sc_PHENIX_try_me_example_.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zJguW7NOV6br",
        "outputId": "261e4a9e-f6a7-429f-e639-9561ffb47490"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting umap-learn\n",
            "  Downloading umap-learn-0.5.3.tar.gz (88 kB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting magic-impute\n",
            "  Downloading magic_impute-3.0.0-py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from umap-learn) (1.22.4)\n",
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            "Collecting pynndescent>=0.5\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from umap-learn) (4.65.0)\n",
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            "\u001b[?25hRequirement already satisfied: future in /usr/local/lib/python3.10/dist-packages (from magic-impute) (0.18.3)\n",
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            "\u001b[?25hCollecting tasklogger>=1.0.0\n",
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            "\u001b[?25hCollecting Deprecated\n",
            "  Downloading Deprecated-1.2.13-py2.py3-none-any.whl (9.6 kB)\n",
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            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.25->magic-impute) (2022.7.1)\n",
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            "Collecting pandas>=0.25\n",
            "  Downloading pandas-1.3.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (11.5 MB)\n",
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            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->magic-impute) (1.0.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->magic-impute) (1.4.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas>=0.25->magic-impute) (1.16.0)\n",
            "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from Deprecated->graphtools>=1.4.0->magic-impute) (1.14.1)\n",
            "Building wheels for collected packages: umap-learn, pynndescent\n",
            "  Building wheel for umap-learn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for umap-learn: filename=umap_learn-0.5.3-py3-none-any.whl size=82830 sha256=473215cc4a1e088c4e6c96be7fec815ce021eb70f76df5b197c511374dec2e9d\n",
            "  Stored in directory: /root/.cache/pip/wheels/a0/e8/c6/a37ea663620bd5200ea1ba0907ab3c217042c1d035ef606acc\n",
            "  Building wheel for pynndescent (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pynndescent: filename=pynndescent-0.5.10-py3-none-any.whl size=55640 sha256=c91124003649a733ec546741a5266101d54e95b656873bdf1a06e552693bed14\n",
            "  Stored in directory: /root/.cache/pip/wheels/4a/38/5d/f60a40a66a9512b7e5e83517ebc2d1b42d857be97d135f1096\n",
            "Successfully built umap-learn pynndescent\n",
            "Installing collected packages: Deprecated, tasklogger, pygsp, pandas, scprep, pynndescent, graphtools, umap-learn, magic-impute\n",
            "  Attempting uninstall: pandas\n",
            "    Found existing installation: pandas 1.5.3\n",
            "    Uninstalling pandas-1.5.3:\n",
            "      Successfully uninstalled pandas-1.5.3\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires pandas~=1.5.3, but you have pandas 1.3.5 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed Deprecated-1.2.13 graphtools-1.5.3 magic-impute-3.0.0 pandas-1.3.5 pygsp-0.5.1 pynndescent-0.5.10 scprep-1.2.2 tasklogger-1.2.0 umap-learn-0.5.3\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting s-gd2==1.7\n",
            "  Downloading s_gd2-1.7.tar.gz (53 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.4/53.4 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy>=1.16 in /usr/local/lib/python3.10/dist-packages (from s-gd2==1.7) (1.22.4)\n",
            "Building wheels for collected packages: s-gd2\n",
            "  Building wheel for s-gd2 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for s-gd2: filename=s_gd2-1.7-cp310-cp310-linux_x86_64.whl size=530687 sha256=8a029f59e3c3b468dd62edeb3cf2aac256e3a199d61cbef12b3928a27ad04578\n",
            "  Stored in directory: /root/.cache/pip/wheels/01/22/1b/8ad6ccdf7a37dbdd2a19afd4958d3b00516c88de69e5860f2d\n",
            "Successfully built s-gd2\n",
            "Installing collected packages: s-gd2\n",
            "Successfully installed s-gd2-1.7\n"
          ]
        }
      ],
      "source": [
        "#Install  UMAP and MAGIC for comparison\n",
        "!pip install umap-learn magic-impute\n",
        "#you need to install s_gd2 https://github.com/jxz12/s_gd2 #this is version (s-gd2==1.7) is stable\n",
        "!pip install s-gd2==1.7"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "U1ZdpiddWBy4"
      },
      "outputs": [],
      "source": [
        "import requests\n",
        "import os\n",
        "import urllib.request\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import umap\n",
        "import magic"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "ft4atMTpW1w7"
      },
