single_cell_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d9f14481",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scanpy as sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "743b5b38",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata = sc.read_csv('GSE171524_RAW/GSM5226574_C51ctr_raw_counts.csv.gz').T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d80aaa59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 6099 × 34546"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1ff8ef5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TAGGTACCATGGCCAC-1_1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATTCACTGTAACAGGC-1_1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAACTTCCAACCACGC-1_1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTGGGTACACGACAAG-1_1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGGCCACAGAGTCACG-1_1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CGCCATTGTTTGCCGG-1_1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CACTGGGGTCTACGTA-1_1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CATACTTGTAGAGGAA-1_1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGGTTTCCACGGAC-1_1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATGCATGAGTCATGAA-1_1</th>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6099 rows × 0 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [TAGGTACCATGGCCAC-1_1, ATTCACTGTAACAGGC-1_1, TAACTTCCAACCACGC-1_1, TTGGGTACACGACAAG-1_1, AGGCCACAGAGTCACG-1_1, CACTGAAGTCGAAGCA-1_1, ACTGATGTCTGCACCT-1_1, TTACCGCCACTCAGAT-1_1, TTGGTTTTCCTAGCTC-1_1, TGGGAAGTCAGTGATC-1_1, CCACGAGTCTCTTAAC-1_1, ACTTCCGCACAACGCC-1_1, GGGAAGTAGCGACCCT-1_1, TGGTAGTTCCCGTGTT-1_1, CGCATAACATGCCGGT-1_1, TCTATCACAAGGCTTT-1_1, ATCCACCAGAGGTATT-1_1, TAACGACAGATGACCG-1_1, TCTTAGTGTATGAGGC-1_1, CACTTCGCAGTACTAC-1_1, GTCAAACAGAACGTGC-1_1, GCAACCGAGGGCAGGA-1_1, CATACTTTCATCACTT-1_1, AAGAACATCGGATTAC-1_1, GGGTATTGTACGATGG-1_1, CTGTAGATCAACGTGT-1_1, GTCATTTGTATCTCGA-1_1, CCTTGTGCAGAGGGTT-1_1, AAGTTCGCAACACGTT-1_1, TCATTCACAAATCAAG-1_1, TCCATGCCAACGACTT-1_1, TCCTTCTCAGTTTCAG-1_1, TGTGAGTCAAATGATG-1_1, AAACGAAGTACAGAGC-1_1, CAACCAAAGTATTCCG-1_1, CTTCTCTCAGAGACTG-1_1, TACAACGGTGGCTGAA-1_1, AACGGGACATGCCGGT-1_1, AACCAACGTTGGGAAC-1_1, TATATCCAGCGTCAGA-1_1, AGACAAACATCCCGTT-1_1, ATGACCAGTCTTCATT-1_1, CTTACCGTCAGACATC-1_1, CGGGACTGTTAGTTCG-1_1, ATTCATCCACTGAGTT-1_1, TCATGAGAGAGGCGGA-1_1, TCCCACATCTAGTACG-1_1, CTTCCTTCATATCTCT-1_1, CCGGACACACTCGATA-1_1, ACACGCGCACCTGTCT-1_1, GAATCGTCAGAAGTGC-1_1, GGTGGCTCAAGCTCTA-1_1, CCTGCATCACATATGC-1_1, GTGGGAAGTTAAAGTG-1_1, CGTTCTGGTACTAGCT-1_1, ACCCTCACAATAGTCC-1_1, GCCCGAACAAACTAAG-1_1, GTGGAAGCACATGACT-1_1, GTTGTGACATCGATAC-1_1, GACAGCCCAGGTCCGT-1_1, TAAGCACGTTGGCTAT-1_1, GGGACAAGTCACCACG-1_1, CTGGCAGGTTCGGTAT-1_1, GACTCAACACTGTGAT-1_1, GCCAGTGGTGTGGTCC-1_1, TCTAACTGTAGGCAGT-1_1, GAAGAATGTAGCTTGT-1_1, TCACTCGCAATCTCTT-1_1, CAAGACTTCCCACAGG-1_1, CAGATACGTGACTCTA-1_1, TGGGATTAGAGGGTCT-1_1, ACCTGAACACTCCTTG-1_1, GACACGCCACTCGATA-1_1, CTCATCGTCACCGCTT-1_1, AGGTAGGGTCCCTGTT-1_1, TACCCGTCAACACTAC-1_1, TGCTGAAAGACGGATC-1_1, ACACCAACACAACGCC-1_1, AAGATAGCAAATGGAT-1_1, CTTCAATGTGACAGGT-1_1, GAAGCGAAGAGTTGAT-1_1, GCCGTGACACAAGCCC-1_1, CCTCAACCATACAGGG-1_1, ACAAAGATCCACAGGC-1_1, CAGATACAGTCCCAGC-1_1, GGCAGTCTCCGGTTCT-1_1, TAAGTCGAGCTGAGCA-1_1, GAGACCCGTCTGTGCG-1_1, TAACACGCATGTGTCA-1_1, TCAATTCGTTCTCGCT-1_1, GCTTTCGCACAGTGTT-1_1, AACCAACAGATAACAC-1_1, ATCGGCGCACATCATG-1_1, TCATCCGCACGAGGAT-1_1, CTGATCCTCTTTACAC-1_1, TCACACCCAACTTCTT-1_1, TGAGGGACACCGTACG-1_1, GTGCACGTCATCTGTT-1_1, GGTAATCAGTTGCATC-1_1, ATACTTCCAAGGTCTT-1_1, ...]\n",
       "\n",
       "[6099 rows x 0 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "88b52ddf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AL627309.1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL627309.5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL627309.4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL669831.2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LINC01409</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VN1R2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL031676.1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMIM34A</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL050402.1</th>\n",
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       "    <tr>\n",
       "      <th>AL445072.1</th>\n",
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       "Columns: []\n",
