docs/ReporterScreen_api.ipynb

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      },
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1tsbLl_yB9lcC_lo4sjwEoI0r1KAjPkoB)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qETZ0KgbbJIe"
      },
      "source": [
        "# CRISPR Reporter Screen analysis with bean"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GQv2gC9IbOak"
      },
      "source": [
        "In this tutorial, we will cover\n",
        "*  Obtaining target variant editing rate from allele count information\n",
        "*  ReporterScreen object slicing (indexing)\n",
        "*  Allele filtering based on significance\n",
        "\n",
        "For the basic API of ReporterScreen object, see [**perturb-tools tutorial of Screen API**](https://github.com/pinellolab/perturb-tools/blob/main/perturb_tools/screen_demo.ipynb)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 1000
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        "id": "b_HqduZRy3DY",
        "outputId": "eec31693-93b0-4266-8c30-a9902e7da4cd"
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Building wheels for collected packages: beans, perturb-tools\n",
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            "  Created wheel for beans: filename=beans-0.1.2-cp37-cp37m-linux_x86_64.whl size=506725 sha256=43eb6b2b41a112f7ea2d37a17e1e9bd91dffd4599710f3958769f84e59b3e6b8\n",
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            "Successfully built beans perturb-tools\n",
            "Installing collected packages: soupsieve, licorice, requests, pyrequisites, fonttools, beautifulsoup4, pool-sharq, matplotlib, biopython, anndata, perturb-tools, beans\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
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            "    Found existing installation: beautifulsoup4 4.6.3\n",
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            "  Attempting uninstall: matplotlib\n",
            "    Found existing installation: matplotlib 3.2.2\n",
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            "      Successfully uninstalled matplotlib-3.2.2\n",
            "Successfully installed anndata-0.8.0 beautifulsoup4-4.11.1 beans-0.1.2 biopython-1.79 fonttools-4.37.3 licorice-0.0.3 matplotlib-3.5.3 perturb-tools-0.1.4 pool-sharq-0.0.12 pyrequisites-0.0.2 requests-2.28.1 soupsieve-2.3.2.post1\n"
          ]
        },
        {
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "matplotlib",
                  "mpl_toolkits"
                ]
              }
            }
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "! pip install beans==0.1.2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "q4U4twaAzLPs"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import bean as be"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8KhlwSn_2x9P",
        "outputId": "1fc11a18-6e01-403d-9e08-73da37eee332"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=18Azb8YmmMvFZo9urc2TxZr540xXWOv_v\n",
            "To: /content/bean_count_072121_ABE_topbot_LDLvar.h5ad\n",
            "100% 40.2M/40.2M [00:00<00:00, 160MB/s]\n"
          ]
        }
      ],
      "source": [
        "!gdown 18Azb8YmmMvFZo9urc2TxZr540xXWOv_v"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "yBRxkKbLbD4F"
      },
      "outputs": [],
      "source": [
        "bdata = be.read_h5ad(\"bean_count_072121_ABE_topbot_LDLvar.h5ad\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
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        "id": "4Q6jF9DgbD4K",
        "outputId": "7abf9b29-2755-456e-fd62-5cb69a4fc27c"
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>165510</th>\n",
              "      <td>rs113408797_Maj_ABE_41_g1</td>\n",
              "      <td>-8:1:+:A&gt;G,-1:8:+:A&gt;G,3:12:+:A&gt;G,5:14:+:A&gt;G,17...</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>165511 rows × 14 columns</p>\n",
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              "                                                     [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",
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              "  "
            ],
            "text/plain": [
              "                               guide  \\\n",
              "0                       LDLR_SA_3_g5   \n",
              "1                       LDLR_SA_3_g5   \n",
              "2                       LDLR_SA_3_g5   \n",
              "3                       LDLR_SA_3_g5   \n",
              "4                       LDLR_SA_3_g5   \n",
              "...                              ...   \n",
              "165506  2:164588224GAG_Maj_ABE_10_g3   \n",
              "165507  2:164588224GAG_Maj_ABE_10_g3   \n",
              "165508      rs4921914_Min_ABE_501_g4   \n",
              "165509    rs191388787_Maj_ABE_121_g2   \n",
              "165510     rs113408797_Maj_ABE_41_g1   \n",
              "\n",