      "outputs": [],
      "source": [
        "#Tasic B, Menon V, Nguyen TN, Kim TK, Jarsky T, et al. (2016) Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat Neurosci 19: 335-346. DOI: 10.1038/nn.4216\n",
        "#https://singlecell.broadinstitute.org/single_cell/study/SCP6/a-transcriptomic-taxonomy-of-adult-mouse-visual-cortex-visp#study-summary\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jht6gofZYI1l",
        "outputId": "a8bcb48b-4bc0-4fc9-f4bb-2a58bb26160c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (4.6.6)\n",
            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.27.1)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.65.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from gdown) (1.16.0)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.11.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.12.0)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.4.1)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2022.12.7)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.4)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.12)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.26.15)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (4.6.6)\n",
            "Collecting gdown\n",
            "  Downloading gdown-4.7.1-py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from gdown) (1.16.0)\n",
            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.27.1)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.11.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.12.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.65.0)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.4.1)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2022.12.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.12)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.26.15)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.4)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
            "Installing collected packages: gdown\n",
            "  Attempting uninstall: gdown\n",
            "    Found existing installation: gdown 4.6.6\n",
            "    Uninstalling gdown-4.6.6:\n",
            "      Successfully uninstalled gdown-4.6.6\n",
            "Successfully installed gdown-4.7.1\n"
          ]
        }
      ],
      "source": [
        "#download the neuronal dataset\n",
        "!pip install gdown\n",
        "# to upgrade\n",
        "!pip install --upgrade gdown"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "ujgPGQgaYNTx",
        "outputId": "3691cbca-c598-4c84-dab4-f48bd9d136bc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From (uriginal): https://drive.google.com/uc?id=1RvQV_ieailND6psKjEnYewud6hcY_pMm\n",
            "From (redirected): https://drive.google.com/uc?id=1RvQV_ieailND6psKjEnYewud6hcY_pMm&confirm=t&uuid=6a4875ff-c903-4472-b852-cee68129ff96\n",
            "To: /content/expression_matrix.txt\n",
            "100%|██████████| 126M/126M [00:01<00:00, 124MB/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'expression_matrix.txt'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "import gdown\n",
        "# same as the above, but with the file ID\n",
        "id = \"1RvQV_ieailND6psKjEnYewud6hcY_pMm\"\n",
        "gdown.download(id=id, output=\"expression_matrix.txt\", quiet=False)\n",
        "\n",
        "#https://drive.google.com/file/d/1RvQV_ieailND6psKjEnYewud6hcY_pMm/view?usp=sharing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "id": "uS5ZN_e2amAB",
        "outputId": "aa867c4d-2c20-4f0c-f313-ed26b557cdb6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1lJ4oorGhW-S8Q29mv6mqBizTo7cF0eAg\n",
            "To: /content/clusters.txt\n",
            "100%|██████████| 52.6k/52.6k [00:00<00:00, 36.6MB/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'clusters.txt'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# same as the above, but with the file ID\n",
        "id = \"1lJ4oorGhW-S8Q29mv6mqBizTo7cF0eAg\"\n",
        "gdown.download(id=id, output=\"clusters.txt\", quiet=False)\n",
        "#https://drive.google.com/file/d/1lJ4oorGhW-S8Q29mv6mqBizTo7cF0eAg/view?usp=sharing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "rjsLKG5WZTt2",
        "outputId": "1606ae2a-6abf-4683-8bc9-f766377fa30b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0  0610005C13Rik  0610007C21Rik  0610007L01Rik  0610007N19Rik  \\\n",
              "A01101401             0.0          76.13           0.05            0.0   \n",
              "A01101402             0.0         512.73          10.11            0.0   \n",
              "A01101403             0.0         172.75           0.00            0.0   \n",
              "A01101404             0.0         170.56           0.02            0.0   \n",
              "A01101405             0.0         113.87           0.04            0.0   \n",
              "...                   ...            ...            ...            ...   \n",
              "Z11041427             0.0         106.52           0.00            0.0   \n",
