{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c000",
   "metadata": {},
   "source": [
    "# Normalize, select, aggregate\n",
    "\n",
    "Plates differ in imaging day, reagent lot and microscope state, and a CellProfiler run gives a few thousand\n",
    "features, most of them redundant. This page puts the cells of a JUMP plate on the scale of its own controls and\n",
    "aggregates them to wells, then reduces the features of a whole screen to the informative ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c001",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T02:25:49.026190Z",
     "iopub.status.busy": "2026-11-11T02:25:49.026058Z",
     "iopub.status.idle": "2026-11-11T02:26:18.644670Z",
     "shell.execute_reply": "2026-11-11T02:26:18.644059Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import mantispy as mt\n",
    "\n",
    "cells = mt.ds.jump_cells()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c002",
   "metadata": {},
   "source": [
    "## Normalization\n",
    "\n",
    "Plates differ in imaging day, reagent lot and microscope state. The standard fix is to express\n",
    "every cell relative to the negative controls on its own plate, which is what\n",
    "`reference=\"negcon\"` together with `by=\"Metadata_Plate\"` does.\n",
    "\n",
    "The default method, `mad_robustize`, subtracts the control median and divides by a robust\n",
    "spread, so a few extreme cells do not set the scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c003",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T02:26:18.646589Z",
     "iopub.status.busy": "2026-11-11T02:26:18.646418Z",
     "iopub.status.idle": "2026-11-11T02:26:33.600983Z",
     "shell.execute_reply": "2026-11-11T02:26:33.600280Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<path>:1: UserWarning: 285 of 5839 features have no spread in at least one group of 'Metadata_Plate' among the rows selected by reference='negcon' (Cells_AreaShape_EulerNumber, Cells_Correlation_Manders_AGP_BFHigh, Cells_Correlation_Manders_AGP_BFLow, Cells_Correlation_Manders_AGP_Brightfield, Cells_Correlation_Manders_AGP_Mito and 280 more). They cannot be scaled there, so their values in those groups are set to NaN. 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",
      "  mt.pp.normalize(cells, method=\"mad_robustize\", by=\"Metadata_Plate\", reference=\"negcon\", keep_raw=True)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<path>:1215: RuntimeWarning: All-NaN slice encountered\n",
      "  return fnb._ureduce(a, func=_nanmedian, keepdims=keepdims,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0.], dtype=float32)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mt.pp.normalize(cells, method=\"mad_robustize\", by=\"Metadata_Plate\", reference=\"negcon\", keep_raw=True)\n",
    "\n",
    "controls = cells[cells.obs[\"Metadata_Control\"].to_numpy()]\n",
    "np.round(np.nanmedian(np.asarray(controls.X), axis=0)[:5], 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c004",
   "metadata": {},
   "source": [
    "The controls now sit at zero on every plate by construction. `keep_raw=True` kept the\n",
    "original values in `layers[\"raw\"]` for the before-and-after plot. It is off by default\n",
    "because the extra layer doubles memory and the original is already on disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c005",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T02:26:33.602476Z",
     "iopub.status.busy": "2026-11-11T02:26:33.602309Z",
     "iopub.status.idle": "2026-11-11T02:26:34.229721Z",
     "shell.execute_reply": "2026-11-11T02:26:34.229158Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mt.pl.feature_distributions(\n",
    "    cells, features=[\"Cells_AreaShape_Area\", \"Nuclei_Intensity_MeanIntensity_DNA\", \"Cells_Intensity_MeanIntensity_Mito\"]\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c006",
   "metadata": {},
   "source": [
    "### Features normalization cannot scale\n",
    "\n",
    "`normalize` warned about features in `var[\"degenerate_scale\"]`. They have no spread among the control cells of\n",
    "some plate, so `mad_robustize` divides them by `epsilon = 1e-18` instead of by zero, as pycytominer\n",
    "{cite:p}`Serrano_2025` does, and multiplies them by up to 1e18."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c007",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T02:26:34.231169Z",
