{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "# Quick start"
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-15T10:20:34.782898Z",
     "start_time": "2025-11-15T10:20:29.711651Z"
    }
   },
   "source": [
    "import rectanglepy as rectangle\n",
    "import pandas as pd\n",
    "from anndata import AnnData, read_h5ad"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "source": "## Creating the input data",
   "metadata": {
    "collapsed": false
   }
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "The tutorial data set contains single-cell RNA-seq counts as a pandas DataFrame, with the cell type annotations as a pandas Series."
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "The scRNA-seq data must come in the form of unnormalized gene counts, together with the associated cell-type annotations for the cell types of interest. The bulk RNA-seq data should be normalized as transcripts per million (TPM) –or counts per millions (CPM) in case of tag-based spatial transcriptomics. The gene nomenclature used for the single-cell and bulk datasets must match (e.g., gene synonyms) to provide meaningful results."
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "To see an example dataset, we can load the (small) tutorial data provided by Rectangle:"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-08T10:47:04.185942Z",
     "start_time": "2025-11-08T10:47:03.424937Z"
    }
   },
   "cell_type": "code",
   "source": "sc_counts, annotations, bulks  = rectangle.load_tutorial_data()",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "The tutorial data set contains (scRNA-seq data counts as a pandas DataFrame (cell by genes), with the cell-type annotations as a pandas Series (cell-type label for every cell ID)."
  },
  {
   "cell_type": "code",
   "source": "sc_counts[['FCGR3A', 'RPL5', 'SF3B4','S100A10', 'ZBTB7B']].head()",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-11-08T10:47:04.605364Z",
     "start_time": "2025-11-08T10:47:04.593638Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                       FCGR3A  RPL5  SF3B4  S100A10  ZBTB7B\n",
       "E2L4_GATGCTACAGGCACAA      37    23      0       10       0\n",
       "L5_AACAACCAGGAACTAT         0    15      0       12       2\n",
       "L5_TCCTTCTGTACTCCGG         0     5      0        1       0\n",
       "L2_GCCCGAACACGTATAC         0    15      1       12       1\n",
       "E2L2_ATGCATGTCACACCCT       0    24      1       18       3"
      ],
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FCGR3A</th>\n",
       "      <th>RPL5</th>\n",
       "      <th>SF3B4</th>\n",
       "      <th>S100A10</th>\n",
       "      <th>ZBTB7B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>E2L4_GATGCTACAGGCACAA</th>\n",
       "      <td>37</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
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       "      <th>L5_AACAACCAGGAACTAT</th>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L5_TCCTTCTGTACTCCGG</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L2_GCCCGAACACGTATAC</th>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E2L2_ATGCATGTCACACCCT</th>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "source": [
    "annotations.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-11-08T10:47:06.231065Z",
     "start_time": "2025-11-08T10:47:06.225943Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "E2L4_GATGCTACAGGCACAA    Monocytes\n",
       "L5_AACAACCAGGAACTAT      Monocytes\n",
       "L5_TCCTTCTGTACTCCGG      Monocytes\n",
       "L2_GCCCGAACACGTATAC      Monocytes\n",
       "E2L2_ATGCATGTCACACCCT    Monocytes\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "And the bulk RNA-seq data as a pandas DataFrame,\n",
    "where the index is the sample ID and the columns are the genes."