       "Index: [AL627309.1, AL627309.5, AL627309.4, AL669831.2, LINC01409, FAM87B, LINC01128, LINC00115, FAM41C, AL645608.6, AL645608.2, LINC02593, SAMD11, NOC2L, KLHL17, PLEKHN1, PERM1, AL645608.7, HES4, ISG15, AL645608.1, AGRN, C1orf159, AL390719.3, LINC01342, AL390719.2, TTLL10-AS1, TTLL10, TNFRSF18, TNFRSF4, SDF4, B3GALT6, C1QTNF12, UBE2J2, LINC01786, SCNN1D, ACAP3, PUSL1, INTS11, AL139287.1, CPTP, DVL1, MXRA8, AURKAIP1, CCNL2, MRPL20-AS1, MRPL20, AL391244.2, ANKRD65, AL391244.1, LINC01770, VWA1, ATAD3C, ATAD3B, ATAD3A, TMEM240, SSU72, AL645728.1, FNDC10, AL691432.4, AL691432.2, MIB2, MMP23B, CDK11B, FO704657.1, SLC35E2B, CDK11A, SLC35E2A, NADK, GNB1, AL109917.1, CALML6, TMEM52, CFAP74, GABRD, AL391845.1, PRKCZ, AL590822.2, PRKCZ-AS1, FAAP20, AL590822.1, SKI, AL590822.3, MORN1, AL589739.1, AL513477.2, RER1, PEX10, PLCH2, AL139246.4, PANK4, HES5, AL139246.5, TNFRSF14-AS1, TNFRSF14, AL139246.3, PRXL2B, MMEL1, TTC34, AC242022.2, ...]\n",
       "\n",
       "[34546 rows x 0 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "df69d3fb",
   "metadata": {},
   "outputs": [
    {
     "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.]], dtype=float32)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "973ea4e9",
   "metadata": {},
   "source": [
    "## Doublet removal - optional but preferred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "eb8b5635",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install scvi-tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "58bc90bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kaiyu/opt/miniconda3/envs/py310/lib/python3.10/site-packages/scvi/_settings.py:63: UserWarning: Since v1.0.0, scvi-tools no longer uses a random seed by default. Run `scvi.settings.seed = 0` to reproduce results from previous versions.\n",
      "  self.seed = seed\n",
      "/Users/kaiyu/opt/miniconda3/envs/py310/lib/python3.10/site-packages/scvi/_settings.py:70: UserWarning: Setting `dl_pin_memory_gpu_training` is deprecated in v1.0 and will be removed in v1.1. Please pass in `pin_memory` to the data loaders instead.\n",
      "  self.dl_pin_memory_gpu_training = (\n"
     ]
    }
   ],
   "source": [
    "import scvi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "09de439c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 6099 × 34546"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "67e5b429",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pp.filter_genes(adata,min_cells =10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "233acb7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pp.highly_variable_genes(adata,n_top_genes = 2000, subset=True, flavor='seurat_v3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3435c0f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (mps), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "/Users/kaiyu/opt/miniconda3/envs/py310/lib/python3.10/site-packages/lightning/pytorch/trainer/setup.py:201: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 400/400: 100%|█| 400/400 [04:23<00:00,  1.51it/s, v_num=1, train_loss_step"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=400` reached.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 400/400: 100%|█| 400/400 [04:23<00:00,  1.52it/s, v_num=1, train_loss_step\n"
     ]
    }
   ],
   "source": [
    "scvi.model.SCVI.setup_anndata(adata)\n",
    "vae = scvi.model.SCVI(adata)\n",
    "vae.train(accelerator=\"cpu\", devices=\"auto\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5ed31b7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mINFO    \u001b[0m Creating doublets, preparing SOLO model.                                                                  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (mps), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "/Users/kaiyu/opt/miniconda3/envs/py310/lib/python3.10/site-packages/lightning/pytorch/trainer/setup.py:201: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 207/400:  52%|▌| 207/400 [00:39<00:37,  5.18it/s, v_num=1, train_loss_step\n",
      "Monitored metric validation_loss did not improve in the last 30 records. Best score: 0.298. Signaling Trainer to stop.\n"
     ]
    }
   ],
   "source": [
    "solo = scvi.external.SOLO.from_scvi_model(vae)\n",
    "solo.train(accelerator=\"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "288bb0ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doublet</th>\n",
       "      <th>singlet</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TAGGTACCATGGCCAC-1</th>\n",
       "      <td>1.329458</td>\n",
       "      <td>-0.939328</td>\n",
       "      <td>doublet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATTCACTGTAACAGGC-1</th>\n",
       "      <td>1.170940</td>\n",
       "      <td>-0.636844</td>\n",
       "      <td>doublet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAACTTCCAACCACGC-1</th>\n",
       "      <td>1.458547</td>\n",