     "iopub.status.busy": "2026-11-11T02:26:34.231000Z",
     "iopub.status.idle": "2026-11-11T02:26:34.234317Z",
     "shell.execute_reply": "2026-11-11T02:26:34.233932Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'flagged degenerate_scale': 285, 'of': 5839}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{\"flagged degenerate_scale\": int(cells.var[\"degenerate_scale\"].sum()), \"of\": cells.n_vars}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c008",
   "metadata": {},
   "source": [
    "A variance threshold does not catch a feature that varies across the plate and is constant among the control\n",
    "wells: it measures variance across all wells, keeps that feature, and the feature then dominates every distance\n",
    "computed afterwards. The flag catches it. {func}`~mantispy.pp.feature_select` drops flagged features first by\n",
    "default and judges the rest without them. Anything that reads the object before selection needs them gone by\n",
    "hand:\n",
    "\n",
    "```python\n",
    "adata = adata[:, ~adata.var[\"degenerate_scale\"].to_numpy()].copy()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c009",
   "metadata": {},
   "source": [
    "## Aggregating to wells\n",
    "\n",
    "Each well's profile is the median across its cells. The median is used instead of the mean so\n",
    "that a few extreme cells do not move the profile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "22ad68c9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T02:26:34.235557Z",
     "iopub.status.busy": "2026-11-11T02:26:34.235419Z",
     "iopub.status.idle": "2026-11-11T02:26:34.238784Z",
     "shell.execute_reply": "2026-11-11T02:26:34.238344Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13,578 single cells x 5839 features, resolution=object\n",
      "across 24 wells on 1 plate(s)\n"
     ]
    }
   ],
   "source": [
    "print(f\"{cells.n_obs:,} single cells x {cells.n_vars} features, resolution={cells.uns['mantispy']['resolution']}\")\n",
    "print(f\"across {cells.obs['Metadata_Well'].nunique()} wells on {cells.obs['Metadata_Plate'].nunique()} plate(s)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c010",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T02:26:34.239971Z",
     "iopub.status.busy": "2026-11-11T02:26:34.239844Z",
     "iopub.status.idle": "2026-11-11T02:26:36.604118Z",
     "shell.execute_reply": "2026-11-11T02:26:36.603466Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 24 × 5839\n",
       "    obs: 'Metadata_Plate', 'Metadata_Well', 'Metadata_CellCount', 'Metadata_SiteCount', 'Metadata_BinningX', 'Metadata_BinningY', 'Metadata_ChannelID', 'Metadata_ChannelName', 'Metadata_Col', 'Metadata_ExposureTime', 'Metadata_ImageResolutionX', 'Metadata_ImageResolutionY', 'Metadata_ImageSizeX', 'Metadata_ImageSizeY', 'Metadata_MainEmissionWavelength', 'Metadata_MainExcitationWavelength', 'Metadata_MaxIntensity', 'Metadata_ObjectiveMagnification', 'Metadata_ObjectiveNA', 'Metadata_PlaneID', 'Metadata_PositionZ', 'Metadata_Row', 'Metadata_ObjectType', 'Metadata_Source', 'Metadata_JCP2022', 'Metadata_InChIKey', 'Metadata_Perturbation', 'Metadata_Perturbation_Type', 'Metadata_Control_Type', 'Metadata_Control'\n",
       "    var: 'object', 'feature_group', 'feature', 'channel', 'scale', 'angle', 'gray_levels', 'radial_bin', 'params', 'is_feature', 'feature_kind', 'selected', 'degenerate_scale'\n",
       "    uns: 'mantispy'\n",
       "    layers: None (.X)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wells = mt.tl.aggregate(cells, by=(\"Metadata_Plate\", \"Metadata_Well\"), func=\"median\")\n",
    "wells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ca661e82",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T02:26:36.605414Z",
     "iopub.status.busy": "2026-11-11T02:26:36.605264Z",
     "iopub.status.idle": "2026-11-11T02:26:36.615278Z",
     "shell.execute_reply": "2026-11-11T02:26:36.614756Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aggregated 13,578 cells -> 24 well profiles (median), resolution=well\n"
     ]
    },
    {
     "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>Metadata_Plate</th>\n",
       "      <th>Metadata_Well</th>\n",
       "      <th>Metadata_CellCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BR00121438_A02</th>\n",