   ]
  },
  {
   "cell_type": "code",
   "source": "bulks[['FCGR3A', 'RPL5', 'SF3B4','S100A10', 'ZBTB7B']].head()",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-24T09:38:02.386713Z",
     "start_time": "2025-10-24T09:38:02.378882Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "             FCGR3A         RPL5      SF3B4  S100A10     ZBTB7B\n",
       "pbmc_1   145.144880  2505.165434  78.708398  415.135  49.318210\n",
       "pbmc_10  137.999249  2296.208027  79.041928  475.642  53.047700\n",
       "pbmc_12  456.966440  2544.939500  74.853167  443.532  45.043961"
      ],
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FCGR3A</th>\n",
       "      <th>RPL5</th>\n",
       "      <th>SF3B4</th>\n",
       "      <th>S100A10</th>\n",
       "      <th>ZBTB7B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pbmc_1</th>\n",
       "      <td>145.144880</td>\n",
       "      <td>2505.165434</td>\n",
       "      <td>78.708398</td>\n",
       "      <td>415.135</td>\n",
       "      <td>49.318210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pbmc_10</th>\n",
       "      <td>137.999249</td>\n",
       "      <td>2296.208027</td>\n",
       "      <td>79.041928</td>\n",
       "      <td>475.642</td>\n",
       "      <td>53.047700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pbmc_12</th>\n",
       "      <td>456.966440</td>\n",
       "      <td>2544.939500</td>\n",
       "      <td>74.853167</td>\n",
       "      <td>443.532</td>\n",
       "      <td>45.043961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 46
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "The count dataframe with its annotations can be easily convert into an AnnData object.\n"
  },
  {
   "cell_type": "code",
   "source": [
    "sc_adata = AnnData(sc_counts, obs=annotations.to_frame(name=\"cell_type\"))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-11-08T10:47:15.140520Z",
     "start_time": "2025-11-08T10:47:15.135731Z"
    }
   },
   "outputs": [],
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "source": "## Single-step deconvolution",
   "metadata": {
    "collapsed": false
   }
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "To deconvolute the bulk data in a single step, use the “rectangle()” function. This method returns the results of the signature-building step (signature_result) and a tuple with the estimated cell type proportions for every sample of the deconvolved bulk dataset (estimations)"
  },
  {
   "cell_type": "code",
   "source": "estimations, signature_result = rectangle.rectangle(sc_adata, bulks)",
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "The rectangle method returns two objects:\n",
    "1. `estimations`: a pandas DataFrame with the estimated cell type proportions for each bulk sample.\n",
    "2. `signature_result`: a [`RectangleSignatureResult`](../generated/rectanglepy.pp.RectangleSignatureResult.rst) object containing  additional information about the signature and the unknown content.\n"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "#subset for better visualisation in the documentation\n",
    "estimations.iloc[:, -5:]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-24T09:47:40.604052Z",
     "start_time": "2025-10-24T09:47:40.598312Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "         T cells CD8     Tregs       mDC       pDC   Unknown\n",
       "pbmc_1      0.161062  0.426049  0.029880  0.002178  0.000000\n",
       "pbmc_10     0.118129  0.392704  0.024096  0.004327  0.000000\n",
       "pbmc_12     0.274570  0.255928  0.014791  0.003015  0.017726"
      ],
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       "      <th></th>\n",
       "      <th>T cells CD8</th>\n",
       "      <th>Tregs</th>\n",
       "      <th>mDC</th>\n",
       "      <th>pDC</th>\n",
       "      <th>Unknown</th>\n",
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       "      <th>pbmc_1</th>\n",
       "      <td>0.161062</td>\n",
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       "      <td>0.002178</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>pbmc_10</th>\n",
       "      <td>0.118129</td>\n",
       "      <td>0.392704</td>\n",
       "      <td>0.024096</td>\n",
       "      <td>0.004327</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>pbmc_12</th>\n",
       "      <td>0.274570</td>\n",
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     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 56
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-08T10:47:56.732459Z",
