{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c000",
   "metadata": {},
   "source": [
    "# Published profiles and JUMP\n",
    "\n",
    "An image-based profiling pipeline hands you a table: one row per well or per cell, a few thousand columns of\n",
    "CellProfiler measurements, and some metadata columns mixed in among them.\n",
    "{func}`~mantispy.io.read_profiles` turns one or many of those tables into an {class}`~anndata.AnnData`.\n",
    "It splits the measurements from the metadata and annotates what each feature name encodes.\n",
    "It handles what differs between real datasets: four spellings of the metadata prefix, missing-value\n",
    "sentinels, files with different columns, and metadata that only exists in the directory name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c001",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T01:03:48.302930Z",
     "iopub.status.busy": "2026-10-21T01:03:48.302354Z",
     "iopub.status.idle": "2026-10-21T01:03:51.820220Z",
     "shell.execute_reply": "2026-10-21T01:03:51.818491Z"
    }
   },
   "outputs": [],
   "source": [
    "import mantispy as mt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c002",
   "metadata": {},
   "source": [
    "## A dataset\n",
    "\n",
    "{mod}`mantispy.ds` downloads whole accessions from the\n",
    "[Cell Painting Gallery](https://github.com/broadinstitute/cellpainting-gallery) {cite:p}`Weisbart_2024`, so there is something real to\n",
    "read.\n",
    "This one is kinase inhibitors at a range of doses, eight plates of it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c003",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T01:03:51.824798Z",
     "iopub.status.busy": "2026-10-21T01:03:51.824300Z",
     "iopub.status.idle": "2026-10-21T01:03:52.153486Z",
     "shell.execute_reply": "2026-10-21T01:03:52.152331Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 3072 × 5839\n",
       "    obs: 'Metadata_plate_map_name', 'Metadata_broad_sample', 'Metadata_mg_per_ml', 'Metadata_mmoles_per_liter', 'Metadata_solvent', 'Metadata_Plate', 'Metadata_Well', 'Metadata_Site_Count', 'Metadata_Count_Cells', 'Metadata_Count_CellsIncludingEdges', 'Metadata_Count_Cytoplasm', 'Metadata_Count_Nuclei', 'Metadata_Count_NucleiIncludingEdges', 'Metadata_Object_Count', 'Metadata_Barcode', 'Metadata_Supplier', 'Metadata_Supplier_Catalog', 'Metadata_pert_type', 'Metadata_control_type', 'Metadata_Row', 'Metadata_Col', 'Metadata_CellCount', 'Metadata_SiteCount', 'Metadata_Control_Type', 'Metadata_Control', 'Metadata_Compound', 'Metadata_Concentration', 'Metadata_ConcentrationUnit', 'Metadata_MOA', 'Metadata_Perturbation', 'Metadata_Perturbation_Type'\n",
       "    var: 'object', 'feature_group', 'feature', 'channel', 'scale', 'angle', 'gray_levels', 'radial_bin', 'params', 'is_feature', 'feature_kind'\n",
       "    uns: 'mantispy'\n",
       "    layers: None (.X)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata = mt.ds.pki()\n",
    "adata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c004",
   "metadata": {},
   "source": [
    "Numeric columns that parse as CellProfiler features became the feature matrix, and everything else became `obs`.\n",
    "Every metadata prefix is normalized to `Metadata_`, and the loader adds the columns an analysis looks for:\n",
    "`Metadata_Compound`, `Metadata_Concentration`, `Metadata_Control` and `Metadata_Perturbation`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c005",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T01:03:52.156399Z",
     "iopub.status.busy": "2026-10-21T01:03:52.156247Z",
     "iopub.status.idle": "2026-10-21T01:03:52.172915Z",
     "shell.execute_reply": "2026-10-21T01:03:52.171928Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Metadata_plate_map_name</th>\n",
       "      <th>Metadata_broad_sample</th>\n",
       "      <th>Metadata_mg_per_ml</th>\n",
       "      <th>Metadata_mmoles_per_liter</th>\n",
       "      <th>Metadata_solvent</th>\n",
       "      <th>Metadata_Plate</th>\n",
       "      <th>Metadata_Well</th>\n",
       "      <th>Metadata_Site_Count</th>\n",
       "      <th>Metadata_Count_Cells</th>\n",
       "      <th>Metadata_Count_CellsIncludingEdges</th>\n",
       "      <th>...</th>\n",
       "      <th>Metadata_CellCount</th>\n",
       "      <th>Metadata_SiteCount</th>\n",
       "      <th>Metadata_Control_Type</th>\n",
       "      <th>Metadata_Control</th>\n",