       "      <td>BR00121438</td>\n",
       "      <td>A02</td>\n",
       "      <td>702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BR00121438_A21</th>\n",
       "      <td>BR00121438</td>\n",
       "      <td>A21</td>\n",
       "      <td>582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BR00121438_B01</th>\n",
       "      <td>BR00121438</td>\n",
       "      <td>B01</td>\n",
       "      <td>401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BR00121438_B04</th>\n",
       "      <td>BR00121438</td>\n",
       "      <td>B04</td>\n",
       "      <td>555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BR00121438_B09</th>\n",
       "      <td>BR00121438</td>\n",
       "      <td>B09</td>\n",
       "      <td>498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Metadata_Plate Metadata_Well  Metadata_CellCount\n",
       "BR00121438_A02     BR00121438           A02                 702\n",
       "BR00121438_A21     BR00121438           A21                 582\n",
       "BR00121438_B01     BR00121438           B01                 401\n",
       "BR00121438_B04     BR00121438           B04                 555\n",
       "BR00121438_B09     BR00121438           B09                 498"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\n",
    "    f\"aggregated {cells.n_obs:,} cells -> {wells.n_obs} well profiles (median), \"\n",
    "    f\"resolution={wells.uns['mantispy']['resolution']}\"\n",
    ")\n",
    "wells.obs[[\"Metadata_Plate\", \"Metadata_Well\", \"Metadata_CellCount\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c011",
   "metadata": {},
   "source": [
    "The result keeps every metadata column that is constant within a\n",
    "well. Columns that vary within a well, such as a per-cell measurement, are dropped because\n",
    "they have no single value per well."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c013",
   "metadata": {},
   "source": [
    "`Metadata_CellCount` is added as well. Cell count is a common confounder, and `mt.pp.regress_out` can remove its\n",
    "influence later.\n",
    "\n",
    "## Feature selection\n",
    "\n",
    "Most of a CellProfiler run's features are redundant. `AreaShape_Area` and `AreaShape_Perimeter` carry much the\n",
    "same information, and so does a texture feature at scale 3 and the same texture at scale 5. Others are constant,\n",
    "or blow up because they are ratios with a near-zero denominator. mantispy reproduces pycytominer's operations\n",
    "{cite:p}`Serrano_2025`, verified feature by feature, because published pipelines depend on their precise behavior.\n",
    "\n",
    "Selection needs a screen rather than one plate's 24 wells. `pki` is a kinase inhibitor screen: eight\n",
    "plates, 3,072 wells, two plates in each of four cell lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c014",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T02:26:36.616521Z",
     "iopub.status.busy": "2026-11-11T02:26:36.616384Z",
     "iopub.status.idle": "2026-11-11T02:26:37.042255Z",
     "shell.execute_reply": "2026-11-11T02:26:37.041590Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<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' (Cells_AreaShape_EulerNumber, Cells_Correlation_Manders_AGP_BFHigh, Cells_Correlation_Manders_AGP_BFLow, Cells_Correlation_Manders_AGP_Brightfield, Cells_Correlation_Manders_BFHigh_BFLow and 128 more). They cannot be scaled there, so their values in those groups are set to NaN. 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",
      "  mt.pp.normalize(screen, method=\"mad_robustize\", by=\"Metadata_Plate\", reference=\"negcon\")\n"
     ]
    },
    {
     "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', 'Metadata_CellLine'\n",
       "    var: 'object', 'feature_group', 'feature', 'channel', 'scale', 'angle', 'gray_levels', 'radial_bin', 'params', 'is_feature', 'feature_kind', 'degenerate_scale'\n",
       "    uns: 'mantispy'\n",
       "    layers: None (.X)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "screen = mt.ds.pki()\n",
    "mt.pp.normalize(screen, method=\"mad_robustize\", by=\"Metadata_Plate\", reference=\"negcon\")\n",
    "screen"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c015",
   "metadata": {},
   "source": [
    "## The operations\n",
    "\n",