     "start_time": "2025-11-08T10:47:56.728768Z"
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   },
   "cell_type": "code",
   "source": "signature_result",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RectangleSignatureResult\n",
       "──────────────────────────────────────────────────\n",
       "  Total Signature genes: 143\n",
       "  Cell types: 11\n",
       "    B cells marker genes: ['ADAM28', 'BLK', 'CD22', 'CD37', 'CD79A', 'FCER2', 'FCRL1', 'HVCN1', 'P2RX5', 'PLPP5', 'RALGPS2', 'SNX2', 'STRBP', 'VPREB3', 'ZCCHC7']\n",
       "    ILC marker genes: ['C16orf54', 'CD7', 'HTATSF1', 'IL18R1']\n",
       "    Monocytes marker genes: ['ADAP2', 'AGTRAP', 'ALDH3B1', 'ATG16L2', 'BCL6', 'BEST1', 'BST1', 'CAMKK2', 'CD300E', 'CD300LF', 'CD302', 'CEBPB', 'CFD', 'CLEC12A', 'CLEC7A', 'CPPED1', 'CREG1', 'CSF3R', 'DUSP1', 'DUSP6', 'EMILIN2']\n",
       "    NK cells marker genes: ['C1orf21', 'CEP78', 'CST7', 'CTBP2', 'CX3CR1', 'FCGR3A', 'FGFBP2', 'GNLY', 'GNPTAB', 'GZMH', 'IL2RB', 'ITGAL', 'KLRC1', 'MYBL1', 'SAMD3', 'SPON2', 'SYNE1', 'TBX21']\n",
       "    Plasma cells marker genes: ['ARSA', 'ATP13A1', 'CALR', 'CDK14', 'CKAP4', 'COPB2', 'DEF8', 'EIF2AK3', 'ERLEC1', 'FAM136A', 'FAM3C', 'FKBP2', 'FNDC3B', 'GMPPA', 'GORASP2', 'HIBCH', 'HM13', 'LAX1', 'MAN1A1', 'MANF', 'MLEC']\n",
       "    Platelet marker genes: ['ACRBP', 'CCL5', 'CMTM5', 'CTDSPL', 'DAB2', 'DAPP1', 'FERMT3', 'GRAP2', 'ILK', 'KIF2A', 'LEPROT', 'MAP3K7CL', 'MAX', 'MGAT4B', 'MMD', 'MPP1', 'MTURN', 'OAZ1', 'PF4', 'PGRMC1', 'PRKAR2B']\n",
       "    T cells CD4 conv marker genes: ['MAL']\n",
       "    T cells CD8 marker genes: ['CD8A']\n",
       "    Tregs marker genes: ['MAL']\n",
       "    mDC marker genes: ['ANXA2', 'ATP1B1', 'BATF3', 'CACNA2D3', 'CIITA', 'EMILIN2', 'FCGR2B', 'FCGRT', 'GPAT3', 'GRASP', 'HLA-DPB1', 'HLA-DQB1', 'HLA-DRB5', 'IGSF6', 'IL13RA1', 'IL18', 'JAML', 'LGALS1', 'LGALS2', 'PAK1', 'RAB34']\n",
       "    pDC marker genes: ['AEBP1', 'ARHGAP27', 'ASIP', 'ATG101', 'BCL7A', 'C12orf75', 'CBX4', 'CCDC186', 'CHML', 'CLN8', 'CRYBG3', 'CUEDC1', 'CXorf21', 'CYP46A1', 'EIF4A3', 'EPHB1', 'FAM129C', 'FLT3', 'FMNL3', 'GAS6', 'GPM6B']\n",
       "  Cluster assignments: ['B cells', 'ILC', 6, 2, 'Plasma cells', 'Platelet', 1, 2, 1, 6, 'pDC']\n",
       "  Unknown cell content analysis: Yes\n",
       "  DGE Optimization result available:\n",
       "    Best cutoffs: p: 0.05, lfc: 1.6"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "source": "## 2-step deconvolution",
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": "Rectangle can also be run in two steps, first creating the signature and then deconvolving the bulk data.",
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": "**1. Create Signature result**",
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "source": [
    "signature_result = rectangle.pp.build_rectangle_signatures(sc_adata, bulks=bulks)"
   ],
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": [
    "This creates a [`RectangleSignatureResult`](../generated/rectanglepy.pp.RectangleSignatureResult.rst) object."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": "**2. Deconvolve bulk data**",
   "metadata": {
    "collapsed": false
   }
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "We can then use the signature result to deconvolve the bulk data. This is done using the `deconvolution` method of the `RectangleSignatureResult` object, which should be fed with the signature_result object obtained at the previous step and the bulk dataset to be deconvolved"
  },
  {
   "cell_type": "code",
   "source": "estimations, _ = rectangle.tl.deconvolution(signature_result, bulks)",
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "source": "This call returns a pandas DataFrame with the estimated cell type proportions.",
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "source": [
    "#subset for better visualisation in the documentation\n",
    "estimations.iloc[:, -5:]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-10-24T09:48:22.528686Z",
     "start_time": "2025-10-24T09:48:22.522789Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "         T cells CD8     Tregs       mDC       pDC   Unknown\n",
       "pbmc_1      0.161062  0.426049  0.029880  0.002178  0.000000\n",
       "pbmc_10     0.118129  0.392704  0.024096  0.004327  0.000000\n",
       "pbmc_12     0.274570  0.255928  0.014791  0.003015  0.017726"
      ],
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       "      <th></th>\n",
       "      <th>T cells CD8</th>\n",
       "      <th>Tregs</th>\n",