              "                                                   allele  rep1_bot  rep2_bot  \\\n",
              "0                                                                26        13   \n",
              "1                                              0:13:+:A>G         6        16   \n",
              "2                                  -12:1:+:A>G,0:13:+:A>G         2        16   \n",
              "3                                   0:13:+:A>G,7:20:+:A>G         2         0   \n",
              "4           -12:1:+:A>G,-8:5:+:A>G,0:13:+:A>G,10:23:+:A>G         1         0   \n",
              "...                                                   ...       ...       ...   \n",
              "165506                             -10:1:+:C>T,-8:3:+:A>T         0         0   \n",
              "165507                  -8:3:+:A>T,-3:8:+:C>A,-1:10:+:G>A         0         0   \n",
              "165508  -9:3:+:A>G,-8:4:+:A>G,-3:9:+:A>G,2:14:+:A>G,4:...         0         0   \n",
              "165509                   -7:3:+:A>G,-5:5:+:A>G,-3:7:+:A>G         0         0   \n",
              "165510  -8:1:+:A>G,-1:8:+:A>G,3:12:+:A>G,5:14:+:A>G,17...         0         0   \n",
              "\n",
              "        rep3_VPA_bot  rep4_VPA_bot  rep1_bulk  rep2_bulk  rep3_VPA_bulk  \\\n",
              "0                  9            13         60         21             27   \n",
              "1                 11            24         29         21             28   \n",
              "2                  1            24         16          5              9   \n",
              "3                  0             0          0          0              0   \n",
              "4                  0             0          0          0              0   \n",
              "...              ...           ...        ...        ...            ...   \n",
              "165506             0             0          0          0              0   \n",
              "165507             0             0          0          0              0   \n",
              "165508             0             0          0          0              0   \n",
              "165509             0             0          0          0              0   \n",
              "165510             0             0          0          0              0   \n",
              "\n",
              "        rep4_VPA_bulk  rep1_top  rep2_top  rep3_VPA_top  rep4_VPA_top  \n",
              "0                  31        32        26            43            43  \n",
              "1                  22        27        11            20            13  \n",
              "2                   4        17        12             7             3  \n",
              "3                   9         0         0             0             0  \n",
              "4                   0         0         0             0             0  \n",
              "...               ...       ...       ...           ...           ...  \n",
              "165506              0         0         0             0             1  \n",
              "165507              0         0         0             0             1  \n",
              "165508              0         0         0             0             1  \n",
              "165509              0         0         0             0             1  \n",
              "165510              0         0         0             0             1  \n",
              "\n",
              "[165511 rows x 14 columns]"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bdata.uns[\"allele_counts\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A_1ERHsEbD4R"
      },
      "source": [
        "## Subsetting & addition\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WCCNOXtQbD4S"
      },
      "source": [
        "Works as anndata, supports allele & edit count operations.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wLQd6h_ubD4S"
      },
      "source": [
        "### Subsetting & selection\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "jaUnqqXt3G2P"
      },
      "outputs": [],
      "source": [
        "bdata.samples[\"replicate\"], bdata.samples[\"sort\"] = zip(*bdata.samples.index.map(lambda s: s.rsplit(\"_\", 1)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "DDVh1WtrbD4T"
      },
      "outputs": [],
      "source": [
        "bdata_subset = bdata[:10,bdata.samples.sort == \"bulk\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "Z_s5M7L1bD4U"
      },
      "outputs": [],
      "source": [
        "bdata.uns[\"allele_counts\"] = bdata.uns['allele_counts'].loc[bdata.uns['allele_counts'].allele.map(str) != \"\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6wzebZzsbD4a"
      },
      "source": [
        "## Getting edit rates from allele counts\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "GMV9enV35HKu",
        "outputId": "e8259a37-98f1-408c-9e16-d5315c2ce06c"
      },
      "outputs": [
        {
          "data": {