    "Each operation tests one criterion, and `feature_select` combines them. It does not drop\n",
    "anything: it writes a boolean column to `var`, and only `subset_features` changes the shape,\n",
    "so you can inspect what would be removed first.\n",
    "\n",
    "| operation | drops a feature when |\n",
    "| --- | --- |\n",
    "| `drop_degenerate` | `normalize` could not scale it and flagged it in `var[\"degenerate_scale\"]` |\n",
    "| `variance_threshold` | its variance is below `min_variance` |\n",
    "| `frequency_threshold` | one value dominates it, or it takes very few distinct values |\n",
    "| `correlation_threshold` | it is highly correlated with another feature |\n",
    "| `drop_na_columns` | too much of it is missing |\n",
    "| `blocklist` | it is on the CellProfiler blocklist of known-unreliable features |\n",
    "| `drop_outliers` | its magnitude has blown up |\n",
    "| `noise_removal` | it varies too much between replicates of the same perturbation |\n",
    "\n",
    "Two of these are easy to get wrong.\n",
    "\n",
    "`variance_threshold` is a plain variance cut. The frequency-and-uniqueness rule often\n",
    "described under that name is a separate operation, `frequency_threshold`, and its two\n",
    "criteria combine with OR.\n",
    "\n",
    "`correlation_threshold` uses the signed correlation, not the absolute value, so two\n",
    "features correlated at −1.0 are both kept. pycytominer does the same, and mantispy matches\n",
    "it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c016",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T02:26:37.043705Z",
     "iopub.status.busy": "2026-11-11T02:26:37.043552Z",
     "iopub.status.idle": "2026-11-11T02:26:45.461593Z",
     "shell.execute_reply": "2026-11-11T02:26:45.460880Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<path>:96: UserWarning: 3 selected feature(s) hold values more than outlier_cutoff=500 robust standard deviations from their own median (Nuclei_ObjectSkeleton_NumberBranchEnds_mito_skel, Nuclei_ObjectSkeleton_NumberNonTrunkBranches_mito_skel, Nuclei_ObjectSkeleton_TotalObjectSkeletonLength_mito_skel). They can dominate distances and PCA. Add 'drop_outliers' to operations to drop them, or check where the extreme values come from; on single cells, before aggregation, since a median profile hides a few extreme cells.\n",
      "  func(target, **params, **extra)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "drop_degenerate          keeps  5706 of 5839\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<path>:3: UserWarning: 6 selected feature(s) still hold NaN in some rows, which pp.sphere and PCA cannot take. Add the drop_na_columns operation with na_cutoff=0.0 and pass subset=True (or run mt.pp.subset_features afterwards) to drop them, or impute before those steps.\n",
      "  mt.pp.feature_select(trial, operations=(operation,))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<path>:96: UserWarning: 3 selected feature(s) hold values more than outlier_cutoff=500 robust standard deviations from their own median (Nuclei_ObjectSkeleton_NumberBranchEnds_mito_skel, Nuclei_ObjectSkeleton_NumberNonTrunkBranches_mito_skel, Nuclei_ObjectSkeleton_TotalObjectSkeletonLength_mito_skel). They can dominate distances and PCA. Add 'drop_outliers' to operations to drop them, or check where the extreme values come from; on single cells, before aggregation, since a median profile hides a few extreme cells.\n",
      "  func(target, **params, **extra)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "variance_threshold       keeps  5712 of 5839\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<path>:3: UserWarning: 132 selected feature(s) still hold NaN in some rows, which pp.sphere and PCA cannot take. Add the drop_na_columns operation with na_cutoff=0.0 and pass subset=True (or run mt.pp.subset_features afterwards) to drop them, or impute before those steps.\n",
      "  mt.pp.feature_select(trial, operations=(operation,))\n",
      "<path>:1215: RuntimeWarning: All-NaN slice encountered\n",
      "  return fnb._ureduce(a, func=_nanmedian, keepdims=keepdims,\n",