       "      <td>-0.437435</td>\n",
       "      <td>doublet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTGGGTACACGACAAG-1</th>\n",
       "      <td>1.128072</td>\n",
       "      <td>-1.089607</td>\n",
       "      <td>doublet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGGCCACAGAGTCACG-1</th>\n",
       "      <td>0.985120</td>\n",
       "      <td>-0.518990</td>\n",
       "      <td>doublet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CGCCATTGTTTGCCGG-1</th>\n",
       "      <td>-1.386511</td>\n",
       "      <td>1.308878</td>\n",
       "      <td>singlet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CACTGGGGTCTACGTA-1</th>\n",
       "      <td>-3.205517</td>\n",
       "      <td>2.424695</td>\n",
       "      <td>singlet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CATACTTGTAGAGGAA-1</th>\n",
       "      <td>-2.564803</td>\n",
       "      <td>2.329313</td>\n",
       "      <td>singlet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGGTTTCCACGGAC-1</th>\n",
       "      <td>-2.709602</td>\n",
       "      <td>3.777692</td>\n",
       "      <td>singlet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATGCATGAGTCATGAA-1</th>\n",
       "      <td>-3.270123</td>\n",
       "      <td>2.501585</td>\n",
       "      <td>singlet</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6099 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     doublet   singlet prediction\n",
       "TAGGTACCATGGCCAC-1  1.329458 -0.939328    doublet\n",
       "ATTCACTGTAACAGGC-1  1.170940 -0.636844    doublet\n",
       "TAACTTCCAACCACGC-1  1.458547 -0.437435    doublet\n",
       "TTGGGTACACGACAAG-1  1.128072 -1.089607    doublet\n",
       "AGGCCACAGAGTCACG-1  0.985120 -0.518990    doublet\n",
       "...                      ...       ...        ...\n",
       "CGCCATTGTTTGCCGG-1 -1.386511  1.308878    singlet\n",
       "CACTGGGGTCTACGTA-1 -3.205517  2.424695    singlet\n",
       "CATACTTGTAGAGGAA-1 -2.564803  2.329313    singlet\n",
       "TTTGGTTTCCACGGAC-1 -2.709602  3.777692    singlet\n",
       "ATGCATGAGTCATGAA-1 -3.270123  2.501585    singlet\n",
       "\n",
       "[6099 rows x 3 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = solo.predict()\n",
    "df['prediction'] = solo.predict(soft=False)\n",
    "\n",
    "df.index = df.index.map(lambda x: x[:-2])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a7d7cf1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doublet</th>\n",
       "      <th>singlet</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prediction</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>doublet</th>\n",
       "      <td>1246</td>\n",
       "      <td>1246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>singlet</th>\n",
       "      <td>4853</td>\n",
       "      <td>4853</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            doublet  singlet\n",
       "prediction                  \n",
       "doublet        1246     1246\n",
       "singlet        4853     4853"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('prediction').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c0a83999",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doublet</th>\n",
       "      <th>singlet</th>\n",
       "      <th>prediction</th>\n",
       "      <th>dif</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TAGGTACCATGGCCAC-1</th>\n",
       "      <td>1.329458</td>\n",
       "      <td>-0.939328</td>\n",
       "      <td>doublet</td>\n",
       "      <td>2.268786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATTCACTGTAACAGGC-1</th>\n",
       "      <td>1.170940</td>\n",
       "      <td>-0.636844</td>\n",
       "      <td>doublet</td>\n",
       "      <td>1.807784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAACTTCCAACCACGC-1</th>\n",
       "      <td>1.458547</td>\n",
       "      <td>-0.437435</td>\n",
       "      <td>doublet</td>\n",
       "      <td>1.895983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTGGGTACACGACAAG-1</th>\n",
       "      <td>1.128072</td>\n",
       "      <td>-1.089607</td>\n",
       "      <td>doublet</td>\n",
       "      <td>2.217679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGGCCACAGAGTCACG-1</th>\n",
       "      <td>0.985120</td>\n",
       "      <td>-0.518990</td>\n",
       "      <td>doublet</td>\n",
       "      <td>1.504110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CGCCATTGTTTGCCGG-1</th>\n",
       "      <td>-1.386511</td>\n",
       "      <td>1.308878</td>\n",
       "      <td>singlet</td>\n",
       "      <td>-2.695389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CACTGGGGTCTACGTA-1</th>\n",
