deconvolution_example.ipynb

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   "source": [
    "import pdb,sys,os\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import anndata\n",
    "import scanpy as sc\n",
    "sc.settings.verbosity = 0\n",
    "import argparse\n",
    "import copy\n",
    "import numpy as np\n",
    "import scipy\n",
    "import timeit\n",
    "\n",
    "from matplotlib.pyplot import figure\n",
    "import matplotlib.pyplot as plt\n",
    "from typing import Tuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c719f911-ac03-494a-b49c-ed81f8fd8abe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scSemiProfiler as semi\n",
    "from scSemiProfiler.utils import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b535086c-880d-4aba-956a-c55db06e282a",
   "metadata": {},
   "source": [
    "## Single-cell deconvolution pipeline\n",
    "This example is for the scenario where the users wish to use scSemiProfiler to deconvolution bulk data of a target sample to single-cell data, providing the bulk and single-cell of reference data. The input format is the same as before. Bulk data of representatives and target samples to deconvolute need to be concatenated together as an h5ad file. Sample IDs should be stored in adata.obs['sample_ids'] and gene names should be stored in adata.var.index. The single-cell reference data is also provided as an h5ad file as the representative before. The model reconstructs the reference and then use tgtinfer function to infer the single-cell data of any target sample. Once target sample's single-cell data is generated, the user can annotate the cell type in various ways, e.g. de novo cell type annotation using any tool. Here we train a classifier using the reference data and then annotate the generated target sample using the classifier. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5dd8ea3-b927-47cf-9d1a-0cd34d7bf0f0",
   "metadata": {},
   "source": [
    "## Step 1: Initial setup: bulk data preparation and reference sample specification"
   ]
  },
  {
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   "id": "13e53a76-08b2-4e99-bde9-6694de9ecc00",
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   "source": [
    "# 'bulkdata.h5ad' contains the reference data and the target bulk data to deconvolute\n",
    "\n",
    "name = 'deconvolution_example'\n",
    "bulk = 'example_data/bulkdata.h5ad' \n",
    "logged = False\n",
    "normed = True\n",
    "geneselection = False\n",
    "batch = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6852bc72-92fc-44dd-81cd-022adb505844",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start initial setup\n",
      "Initial setup finished. Among 12 total samples, selected 2 representatives:\n",
      "BGCV09_CV0279\n",
      "MH9143426\n"
     ]
    }
   ],
   "source": [
    "# run the same initial setup\n",
    "semi.initsetup(name,bulk,logged=logged,normed=normed,geneselection=geneselection,batch=batch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f88c7c50-c066-4d30-af7f-73e662169303",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 AP8\n",
      "1 BGCV02_CV0068\n",
      "2 BGCV03_CV0084\n",
      "3 BGCV03_CV0176\n",
      "4 BGCV04_CV0164\n",
      "5 BGCV07_CV0137\n",
      "6 BGCV09_CV0279\n",
      "7 MH8919226\n",
      "8 MH8919227\n",
      "9 MH9143322\n",
      "10 MH9143426\n",
      "11 newcastle20\n"
     ]
    }
   ],
   "source": [
    "# view bulk samples\n",
    "\n",
    "bulkdata = anndata.read_h5ad(bulk)\n",
    "for i, sid in enumerate(bulkdata.obs['sample_ids']):\n",
    "    print(i,sid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b05218f2-8d77-4a4e-8f95-2a9dc9cb8a3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select 6 as reference data, setup representative and obtain representative single-cell data\n",
    "\n",
    "set_representatives(name=name, representatives_list = [6])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8aeddfeb-bf53-443b-9af6-1a368aad47d1",
   "metadata": {},
   "source": [
    "## Step 2: Reference data processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1f4a2482-168c-4d34-8faa-0b0777bc746e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obtained single-cell data for representatives.\n"
     ]
    }
   ],
   "source": [
    "# provide reference single-cell data at 'name/representative_sc.h5ad'\n",
    "\n",
    "# for this example we use this function to acquire data.\n",
    "semi.get_eg_representatives(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bd248edf-c739-4e6c-912d-5c0eaa1859ad",
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Processing representative single-cell data\n",
      "Removing background noise\n",
      "Computing human geneset scores\n",
      "GMT file c2.cp.v7.4.symbols.gmt loading ... \n",
      "2922\n",
      "Number of genes in c2.cp.v7.4.symbols.gmt 4240\n",