              "Z11041428             0.0         349.73           0.00            0.0   \n",
              "Z11041429             0.0           1.54           1.17            0.0   \n",
              "Z11041430             0.0           0.54           7.65            0.0   \n",
              "Z11041431             0.0         679.19           0.12            0.0   \n",
              "\n",
              "Unnamed: 0  0610007P08Rik  0610007P14Rik  0610007P22Rik  0610008F07Rik  \\\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import umap\n",
        "#este funciona\n",
        "data = pd.read_table('/content/expression_matrix.txt')\n",
        "data = data.set_index('Unnamed: 0')\n",
        "data = data.T\n",
        "data\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "vjHaGf_Yaiex",
        "outputId": "bd81a0e1-dbcf-45a2-8352-686af148fc36"
      },
      "outputs": [
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              "1677  Z11041430  GABA_Vip    Vip_Chat\n",
              "1678  Z11041431  GABA_Vip    Vip_Chat\n",
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          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "clusters = pd.read_table('clusters.txt',skiprows=1)\n",
        "clusters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zMahUSmva5v1",
        "outputId": "d4d3846c-566d-4ad6-9fe3-6cafc306e871"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "clusters.txt  expression_matrix.txt  sample_data  sc_PHENIX.py\n"
          ]
        }
      ],
      "source": [
        "url_sc_phenix = 'https://raw.githubusercontent.com/resendislab/sc-PHENIX/main/sc-PHENIX%20tutorial%20colab/sc_PHENIX.py'\n",
        "urllib.request.urlretrieve(url_sc_phenix, 'sc_PHENIX.py')\n",
        "os.listdir()\n",
        "!cd /content\n",
        "!ls"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "70eaEeKMgkTH",
        "outputId": "f49c1a48-5d8d-4c43-ea8f-77403f88f9c6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['GABA_Vip', 'Gluta_L4', 'Gluta_L2_3', 'Gluta_L2', 'GABA_Pvalb',\n",
              "       'GABA_Ndnf', 'Gluta_L5a', 'NonNeu_SMC', 'NonNeu_Astro', 'Gluta_L5',\n",
              "       'NonNeu_Micro', 'NonNeu_Endo', 'GABA_Sst', 'Gluta_L6b',\n",
              "       'GABA_Sncg', 'GABA_Igtp', 'NonNeu_Oligo', 'GABA_Smad3',\n",
              "       'NonNeu_OPC', 'Gluta_L5b', 'Gluta_L6a'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "clusters.group.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211,
          "referenced_widgets": [
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        },
        "id": "VW7-JMnngk8h",
        "outputId": "946eaa46-8641-4a87-a05d-d560040c976c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "UMAP(angular_rp_forest=True, learning_rate=0.1, local_connectivity=3, metric='cosine', min_dist=0.5, n_epochs=1500, n_neighbors=10, random_state=42, set_op_mix_ratio=0.8, verbose=True)\n",
            "Thu May  4 02:21:21 2023 Construct fuzzy simplicial set\n",
            "Thu May  4 02:22:40 2023 Finding Nearest Neighbors\n",
            "Thu May  4 02:22:43 2023 Finished Nearest Neighbor Search\n",
            "Thu May  4 02:22:46 2023 Construct embedding\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epochs completed:   0%|            0/1500 [00:00]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5bd98d6a99204220984f83dbbde8f17b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Thu May  4 02:22:54 2023 Finished embedding\n",
            "CPU times: user 1min 28s, sys: 503 ms, total: 1min 28s\n",
            "Wall time: 1min 34s\n"
          ]
        }
      ],
      "source": [
        "from sklearn.decomposition import PCA\n",
        "pca = PCA(n_components=30, random_state=1)\n",
        "pca_data= pca.fit_transform(data)\n",
        "#### import umap\n",
        "fit = umap.UMAP(n_neighbors=10,verbose= True,metric='cosine',min_dist=0.5,n_epochs=1500,negative_sample_rate=5,set_op_mix_ratio=0.8,learning_rate=0.1,local_connectivity=3,random_state=42)\n",
        "%time u_no = fit.fit_transform(data)\n",
        "u_noimp_umap = pd.DataFrame(u_no, columns=['UMAP1', 'UMAP2'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "Zq1xDM6ZgopP",
        "outputId": "74a34808-9dc7-49d7-99fa-670c6ecee4ab"
      },
      "outputs": [
        {
          "output_type": "display_data",
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                       {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('0363f7ed-622c-41be-9a3d-301f1ead584c');\n",
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              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
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              "    x.observe(notebookContainer, {childList: true});\n",
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              "    x.observe(outputEl, {childList: true});\n",