       "      <td>-3.205517</td>\n",
       "      <td>2.424695</td>\n",
       "      <td>singlet</td>\n",
       "      <td>-5.630212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CATACTTGTAGAGGAA-1</th>\n",
       "      <td>-2.564803</td>\n",
       "      <td>2.329313</td>\n",
       "      <td>singlet</td>\n",
       "      <td>-4.894115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGGTTTCCACGGAC-1</th>\n",
       "      <td>-2.709602</td>\n",
       "      <td>3.777692</td>\n",
       "      <td>singlet</td>\n",
       "      <td>-6.487294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATGCATGAGTCATGAA-1</th>\n",
       "      <td>-3.270123</td>\n",
       "      <td>2.501585</td>\n",
       "      <td>singlet</td>\n",
       "      <td>-5.771708</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6099 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     doublet   singlet prediction       dif\n",
       "TAGGTACCATGGCCAC-1  1.329458 -0.939328    doublet  2.268786\n",
       "ATTCACTGTAACAGGC-1  1.170940 -0.636844    doublet  1.807784\n",
       "TAACTTCCAACCACGC-1  1.458547 -0.437435    doublet  1.895983\n",
       "TTGGGTACACGACAAG-1  1.128072 -1.089607    doublet  2.217679\n",
       "AGGCCACAGAGTCACG-1  0.985120 -0.518990    doublet  1.504110\n",
       "...                      ...       ...        ...       ...\n",
       "CGCCATTGTTTGCCGG-1 -1.386511  1.308878    singlet -2.695389\n",
       "CACTGGGGTCTACGTA-1 -3.205517  2.424695    singlet -5.630212\n",
       "CATACTTGTAGAGGAA-1 -2.564803  2.329313    singlet -4.894115\n",
       "TTTGGTTTCCACGGAC-1 -2.709602  3.777692    singlet -6.487294\n",
       "ATGCATGAGTCATGAA-1 -3.270123  2.501585    singlet -5.771708\n",
       "\n",
       "[6099 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['dif'] = df.doublet - df.singlet\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "946be5dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cbb8c29f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kaiyu/opt/miniconda3/envs/py310/lib/python3.10/site-packages/seaborn/axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2ccd61240>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df[df.prediction=='doublet'],x='dif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "087bca42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doublet</th>\n",
       "      <th>singlet</th>\n",
       "      <th>prediction</th>\n",
       "      <th>dif</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TAGGTACCATGGCCAC-1</th>\n",
       "      <td>1.329458</td>\n",
       "      <td>-0.939328</td>\n",
       "      <td>doublet</td>\n",
       "      <td>2.268786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATTCACTGTAACAGGC-1</th>\n",
       "      <td>1.170940</td>\n",
       "      <td>-0.636844</td>\n",
       "      <td>doublet</td>\n",
       "      <td>1.807784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAACTTCCAACCACGC-1</th>\n",
       "      <td>1.458547</td>\n",
       "      <td>-0.437435</td>\n",
       "      <td>doublet</td>\n",
       "      <td>1.895983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTGGGTACACGACAAG-1</th>\n",
       "      <td>1.128072</td>\n",
       "      <td>-1.089607</td>\n",
       "      <td>doublet</td>\n",
       "      <td>2.217679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGGCCACAGAGTCACG-1</th>\n",
       "      <td>0.985120</td>\n",
       "      <td>-0.518990</td>\n",
       "      <td>doublet</td>\n",
       "      <td>1.504110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATCACGATCATCGACA-1</th>\n",
       "      <td>1.067070</td>\n",
       "      <td>-0.423827</td>\n",
       "      <td>doublet</td>\n",
       "      <td>1.490897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TGAGACTCAGGACTAG-1</th>\n",
       "      <td>1.107163</td>\n",
       "      <td>-0.665635</td>\n",
       "      <td>doublet</td>\n",
       "      <td>1.772798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAATACGCAATGTGGG-1</th>\n",
       "      <td>0.405565</td>\n",
       "      <td>-0.701353</td>\n",
       "      <td>doublet</td>\n",
       "      <td>1.106918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATCAGGTGTCGTCTCT-1</th>\n",
       "      <td>1.473204</td>\n",
       "      <td>-0.084332</td>\n",
       "      <td>doublet</td>\n",
       "      <td>1.557536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACGTAACCATGACACT-1</th>\n",
       "      <td>0.927097</td>\n",
       "      <td>-0.343158</td>\n",
       "      <td>doublet</td>\n",
