9   Factor4         proteomics  group1   8.080041\n",
              "10  Factor4         lipidomics  group1  18.938944\n",
              "11  Factor4  phosphoproteomics  group1  14.679820\n",
              "12  Factor5         proteomics  group1   9.700683\n",
              "13  Factor5         lipidomics  group1   1.524241\n",
              "14  Factor5  phosphoproteomics  group1   4.601165"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b2c0f230-9ec0-4349-8497-aefc136c40f5\" class=\"colab-df-container\">\n",
              "    <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>Factor</th>\n",
              "      <th>View</th>\n",
              "      <th>Group</th>\n",
              "      <th>R2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Factor1</td>\n",
              "      <td>proteomics</td>\n",
              "      <td>group1</td>\n",
              "      <td>10.185313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Factor1</td>\n",
              "      <td>lipidomics</td>\n",
              "      <td>group1</td>\n",
              "      <td>46.564318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Factor1</td>\n",
              "      <td>phosphoproteomics</td>\n",
              "      <td>group1</td>\n",
              "      <td>16.784940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Factor2</td>\n",
              "      <td>proteomics</td>\n",
              "      <td>group1</td>\n",
              "      <td>23.776308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Factor2</td>\n",
              "      <td>lipidomics</td>\n",
              "      <td>group1</td>\n",
              "      <td>7.065490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Factor2</td>\n",
              "      <td>phosphoproteomics</td>\n",
              "      <td>group1</td>\n",
              "      <td>12.936879</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Factor3</td>\n",
              "      <td>proteomics</td>\n",
              "      <td>group1</td>\n",
              "      <td>12.739802</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Factor3</td>\n",
              "      <td>lipidomics</td>\n",
              "      <td>group1</td>\n",
              "      <td>8.921560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Factor3</td>\n",
              "      <td>phosphoproteomics</td>\n",
              "      <td>group1</td>\n",
              "      <td>21.683354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Factor4</td>\n",
              "      <td>proteomics</td>\n",
              "      <td>group1</td>\n",
              "      <td>8.080041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Factor4</td>\n",
              "      <td>lipidomics</td>\n",
              "      <td>group1</td>\n",
              "      <td>18.938944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Factor4</td>\n",
              "      <td>phosphoproteomics</td>\n",
              "      <td>group1</td>\n",
              "      <td>14.679820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Factor5</td>\n",
              "      <td>proteomics</td>\n",
              "      <td>group1</td>\n",
              "      <td>9.700683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Factor5</td>\n",
              "      <td>lipidomics</td>\n",
              "      <td>group1</td>\n",
              "      <td>1.524241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Factor5</td>\n",
              "      <td>phosphoproteomics</td>\n",
              "      <td>group1</td>\n",
              "      <td>4.601165</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
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              "variable_name": "variance_explained_df",
              "summary": "{\n  \"name\": \"variance_explained_df\",\n  \"rows\": 15,\n  \"fields\": [\n    {\n      \"column\": \"Factor\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Factor2\",\n          \"Factor5\",\n          \"Factor3\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"View\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"proteomics\",\n          \"lipidomics\",\n          \"phosphoproteomics\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Group\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"group1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.796338672017757,\n        \"min\": 1.5242414897971734,\n        \"max\": 46.564317598170135,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          8.080040726230086\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mofa_features_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "7WoUVy6Vinuu",
        "outputId": "54cebbad-6226-4fcb-a686-19f2a06a28e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             view         feature\n",
              "0      lipidomics   OAHFA(37:3)+H\n",
              "1      lipidomics   WE(O-60:7)+Na\n",
              "2      lipidomics   OAHFA(37:2)+H\n",
              "3      lipidomics   OAHFA(37:2)-H\n",
              "4      lipidomics  WE(O-25:1)+NH4\n",
              "...           ...             ...\n",
              "27427  proteomics          O95218\n",
              "27428  proteomics          O43264\n",
              "27429  proteomics          Q9C0D3\n",
              "27430  proteomics          Q15942\n",
              "27431  proteomics          O43149\n",
              "\n",
              "[27432 rows x 2 columns]"
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              "\n",
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              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "mofa_features_df",
              "summary": "{\n  \"name\": \"mofa_features_df\",\n  \"rows\": 27432,\n  \"fields\": [\n    {\n      \"column\": \"view\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"lipidomics\",\n          \"phosphoproteomics\",\n          \"proteomics\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 27390,\n        \"samples\": [\n          \"A0A1B0GTU7\",\n          \"MAP4_S2086_M1\",\n          \"DFFA_Y27_M1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mofa_weights_df = pd.DataFrame(mfx.mofa_model.get_weights(model))"
      ],
      "metadata": {
        "id": "3vmtvHmlis_6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mofa_weights_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "5SjfofmZi6AE",
        "outputId": "2141e3da-d092-4ff7-e1e9-6e48811e56b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "27431 -0.005258 -0.432119  0.066860 -0.001893 -0.089535\n",
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              "      <td>0.023448</td>\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mfx.mofa_model.get_variance_explained(model)"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "ySSnVgM0kZ_7",
        "outputId": "a24792ca-cc90-4e0f-fa94-ae38ea519229"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Factor               View   Group         R2\n",
              "0   Factor1         proteomics  group1  10.185313\n",