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       "      <th>pbmc_1</th>\n",
       "      <td>0.161062</td>\n",
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       "      <td>0.002178</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>pbmc_10</th>\n",
       "      <td>0.118129</td>\n",
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       "      <td>0.024096</td>\n",
       "      <td>0.004327</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>pbmc_12</th>\n",
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       "      <td>0.003015</td>\n",
       "      <td>0.017726</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 57
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Bulk RNA-seq deconvolution"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "To apply Rectangle to a more meaningful use case, we will perform the deconvolution of 9 bulk RNA-seq samples from blood-cell mixtures generated in the {cite}`Finotello2019` study.\n",
    "We can access this dataset from [deconvData](https://figshare.com/articles/dataset/Validation_real/25347757?file=44877523){cite}`Dietrich2024`\n"
   ]
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-09T12:18:22.402668Z",
     "start_time": "2025-08-09T12:18:21.529051Z"
    }
   },
   "cell_type": "code",
   "source": [
    "url = \"https://figshare.com/ndownloader/files/44877523\"\n",
    "bulks_fino = pd.read_csv(url, index_col=0)\n",
    "bulks_fino.T[['LYZ', 'CD14', 'FCGR3A', 'CD8A', 'CD3D']].head()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "             LYZ      CD14      FCGR3A        CD8A       CD3D\n",
       "pbmc_1   7912.41  645.8500  145.144880  123.580148  357.85446\n",
       "pbmc_10  9331.48  800.9088  137.999249   99.817693  258.58275\n",
       "pbmc_12  9506.39  512.9779  456.966440  179.161160  328.40886\n",
       "pbmc_2   6048.82  505.6502  158.455478   87.202916  320.96350\n",
       "pbmc_4   3547.27  288.5666  314.124590  175.999580  267.81102"
      ],
      "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>LYZ</th>\n",
       "      <th>CD14</th>\n",
       "      <th>FCGR3A</th>\n",
       "      <th>CD8A</th>\n",
       "      <th>CD3D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pbmc_1</th>\n",
       "      <td>7912.41</td>\n",
       "      <td>645.8500</td>\n",
       "      <td>145.144880</td>\n",
       "      <td>123.580148</td>\n",
       "      <td>357.85446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pbmc_10</th>\n",
       "      <td>9331.48</td>\n",
       "      <td>800.9088</td>\n",
       "      <td>137.999249</td>\n",
       "      <td>99.817693</td>\n",
       "      <td>258.58275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pbmc_12</th>\n",
       "      <td>9506.39</td>\n",
       "      <td>512.9779</td>\n",
       "      <td>456.966440</td>\n",
       "      <td>179.161160</td>\n",
       "      <td>328.40886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pbmc_2</th>\n",
       "      <td>6048.82</td>\n",
       "      <td>505.6502</td>\n",
       "      <td>158.455478</td>\n",
       "      <td>87.202916</td>\n",
       "      <td>320.96350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pbmc_4</th>\n",
       "      <td>3547.27</td>\n",
       "      <td>288.5666</td>\n",
       "      <td>314.124590</td>\n",
       "      <td>175.999580</td>\n",
       "      <td>267.81102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 19
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "To train rectangle and build the signature matrix, we will use single-cell dataset human peripheral blood mononuclear cells (PBMC) profiled with the CITE-seq technology in the Hao et al. study {cite}`Hao2021`. We will use a reduced and reannotated version of this dataset which is accessible through [deconvData](https://figshare.com/articles/dataset/Training_sc/25348051?file=56063936)\n"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-07-27T18:50:11.766153Z",
     "start_time": "2025-07-27T18:50:10.958546Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# the files are originaly in an rds format,\n",
    "# we skip the conversion step here and load an AnnData object directly\n",
    "haoSub_adata = read_h5ad(\"../data/haoSub_adata.h5ad\")\n",
    "haoSub_adata"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 14744 × 24049\n",
       "    obs: 'cell_type'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "We can now use the `rectangle` method to deconvolute the bulk data guided by the single-cell expression profiles and annotations using just a single command."