       "      <td>1.270255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>511 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     doublet   singlet prediction       dif\n",
       "TAGGTACCATGGCCAC-1  1.329458 -0.939328    doublet  2.268786\n",
       "ATTCACTGTAACAGGC-1  1.170940 -0.636844    doublet  1.807784\n",
       "TAACTTCCAACCACGC-1  1.458547 -0.437435    doublet  1.895983\n",
       "TTGGGTACACGACAAG-1  1.128072 -1.089607    doublet  2.217679\n",
       "AGGCCACAGAGTCACG-1  0.985120 -0.518990    doublet  1.504110\n",
       "...                      ...       ...        ...       ...\n",
       "ATCACGATCATCGACA-1  1.067070 -0.423827    doublet  1.490897\n",
       "TGAGACTCAGGACTAG-1  1.107163 -0.665635    doublet  1.772798\n",
       "CAATACGCAATGTGGG-1  0.405565 -0.701353    doublet  1.106918\n",
       "ATCAGGTGTCGTCTCT-1  1.473204 -0.084332    doublet  1.557536\n",
       "ACGTAACCATGACACT-1  0.927097 -0.343158    doublet  1.270255\n",
       "\n",
       "[511 rows x 4 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doublets = df[(df.prediction=='doublet')&(df.dif>1)]\n",
    "doublets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a4cce715",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata =sc.read_csv('GSE171524_RAW/GSM5226574_C51ctr_raw_counts.csv.gz').T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6ce0c261",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.obs['doublet'] = adata.obs.index.isin(doublets.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a2d0426f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doublet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TAGGTACCATGGCCAC-1_1</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATTCACTGTAACAGGC-1_1</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAACTTCCAACCACGC-1_1</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTGGGTACACGACAAG-1_1</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGGCCACAGAGTCACG-1_1</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CGCCATTGTTTGCCGG-1_1</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CACTGGGGTCTACGTA-1_1</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CATACTTGTAGAGGAA-1_1</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TTTGGTTTCCACGGAC-1_1</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATGCATGAGTCATGAA-1_1</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6099 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      doublet\n",
       "TAGGTACCATGGCCAC-1_1    False\n",
       "ATTCACTGTAACAGGC-1_1    False\n",
       "TAACTTCCAACCACGC-1_1    False\n",
       "TTGGGTACACGACAAG-1_1    False\n",
       "AGGCCACAGAGTCACG-1_1    False\n",
       "...                       ...\n",
       "CGCCATTGTTTGCCGG-1_1    False\n",
       "CACTGGGGTCTACGTA-1_1    False\n",
       "CATACTTGTAGAGGAA-1_1    False\n",
       "TTTGGTTTCCACGGAC-1_1    False\n",
       "ATGCATGAGTCATGAA-1_1    False\n",
       "\n",
       "[6099 rows x 1 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "02868aff",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata = adata[~adata.obs.doublet]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "365babff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "View of AnnData object with n_obs × n_vars = 6099 × 34546\n",
       "    obs: 'doublet'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da895408",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "75079ab3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/cn/h_gbhrfs0rxbf8j5tzd54cg00000gn/T/ipykernel_5694/310233584.py:1: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n",
      "  adata.var['mt'] = adata.var.index.str.startswith('MT-')\n"
     ]
    }
   ],
   "source": [
    "adata.var['mt'] = adata.var.index.str.startswith('MT-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8c6d51e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>mt</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AL627309.1</th>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>AL627309.5</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL627309.4</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL669831.2</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LINC01409</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VN1R2</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL031676.1</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMIM34A</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL050402.1</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL445072.1</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>34546 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               mt\n",
       "AL627309.1  False\n",