      "Augmenting and saving single-cell data.\n",
      "Finished processing representative single-cell data\n"
     ]
    }
   ],
   "source": [
    "# single-cell processing of the reference data\n",
    "semi.scprocess(name=name,singlecell=name+'/representative_sc.h5ad',normed=True,logged=False,cellfilter=False,threshold=1e-3,geneset=True,weight=0.5,k=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ac2c874-bc9d-4f4e-a61a-b6d6274eeb4a",
   "metadata": {},
   "source": [
    "## Step 3: Infer target sample single-cell data"
   ]
  },
  {
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   "execution_count": 8,
   "id": "01024e35-c9c5-4398-b902-1f0badc8c2f4",
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     "text": [
      "pretrain 1: representative reconstruction\n",
      "\u001b[34mINFO    \u001b[0m Generating sequential column names                                                                        \n"
     ]
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     "text": [
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "You are using a CUDA device ('NVIDIA A16') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n"
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      "Epoch 100/100: 100%|██████████████| 100/100 [00:52<00:00,  1.94it/s, v_num=1, train_loss_step=427, train_loss_epoch=418]"
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     "text": [
      "`Trainer.fit` stopped: `max_epochs=100` reached.\n"
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     "text": [
      "Epoch 100/100: 100%|██████████████| 100/100 [00:52<00:00,  1.90it/s, v_num=1, train_loss_step=427, train_loss_epoch=418]"
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      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "You are using a CUDA device ('NVIDIA A16') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n"
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      "\n",
      "Epoch 300/300: 100%|██████████████| 300/300 [02:00<00:00,  2.49it/s, v_num=1, train_loss_step=369, train_loss_epoch=379]"
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      "`Trainer.fit` stopped: `max_epochs=300` reached.\n"
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      "Epoch 300/300: 100%|██████████████| 300/300 [02:00<00:00,  2.49it/s, v_num=1, train_loss_step=369, train_loss_epoch=379]\n",
      "pretrain2: reconstruction with representative bulk loss\n",
      "\u001b[34mINFO    \u001b[0m Generating sequential column names                                                                        \n",
      "\u001b[34mINFO    \u001b[0m Generating sequential column names                                                                        \n"
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      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "You are using a CUDA device ('NVIDIA A16') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n"
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     "text": [
      "Epoch 50/50: 100%|██████████████████| 50/50 [00:27<00:00,  1.81it/s, v_num=1, train_loss_step=337, train_loss_epoch=343]"
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     "text": [
      "`Trainer.fit` stopped: `max_epochs=50` reached.\n"
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     "name": "stdout",
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     "text": [
      "Epoch 50/50: 100%|██████████████████| 50/50 [00:27<00:00,  1.80it/s, v_num=1, train_loss_step=337, train_loss_epoch=343]"
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      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "You are using a CUDA device ('NVIDIA A16') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n"
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      "\n",
      "Epoch 150/150: 100%|██████████████| 150/150 [01:03<00:00,  2.75it/s, v_num=1, train_loss_step=359, train_loss_epoch=355]"
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     "text": [
      "`Trainer.fit` stopped: `max_epochs=150` reached.\n"
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     "text": [
      "Epoch 150/150: 100%|██████████████| 150/150 [01:03<00:00,  2.37it/s, v_num=1, train_loss_step=359, train_loss_epoch=355]\n",
      "\u001b[34mINFO    \u001b[0m Generating sequential column names                                                                        \n",
      "\u001b[34mINFO    \u001b[0m Generating sequential column names                                                                        \n"
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      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "You are using a CUDA device ('NVIDIA A16') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
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      "Epoch 150/150: 100%|██████████████| 150/150 [00:46<00:00,  3.26it/s, v_num=1, train_loss_step=357, train_loss_epoch=357]"