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "estimations, signature_result = rectangle.rectangle(haoSub_adata, bulks_fino.T)",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "The estimated cell-type fractions can be visualized in Python as a stacked barplot using matplotlib:"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-16T08:30:31.085182Z",
     "start_time": "2025-08-16T08:30:30.879604Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(dpi=200)  # Increased resolution\n",
    "colors = plt.get_cmap('tab20').colors  # More distinct colors\n",
    "estimations.plot(kind='bar', stacked=True, ax=ax, color=colors)\n",
    "ax.set_title('Rectangle deconvolution Results (Finotello dataset)', fontsize=16)\n",
    "ax.set_ylabel('Cell fractions (%)', fontsize=14)\n",
    "ax.set_xlabel('Bulk sample', fontsize=14)\n",
    "ax.legend(title='Cell type', bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=12)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ],
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "The Finotello dataset also provides ground-truth cell fractions obtained via fluorescence-activated cell sorting (FACS) from all the analyzed bulk samples, which can be used to validate Rectangle estimates.\n",
    "\n",
    "So let us further explore the  estimation performance by plotting the estimated cell type proportions against the true cell type proportions we can again obtain from [deconvData](https://figshare.com/articles/dataset/Validation_real/25347757?file=44877526).\n"
   ]
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-16T08:30:52.986044Z",
     "start_time": "2025-08-16T08:30:52.518954Z"
    }
   },
   "cell_type": "code",
   "source": [
    "url = \"https://figshare.com/ndownloader/files/44877526\"\n",
    "finotello_true = pd.read_csv(url, index_col=0).T\n",
    "# rename the columns to match the cell types in the estimations\n",
    "finotello_true.columns = [\"NK cells\", 'B cells', 'Tregs', 'mDC', 'Monocytes', 'Neutrophils', 'T cells CD8',\n",
    "                         'T cells CD4 conv', 'Other']"
   ],
   "outputs": [],
   "execution_count": 7
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "First, we need to do some data wrangling to make the data compatible.\n"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-16T08:30:59.274436Z",
     "start_time": "2025-08-16T08:30:59.263514Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# funtion to match the dataframes by index and columns\n",
    "# and sort them by index and columns\n",
    "def match_dfs(df1, df2):\n",
    "    common_rows = df1.index.intersection(df2.index)\n",
    "    common_cols = df1.columns.intersection(df2.columns)\n",
    "    df1_matched = df1.loc[common_rows, common_cols].sort_index().sort_index(axis=1)\n",
    "    df2_matched = df2.loc[common_rows, common_cols].sort_index().sort_index(axis=1)\n",
    "    return df1_matched, df2_matched\n",
    "\n",
    "# function to create a dataframe for evaluation\n",
    "# it takes the expected and estimated dataframes and returns a melted dataframe\n",
    "def create_evaluation_df(expected_df, estimated_df):\n",
    "    evaluation_df = expected_df.T.melt()\n",
    "    estimated_df_e = estimated_df.T.melt()\n",
    "    evaluation_df['result'] = estimated_df_e['value']\n",
    "    evaluation_df.columns = ['Cell type','True Fractions' , 'Estimated Fractions']\n",
    "    return evaluation_df\n",
    "\n",
    "true_m, finotello_m = match_dfs(finotello_true.T, estimations.T)\n",
    "true_m = true_m.sort_index(key=lambda x: x.str.lower())\n",