       "AL627309.5  False\n",
       "AL627309.4  False\n",
       "AL669831.2  False\n",
       "LINC01409   False\n",
       "...           ...\n",
       "VN1R2       False\n",
       "AL031676.1  False\n",
       "SMIM34A     False\n",
       "AL050402.1  False\n",
       "AL445072.1  False\n",
       "\n",
       "[34546 rows x 1 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "47926da9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a017e921",
   "metadata": {},
   "outputs": [],
   "source": [
    "ribo_url = 'http://software.broadinstitute.org/gsea/msigdb/download_geneset.jsp?geneSetName=KEGG_RIBOSOME&fileType=txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "93fcc320",
   "metadata": {},
   "outputs": [],
   "source": [
    "ribo_genes = pd.read_table(ribo_url,skiprows=2,header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7c879b62",
   "metadata": {},
   "outputs": [
    {
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       "      <th>4</th>\n",
       "      <td>RPL10L</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>83</th>\n",
       "      <td>RPS9</td>\n",
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       "      <th>84</th>\n",
       "      <td>RPSA</td>\n",
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       "      <th>85</th>\n",
       "      <td>RSL24D1</td>\n",
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       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>RSL24D1P11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>UBA52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88 rows × 1 columns</p>\n",
       "</div>"
      ],
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       "             0\n",
       "0          FAU\n",
       "1       MRPL13\n",
       "2        RPL10\n",
       "3       RPL10A\n",
       "4       RPL10L\n",
       "..         ...\n",
       "83        RPS9\n",
       "84        RPSA\n",
       "85     RSL24D1\n",
       "86  RSL24D1P11\n",
       "87       UBA52\n",
       "\n",
       "[88 rows x 1 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ribo_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b9481b5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.var['ribo'] = adata.var_names.isin(ribo_genes[0].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8998cb7c",
   "metadata": {},
   "outputs": [
    {
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>AL669831.2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LINC01409</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>VN1R2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>AL031676.1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMIM34A</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL050402.1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL445072.1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>34546 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               mt   ribo\n",
       "AL627309.1  False  False\n",
       "AL627309.5  False  False\n",
       "AL627309.4  False  False\n",
       "AL669831.2  False  False\n",
       "LINC01409   False  False\n",
       "...           ...    ...\n",
       "VN1R2       False  False\n",
       "AL031676.1  False  False\n",
       "SMIM34A     False  False\n",
       "AL050402.1  False  False\n",
       "AL445072.1  False  False\n",
       "\n",
       "[34546 rows x 2 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8b6ed86d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pp.calculate_qc_metrics(adata,qc_vars=['mt','ribo'],percent_top=None,log1p=False,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "0affcf26",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mt</th>\n",
       "      <th>ribo</th>\n",
       "      <th>n_cells_by_counts</th>\n",
       "      <th>mean_counts</th>\n",
       "      <th>pct_dropout_by_counts</th>\n",
       "      <th>total_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AL627309.1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "      <td>0.001312</td>\n",
       "      <td>99.868831</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL627309.5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>33</td>\n",
       "      <td>0.005411</td>\n",
       "      <td>99.458928</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL627309.4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000656</td>\n",
       "      <td>99.934415</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL669831.2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000328</td>\n",
       "      <td>99.967208</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LINC01409</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>274</td>\n",