              "1   Factor1         lipidomics  group1  46.564318\n",
              "2   Factor1  phosphoproteomics  group1  16.784940\n",
              "3   Factor2         proteomics  group1  23.776308\n",
              "4   Factor2         lipidomics  group1   7.065490\n",
              "5   Factor2  phosphoproteomics  group1  12.936879\n",
              "6   Factor3         proteomics  group1  12.739802\n",
              "7   Factor3         lipidomics  group1   8.921560\n",
              "8   Factor3  phosphoproteomics  group1  21.683354\n",
              "9   Factor4         proteomics  group1   8.080041\n",
              "10  Factor4         lipidomics  group1  18.938944\n",
              "11  Factor4  phosphoproteomics  group1  14.679820\n",
              "12  Factor5         proteomics  group1   9.700683\n",
              "13  Factor5         lipidomics  group1   1.524241\n",
              "14  Factor5  phosphoproteomics  group1   4.601165"
            ],
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              "      <th>Factor</th>\n",
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              "      <td>46.564318</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Factor1</td>\n",
              "      <td>phosphoproteomics</td>\n",
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              "      <th>4</th>\n",
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              "      <td>group1</td>\n",
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              "      <th>5</th>\n",
              "      <td>Factor2</td>\n",
              "      <td>phosphoproteomics</td>\n",
              "      <td>group1</td>\n",
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              "      <td>12.739802</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Factor3</td>\n",
              "      <td>lipidomics</td>\n",
              "      <td>group1</td>\n",
              "      <td>8.921560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Factor3</td>\n",
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              "      <td>group1</td>\n",
              "      <td>21.683354</td>\n",
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              "      <th>9</th>\n",
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              "      <td>18.938944</td>\n",
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              "      <td>group1</td>\n",
              "      <td>14.679820</td>\n",
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              "      <td>9.700683</td>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e0a277bd-1992-454c-b9ed-5da345489094 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"mfx\",\n  \"rows\": 15,\n  \"fields\": [\n    {\n      \"column\": \"Factor\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Factor2\",\n          \"Factor5\",\n          \"Factor3\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"View\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"proteomics\",\n          \"lipidomics\",\n          \"phosphoproteomics\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Group\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"group1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.796338672017757,\n        \"min\": 1.5242414897971734,\n        \"max\": 46.564317598170135,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          8.080040726230086\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mfx.mofa_model.get_views_contributions(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "ucW5_qPikiyp",
        "outputId": "92a5b3c3-dd29-4fce-915e-49c76f2d2b4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "View      proteomics  lipidomics  phosphoproteomics\n",
              "sample                                             \n",
              "HFpEF_11    0.225833    0.485881           0.288286\n",
              "HFpEF_8     0.312294    0.342607           0.345100\n",
              "HFpEF_1     0.473739    0.225436           0.300825\n",
              "HFpEF_3     0.341376    0.297788           0.360836\n",
              "HFpEF_6     0.404957    0.216491           0.378551"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>View</th>\n",
              "      <th>proteomics</th>\n",
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              "  <tbody>\n",
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              "      <th>HFpEF_11</th>\n",
              "      <td>0.225833</td>\n",
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              "      <td>0.288286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HFpEF_8</th>\n",
              "      <td>0.312294</td>\n",
              "      <td>0.342607</td>\n",
              "      <td>0.345100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HFpEF_1</th>\n",
              "      <td>0.473739</td>\n",
              "      <td>0.225436</td>\n",
              "      <td>0.300825</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HFpEF_3</th>\n",
              "      <td>0.341376</td>\n",
              "      <td>0.297788</td>\n",
              "      <td>0.360836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HFpEF_6</th>\n",
              "      <td>0.404957</td>\n",
              "      <td>0.216491</td>\n",
              "      <td>0.378551</td>\n",
              "    </tr>\n",
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              "\n",
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              "      buttonEl.style.display =\n",
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              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c560fc28-0427-4e96-ad51-8c2eb2d9c290 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"mfx\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"sample\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"HFpEF_8\",\n          \"HFpEF_6\",\n          \"HFpEF_1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"proteomics\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0938504702527079,\n        \"min\": 0.2258334153110708,\n        \"max\": 0.47373883968568375,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.31229357889779163,\n          0.40495726034181545,\n          0.47373883968568375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lipidomics\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.10951589417934447,\n        \"min\": 0.21649139530618353,\n        \"max\": 0.48588069105245796,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3426067444068212,\n          0.21649139530618353,\n          0.22543649715671601\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"phosphoproteomics\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03878140344616725,\n        \"min\": 0.28828589363647134,\n        \"max\": 0.378551344352001,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3450996766953872,\n          0.378551344352001,\n          0.30082466315760026\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    }
  ]
}