       "      <th>Metadata_Compound</th>\n",
       "      <th>Metadata_Concentration</th>\n",
       "      <th>Metadata_ConcentrationUnit</th>\n",
       "      <th>Metadata_MOA</th>\n",
       "      <th>Metadata_Perturbation</th>\n",
       "      <th>Metadata_Perturbation_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C-7210-01-CMP-014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>BR00122970</td>\n",
       "      <td>A01</td>\n",
       "      <td>9</td>\n",
       "      <td>1758</td>\n",
       "      <td>1890</td>\n",
       "      <td>...</td>\n",
       "      <td>1758.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>negcon</td>\n",
       "      <td>True</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>compound</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C-7210-01-CMP-014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>BR00122970</td>\n",
       "      <td>A02</td>\n",
       "      <td>9</td>\n",
       "      <td>1941</td>\n",
       "      <td>2137</td>\n",
       "      <td>...</td>\n",
       "      <td>1941.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>negcon</td>\n",
       "      <td>True</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>compound</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>C-7210-01-CMP-014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>BR00122970</td>\n",
       "      <td>A11</td>\n",
       "      <td>9</td>\n",
       "      <td>2357</td>\n",
       "      <td>2564</td>\n",
       "      <td>...</td>\n",
       "      <td>2357.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>negcon</td>\n",
       "      <td>True</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>compound</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>C-7210-01-CMP-014</td>\n",
       "      <td>BRD-U00086680-001-01-9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.04</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>BR00122970</td>\n",
       "      <td>E05</td>\n",
       "      <td>9</td>\n",
       "      <td>2001</td>\n",
       "      <td>2172</td>\n",
       "      <td>...</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>treatment</td>\n",
       "      <td>False</td>\n",
       "      <td>BRD-U00086680-001-01-9</td>\n",
       "      <td>0.04</td>\n",
       "      <td>mM</td>\n",
       "      <td>ERK5 inhibitor</td>\n",
       "      <td>BRD-U00086680-001-01-9@0.04</td>\n",
       "      <td>compound</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>C-7210-01-CMP-014</td>\n",
       "      <td>BRD-U00086676-001-01-9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.01</td>\n",
       "      <td>DMSO</td>\n",
       "      <td>BR00122972</td>\n",
       "      <td>J17</td>\n",
       "      <td>9</td>\n",
       "      <td>521</td>\n",
       "      <td>557</td>\n",
       "      <td>...</td>\n",
       "      <td>521.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>treatment</td>\n",
       "      <td>False</td>\n",
       "      <td>BRD-U00086676-001-01-9</td>\n",
       "      <td>0.01</td>\n",
       "      <td>mM</td>\n",
       "      <td>undisclosed target PROTAC</td>\n",
       "      <td>BRD-U00086676-001-01-9@0.01</td>\n",
       "      <td>compound</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Metadata_plate_map_name   Metadata_broad_sample  Metadata_mg_per_ml  \\\n",
       "0          C-7210-01-CMP-014                     NaN                 NaN   \n",
       "1          C-7210-01-CMP-014                     NaN                 NaN   \n",
       "10         C-7210-01-CMP-014                     NaN                 NaN   \n",
       "100        C-7210-01-CMP-014  BRD-U00086680-001-01-9                 NaN   \n",
       "1000       C-7210-01-CMP-014  BRD-U00086676-001-01-9                 NaN   \n",
       "\n",
       "      Metadata_mmoles_per_liter Metadata_solvent Metadata_Plate Metadata_Well  \\\n",
       "0                           NaN             DMSO     BR00122970           A01   \n",
       "1                           NaN             DMSO     BR00122970           A02   \n",
       "10                          NaN             DMSO     BR00122970           A11   \n",
       "100                        0.04             DMSO     BR00122970           E05   \n",
       "1000                       0.01             DMSO     BR00122972           J17   \n",
       "\n",