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              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "import plotly.express as px\n",
        "fig = px.scatter(u_noimp_umap, x=\"UMAP1\", y=\"UMAP2\",color = clusters['group'])\n",
        "fig.update_layout({'plot_bgcolor':'rgba(0,0,0,0)'\n",
        "   \n",
        "})\n",
        "fig.update_traces(marker=dict(size=3))\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "J_qOK_3XheYW"
      },
      "outputs": [],
      "source": [
        "color_continuous_scale=[(0, \"rgb(240, 239, 238)\"),(0.0001, \"black\"),(0.001, \"black\"),(0.035, \"yellow\"),(0.5, \"#CCFFCC\"), (1, \"green\") ]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s1wY3KUEj_i_"
      },
      "source": [
        "#NON IMPUTATION "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "6zNCnclThf84",
        "outputId": "3ed8e677-b3d2-48e4-a052-eb659596cbfb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        }
      ],
      "source": [
        "import plotly.express as px\n",
        "fig = px.scatter(u_noimp_umap, x=\"UMAP1\", y=\"UMAP2\",color = data['Flt1'],color_continuous_scale=color_continuous_scale)\n",
        "fig.update_layout({'plot_bgcolor':'rgba(0,0,0,0)'   \n",
        "})\n",
        "fig.update_traces(marker=dict(size=3))\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5NI8FkayhzoQ"
      },
      "source": [
        "#Imputation with MAGIC\n",
        "Try this first **t = 15, n_pca = 30, knn = 15** and compared with sc-PHENIX in the orther section\n",
        "\n",
        "Try local imputation with parameters\n",
        "t = 3, n_pca = 30, knn = 3\n",
        "this means due to the kernel of MAGIC and sc-PHENIX will gene expression will be shared by approx to 3 local knn. Take in mind that local imputation can be similar to non-imputation for GSEA analysis obtening few in information about biological insights.\n",
        "\n",
        "**1. Cautions with MAGIC** few PCA dimensions can lead to a distord manifolds try 3 pca dimensions, same applies to sc-PHENIX, high dimensional space is distord in a very low dimensions, specially with linear aproaches such as PCA.\n",
        "\n",
        "**2. Cautions with MAGIC** \n",
        "Also, too many PCA dimensions and you will encounter the curse of dimensionality try 500 PCA dimensions, distinct clusters will be too close to each other and this will be reflected in over-smoothing Flt1 expression will spread to other phenotypes different from non-neuronal cells for example Endo and SMC. \n",
        "\n",
        "\n",
        "sc-PHENIX is robust agains more values of PCA dimensions(see IMPUTATION WITH sc-PHENIX) and other parameters (t and knn)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VogEMHymhzaL",
        "outputId": "e3e8c741-d41f-4a93-bf05-1291343f2709"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Calculating MAGIC...\n",
            "  Running MAGIC on 1679 cells and 24057 genes.\n",
            "  Calculating graph and diffusion operator...\n",
            "    Calculating PCA...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/magic/magic.py:425: UserWarning:\n",
            "\n",
            "Input matrix contains unexpressed genes. Please remove them prior to running MAGIC.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    Calculated PCA in 23.69 seconds.\n",
            "    Calculating KNN search...\n",
            "    Calculated KNN search in 2.26 seconds.\n",
            "    Calculating affinities...\n",
            "    Calculated affinities in 2.19 seconds.\n",
            "  Calculated graph and diffusion operator in 28.32 seconds.\n",
            "  Running MAGIC with `solver='exact'` on 24057-dimensional data may take a long time. Consider denoising specific genes with `genes=<list-like>` or using `solver='approximate'`.\n",
            "  Calculating imputation...\n",
            "  Calculated imputation in 6.55 seconds.\n",
            "Calculated MAGIC in 35.04 seconds.\n"
          ]
        }
      ],
      "source": [
        "import magic\n",
        "magic_operator = magic.MAGIC(t = 15, n_pca = 500, knn = 15, random_state=1)\n",
        "imputated_magic =magic_operator.fit_transform(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "9AlgbRAIhyOa",
        "outputId": "66921a55-7fed-4c91-c328-2fa0f70a3b21"
      },
      "outputs": [
        {
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          "metadata": {}
        }
      ],
      "source": [
        "import plotly.express as px\n",
        "fig = px.scatter(u_noimp_umap, x=\"UMAP1\", y=\"UMAP2\",color = imputated_magic['Flt1'],color_continuous_scale=color_continuous_scale)\n",
        "fig.update_layout({'plot_bgcolor':'rgba(0,0,0,0)'   \n",
        "})\n",
        "fig.update_traces(marker=dict(size=3))\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sjt-nK0ZkFLJ"
      },
      "source": [
        "#IMPUTATION WITH sc-PHENIX\n",
        "**t = 15, n_pca(n_components) = 30, knn = 15**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 229,