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8bedf952-1ebb-4ab0-97e3-c1ac26dfa8fe button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8bedf952-1ebb-4ab0-97e3-c1ac26dfa8fe');\n",
              "          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",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                               guide  \\\n",
              "1                       LDLR_SA_3_g5   \n",
              "2                       LDLR_SA_3_g5   \n",
              "3                       LDLR_SA_3_g5   \n",
              "4                       LDLR_SA_3_g5   \n",
              "5                       LDLR_SA_3_g5   \n",
              "...                              ...   \n",
              "165506  2:164588224GAG_Maj_ABE_10_g3   \n",
              "165507  2:164588224GAG_Maj_ABE_10_g3   \n",
              "165508      rs4921914_Min_ABE_501_g4   \n",
              "165509    rs191388787_Maj_ABE_121_g2   \n",
              "165510     rs113408797_Maj_ABE_41_g1   \n",
              "\n",
              "                                                   allele  rep1_bot  rep2_bot  \\\n",
              "1                                              0:13:+:A>G         6        16   \n",
              "2                                  -12:1:+:A>G,0:13:+:A>G         2        16   \n",
              "3                                   0:13:+:A>G,7:20:+:A>G         2         0   \n",
              "4           -12:1:+:A>G,-8:5:+:A>G,0:13:+:A>G,10:23:+:A>G         1         0   \n",
              "5                       -12:1:+:A>G,0:13:+:A>G,7:20:+:A>G         1         0   \n",
              "...                                                   ...       ...       ...   \n",
              "165506                             -10:1:+:C>T,-8:3:+:A>T         0         0   \n",
              "165507                  -8:3:+:A>T,-3:8:+:C>A,-1:10:+:G>A         0         0   \n",
              "165508  -9:3:+:A>G,-8:4:+:A>G,-3:9:+:A>G,2:14:+:A>G,4:...         0         0   \n",
              "165509                   -7:3:+:A>G,-5:5:+:A>G,-3:7:+:A>G         0         0   \n",
              "165510  -8:1:+:A>G,-1:8:+:A>G,3:12:+:A>G,5:14:+:A>G,17...         0         0   \n",
              "\n",
              "        rep3_VPA_bot  rep4_VPA_bot  rep1_bulk  rep2_bulk  rep3_VPA_bulk  \\\n",
              "1                 11            24         29         21             28   \n",
              "2                  1            24         16          5              9   \n",
              "3                  0             0          0          0              0   \n",
              "4                  0             0          0          0              0   \n",
              "5                  0             0          0          2              2   \n",
              "...              ...           ...        ...        ...            ...   \n",
              "165506             0             0          0          0              0   \n",
              "165507             0             0          0          0              0   \n",
              "165508             0             0          0          0              0   \n",
              "165509             0             0          0          0              0   \n",
              "165510             0             0          0          0              0   \n",
              "\n",
              "        rep4_VPA_bulk  rep1_top  rep2_top  rep3_VPA_top  rep4_VPA_top  \n",
              "1                  22        27        11            20            13  \n",
              "2                   4        17        12             7             3  \n",
              "3                   9         0         0             0             0  \n",
              "4                   0         0         0             0             0  \n",
              "5                   0         1         0             0             0  \n",
              "...               ...       ...       ...           ...           ...  \n",
              "165506              0         0         0             0             1  \n",
              "165507              0         0         0             0             1  \n",
              "165508              0         0         0             0             1  \n",
              "165509              0         0         0             0             1  \n",
              "165510              0         0         0             0             1  \n",
              "\n",
              "[162065 rows x 14 columns]"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bdata.uns['allele_counts']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "pEX2eOem4uka"
      },
      "outputs": [],
      "source": [
        "bdata.uns[\"edit_counts\"] = bdata.get_edit_from_allele()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FwKNCaIm7JSH",