       "      Metadata_Site_Count  Metadata_Count_Cells  \\\n",
       "0                       9                  1758   \n",
       "1                       9                  1941   \n",
       "10                      9                  2357   \n",
       "100                     9                  2001   \n",
       "1000                    9                   521   \n",
       "\n",
       "      Metadata_Count_CellsIncludingEdges  ...  Metadata_CellCount  \\\n",
       "0                                   1890  ...              1758.0   \n",
       "1                                   2137  ...              1941.0   \n",
       "10                                  2564  ...              2357.0   \n",
       "100                                 2172  ...              2001.0   \n",
       "1000                                 557  ...               521.0   \n",
       "\n",
       "      Metadata_SiteCount  Metadata_Control_Type  Metadata_Control  \\\n",
       "0                    9.0                 negcon              True   \n",
       "1                    9.0                 negcon              True   \n",
       "10                   9.0                 negcon              True   \n",
       "100                  9.0              treatment             False   \n",
       "1000                 9.0              treatment             False   \n",
       "\n",
       "           Metadata_Compound Metadata_Concentration  \\\n",
       "0                       DMSO                    NaN   \n",
       "1                       DMSO                    NaN   \n",
       "10                      DMSO                    NaN   \n",
       "100   BRD-U00086680-001-01-9                   0.04   \n",
       "1000  BRD-U00086676-001-01-9                   0.01   \n",
       "\n",
       "     Metadata_ConcentrationUnit               Metadata_MOA  \\\n",
       "0                            mM                        NaN   \n",
       "1                            mM                        NaN   \n",
       "10                           mM                        NaN   \n",
       "100                          mM             ERK5 inhibitor   \n",
       "1000                         mM  undisclosed target PROTAC   \n",
       "\n",
       "            Metadata_Perturbation  Metadata_Perturbation_Type  \n",
       "0                            DMSO                    compound  \n",
       "1                            DMSO                    compound  \n",
       "10                           DMSO                    compound  \n",
       "100   BRD-U00086680-001-01-9@0.04                    compound  \n",
       "1000  BRD-U00086676-001-01-9@0.01                    compound  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c006",
   "metadata": {},
   "source": [
    "Plates of one screen can disagree on their columns when a channel failed on one of them, so the eight are read\n",
    "down to the features they share.\n",
    "That is `on_column_mismatch=\"intersect\"`; the default is to raise instead, so a mismatch is never silent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c007",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T01:03:52.175971Z",
     "iopub.status.busy": "2026-10-21T01:03:52.175835Z",
     "iopub.status.idle": "2026-10-21T01:03:52.180896Z",
     "shell.execute_reply": "2026-10-21T01:03:52.180006Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Metadata_Plate\n",
       "BR00122970    384\n",
       "BR00122971    384\n",
       "BR00122972    384\n",
       "BR00122973    384\n",
       "BR00122974    384\n",
       "BR00122975    384\n",
       "BR00122977    384\n",
       "BR00122978    384\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs.groupby(\"Metadata_Plate\", observed=True).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c008",
   "metadata": {},
   "source": [
    "## What a feature name says\n",
    "\n",
    "CellProfiler names a feature `<object>_<feature group>_<feature>[_<channel>][_<parameters>]`, and\n",
    "{func}`~mantispy.io.read_profiles` pulls that apart into `var`.\n",
    "`Correlation` features measure colocalization between a pair of channels, recorded as a pair such as `AGP|Brightfield`.\n",
    "`AreaShape` is geometry and carries none."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c009",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T01:03:52.184118Z",
     "iopub.status.busy": "2026-10-21T01:03:52.183984Z",