      "<path>:96: UserWarning: 1 selected feature(s) hold values more than outlier_cutoff=500 robust standard deviations from their own median (Nuclei_ObjectSkeleton_NumberNonTrunkBranches_mito_skel). They can dominate distances and PCA. Add 'drop_outliers' to operations to drop them, or check where the extreme values come from; on single cells, before aggregation, since a median profile hides a few extreme cells.\n",
      "  func(target, **params, **extra)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "correlation_threshold    keeps  1002 of 5839\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<path>:96: UserWarning: 3 selected feature(s) hold values more than outlier_cutoff=500 robust standard deviations from their own median (Nuclei_ObjectSkeleton_NumberBranchEnds_mito_skel, Nuclei_ObjectSkeleton_NumberNonTrunkBranches_mito_skel, Nuclei_ObjectSkeleton_TotalObjectSkeletonLength_mito_skel). They can dominate distances and PCA. Add 'drop_outliers' to operations to drop them, or check where the extreme values come from; on single cells, before aggregation, since a median profile hides a few extreme cells.\n",
      "  func(target, **params, **extra)\n",
      "<path>:3: UserWarning: 133 selected feature(s) still hold NaN in some rows, which pp.sphere and PCA cannot take. Add the drop_na_columns operation with na_cutoff=0.0 and pass subset=True (or run mt.pp.subset_features afterwards) to drop them, or impute before those steps.\n",
      "  mt.pp.feature_select(trial, operations=(operation,))\n",
      "<path>:1215: RuntimeWarning: All-NaN slice encountered\n",
      "  return fnb._ureduce(a, func=_nanmedian, keepdims=keepdims,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "drop_na_columns          keeps  5706 of 5839\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "blocklist                keeps  5784 of 5839\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<path>:96: UserWarning: 3 selected feature(s) hold values more than outlier_cutoff=500 robust standard deviations from their own median (Nuclei_ObjectSkeleton_NumberBranchEnds_mito_skel, Nuclei_ObjectSkeleton_NumberNonTrunkBranches_mito_skel, Nuclei_ObjectSkeleton_TotalObjectSkeletonLength_mito_skel). They can dominate distances and PCA. Add 'drop_outliers' to operations to drop them, or check where the extreme values come from; on single cells, before aggregation, since a median profile hides a few extreme cells.\n",
      "  func(target, **params, **extra)\n"
     ]
    }
   ],
   "source": [
    "for operation in (\"drop_degenerate\", \"variance_threshold\", \"correlation_threshold\", \"drop_na_columns\", \"blocklist\"):\n",
    "    trial = screen.copy()\n",
    "    mt.pp.feature_select(trial, operations=(operation,))\n",
    "    print(f\"{operation:24s} keeps {int(trial.var['selected'].sum()):5d} of {trial.n_vars}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c017",
   "metadata": {},
   "source": [
    "Each operation is judged on its own here. `correlation_threshold` does the most, because most of a CellProfiler\n",
    "run is near-duplicates of other features.\n",
    "\n",
    "## Running the default pipeline\n",
    "\n",
    "The default runs five of the eight operations: `drop_degenerate`, which runs first, and pycytominer's own default\n",
    "of four. `frequency_threshold` and `drop_outliers` are off because both can remove an informative feature on a\n",
    "screen with few conditions, and `noise_removal` is off because it needs replicate structure, which not every\n",
    "object has."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c018",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T02:26:45.463294Z",
     "iopub.status.busy": "2026-11-11T02:26:45.463128Z",
     "iopub.status.idle": "2026-11-11T02:26:48.571307Z",
     "shell.execute_reply": "2026-11-11T02:26:48.570628Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<path>:96: UserWarning: 1 selected feature(s) hold values more than outlier_cutoff=500 robust standard deviations from their own median (Nuclei_ObjectSkeleton_NumberNonTrunkBranches_mito_skel). They can dominate distances and PCA. Add 'drop_outliers' to operations to drop them, or check where the extreme values come from; on single cells, before aggregation, since a median profile hides a few extreme cells.\n",
      "  func(target, **params, **extra)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'drop_degenerate': 133,\n",
       " 'blocklist': 55,\n",
       " 'drop_na_columns': 0,\n",
       " 'variance_threshold': 0,\n",