       "      <td>0.048697</td>\n",
       "      <td>95.507460</td>\n",
       "      <td>297.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VN1R2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL031676.1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMIM34A</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL050402.1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL445072.1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>34546 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               mt   ribo  n_cells_by_counts  mean_counts  \\\n",
       "AL627309.1  False  False                  8     0.001312   \n",
       "AL627309.5  False  False                 33     0.005411   \n",
       "AL627309.4  False  False                  4     0.000656   \n",
       "AL669831.2  False  False                  2     0.000328   \n",
       "LINC01409   False  False                274     0.048697   \n",
       "...           ...    ...                ...          ...   \n",
       "VN1R2       False  False                  0     0.000000   \n",
       "AL031676.1  False  False                  0     0.000000   \n",
       "SMIM34A     False  False                  0     0.000000   \n",
       "AL050402.1  False  False                  0     0.000000   \n",
       "AL445072.1  False  False                  0     0.000000   \n",
       "\n",
       "            pct_dropout_by_counts  total_counts  \n",
       "AL627309.1              99.868831           8.0  \n",
       "AL627309.5              99.458928          33.0  \n",
       "AL627309.4              99.934415           4.0  \n",
       "AL669831.2              99.967208           2.0  \n",
       "LINC01409               95.507460         297.0  \n",
       "...                           ...           ...  \n",
       "VN1R2                  100.000000           0.0  \n",
       "AL031676.1             100.000000           0.0  \n",
       "SMIM34A                100.000000           0.0  \n",
       "AL050402.1             100.000000           0.0  \n",
       "AL445072.1             100.000000           0.0  \n",
       "\n",
       "[34546 rows x 6 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "fb70dc01",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>TAGGTACCATGGCCAC-1_1</th>\n",
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       "      <th>CACTGGGGTCTACGTA-1_1</th>\n",
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       "      <td>346</td>\n",
       "      <td>403.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>CATACTTGTAGAGGAA-1_1</th>\n",
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       "      <td>410.0</td>\n",
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       "      <th>TTTGGTTTCCACGGAC-1_1</th>\n",
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       "                      doublet  n_genes_by_counts  total_counts  \\\n",
       "TAGGTACCATGGCCAC-1_1    False               6764       24256.0   \n",
       "ATTCACTGTAACAGGC-1_1    False               6686       19020.0   \n",
       "TAACTTCCAACCACGC-1_1    False               5158       15645.0   \n",
       "TTGGGTACACGACAAG-1_1    False               4900       15220.0   \n",
       "AGGCCACAGAGTCACG-1_1    False               5544       13217.0   \n",
       "...                       ...                ...           ...   \n",
       "CGCCATTGTTTGCCGG-1_1    False                355         410.0   \n",
       "CACTGGGGTCTACGTA-1_1    False                346         403.0   \n",
       "CATACTTGTAGAGGAA-1_1    False                360         410.0   \n",
       "TTTGGTTTCCACGGAC-1_1    False                299         405.0   \n",
       "ATGCATGAGTCATGAA-1_1    False                351         411.0   \n",
       "\n",
       "                      total_counts_mt  pct_counts_mt  total_counts_ribo  \\\n",
       "TAGGTACCATGGCCAC-1_1            153.0       0.630772               55.0   \n",
       "ATTCACTGTAACAGGC-1_1            404.0       2.124080              115.0   \n",
       "TAACTTCCAACCACGC-1_1            221.0       1.412592              211.0   \n",
       "TTGGGTACACGACAAG-1_1              3.0       0.019711                5.0   \n",
       "AGGCCACAGAGTCACG-1_1            417.0       3.155028               82.0   \n",
       "...                               ...            ...                ...   \n",
       "CGCCATTGTTTGCCGG-1_1              3.0       0.731707                0.0   \n",
       "CACTGGGGTCTACGTA-1_1              0.0       0.000000                0.0   \n",