     "iopub.status.idle": "2026-10-21T01:03:52.193547Z",
     "shell.execute_reply": "2026-10-21T01:03:52.192552Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>object</th>\n",
       "      <th>feature_group</th>\n",
       "      <th>feature</th>\n",
       "      <th>channel</th>\n",
       "      <th>scale</th>\n",
       "      <th>angle</th>\n",
       "      <th>gray_levels</th>\n",
       "      <th>radial_bin</th>\n",
       "      <th>params</th>\n",
       "      <th>is_feature</th>\n",
       "      <th>feature_kind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cells_AreaShape_Area</th>\n",
       "      <td>Cells</td>\n",
       "      <td>AreaShape</td>\n",
       "      <td>Area</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>measurement</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cells_AreaShape_BoundingBoxArea</th>\n",
       "      <td>Cells</td>\n",
       "      <td>AreaShape</td>\n",
       "      <td>BoundingBoxArea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>measurement</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cells_AreaShape_Compactness</th>\n",
       "      <td>Cells</td>\n",
       "      <td>AreaShape</td>\n",
       "      <td>Compactness</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>measurement</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cells_AreaShape_Eccentricity</th>\n",
       "      <td>Cells</td>\n",
       "      <td>AreaShape</td>\n",
       "      <td>Eccentricity</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>measurement</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cells_AreaShape_EquivalentDiameter</th>\n",
       "      <td>Cells</td>\n",
       "      <td>AreaShape</td>\n",
       "      <td>EquivalentDiameter</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>measurement</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   object feature_group             feature  \\\n",
       "Cells_AreaShape_Area                Cells     AreaShape                Area   \n",
       "Cells_AreaShape_BoundingBoxArea     Cells     AreaShape     BoundingBoxArea   \n",
       "Cells_AreaShape_Compactness         Cells     AreaShape         Compactness   \n",
       "Cells_AreaShape_Eccentricity        Cells     AreaShape        Eccentricity   \n",
       "Cells_AreaShape_EquivalentDiameter  Cells     AreaShape  EquivalentDiameter   \n",
       "\n",
       "                                   channel  scale  angle  gray_levels  \\\n",
       "Cells_AreaShape_Area                   NaN    NaN    NaN          NaN   \n",
       "Cells_AreaShape_BoundingBoxArea        NaN    NaN    NaN          NaN   \n",
       "Cells_AreaShape_Compactness            NaN    NaN    NaN          NaN   \n",
       "Cells_AreaShape_Eccentricity           NaN    NaN    NaN          NaN   \n",
       "Cells_AreaShape_EquivalentDiameter     NaN    NaN    NaN          NaN   \n",
       "\n",
       "                                   radial_bin params  is_feature feature_kind  \n",
       "Cells_AreaShape_Area                      NaN    NaN        True  measurement  \n",
       "Cells_AreaShape_BoundingBoxArea           NaN    NaN        True  measurement  \n",
       "Cells_AreaShape_Compactness               NaN    NaN        True  measurement  \n",
       "Cells_AreaShape_Eccentricity              NaN    NaN        True  measurement  \n",
       "Cells_AreaShape_EquivalentDiameter        NaN    NaN        True  measurement  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.var.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c010",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T01:03:52.196554Z",
     "iopub.status.busy": "2026-10-21T01:03:52.196419Z",
     "iopub.status.idle": "2026-10-21T01:03:52.206118Z",
     "shell.execute_reply": "2026-10-21T01:03:52.205235Z"
    }
   },
   "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>object</th>\n",
       "      <th>feature_group</th>\n",
       "      <th>feature</th>\n",
       "      <th>channel</th>\n",
       "      <th>scale</th>\n",
       "      <th>angle</th>\n",
       "      <th>gray_levels</th>\n",
       "      <th>radial_bin</th>\n",
       "      <th>params</th>\n",
       "      <th>is_feature</th>\n",
       "      <th>feature_kind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cells_Correlation_Correlation_AGP_BFHigh</th>\n",
       "      <td>Cells</td>\n",
       "      <td>Correlation</td>\n",