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     "text": [
      "`Trainer.fit` stopped: `max_epochs=150` reached.\n"
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     "name": "stdout",
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     "text": [
      "Epoch 150/150: 100%|██████████████| 150/150 [00:46<00:00,  3.26it/s, v_num=1, train_loss_step=357, train_loss_epoch=357]\n"
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      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "You are using a CUDA device ('NVIDIA A16') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n"
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      "Epoch 150/150: 100%|██████████████| 150/150 [00:46<00:00,  3.25it/s, v_num=1, train_loss_step=363, train_loss_epoch=363]"
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      "`Trainer.fit` stopped: `max_epochs=150` reached.\n"
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     "text": [
      "Epoch 150/150: 100%|██████████████| 150/150 [00:46<00:00,  3.25it/s, v_num=1, train_loss_step=363, train_loss_epoch=363]\n"
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      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "You are using a CUDA device ('NVIDIA A16') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
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      "Epoch 150/150: 100%|██████████████| 150/150 [00:46<00:00,  3.26it/s, v_num=1, train_loss_step=368, train_loss_epoch=368]"
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      "`Trainer.fit` stopped: `max_epochs=150` reached.\n"
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     "text": [
      "Epoch 150/150: 100%|██████████████| 150/150 [00:46<00:00,  3.25it/s, v_num=1, train_loss_step=368, train_loss_epoch=368]\n"
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      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "You are using a CUDA device ('NVIDIA A16') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n"
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      "Epoch 150/150: 100%|██████████████| 150/150 [00:45<00:00,  3.26it/s, v_num=1, train_loss_step=374, train_loss_epoch=374]"
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     "text": [
      "`Trainer.fit` stopped: `max_epochs=150` reached.\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 150/150: 100%|██████████████| 150/150 [00:45<00:00,  3.26it/s, v_num=1, train_loss_step=374, train_loss_epoch=374]\n"
     ]
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    {
     "name": "stderr",
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     "text": [
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "You are using a CUDA device ('NVIDIA A16') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\n"
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     "text": [
      "Epoch 150/150: 100%|██████████████| 150/150 [00:45<00:00,  3.27it/s, v_num=1, train_loss_step=377, train_loss_epoch=377]"
     ]
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    {
     "name": "stderr",
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     "text": [
      "`Trainer.fit` stopped: `max_epochs=150` reached.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 150/150: 100%|██████████████| 150/150 [00:45<00:00,  3.26it/s, v_num=1, train_loss_step=377, train_loss_epoch=377]\n",
      "Finished target sample single-cell inference\n"
     ]
    }
   ],
   "source": [
    "# use the reference data to infer the target single-cell data\n",
    "# e.g. infer sample 1's single-cell\n",
    "\n",
    "semi.tgtinfer(name = name, representative = 6, target = 1, bulktype='pseudobulk')\n"
   ]
  },
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   "id": "ef6558c2-e77f-429a-8791-87cb411e786f",
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     "text": [
      "\u001b[34mINFO    \u001b[0m Generating sequential column names                                                                        \n"
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    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_recon(name, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "384e61c5-e94a-4995-9620-240b5faede7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check the inference performance\n",
    "\n",
    "visualize_inferred(name=name, target=1,  representatives=[6], cluster_labels = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) # only 1 representative, so in cluster_label all targets have representative No.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bad7d796-6aed-4e8c-a1ea-5b676823c685",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "743db15e-b39a-460c-afaa-3587e1842aa5",
   "metadata": {},
   "source": [