       "CATACTTGTAGAGGAA-1_1              2.0       0.487805                0.0   \n",
       "TTTGGTTTCCACGGAC-1_1              0.0       0.000000                2.0   \n",
       "ATGCATGAGTCATGAA-1_1              0.0       0.000000                0.0   \n",
       "\n",
       "                      pct_counts_ribo  \n",
       "TAGGTACCATGGCCAC-1_1         0.226748  \n",
       "ATTCACTGTAACAGGC-1_1         0.604627  \n",
       "TAACTTCCAACCACGC-1_1         1.348674  \n",
       "TTGGGTACACGACAAG-1_1         0.032852  \n",
       "AGGCCACAGAGTCACG-1_1         0.620413  \n",
       "...                               ...  \n",
       "CGCCATTGTTTGCCGG-1_1         0.000000  \n",
       "CACTGGGGTCTACGTA-1_1         0.000000  \n",
       "CATACTTGTAGAGGAA-1_1         0.000000  \n",
       "TTTGGTTTCCACGGAC-1_1         0.493827  \n",
       "ATGCATGAGTCATGAA-1_1         0.000000  \n",
       "\n",
       "[6099 rows x 7 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "79653234",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.pp.filter_genes(adata,min_cells=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b4a60d65",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.var.sort_values=('n_cells_by_counts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "fa6574d7",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                      doublet  n_genes_by_counts  total_counts  \\\n",
       "TGGTACAGTTGGTGTT-1_1    False                323         401.0   \n",
       "CTCAACCGTTTGGGAG-1_1    False                325         401.0   \n",
       "GTCGTTCTCCAAGGGA-1_1    False                300         401.0   \n",
       "CGAGAAGGTGAACTAA-1_1    False                308         401.0   \n",
       "CAGGGCTTCATGCGGC-1_1    False                330         401.0   \n",
       "...                       ...                ...           ...   \n",
       "AGGCCACAGAGTCACG-1_1    False               5544       13217.0   \n",
       "TTGGGTACACGACAAG-1_1    False               4900       15220.0   \n",
       "TAACTTCCAACCACGC-1_1    False               5158       15645.0   \n",
       "ATTCACTGTAACAGGC-1_1    False               6686       19020.0   \n",
       "TAGGTACCATGGCCAC-1_1    False               6764       24256.0   \n",
       "\n",
       "                      total_counts_mt  pct_counts_mt  total_counts_ribo  \\\n",
       "TGGTACAGTTGGTGTT-1_1              0.0       0.000000                0.0   \n",
       "CTCAACCGTTTGGGAG-1_1              0.0       0.000000                0.0   \n",
       "GTCGTTCTCCAAGGGA-1_1              0.0       0.000000                0.0   \n",
       "CGAGAAGGTGAACTAA-1_1              0.0       0.000000                0.0   \n",
       "CAGGGCTTCATGCGGC-1_1              7.0       1.745636                1.0   \n",
       "...                               ...            ...                ...   \n",
       "AGGCCACAGAGTCACG-1_1            417.0       3.155028               82.0   \n",
       "TTGGGTACACGACAAG-1_1              3.0       0.019711                5.0   \n",
       "TAACTTCCAACCACGC-1_1            221.0       1.412592              211.0   \n",
       "ATTCACTGTAACAGGC-1_1            404.0       2.124080              115.0   \n",
       "TAGGTACCATGGCCAC-1_1            153.0       0.630772               55.0   \n",
       "\n",
       "                      pct_counts_ribo  \n",
       "TGGTACAGTTGGTGTT-1_1         0.000000  \n",
       "CTCAACCGTTTGGGAG-1_1         0.000000  \n",
       "GTCGTTCTCCAAGGGA-1_1         0.000000  \n",
       "CGAGAAGGTGAACTAA-1_1         0.000000  \n",
       "CAGGGCTTCATGCGGC-1_1         0.249377  \n",
       "...                               ...  \n",
       "AGGCCACAGAGTCACG-1_1         0.620413  \n",
       "TTGGGTACACGACAAG-1_1         0.032852  \n",
       "TAACTTCCAACCACGC-1_1         1.348674  \n",
       "ATTCACTGTAACAGGC-1_1         0.604627  \n",
       "TAGGTACCATGGCCAC-1_1         0.226748  \n",
       "\n",
       "[6099 rows x 7 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs.sort_values('total_counts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "56ba40ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sc.pp.filter_cells(adata,min_genes=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ece4cd5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kaiyu/opt/miniconda3/envs/py310/lib/python3.10/site-packages/seaborn/axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.violin(adata,['n_genes_by_counts','total_counts','pct_counts_mt','pct_counts_ribo'],jitter=0.4,multi_panel=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32faec1e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}