        "outputId": "f874adbf-6286-46a5-8b6b-5f80a204da58"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "New edit matrix saved in .layers['edits']. Returning old edits.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "array([[0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       ...,\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.]])"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bdata.get_edit_mat_from_uns(\"A\", \"G\", target_pos_col = \"target_pos\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "aJXnxwbb4F3G"
      },
      "outputs": [],
      "source": [
        "window_edit_rate= bdata.get_guide_edit_rate(normalize_by_editable_base = True,\n",
        "                   edited_base = \"A\",\n",
        "                   editable_base_start = 3,\n",
        "                   editable_base_end = 8,\n",
        "                   bcmatch_thres = 5,\n",
        "                   prior_weight = 1,\n",
        "                   return_result = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "dgM4MptLbD4c",
        "outputId": "94f97a73-0e8e-4ffc-9833-8634053c3f42"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(array([579., 364., 299., 249., 221., 214., 191., 134., 159., 133.,  97.,\n",
              "         90.,  83.,  76.,  70.,  67.,  48.,  53.,  45.,  56.,  38.,  37.,\n",
              "         32.,  21.,  24.,  15.,   8.,   7.,   1.,   2.]),\n",
              " array([4.13052458e-04, 2.89016995e-02, 5.73903465e-02, 8.58789936e-02,\n",
              "        1.14367641e-01, 1.42856288e-01, 1.71344935e-01, 1.99833582e-01,\n",
              "        2.28322229e-01, 2.56810876e-01, 2.85299523e-01, 3.13788170e-01,\n",
              "        3.42276817e-01, 3.70765464e-01, 3.99254111e-01, 4.27742758e-01,\n",
              "        4.56231405e-01, 4.84720052e-01, 5.13208699e-01, 5.41697346e-01,\n",
              "        5.70185993e-01, 5.98674640e-01, 6.27163287e-01, 6.55651934e-01,\n",
              "        6.84140582e-01, 7.12629229e-01, 7.41117876e-01, 7.69606523e-01,\n",
              "        7.98095170e-01, 8.26583817e-01, 8.55072464e-01]),\n",
              " <BarContainer object of 30 artists>)"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.hist(window_edit_rate, bins=30)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YkgDKePa8ldu"
      },
      "source": [
        "# Allele filtering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KB1B2sqX8qaA"
      },
      "source": [
        "If you have non-edited control data of reporter (e.g., plasmid library), you can filter out the reporter to select the significant base edits per guides. Significance is determined by Fisher's exact test."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8tDcYNhB8KGj",
        "outputId": "7f0523e8-01b7-4139-a6a2-9fb9850caf01"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=153fKJntS-4vlrodC6xlZ9oINQo1XT_i9\n",
            "To: /content/bean_count_LDLvar_plasmid.h5ad\n",
            "\r  0% 0.00/5.10M [00:00<?, ?B/s]\r100% 5.10M/5.10M [00:00<00:00, 148MB/s]\n"
          ]
        }
      ],
      "source": [
        "! gdown 153fKJntS-4vlrodC6xlZ9oINQo1XT_i9"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "DsukzmLgbD4e"
      },
      "outputs": [],
      "source": [
        "bdata_plasmid = be.read_h5ad(\"bean_count_LDLvar_plasmid.h5ad\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "Dw6JJLXf9YL3"
      },
      "outputs": [],
      "source": [
        "bdata_plasmid.uns[\"allele_counts\"] = bdata_plasmid.uns['allele_counts'].loc[bdata_plasmid.uns['allele_counts'].allele.map(str) != \"\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wZ8xiC2n9r93"
      },
      "source": [
        "Allele filtering may take few minutes depending on the number of alleles in the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180,
          "referenced_widgets": [
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        "id": "G2FRRENq9NC1",
        "outputId": "2b0c9b6a-12e7-423b-a232-a34174654f14"
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Running Fisher's exact test to get significant edits compared to control...\n",
            "Done calculating significance.\n",
            "\n",
            "\n",
            "Filtering alleles for those containing significant edits (q < 0.05)...\n",
            "Running 30 parallel processes to filter alleles...\n",
            "Done filtering alleles, merging the result...\n"
          ]
        },
        {
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              "model_id": "494439c2d852487da85750b5a52a196d",
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            "text/plain": [
              "Mapping alleles to closest filtered alleles:   0%|          | 0/3446 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "q_val_sig, bdata.uns['sig_allele_counts'] = be.filter_alleles(\n",
        "    bdata, bdata_plasmid, filter_each_sample = True, run_parallel = True, allele_counts_key=\"allele_counts\", map_to_filtered = True)"
      ]
    }
  ],
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