    "finotello_m = finotello_m.sort_index(key=lambda x: x.str.lower())\n",
    "\n",
    "evaluation_df = create_evaluation_df(true_m, finotello_m)\n"
   ],
   "outputs": [],
   "execution_count": 8
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Next, we quantitatively compare Rectangle estimates to the ground-truth cell fractions using Pearson correlation (r) and root-mean square error (RMSE), as well as cell-fraction scatter plots:"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-16T08:32:00.376615Z",
     "start_time": "2025-08-16T08:32:00.211647Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "from scipy.stats import pearsonr\n",
    "import seaborn as sns\n",
    "\n",
    "def evaulate_total_df(df):\n",
    "    rsme_total = np.sqrt(np.mean((df['Estimated Fractions'] - df['True Fractions']) ** 2))\n",
    "    r_value_total, p_value_total =  pearsonr(df['Estimated Fractions'], df['True Fractions'])\n",
    "    return rsme_total, r_value_total, p_value_total\n",
    "\n",
    "rsme_finotello_rectangle, r_finotello_rectangle, p_finotello_rectangle = evaulate_total_df(evaluation_df)\n",
    "\n",
    "def create_scatter_plot_total(df, rsme, p_value, r_value, title=\"Total\", y_axis_label=\"Estimated Fractions\", x_axis_label=\"True Fractions\", hue=\"Cell type\"):\n",
    "    sns.set_style(\"white\")\n",
    "    fig, ax = plt.subplots(dpi=200)  # Increased resolution\n",
    "    g = sns.scatterplot(x=\"True Fractions\", y=\"Estimated Fractions\",hue=hue, data=df, ax=ax)\n",
    "    sns.regplot(x=\"True Fractions\", y=\"Estimated Fractions\", data=df, scatter=False, color='#444444', ci=None, ax=ax)\n",
    "    text = f\"r = {round(r_value, 3)}, p = {round(p_value, 10)} \\nRMSE = {round(rsme, 3)}\\nn = {len(df)}\"\n",
    "    g.set(xlabel=x_axis_label, ylabel=y_axis_label)\n",
    "    g.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "    g.annotate(text, xy=(0.01, 0.95), xycoords=\"axes fraction\",\n",
    "                xytext=(5, -5), textcoords=\"offset points\",\n",
    "                ha=\"left\", va=\"top\")\n",
    "    g.set_xlim(0, g.get_xlim()[1])\n",
    "    g.set_ylim(0, g.get_ylim()[1])\n",
    "    return g\n",
    "\n",
    "rsme_finotello_rectangle, r_finotello_rectangle, p_finotello_rectangle = evaulate_total_df(evaluation_df)\n",
    "create_scatter_plot_total(evaluation_df, rsme_finotello_rectangle, p_finotello_rectangle, r_finotello_rectangle)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='True Fractions', ylabel='Estimated Fractions'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Spatial deconvolution"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Rectangle can be also effectively applied to spot-based spatial transcriptomics data, seamlessly integrating with the `spatialdata` package.\n",
    "See the [spatialdata documentation](https://spatialdata.scverse.org/en/stable/index.html) for more details.\n",
    "\n",
    "The mouse brain slide \"ST8059048\" was downloaded from ArrayExpress, with access number E-MTAB-11114 {cite}`Hajdarovic2022`.\n",
    "Find the dataset in [https://figshare.com/articles/dataset/Tutorial_data/30104548?file=58967194](https://figshare.com/articles/dataset/Tutorial_data/30104548?file=58967194)\n"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "import spatialdata_io\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from anndata import AnnData, read_h5ad\n",
    "\n",
    "s_data = spatialdata_io.visium(path='../data/spatial_ST8059048', dataset_id='ST8059048', scalefactors_file='scalefactors_json.json', tissue_positions_file='tissue_positions_list.csv', counts_file='ST8059048_filtered_feature_bc_matrix.h5')\n",