       "      <td>Correlation</td>\n",
       "      <td>AGP|BFHigh</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>measurement</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cells_Correlation_Correlation_AGP_BFLow</th>\n",
       "      <td>Cells</td>\n",
       "      <td>Correlation</td>\n",
       "      <td>Correlation</td>\n",
       "      <td>AGP|BFLow</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>measurement</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cells_Correlation_Correlation_AGP_Brightfield</th>\n",
       "      <td>Cells</td>\n",
       "      <td>Correlation</td>\n",
       "      <td>Correlation</td>\n",
       "      <td>AGP|Brightfield</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>measurement</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              object feature_group  \\\n",
       "Cells_Correlation_Correlation_AGP_BFHigh       Cells   Correlation   \n",
       "Cells_Correlation_Correlation_AGP_BFLow        Cells   Correlation   \n",
       "Cells_Correlation_Correlation_AGP_Brightfield  Cells   Correlation   \n",
       "\n",
       "                                                   feature          channel  \\\n",
       "Cells_Correlation_Correlation_AGP_BFHigh       Correlation       AGP|BFHigh   \n",
       "Cells_Correlation_Correlation_AGP_BFLow        Correlation        AGP|BFLow   \n",
       "Cells_Correlation_Correlation_AGP_Brightfield  Correlation  AGP|Brightfield   \n",
       "\n",
       "                                               scale  angle  gray_levels  \\\n",
       "Cells_Correlation_Correlation_AGP_BFHigh         NaN    NaN          NaN   \n",
       "Cells_Correlation_Correlation_AGP_BFLow          NaN    NaN          NaN   \n",
       "Cells_Correlation_Correlation_AGP_Brightfield    NaN    NaN          NaN   \n",
       "\n",
       "                                              radial_bin params  is_feature  \\\n",
       "Cells_Correlation_Correlation_AGP_BFHigh             NaN    NaN        True   \n",
       "Cells_Correlation_Correlation_AGP_BFLow              NaN    NaN        True   \n",
       "Cells_Correlation_Correlation_AGP_Brightfield        NaN    NaN        True   \n",
       "\n",
       "                                              feature_kind  \n",
       "Cells_Correlation_Correlation_AGP_BFHigh       measurement  \n",
       "Cells_Correlation_Correlation_AGP_BFLow        measurement  \n",
       "Cells_Correlation_Correlation_AGP_Brightfield  measurement  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.var[adata.var[\"feature_group\"] == \"Correlation\"].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c011",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T01:03:52.209300Z",
     "iopub.status.busy": "2026-10-21T01:03:52.209166Z",
     "iopub.status.idle": "2026-10-21T01:03:52.215488Z",
     "shell.execute_reply": "2026-10-21T01:03:52.214592Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "object     feature_group     \n",
       "Cells      Texture               1248\n",
       "Cytoplasm  Texture               1248\n",
       "Nuclei     Texture               1248\n",
       "Cytoplasm  Correlation            224\n",
       "Cells      Correlation            224\n",
       "Nuclei     Correlation            224\n",
       "Cells      RadialDistribution     207\n",
       "Cytoplasm  RadialDistribution     207\n",
       "Cells      Granularity            128\n",
       "Nuclei     Granularity            128\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.var.groupby([\"object\", \"feature_group\"], observed=True).size().sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c012",
   "metadata": {},
   "source": [
    "A slice of the measurements is then a query on `var` instead of a regex on the names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c013",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T01:03:52.218679Z",
     "iopub.status.busy": "2026-10-21T01:03:52.218546Z",
     "iopub.status.idle": "2026-10-21T01:03:52.237566Z",
     "shell.execute_reply": "2026-10-21T01:03:52.236541Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3072, 45)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dna_intensity = adata[:, (adata.var[\"channel\"] == \"DNA\") & (adata.var[\"feature_group\"] == \"Intensity\")]\n",
    "dna_intensity.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c014",