          "referenced_widgets": [
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            "d61a6aa5a13e420e9efeb1a0f5f17612",
            "4d7a861d87254ee2aaf8070069ec2486"
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        },
        "id": "LK3I-tLfhv-I",
        "outputId": "74f41388-975c-4080-90c1-0f27a4292565"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "process start Thu May  4 02:23:32 2023\n",
            "UMAP(angular_rp_forest=True, metric='cosine', n_components=50, n_neighbors=10, random_state=42, verbose=True)\n",
            "Thu May  4 02:23:52 2023 Construct fuzzy simplicial set\n",
            "Thu May  4 02:23:54 2023 Finding Nearest Neighbors\n",
            "Thu May  4 02:23:55 2023 Finished Nearest Neighbor Search\n",
            "Thu May  4 02:23:55 2023 Construct embedding\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epochs completed:   0%|            0/500 [00:00]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8e3116c5edd1460a948bad33c173939c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Thu May  4 02:24:08 2023 Finished embedding\n",
            "CPU times: user 14 s, sys: 241 ms, total: 14.3 s\n",
            "Wall time: 15.9 s\n"
          ]
        }
      ],
      "source": [
        "from sc_PHENIX import run_pca, run_umap, sc_PHENIX\n",
        "pca_data= run_pca(data,n_components=500, random_state=1)\n",
        "\n",
        "import umap\n",
        "#umap parameters we reduced the 500 PCA dimensions to 50 umap dimensions\n",
        "fit = umap.UMAP(n_components=50,n_neighbors=10,verbose= True,metric='cosine',random_state=42)\n",
        "%time u_no_3 = fit.fit_transform(pca_data) #u_no_3 variable is the 50 umap dimenions coordinates for sc-PHENIX\n",
        "#the default output from UMAP is a euclidean interpretable space, but can be changed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 753
        },
        "id": "flfbv1OAiQlI",
        "outputId": "7a169b91-893b-4c98-d473-39ae940fb621"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "calculando distancias\n",
            "(1679, 1679)\n",
            "done distance matrix\n",
            "knn\n",
            " knn done\n",
            " afinity matrix\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/content/sc_PHENIX.py:80: RuntimeWarning:\n",
            "\n",
            "overflow encountered in power\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " afinity matrix done\n",
            "imputation\n",
            "done\n",
            "done Thu May  4 02:24:15 2023\n"
          ]
        },
        {
          "output_type": "execute_result",
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      ],
      "source": [
        "neuro_phenix = sc_PHENIX(data, u_no_3,t=15,metric='euclidean',knn=15,decay=500)\n",
        "neuro_phenix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "9NbQydpHf6fl",
        "outputId": "76c45f16-6d00-4489-d06d-39c855ad24d4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        }
      ],
      "source": [
        "import plotly.express as px\n",
        "fig = px.scatter(u_noimp_umap, x=\"UMAP1\", y=\"UMAP2\",color = neuro_phenix['Flt1'],color_continuous_scale=color_continuous_scale)\n",
        "fig.update_layout({'plot_bgcolor':'rgba(0,0,0,0)'   \n",
        "})\n",
        "fig.update_traces(marker=dict(size=3))\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bw5Hd5Itm0da"
      },
      "source": [
        "##Visualization of the markovian matrix\n",
        "\n",
        "This section can give an idea to the user how to handle parameters visually. Also we showed the differences about the markovian matrix obtained from MAGIC and sc-PHENIX, and the explication why diffusion on PCA(a.k.a. MAGIC) tends to over-smooth data more easily than diffusion on PCA-UMAP(sc-PHENIX)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "_1Onaozfm55T"
      },
      "outputs": [],
      "source": [
        "#just used this code for getting the exp markovian matrix\n",
        "def sc_PHENIX_vis(data, umap_data,t=10,decay=15,metric='euclidean',knn=10):\n",
        "    import numpy as np\n",
        "    import pandas as pd\n",
        "    from sklearn.decomposition import PCA\n",
        "    from scipy.spatial.distance import pdist\n",
        "    from scipy.spatial.distance import squareform\n",
        "    import time\n",
        "    print('calculando distancias')\n",
        "    \n",
        "    distance_matrix =pdist(umap_data, metric)\n",
        "    distance_matrix = (squareform(distance_matrix))\n",
        "    D = distance_matrix\n",
        "    \n",
        "    print(distance_matrix.shape)\n",
        "    print('done distance matrix')\n",
        "    print('knn')\n",
        "\n",
        "    print(' knn done')\n",
        "    print(' afinity matrix')\n",
        "\n",
        "\n",
        "    n,m =D.shape\n",
        "\n",
        "    E = np.zeros((m,m))\n",
        "    \n",
        "    knn_dst = np.sort(distance_matrix, axis=1)\n",