       " 'correlation_threshold': 4797}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mt.pp.feature_select(screen)\n",
    "screen.uns[\"mantispy\"][\"feature_select\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c019",
   "metadata": {},
   "source": [
    "### Before and after\n",
    "\n",
    "The correlation heatmap is ordered by feature group, with a line at each group boundary. Here it is for the shape\n",
    "measurements of the whole cell, before selection and after. Blocks along the diagonal are features that measure\n",
    "nearly the same thing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c020",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T02:26:48.572580Z",
     "iopub.status.busy": "2026-11-11T02:26:48.572439Z",
     "iopub.status.idle": "2026-11-11T02:26:49.301777Z",
     "shell.execute_reply": "2026-11-11T02:26:49.301100Z"
    }
   },
   "outputs": [
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S0Ohr8ui\\u002fsLQ0TN9v7z95MJymhvPuPyG+9v07Euw\\u002fo87sh2++0b9QX9P8KHzvP1E41ZWWt+C\\u002fOkRnx3oVrr84v2phJlvrvw1VhVXWOe8\\u002fD9kEFbEH7z8AAAAAAADwPytepjno+O8\\u002fXjHDHbLx7z\\u002f3YXbV\\u002fmXvP4huN2vmnu8\\u002f33e\\u002fa9ykoL+IcfgqCQ3vPx5M0ERriNy\\u002fAO9BBdlPuL9sdwyCPG7MP4\\u002fLw94aI6W\\u002f1Trptwgd6r\\u002f0kLApBPrAP0EXxEzBIeK\\u002f6yqXQiOTzT\\u002ftG\\u002f0EUCfNv9bFBapKx+q\\u002f+LrZRdfi2j\\u002fuo6fxfGSLvxqYlH2ek+O\\u002fuqkrxgrB0T8L3z52pJLMPwtV2qCyd7+\\u002fY9uv1AJc67+5dWZ8yrTWP5GzYtlp4cI\\u002f3wOO6pO1tr8LjfnpOljmv+1LELE9ksQ\\u002fUXmeVoJcwz+\\u002fOoJlSEW3P8HNF8Qpxbu\\u002fj3tQV8Yo679VlO6z52XRP5CM923JwLY\\u002fwXMeMwalrT+WM09FTG68v0QY\\u002fjLpdOe\\u002fwPwhzVRP7z8coq0BEbvuP0UkOfxHoes\\u002fn\\u002fvd\\u002f9bB0r9GPb2fYlvvPxEYPsKEiuC\\u002fldfqnPVjn79nU+RA+gDrv6mdanCpFO8\\u002fSsJBx73a7j8rXqY56PjvPwAAAAAAAPA\\u002fJsPrgC3+7z8RT4T5OzTvP8D+Tdy2de8\\u002fEeb523\\u002fgn79c035JZtHuP+RNt5Mvqdq\\u002fijERz+Ewsr+8Bya\\u002fhQXMP3iTLEmiK5K\\u002f+F1Hh73J6b823x738Ci8P\\u002flxw20yIeK\\u002fm6bovY9\\u002fzz+bfyPlMTTPv1snB57Isuq\\u002f9qPXEFBs2j8kEi13pKegv+z3xRLGb+O\\u002fQpAEMlWz0T\\u002fjzDFzBEfNP2h1MVJD5cG\\u002f0lHyR+Jb67+3pvRaRVvWP+CxTH+hIME\\u002f8QCIwcT8ur8OTXdxDz7mv8DAWNBDOsQ\\u002fs40TSZYBwj+HNH5Ll4C1P\\u002fduTObtBsC\\u002fifXX7NEd67\\u002fu7SDyjRnRPy33HUj8FLM\\u002f190eYYBlpj\\u002fhIfWlhhjAv2juy6c7Qee\\u002fcWBfgro+7z8gu4L4NKbuP68Ui2mXbus\\u002fYbETh\\u002fII07\\u002fxTkJngEvvP1wJ5JOqcuC\\u002fBWBIR+1Olr9hVPhPjd7qv7o408OHB+8\\u002fXWJveg7L7j9eMcMdsvHvPybD64At\\u002fu8\\u002fAAAAAAAA8D\\u002fHdggZxhzvPyVrl8GGZO8\\u002fDuXMLuBrn7\\u002ff20ljy7vuP5g5ca3D9Nm\\u002fMHHIt8ZZsL+Yjx0X99HLPzfTjjd91YK\\u002fOYLkYSSW6b\\u002fEuPPSMVu6P81bNYnGFOK\\u002fYeNluP0I0D\\u002fGrQdwc4zPv5A42DokoOq\\u002fbWdPW3I12j9plILT7oSjv1vM3mVJW+O\\u002f++BLehuh0T+2JSWau43NP9BEM+oJbsK\\u002frBkhQb1G67+xCP6ebDDWP5HZfiWGd8A\\u002fXl8BitNFvL+fsR4EGyzmv5aN5RKX4MM\\u002fn5leJh6RwT8tV8\\u002fOJt60P2RAxQVrncC\\u002fH2\\u002fngKMW678osvGV\\u002fOnQP3t8z\\u002fQ\\u002fqbE\\u002fDvYf0Nf7oz9KOunboqPAv0cYbXbtLee\\u002fLQ+g9xiz7z8JKME795jvP2ig+73L8u0\\u002f54IOxuFVxL+ODCefCK7vP5W\\u002flq71geG\\u002fd6b8Hcwoy7\\u002f60sUXF+rsv9Q3z3rjVO8\\u002ff4tBE9N67z\\u002f3YXbV\\u002fmXvPxFPhPk7NO8\\u002fx3YIGcYc7z8AAAAAAADwP19QfOyW7e8\\u002fqUFJZ6CUqL8Az0a\\u002fhLrvPw6YK43KwOK\\u002fE5ztmny2zb9aShBE0y3OP5xuP\\u002fLRdMS\\u002f8sXCFDOL6r\\u002fbtnG6OCDRPwXWWtQeqOG\\u002fnEhQA18kvj8B6PMOZPq+v99Xrr5PxOm\\u002fXihR6QUx3D+ezCCiwCu6P1gMiUh+weO\\u002fr\\u002fYrRawh0D9Xq55aWH7HP+0hGcmVI4q\\u002f9HvWh5qu6r+CfG4g5tHXP4H1Zh3PaMw\\u002fEtaSDNgnhj8g4h3BHp\\u002fmv+lStTEGqsM\\u002fFWTbLspQyj9pmEqhBEDBP1cOK12UBoe\\u002fwNkabm7+6r+iLM38\\u002fMTRP\\u002fSvwYyiK8Q\\u002fiY2yWDpUwT\\u002f7ErJQJYKevwUETS7JHei\\u002fj7f4eS+f7z9wcGWJP5bvP\\u002fpoknwXgu0\\u002fNm38FOLOyb8Pq7TRqpXvP9nOgnVYTeG\\u002fPTDg+0DHxb\\u002fbKrnPV5Psv7P\\u002f5sATWe8\\u002ftHqaBxl07z+Ibjdr5p7vP8D+Tdy2de8\\u002fJWuXwYZk7z9fUHzslu3vPwAAAAAAAPA\\u002f83wJcCmkpL9kgL5qr7fvP64PI+GeneG\\u002fv7ymhhhUyL\\u002fxpnuQrEvPP1m\\u002fLTlbXMC\\u002fK0vTY9yI6r9gaMPfMn\\u002fNP5Yy3uwSleG\\u002ftszvAh0Rwz+Yp46rhxLEvxP1Zt5jSuq\\u002fh7Bw4A3u2z96PAxC4IOxP95RcsYPneO\\u002fhMpfoZOx0D+usHnJAvDIP7j7hO2\\u002fQ6q\\u002fKGx1EYoB679twMhS3ZnXP+iR6Y7Clck\\u002fvP7dhMVjk78\\u002f3IjJPnTmv\\u002f9McLL57sM\\u002fF\\u002fPfB91LyD\\u002fPJYZz11q+P35EEVl3Bqi\\u002fRrA1FvBt679M491vH73RP1CYhAF8WME\\u002fflsbFUKnvD87ZIxlte+rv+cFtJKk8+e\\u002fVWlZ0yZjrb9FD2M8Zuuqv8lTRz\\