   "metadata": {},
   "source": [
    "## Reading your own files\n",
    "\n",
    "The remaining arguments cover what has differed between datasets:\n",
    "\n",
    "```python\n",
    "adata = mt.io.read_profiles(\n",
    "    sorted(Path(\"profiles\").glob(\"*/*.parquet\")),\n",
    "    sentinels=-999,                          # values standing for missing, replaced with NaN\n",
    "    index_columns=(\"Metadata_Plate\", \"Metadata_Well\"),  # joined with \":\" into the observation index\n",
    "    metadata_columns=(\"ImageNumber\",),       # numeric columns that are not features\n",
    "    path_columns={\"Metadata_Batch\": 2},      # metadata read off the file path\n",
    "    on_column_mismatch=\"intersect\",\n",
    ")\n",
    "```\n",
    "\n",
    "`path_columns` maps a column name to how many directories up from the file to read the name of.\n",
    "That is how a batch or a plate that exists only in the directory layout gets into `obs`.\n",
    "\n",
    "Pointed at a directory instead, the same call reads a CellProfiler `ExportToSpreadsheet` run, one row per cell\n",
    "with every object joined on, or the parquet parts CytoTable writes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c015",
   "metadata": {},
   "source": [
    "## A first look\n",
    "\n",
    "These are raw measurements, most of them redundant.\n",
    "Default feature selection keeps a few hundred, and scaling those is enough for a first look with scanpy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c016",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T01:03:52.240396Z",
     "iopub.status.busy": "2026-10-21T01:03:52.240261Z",
     "iopub.status.idle": "2026-10-21T01:04:12.239314Z",
     "shell.execute_reply": "2026-10-21T01:04:12.238201Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1455.6x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scanpy as sc\n",
    "\n",
    "mt.pp.feature_select(adata, na_cutoff=0.0)\n",
    "adata = mt.pp.subset_features(adata)\n",
    "sc.pp.scale(adata)\n",
    "\n",
    "sc.pp.pca(adata)\n",
    "sc.pp.neighbors(adata)\n",
    "sc.tl.umap(adata)\n",
    "sc.pl.umap(adata, color=[\"Metadata_Plate\", \"Metadata_Concentration\"], ncols=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c017",
   "metadata": {},
   "source": [
    "The eight plates fall into four clusters of two, and the DMSO wells split among them. All eight share one plate\n",
    "map, so the clusters come from how the plates were run rather than from what they received, which a real analysis\n",
    "has to account for before interpreting the rest of the embedding."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c018",
   "metadata": {},
   "source": [
    "## JUMP\n",
    "\n",
    "A JUMP plate parquet has three metadata columns, source, plate and well, and nothing about what each well\n",
    "received. {func}`~mantispy.io.read_jump` reads the plates and joins the perturbation from the JUMP metadata\n",
    "repository, which is what {func}`~mantispy.ds.jump_target2` does for you:\n",
    "\n",
    "```python\n",
    "wells = mt.io.read_jump(sorted(Path(\"jump\").glob(\"*.parquet\")), on_column_mismatch=\"intersect\")\n",
    "```\n",
    "\n",
    "That annotation names compounds. For the CRISPR plates, `mt.pp.annotate_jump(wells, kind=\"crispr\")` joins the\n",
    "gene each well targets instead, as {func}`~mantispy.ds.jump_crispr` does.\n",
    "\n",
    "The loaders for the public JUMP data are {func}`~mantispy.ds.jump_target2` (compound plates from every source), {func}`~mantispy.ds.jump_crispr` (the CRISPR arm), {func}`~mantispy.ds.jump_lite` (four plates under six feature sets), and {func}`~mantispy.ds.jump_plate` and {func}`~mantispy.ds.jump_cells` (one plate as images and as single cells).\n",
    "The [dataset overview](../../datasets/overview.ipynb) says which one to start from.\n",
    "\n",
    "JUMP calls a participating laboratory a source, `Metadata_Source`. In a CellProfiler export,\n",
    "`Metadata_Site` is a field of view inside a well, a different thing.\n",
    "\n",
    "## Working from disk\n",
    "\n",
    "`mt.io.read(path, backed=\"r\")` leaves `X` in the file. `obs` and `var` are loaded into memory, so metadata,\n",
    "QC flags and feature selection work as before, and grouped operations read one group at a time.\n",
    "\n",