        "    \n",
        "\n",
        "    epsilon = knn_dst[:,knn]\n",
        "    \n",
        "    \n",
        "    pdx_scale = (distance_matrix / epsilon).T \n",
        "    \n",
        "    E = np.exp(-1 * ( pdx_scale ** decay))\n",
        "    \n",
        "    print(' afinity matrix done')\n",
        "    \n",
        "    A = (E + E.T)\n",
        "    \n",
        "    diff_deg = np.diag(np.sum(A,0))\n",
        "    \n",
        "    \n",
        "    diff_op = np.dot(np.diag(np.diag(diff_deg)**(-1)),A)\n",
        "\n",
        "    new_matrix =  np.linalg.matrix_power(diff_op, t)\n",
        "    \n",
        "    return new_matrix\n",
        "\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "VvnmoO7gwfUu"
      },
      "outputs": [],
      "source": [
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "mHy65jvBwypL"
      },
      "outputs": [],
      "source": [
        "# we are making the embbeding with s_gd2\n",
        "import s_gd2\n",
        "from scipy.spatial.distance import pdist, squareform\n",
        "def sgd(D, n_components=2, random_state=None, init=None,num_dimensions=None):\n",
        "    \"\"\"Metric MDS using stochastic gradient descent\n",
        "    Parameters\n",
        "    ----------\n",
        "    D : array-like, shape=[n_samples, n_samples]\n",
        "        pairwise distances\n",
        "    n_components : int, optional (default: 2)\n",
        "        number of dimensions in which to embed `D`\n",
        "    random_state : int or None, optional (default: None)\n",
        "        numpy random state\n",
        "    init : array-like or None\n",
        "        Initialization algorithm or state to use for MMDS\n",
        "    Returns\n",
        "    -------\n",
        "    Y : array-like, embedded data [n_sample, ndim]\n",
        "    \"\"\"\n",
        "\n",
        "    N = D.shape[0]\n",
        "    D = squareform(D)\n",
        "    # Metric MDS from s_gd2\n",
        "    Y = s_gd2.mds_direct(N, D, init=init, random_seed=random_state,num_dimensions=num_dimensions)\n",
        "    return Y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "dOg2aiCV1daU"
      },
      "outputs": [],
      "source": [
        "#just visualization \n",
        "from matplotlib.colors import ListedColormap\n",
        "target=pd.Series(clusters.group)\n",
        "list_clust = list(np.arange(21))\n",
        "lut = dict(zip(clusters.group.unique(), list_clust))\n",
        "row_colors = target.map(lut)\n",
        "all_label_1= ['GABA_Vip', 'Gluta_L4', 'Gluta_L2_3', 'Gluta_L2', 'GABA_Pvalb',\n",
        "       'GABA_Ndnf', 'Gluta_L5a', 'NonNeu_SMC', 'NonNeu_Astro', 'Gluta_L5',\n",
        "       'NonNeu_Micro', 'NonNeu_Endo', 'GABA_Sst', 'Gluta_L6b',\n",
        "       'GABA_Sncg', 'GABA_Igtp', 'NonNeu_Oligo', 'GABA_Smad3',\n",
        "       'NonNeu_OPC', 'Gluta_L5b', 'Gluta_L6a']\n",
        "cmap = ListedColormap(['#FFFF91', '#0055FA', '#1C67F9', '#3E7FFE', '#F91D12',\n",
        "       '#FFA900', '#6095FC', '#6FAB91', '#C3F2DE', '#0793F0',\n",
        "       '#397159', '#0E472E', '#FFFF00', '#7FC3F1',\n",
        "       '#E06A40', '#E9B22C', '#179860', '#FAF712',\n",
        "       '#03F216', '#14DEFA', '#03F2EB'])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Visualization of the exponantiatex markovian matrix with sc-PHENIX with **Diffusion on PCA**\n",
        "Try different parameters with sc-PHENIX as MAGIC using only PCA as initialization \n",
        "\n",
        "We this parameters:\n",
        "\n",
        "```python\n",
        "\n",
        "from sc_PHENIX import run_pca, run_umap, sc_PHENIX\n",
        "pca_data= run_pca(data,n_components=500, random_state=1)\n",
        "\n",
        "import umap\n",
        "#umap parameters we reduced the 500 PCA dimensions to 50 umap dimensions\n",
        "fit = umap.UMAP(n_components=50,n_neighbors=10,verbose= True,metric='cosine',random_state=42)\n",
        "%time u_no_3 = fit.fit_transform(pca_data) #u_no_3 variable is the 50 umap dimenions coordinates for sc-PHENIX\n",
        "#the default output from UMAP is a euclidean interpretable space, but can be changed.\n",
        "\n",
        "```\n",
        "you will observe that  subclusters are separated easily (well defined local structure) and global structure is more defined than MAGIC(next section)\n",
        "\n",
        "```python\n",
        "We this parameters:\n",
        "#We need to run sc_PHENIX_vis for visualization in 2d MDS plot, u_no3 has de umap coordinates \n",
        "#data is non-imputated matrix\n",
        "\n",
        "from sc_PHENIX import run_pca, run_umap, sc_PHENIX\n",
        "pca_data= run_pca(data,n_components=30, random_state=1)\n",
        "\n",
        "import umap\n",
        "#umap parameters we reduced the 500 PCA dimensions to 50 umap dimensions\n",
        "fit = umap.UMAP(n_components=50,n_neighbors=10,verbose= True,metric='cosine',random_state=42)\n",