    "\n"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Single-cell data: scRNA-seq data from the mouse brain was downloaded from Array Express, with accession number E-MTAB-11115 {cite}`Kleshchevnikov2022`.\n",
    "You can find a description of the preprocessing steps in [https://figshare.com/articles/dataset/Tutorial_data/30104548?file=58978408](https://figshare.com/articles/dataset/Tutorial_data/30104548?file=58978408)\n",
    "\n",
    "Find the processed data in [https://figshare.com/articles/dataset/Tutorial_data/30104548?file=58978435](https://figshare.com/articles/dataset/Tutorial_data/30104548?file=58978435)"
   ]
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-15T10:39:53.684794Z",
     "start_time": "2025-11-15T10:39:53.227346Z"
    }
   },
   "cell_type": "code",
   "source": "adata = read_h5ad('../data/cell2location_single_cell_processed.h5ad')\n",
   "outputs": [],
   "execution_count": 13
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "We can run Rectangle on the spatial data using the rectangle method, by using the ‘table’ object of the `SpatialData` object. As the spatial expression data is summarized as raw gene counts, we will first convert it to CPM."
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "\n",
    "data_table = s_data['table']\n",
    "bulks = data_table.to_df()\n",
    "bulks_cpm = bulks.div(bulks.sum(axis=1), axis=0) * 1e6\n"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "estimations, signature_result = rectangle.rectangle(adata, bulks_cpm)",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# remove disconnected spots\n",
    "disconnected_spots = [\"AATCGAGGTCTCAAGG-1\", \"ACCACACGGTTGATGG-1\",\n",
    "                   \"ACCGAGTCTCCTTATT-1\", \"AGCAGTCGAAGCATGC-1\",\n",
    "                   \"AGTACAGAAGCTTATA-1\", \"CCTACAGTTGAGGGAG-1\",\n",
    "                   \"CGCTTTCCGCCAAGGT-1\", \"CGGTATGGGCACTCTG-1\",\n",
    "                   \"CGGTTGGGCAGGGTCC-1\", \"CTATCACAACGCTGGA-1\",\n",
    "                   \"CTCTAACACCGGCAGC-1\", \"CTTACATAGATTTCTT-1\",\n",
    "                   \"CTTCATCACCAGGGCT-1\", \"GAAGGGTCATTAAGAC-1\",\n",
    "                   \"GACCACACTTCCCTTT-1\", \"GCACGCCGATTCCCGC-1\",\n",
    "                   \"GCGGCTTTAGCAAGTT-1\", \"GCGTCGTAACATGGTC-1\",\n",
    "                   \"GGATTAATCATGGACC-1\", \"GTACTACGGCCTCGTT-1\",\n",
    "                   \"TACACAGCCGTGGTGC-1\", \"TCAGCGCACGCCGTTT-1\",\n",
    "                   \"TGCCCGATAGTTAGAA-1\", \"TGTAGCCAATTCCGTT-1\"]\n",
    "estimations = estimations.drop(index=disconnected_spots)"
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "We can add our estimations to the spatial data by creating a new `AnnData` object from the estimations and adding it to the `SpatialData` object."
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "from spatialdata.models import TableModel\n",
    "\n",
    "# Create AnnData from estimations\n",
    "table_data = AnnData(estimations)\n",
    "\n",
    "# Add the required metadata columns\n",
    "table_data.obs['region'] = 'ST8059048'\n",
    "table_data.obs['instance_id'] = table_data.obs.index\n",
    "\n",
    "adata_for_sdata = TableModel.parse(\n",
    "    table_data,\n",
    "    region='ST8059048',\n",
    "    region_key='region',\n",
    "    instance_key='instance_id'\n",
    ")\n",
    "\n",
    "s_data.tables[\"rectangle_results\"] = adata_for_sdata"
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": ""
  }
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