    "Backed mode is slower than working in memory, so use it only on data that does not fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c019",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T01:04:12.245521Z",
     "iopub.status.busy": "2026-10-21T01:04:12.245093Z",
     "iopub.status.idle": "2026-10-21T01:04:12.865781Z",
     "shell.execute_reply": "2026-10-21T01:04:12.864371Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'backed': (True, 'Dataset'),\n",
       " 'in memory': (False, 'ndarray'),\n",
       " 'obs is in memory either way': 'DataFrame'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tempfile\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "path = Path(tempfile.mkdtemp()) / \"pki.h5ad\"\n",
    "mt.io.write(mt.ds.pki(), path)\n",
    "\n",
    "backed = mt.io.read(path, backed=\"r\")\n",
    "in_memory = mt.io.read(path)\n",
    "\n",
    "{\n",
    "    \"backed\": (backed.isbacked, type(backed.X).__name__),\n",
    "    \"in memory\": (in_memory.isbacked, type(in_memory.X).__name__),\n",
    "    \"obs is in memory either way\": type(backed.obs).__name__,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c020",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T01:04:12.870003Z",
     "iopub.status.busy": "2026-10-21T01:04:12.869599Z",
     "iopub.status.idle": "2026-10-21T01:04:13.450880Z",
     "shell.execute_reply": "2026-10-21T01:04:13.450106Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<path>:1: UserWarning: 133 of 5839 features have no spread in at least one group of 'Metadata_Plate' among the rows selected by reference='negcon'. Adding epsilon=1e-18 to their scale multiplies them by up to 1e18, so they dominate every distance downstream. They are flagged in var['degenerate_scale'], and pp.feature_select drops them by default; to drop them now, adata = adata[:, ~adata.var['degenerate_scale']].copy(). A variance or outlier cut does not catch a feature that varies across a plate but is constant among its control wells; the flag does.\n",
      "  from_disk = mt.pp.normalize(backed, by=\"Metadata_Plate\", reference=\"negcon\", copy=True)\n",
      "<path>:2: UserWarning: 133 of 5839 features have no spread in at least one group of 'Metadata_Plate' among the rows selected by reference='negcon'. Adding epsilon=1e-18 to their scale multiplies them by up to 1e18, so they dominate every distance downstream. They are flagged in var['degenerate_scale'], and pp.feature_select drops them by default; to drop them now, adata = adata[:, ~adata.var['degenerate_scale']].copy(). A variance or outlier cut does not catch a feature that varies across a plate but is constant among its control wells; the flag does.\n",
      "  resident = mt.pp.normalize(in_memory, by=\"Metadata_Plate\", reference=\"negcon\", copy=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'identical': True, 'largest difference': 0.0}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from_disk = mt.pp.normalize(backed, by=\"Metadata_Plate\", reference=\"negcon\", copy=True)\n",
    "resident = mt.pp.normalize(in_memory, by=\"Metadata_Plate\", reference=\"negcon\", copy=True)\n",
    "\n",
    "{\n",
    "    \"identical\": bool(np.allclose(np.asarray(from_disk.X), np.asarray(resident.X), rtol=1e-6)),\n",
    "    \"largest difference\": float(np.abs(np.asarray(from_disk.X) - np.asarray(resident.X)).max()),\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c021",
   "metadata": {},
   "source": [
    "The results are identical. The backed path reduces group by group while the in-memory path\n",
    "makes one kernel call, yet they land on the same values.\n",
    "\n",
    "Functions that rewrite `X` need `copy=True` here. The file is open read-only, so they raise a\n",
    "clear error instead of failing inside h5py:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c022",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T01:04:13.452780Z",
     "iopub.status.busy": "2026-10-21T01:04:13.452624Z",
     "iopub.status.idle": "2026-10-21T01:04:13.456283Z",
     "shell.execute_reply": "2026-10-21T01:04:13.455420Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "normalize rewrites X, which a backed object holds read-only on disk. Pass copy=True to get the result in memory, or call adata.to_memory() first.\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    mt.pp.normalize(backed, by=\"Metadata_Plate\")\n",
    "except ValueError as error:\n",
    "    print(error)"
   ]
  }
 ],
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