        "%time u_no_3 = fit.fit_transform(pca_data) #u_no_3 variable is the 50 umap dimenions coordinates for sc-PHENIX\n",
        "#the default output from UMAP is a euclidean interpretable space, but can be changed.\n",
        "exp_markovian_matrix = sc_PHENIX_vis(data,u_no_3,t=15,decay=500,metric='euclidean',knn=15)\n",
        "```\n",
        "Also(less pcas), you will observe that subclusters are separated easily (well defined local structure) and global structure is more defined than MAGIC(next section)\n",
        "\n"
      ],
      "metadata": {
        "id": "TET_FiLlX23v"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424,
          "referenced_widgets": [
            "837f12ef4413483cad6bd4f0ef9f3c7d",
            "c33075e7bee943d7a9f1f94dc9229f17",
            "cb3f059b49524b3fa013718f852f4b46",
            "d6caef00d7f646e2a3f6b3b90d45f1eb",
            "adcf12e42acb4575bf5d1a5cfe10fc81",
            "e5ec60fc932249e19222080894df65ad",
            "813b7721246a4762979e357e83e19ac0",
            "0db8d716a214485ebe28ffe83a42026e",
            "14352d6380db4ea39db7d6081bdf2d8e",
            "58120fbb2ecb4944b13e0272650f5600",
            "3ff8bc7d901745f0aeb991baf141dcf5"
          ]
        },
        "id": "S8UDRMvkxIQh",
        "outputId": "cb39fc27-9cd1-4749-d024-f9caf5a4a303"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "process start Thu May  4 02:24:16 2023\n",
            "UMAP(angular_rp_forest=True, metric='cosine', n_components=50, n_neighbors=10, random_state=42, verbose=True)\n",
            "Thu May  4 02:24:21 2023 Construct fuzzy simplicial set\n",
            "Thu May  4 02:24:22 2023 Finding Nearest Neighbors\n",
            "Thu May  4 02:24:22 2023 Finished Nearest Neighbor Search\n",
            "Thu May  4 02:24:22 2023 Construct embedding\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epochs completed:   0%|            0/500 [00:00]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "837f12ef4413483cad6bd4f0ef9f3c7d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Thu May  4 02:24:33 2023 Finished embedding\n",
            "CPU times: user 12.7 s, sys: 252 ms, total: 12.9 s\n",
            "Wall time: 12.9 s\n",
            "calculando distancias\n",
            "(1679, 1679)\n",
            "done distance matrix\n",
            "knn\n",
            " knn done\n",
            " afinity matrix\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-20-0d9639178b67>:35: RuntimeWarning:\n",
            "\n",
            "overflow encountered in power\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " afinity matrix done\n"
          ]
        }
      ],
      "source": [
        "#We need to run sc_PHENIX_vis for visualization in 2d MDS plot, u_no3 has de umap coordinates \n",
        "#data is non-imputated matrix\n",
        "\n",
        "from sc_PHENIX import run_pca, run_umap, sc_PHENIX\n",
        "pca_data= run_pca(data,n_components=30, random_state=1)\n",
        "\n",
        "import umap\n",
        "#umap parameters we reduced the 500 PCA dimensions to 50 umap dimensions\n",
        "fit = umap.UMAP(n_components=50,n_neighbors=10,verbose= True,metric='cosine',random_state=42)\n",
        "%time u_no_3 = fit.fit_transform(pca_data) #u_no_3 variable is the 50 umap dimenions coordinates for sc-PHENIX\n",
        "#the default output from UMAP is a euclidean interpretable space, but can be changed.\n",
        "exp_markovian_matrix = sc_PHENIX_vis(data,u_no_3,t=15,decay=500,metric='euclidean',knn=15)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c_hnXxs6xgJq",
        "outputId": "cac1f6fb-a232-4d90-e4bf-51b8bda2c3d4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "process start Thu May  4 02:24:36 2023\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.01243173, -0.05287535],\n",
              "       [-0.02313651, -0.07628784],\n",
              "       [-0.01339023,  0.06862717],\n",
              "       ...,\n",
              "       [-0.02576474, -0.08035101],\n",
              "       [-0.02805977, -0.07978654],\n",
              "       [-0.0265357 , -0.08155274]])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "#pca initialization (\"pca_data_2d\" variable)for the sgd function for the MDS embbeding \n",
        "pca_data_2d= run_pca(neuro_phenix,n_components=2, random_state=4000000)\n",
        "distance_matrix_dist_nuero =pdist(exp_markovian_matrix)\n",
        "distance_matrix_pca_diff_neuro = (squareform(distance_matrix_dist_nuero))\n",
        "PCA_diff_2D_MSD_sdg_neu = sgd(distance_matrix_pca_diff_neuro,num_dimensions=2,random_state=10,init=pca_data_2d)\n",
        "PCA_diff_2D_MSD_sdg_neu "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "id": "AASjNsGRxJbw",
        "outputId": "8f8d8dba-cea1-4e35-e0af-b6a9bc5e0fa1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12, 10))\n",
        "plt.scatter(PCA_diff_2D_MSD_sdg_neu[:, 0], PCA_diff_2D_MSD_sdg_neu[:, 1], c=row_colors.astype('category'), cmap=cmap, s=5)\n",
        "plt.setp(ax, xticks=[], yticks=[])\n",