u002fH6q2\\u002fx32Rgltkt783uiTIUAmuvzAkMNNzwp0\\u002fPkmwg0nKtj8GD1SmZq6uP2GzskoXka2\\u002fn7CAq\\u002fLArr\\u002ffd79r3KSgvxHm+dt\\u002f4J+\\u002fDuXMLuBrn7+pQUlnoJSov\\u002fN8CXAppKS\\u002fAAAAAAAA8D+Zqp5dit2pv5gY8RzQUbM\\u002fdp6O8eYNuz8grxOFcCKzP+o6Zrg9Bok\\u002fUUwZuCEdoD9MDD8L49Owv3aS324GDKQ\\u002fB4VQqcwxsj\\u002f74ie3A5u1v1k1BlYuHJW\\u002fX1klyg6Gdj8oC2sYXXmqv+byoowH56Q\\u002ft7Rf7XgdoT\\u002fGxWJV6OSNv4RgdNi5iLa\\u002fkWRsa6vokL9yn+FIU+KBv+1SNwZqaK2\\u002f+Vuo5rQ4rL\\u002f8PFdUQ5WnP5Bq9Qw6dJ0\\u002ffF6l2Kdzrr9LCzaPI363v\\u002foK08Mhs76\\u002f8n37XwHoj79EBgsw7Zt2v1sec\\u002fVfOKq\\u002f\\u002f5ddONSjqL9Y60ZzdDu2vx63OvDc9qA\\u002f1UhfBkp+7z\\u002fO1XDv2+fvPwiY97r3hO4\\u002fFFjz6Y78vb9Ui28uM03vP50qqiKeSuG\\u002fTq8DviMO0L+ipd47B5ntv2WfPVTFle8\\u002fzmXAPVDE7z+IcfgqCQ3vP1zTfklm0e4\\u002f39tJY8u77j8Az0a\\u002fhLrvP2SAvmqvt+8\\u002fmaqeXYrdqb8AAAAAAADwP+CR9ruwk+O\\u002fU140gLKS0b\\u002fy65YzUhbMPzQRGERPVci\\u002f\\u002fatzVr8P6r9CX\\u002fFIYOjSP7urTyB4VuK\\u002f2n8LcyV3vD8C38RZmSa1vyk7cXaTbum\\u002fbZn3HheF3D\\u002fsWMv4xUC9P4FUrTJsVeS\\u002fF7CrtZgY0D9tmj\\u002ffi8\\u002fIP+GXMscPOZM\\u002flE79RTIQ6r\\u002fizioKPrnXP5t6YNUKws0\\u002fKNEFWLkdmD8iaCM4\\u002fBLnvws+dfLMd8I\\u002fPmcxVaYUzD+zjaBixl7DP\\u002fgH7f9i1pM\\u002fyU\\u002fX9y7G6r8OjyML\\u002ftLRP0RmbMQfDcU\\u002fjIyWXeTwwz8CkTuk7yiTvxdVH9cVqOi\\u002fao4\\u002frzCA4b\\u002franKwJSDjv5XiCJYSEum\\u002fn9FkSOHg4L\\u002f83P5lY8Lhv2Sy\\u002fn2wxNo\\u002f6drIvJmS6z\\u002fwvHU+E23oPwkB53xxZeK\\u002fdqTTrdlN478eTNBEa4jcv+RNt5Mvqdq\\u002fmDlxrcP02b8OmCuNysDiv64PI+GeneG\\u002fmBjxHNBRsz\\u002fgkfa7sJPjvwAAAAAAAPA\\u002f1WUx37dd6j9VqsOeOW64vzPyXv9G0+U\\u002fCgcHPsmG3z9N9jFGwGPpv+kP5t1mbtQ\\u002fCb+s7Wek2T\\u002fTxx1gjh3gv2MkQHPP3tI\\u002fQt6TOqV+17\\u002fXRNovNWfjv5wLdz+MBNs\\u002f5+xMoo3Rl7\\u002f4q0NKYjKmPzMG9GlSi+K\\u002f6xfYEXmS0z+obW+TqHnRv0Z8NGemAuC\\u002fHwOwIhN+4L\\u002f59KfXHHndP1PLR3a8IKK\\u002f1J0bkWDY17\\u002fVDC1+QhTVv\\u002fFLCy6tc+G\\u002fFy8ZSNTv1j97Lxc2mqbGv1YgAWpok9u\\u002feDPGoJhj4L\\u002fsNlIxrozbv\\u002fiZji3z++A\\u002f\\u002fkbzyl\\u002fwy7+0whPEYdjQv82BC+kcFt6\\u002fljCmsbUy7L+rGEkqKWTOv0MOAS1X68w\\u002fh2dB7xOA7j\\u002fxzMvjnlHfP7lrfwuqq9G\\u002fh+xZREZH078A70EF2U+4v4oxEc\\u002fhMLK\\u002fMHHIt8ZZsL8TnO2afLbNv7+8poYYVMi\\u002fdp6O8eYNuz9TXjSAspLRv9VlMd+3Xeo\\u002fAAAAAAAA8D\\u002fw2y9jj7y\\u002fP7QJyBUuxeQ\\u002fN8CXCIPhvD9qc7wdSezrv4eZhxihOc4\\u002fQkCB8woV4T\\u002fCzEDNO6jqv9XOgjOHsL6\\u002fqJZO5YEbxb8VYl5SA+Lkv7m4H5\\u002fscNQ\\u002fuab6v\\u002fL5uj85P8fADk7BP4BJegOP8Oq\\u002f+gAo526avL9F3\\u002f9i8MG\\u002fv6\\u002fRc0WQ992\\u002flu+RI4QN47\\u002frBBuFhNnUP0egHwOvc6s\\u002fmvjwM0862b9+AIipvZzWvwRFUghhXem\\u002frd3aYvhQpb9X4v510BOqv4xdpC1vUNy\\u002fjxncAhYz4b91OLD0hVTgv14jZMcNm9c\\u002fp+SyfYkPyz8P3MDcIxvNPwUYlGgwacY\\u002f\\u002feBAs8JNz7\\u002fWqsYgJNbHP6Lrbt7Gj7O\\u002fL0gbMzYIoT8q8nkjGEy\\u002fvwZO6XgmvMQ\\u002fxBiidK9Lxj9sdwyCPG7MP7wHJr+FBcw\\u002fmI8dF\\u002ffRyz9aShBE0y3OP\\u002fGme5CsS88\\u002fIK8ThXAisz\\u002fy65YzUhbMP1Wqw545bri\\u002f8NsvY4+8vz8AAAAAAADwP7y3tzYROcW\\u002fFvVkWEC70b\\u002fQvxmv1KXPP0aIC+YVo6w\\u002faajaqKwGv7\\u002fpw8jW3zPSv+DplNbfTdS\\u002fwFQ6PD+c3D\\u002fOvzp99buiP6YiEknEyKM\\u002fV+n\\u002fm1ydrL8F9wMbRR+hv23BDoZc\\u002fM+\\u002fBxfip5gF1b\\u002fMBApFFUfPPxbfuG44YLM\\u002fKjDhBnwemL\\u002fUxJwF5NiTv18LB3TyYWu\\u002f+ONOuRPQkL9qC8QblOjAv\\u002fs6KZo8A9K\\u002fC6maNNl01r+vlbB1LuvAP9AknC4YM6Y\\u002fxNNdsFuEmL9vQWx1l+q0v9n9D7P9dbG\\u002fWSGTNh8Fwr8tIO5CIiXJv+tdYCQo9de\\u002fiyyF+Yw84b+CIm1j8r3Av4yce2t0Xqg\\u002foQvdG4xr5z8q9avNIffSPwlreEqe1cS\\u002fGzsbFBg1x7+Py8PeGiOlv3iTLEmiK5K\\u002fN9OON33Vgr+cbj\\u002fy0XTEv1m\\u002fLTlbXMC\\u002f6jpmuD0GiT80ERhET1XIvzPyXv9G0+U\\u002ftAnIFS7F5D+8t7c2ETnFvwAAAAAAAPA\\u002fb6ZUqcZhwD\\u002fhtGzppKflv5tI\\u002fQxyu8E\\u002fVjy1wvwAyD9+Y\\u002f4DshPdv8\\u002fxi\\u002fiGHJc\\u002fou8Ixcqz1b\\u002fJPv25+ILbv1\\u002fzn2rNoMo\\u002fdWfRWoQ2tr8okmr0pdutP6qEGKDa3dy\\u002fNsT2tuqkqD\\u002fS6C9muhqxvynihowKytS\\u002fDNjKAO7C1b9TwXWyOaPLP575LP1zeqq\\u002fzJlaNf2izr94c\\u002fsIuUHIvzsgrmzmyNe\\u002feB4qyoE8tT9+xCueujunvzOe2KsZ09C\\u002fHHQN7P5N0r9wy0SF9VbLv+jOD2X2CdI\\u002fJ8tFwm2b6b9eKo4UxbPpvxEGXDJj8+i\\u002fLOqRXrBK1T\\u002f6ZrFm+9jpv1VPblnix9s\\u002f8n6ZPzFewj8n8YrGv8znP6nqKZUthem\\u002fqoQDLJHG6b\\u002fVOum3CB3qv\\u002fhdR4e9yem\\u002fOYLkYSSW6b\\u002fyxcIUM4vqvytL02PciOq\\u002fUUwZuCEdoD\\u002f9q3NWvw\\u002fqvwoHBz7Jht8\\u002fN8CXCIPhvD8W9WRYQLvRv