    "After inference, a cell-by-gene matrix for the target sample will be stored in 'name/inferreddata/reference_to_target.npy'. We convert it to h5ad for downstream task - cell type annotation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "684d6816-b46c-496c-b0a5-13a75e8dc275",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['BGCV09_CV0279_to_BGCV02_CV0068.npy']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list inferred target sample:\n",
    "os.listdir(name + '/inferreddata')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "81f195fc-b5a4-485d-b1af-888c02b45bea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert to anndata object\n",
    "tgt_mtx = np.load(name + '/inferreddata/BGCV09_CV0279_to_BGCV02_CV0068.npy')\n",
    "tgtdata = anndata.AnnData(tgt_mtx)\n",
    "tgtdata.var.index = bulkdata.var.index # add gene names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05a734aa-d47c-4b6b-9c06-eda5866e0682",
   "metadata": {},
   "source": [
    "## Step 4: Cell type annotation for target single-cell data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15d2992e-6d8e-4799-9c09-faecd38028bd",
   "metadata": {},
   "source": [
    "### Option 1: De novo annotation\n",
    "The user can choose any existing methods to annotate the generated single-cell matrix, for example:\n",
    "[Cellar](https://github.com/euxhenh/cellar) or [Scanpy](https://scanpy.readthedocs.io/en/stable/tutorials/basics/clustering.html#dimensionality-reduction) (note: The data is already preprocessed. Please start from the 'dimensionality-reduction' step.)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8870e376-47a4-4388-b8d4-ae0d95ef7d03",
   "metadata": {},
   "source": [
    "### Option 2: Annotate using a classifier trained on reference samples\n",
    "We can train a classifier usign the reference samples and use it to annotate the target sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c8ee681e-a0e7-4280-b82a-a6829199be5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get training data\n",
    "scdata = anndata.read_h5ad(name + '/representative_sc.h5ad')\n",
    "reference_x = np.log1p(scdata.X)\n",
    "reference_y = np.array(scdata.obs['celltypes']) # reference data cell types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9c7bb572-5037-43cf-904b-0cd4e7ce24d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get target expression data\n",
    "target_x = np.log1p(tgtdata.X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "679a1d91-8d6b-4ce1-8b4b-74c71c15382f",
   "metadata": {},
   "source": [
    "Train a sklearn MLP classifier. Feel free to choose any other models that you believe work better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "54dc3ab4-beeb-4c8d-bfe8-7c59ad9b66f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training cell type annotator.\n",
      "Finished. Cost 75.04079421889037 seconds.\n"
     ]
    }
   ],
   "source": [
    "annotator = MLPClassifier(hidden_layer_sizes=(200,))\n",
    "\n",
    "print('Training cell type annotator.')\n",
    "st =  timeit.default_timer()\n",
    "#annotator = MLPClassifier(hidden_layer_sizes=(200,)) \n",
    "annotator.fit((reference_x),reference_y)\n",
    "ed =  timeit.default_timer()\n",
    "print('Finished. Cost ' + str(ed-st) + ' seconds.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfa24a84-89f0-47c6-8c88-ec6fb91750b2",
   "metadata": {},
   "source": [
    "### Get predicted cell type and compare with the ground truth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d8182c0b-d247-4ba2-af14-cb3210927807",
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_celltype = annotator.predict(target_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5f7ed4d2-fc6b-4f60-9f12-b5b4f94e57ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "tgtdata.obs['celltypes'] = predicted_celltype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1ab5e47e-a11d-4f38-b1d7-c3a1bbb1599b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ground truth and predicted cell type proportion comparison\n",
      "CCC: 0.9875996401037704\n",
      "Pearson correlation: PearsonRResult(statistic=0.9946967172114376, pvalue=3.103562672461389e-16)\n",
      "RMSE: 0.017659669657844133\n"
     ]
    }
   ],
   "source": [
    "totaltypes = np.unique(reference_y)\n",
    "real_prop = celltype_proportion(scdata,totaltypes)\n",
    "tgt_prop = celltype_proportion(tgtdata,totaltypes)\n",
    "\n",
    "print('ground truth and predicted cell type proportion comparison')\n",
    "print('CCC:',CCCscore(real_prop,tgt_prop))\n",
    "print('Pearson correlation:',scipy.stats.pearsonr(real_prop,tgt_prop))\n",
    "print('RMSE:',RMSE(real_prop,tgt_prop))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2eb0847a-0558-4e87-881d-f06d5792726b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf509a0c-88b2-4c50-a86f-55d547740d3d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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