        "cbar = plt.colorbar(boundaries=np.arange(22)-0.5)\n",
        "cbar.set_ticks(np.arange(21))# this i\n",
        "cbar.set_ticklabels(all_label_1)\n",
        "plt.title('2D MDS plot diffusion on PCA-UMAP');"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "fig = px.scatter(pd.DataFrame(PCA_diff_2D_MSD_sdg_neu,columns= ['MDS1','MDS2']), x=\"MDS1\", y=\"MDS2\",color = clusters['group'])\n",
        "fig.update_layout({'plot_bgcolor':'rgba(0,0,0,0)'\n",
        "   \n",
        "})\n",
        "fig.update_traces(marker=dict(size=3))\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "piTV-EO-RUVa",
        "outputId": "13621dd9-1484-436d-8b80-ab68ee014e4b"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
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    {
      "cell_type": "markdown",
      "source": [
        "#Visualization of the exponantiatex markovian matrix as MAGIC\n",
        "\n",
        "**Diffusion on PCA**\n",
        "Try different parameters with sc-PHENIX as MAGIC using only PCA as initialization \n",
        "We this parameters:\n",
        "\n",
        "```python\n",
        "\n",
        "pca_data= run_pca(data,n_components=30, random_state=1)\n",
        "\n",
        "exp_markovian_matrix_like_magic = sc_PHENIX_vis(data,pca_data,t=15,decay=500,metric='euclidean',knn=15)\n",
        "\n",
        "```\n",
        "you will observe that gaba subclusters are not posible to visualize, indicating that gaba subclusters are grouped in a single dense cluster, at least visually.\n",
        "\n",
        "We this parameters:\n",
        "```python\n",
        "pca_data= run_pca(data,n_components=500, random_state=1)\n",
        "\n",
        "exp_markovian_matrix_like_magic = sc_PHENIX_vis(data,pca_data,t=15,decay=500,metric='euclidean',knn=15)\n",
        "\n",
        "```\n",
        "\n",
        "Several distinct clusters are grouped, this indicates that if you shared information with local neighbors in this distorted manifold you will obtain over-smoothing\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "kD23uxHT3b_7"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3wj4Aw5u83A8",
        "outputId": "f4919360-f882-4c13-ccd0-688a50e30cf3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "process start Thu May  4 02:24:45 2023\n",
            "calculando distancias\n",
            "(1679, 1679)\n",
            "done distance matrix\n",
            "knn\n",
            " knn done\n",
            " afinity matrix\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-20-0d9639178b67>:35: RuntimeWarning:\n",
            "\n",
            "overflow encountered in power\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " afinity matrix done\n"
          ]
        }
      ],
      "source": [
        "pca_data= run_pca(data,n_components=500, random_state=1)\n",
        "\n",
        "exp_markovian_matrix_like_magic = sc_PHENIX_vis(data,pca_data,t=15,decay=500,metric='euclidean',knn=15)\n",
        "#INSTED OF USING PCA-UMAP SPACE WE USED PCA SPACE FOR SC_PHENIX_vis to run as MAGIC"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vdxWMq5A-4Co",
        "outputId": "c799a4ce-171d-4128-8a8b-a0a8a7846194"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.43860918, 0.48383506],\n",
              "       [0.44910771, 0.48802274],\n",
              "       [0.48534925, 0.54520598],\n",
              "       ...,\n",
              "       [0.44593353, 0.48891246],\n",
              "       [0.44309175, 0.48491041],\n",
              "       [0.44146194, 0.48619008]])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "distance_matrix_dist_nuero =pdist(exp_markovian_matrix_like_magic)\n",
        "distance_matrix_pca_diff_neuro = (squareform(distance_matrix_dist_nuero))\n",
        "PCA_diff_2D_MSD_sdg_neu = sgd(distance_matrix_pca_diff_neuro,num_dimensions=2,random_state=5)\n",
        "PCA_diff_2D_MSD_sdg_neu"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "id": "7KTnavzi-7rq",
        "outputId": "d336a6a6-34a6-4e15-f7a0-a068c2ce9366"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12, 10))\n",
        "plt.scatter(PCA_diff_2D_MSD_sdg_neu[:, 0], PCA_diff_2D_MSD_sdg_neu[:, 1], c=row_colors.astype('category'), cmap=cmap, s=5)\n",
        "plt.setp(ax, xticks=[], yticks=[])\n",
        "cbar = plt.colorbar(boundaries=np.arange(22)-0.5)\n",
        "cbar.set_ticks(np.arange(21))\n",
        "cbar.set_ticklabels(all_label_1)\n",
        "plt.title('2D MDS plot diffusion on PCA, a.k.a MAGIC'+'´'+'s markovian matrix');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "MQsqJqRf8TTz",
        "outputId": "f415eedc-1df4-4473-ea16-9b52bb5a1b60",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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        "import plotly.express as px\n",
        "fig = px.scatter(pd.DataFrame(PCA_diff_2D_MSD_sdg_neu,columns= ['MDS1','MDS2']), x=\"MDS1\", y=\"MDS2\",color = clusters['group'])\n",
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