2+mVKnGYcA\\u002fAAAAAAAA8D\\u002fZqH6J7z3HvwcbsRLe5do\\u002fpDt2yRHyzb+Z7gUKTRHVP1CH5muIy+g\\u002fO+uIVPz22r9dQ58TWFenv1Ogwl9NeuE\\u002fC5obqJDg1b\\u002fJ\\u002fhganmi7vyklGiC9\\u002f7Y\\u002fFVivZnGf6D\\u002fkMh2vul3Xv6IQPodGZcG\\u002foz4jhQB0pT9fNl84GfPiP7+cvwBQYMy\\u002f4GQ8wltmwL\\u002fNS+cIYeagv9ejxV0Tw6k\\u002f0pVRHpKy5z+WX6bkidTRv73AfMwL\\u002fbW\\u002fNQwwBEF6r78nNJTb38upPwZ1HCW2rOM\\u002f5gSvLDo8zz8T9uekDW7SPxwA0168sN0\\u002fviLmyV6w5z\\u002fH+MiXoD3QP3qmvjPYssm\\u002fqIpdQbnX67\\u002fMgCqtsgDev1+csOp+sdE\\u002f0nsElgKH0z\\u002f0kLApBPrAPzbfHvfwKLw\\u002fxLjz0jFbuj\\u002fbtnG6OCDRP2Bow98yf80\\u002fTAw\\u002fC+PTsL9CX\\u002fFIYOjSP032MUbAY+m\\u002fanO8HUns67\\u002fQvxmv1KXPP+G0bOmkp+W\\u002f2ah+ie89x78AAAAAAADwP0HZTLTh6sS\\u002fMgb0FdbU4r\\u002fPyRWBqDzlP20crWign6Y\\u002fhx3dZHSh0T+kSUixSjLkP2JR7eKmkNC\\u002fXBUIi98zyb\\u002fJ6kQgc+DMv9w460BQoeY\\u002f3H+SvR45mD\\u002fwHexEGLXQPwl2afMFu9w\\u002fPkeZr+jy4T+TrqFY+PjRvy82tB+77bS\\u002f93VP9Jsk1T\\u002fhEhUE4CbQPwiY77emeuQ\\u002fqAiLsSjzqL+gpZbrFWvAP\\u002fsxhJlt7ts\\u002f\\u002fwca0mcy3z8\\u002fuf2gC7\\u002fdP3HlN7tjy9W\\u002fpDtSZCPS4b9oLzlg0PHhv1Gp5IQzYuK\\u002fELnw2\\u002fjptL9bWmSoLk\\u002fiv4DTQsUiX9I\\u002f2FJp9GtqwT+rtPRGNa\\u002fiP9LD6xRMWuO\\u002f0NoN1as\\u002f479BF8RMwSHiv\\u002flxw20yIeK\\u002fzVs1icYU4r8F1lrUHqjhv5Yy3uwSleG\\u002fdpLfbgYMpD+7q08geFbiv+kP5t1mbtQ\\u002fh5mHGKE5zj9GiAvmFaOsP5tI\\u002fQxyu8E\\u002fBxuxEt7l2j9B2Uy04erEvwAAAAAAAPA\\u002fsxNK47eJ0b9ZYxHkHKKev1qHlsq8Zts\\u002fLDL29lUjzr+PWeJTxCrRP558pFSum+U\\u002fMjgM59MNzr85dreqxB\\u002fJvx0Pvf3w5rW\\u002fH83UJf2e3D9WDdQI8s\\u002fHv7CtJzPMyoy\\u002fKC3dUkQSvT+ViYVV6kHlP8kgOQMsd8e\\u002fPFH55DOTw7\\u002fSzu2Gq7mxv7YMjTTYAbe\\u002fJZcErwFf3D8rYlAUwhLKv6\\u002fqmhlmF46\\u002f6AOd4c0Rgr9KLwYfJnLAP6tXeWuVa+Q\\u002fpVMJe5aSwT8bgibiRKu9P6pswdoW8oK\\u002fDU3fVdzg4r\\u002f+47mEX8bAP3+CbR5FrbE\\u002fNORa2w9L4T+yts6NBdagPyNLf4JSn8E\\u002f1YUCqo0UvT\\u002frKpdCI5PNP5um6L2Pf88\\u002fYeNluP0I0D+cSFADXyS+P7bM7wIdEcM\\u002fB4VQqcwxsj\\u002fafwtzJXe8Pwm\\u002frO1npNk\\u002fQkCB8woV4T9pqNqorAa\\u002fv1Y8tcL8AMg\\u002fpDt2yRHyzb8yBvQV1tTiv7MTSuO3idG\\u002fAAAAAAAA8D+efhhiud3ivzhqJFc7JNe\\u002fLnfLgcW+hT+p0ilQU23kvwSYab31sMi\\u002f2NY4fX4A1j\\u002fYgar8N\\u002ffNP+qILZgdjOG\\u002f9xMZDxQ11b\\u002fLHH2nStq\\u002fv4xoSOeX3Na\\u002fmS+wEsiO4r\\u002flHHlGZ9\\u002fHv5kR+JlWrco\\u002fjYipx7P+z7\\u002fB5oselqnOv\\u002f4AifSnFuC\\u002fhF6\\u002f5AGP0b+W3+2\\u002f9UG2P\\u002fqWtzyrI9i\\u002fFx7XLN6U3L8sF0Rm6dvgv3rJT5xVs8C\\u002fCZ6BMfpdvr\\u002fkkwDME2K3v7vO6DyhxrU\\u002fWdyA1qNm7T9IGPeA1+q4vyCIma8AH6G\\u002fZ0TqsRdm6L8hobRwHpLAv5EG2107XLG\\u002fZyp6iQ4LrL\\u002ftG\\u002f0EUCfNv5t\\u002fI+UxNM+\\u002fxq0HcHOMz78B6PMOZPq+v5injquHEsS\\u002f++IntwObtb8C38RZmSa1v9PHHWCOHeC\\u002fwsxAzTuo6r\\u002fpw8jW3zPSv35j\\u002fgOyE92\\u002fme4FCk0R1T\\u002fPyRWBqDzlP1ljEeQcop6\\u002fnn4YYrnd4r8AAAAAAADwPxG\\u002foWp3Ets\\u002fei9VqlJcuL\\u002f6YEqNQrnhPxvCYSwpn62\\u002f1BWaIxYQ0L8Bc+XiMvbMv6PXuXKwT+o\\u002ff08f\\u002fGi+2j+28wMqaPizv3UV6sVJ5NU\\u002fhfTGbjnl4T\\u002fZ8P5\\u002fC66ov0MbQzAC3cO\\u002fbvEnBmNy1D86HUama4nTP06HZ2kJGug\\u002fOR\\u002finFRm1j\\u002fBI8usHHa4v9eyWlTbUNc\\u002fZBgcm8hR3D9fCo8jY\\u002fbeP6CNhduEybS\\u002f\\u002fHRnvXw16b\\u002fTcmTLWjbpvyB+VUtLdua\\u002fcVPhvSEb3j\\u002fK9cxlIiLpv47eGH4hX9o\\u002f0aN8CZI0t7+EKQs4FTHlPwpqnqEX6ui\\u002f4a18PIfB6L\\u002fWxQWqSsfqv1snB57Isuq\\u002fkDjYOiSg6r\\u002ffV66+T8TpvxP1Zt5jSuq\\u002fWTUGVi4clb8pO3F2k27pv2MkQHPP3tI\\u002f1c6CM4ewvr\\u002fg6ZTW303Uv8\\u002fxi\\u002fiGHJc\\u002fUIfma4jL6D9tHK1ooJ+mP1qHlsq8Zts\\u002fOGokVzsk178Rv6FqdxLbPwAAAAAAAPA\\u002fMXuAgi322r8OsKbBdXS9PzQg8\\u002fbj79s\\u002fycy8bxK21L9eZMr7CsLQv70zXP0XV9s\\u002fDuUHpRx56z8zEU73\\u002fGnUv6env6uQhKu\\u002fKWDphAWNyD8bkJDgXK7hPxhh3E+svMi\\u002fmLDM4vqBrb9UX\\u002fSRVzGsPxeSOJf8vNM\\u002f4tBUYt5h6j+iMFBIIqPRv3iViqmk6Wg\\u002fpnj1u7Dysz8idr5bn9rOP0G\\u002fQ81ZZeI\\u002fE\\u002fTsR98y3D9YblIMh+jcP8a3UklBed0\\u002f1tUmRH31or+cQKd6Jl3bP2yUOSpvGc+\\u002fMG5QRl0Jy79TdHUIgEbavwY0aH299ds\\u002fPhCCMW\\u002fs2z\\u002f4utlF1+LaP\\u002faj1xBQbNo\\u002fbWdPW3I12j9eKFHpBTHcP4ewcOAN7ts\\u002fX1klyg6Gdj9tmfceF4XcP0Lekzqlfte\\u002fqJZO5YEbxb\\u002fAVDo8P5zcP6LvCMXKs9W\\u002fO+uIVPz22r+HHd1kdKHRPywy9vZVI86\\u002fLnfLgcW+hT96L1WqUly4vzF7gIIt9tq\\u002fAAAAAAAA8D+xOK5wn+zBPyNmimeB1c6\\u002fMY5T3GZURL+78LCT6hy5P9aQz+5iMaO\\u002fHovrK7N12r\\u002fA2YB8R5HRPyJcYZeD2sw\\u002fK7W1ETzYsj9Boyi+HOHSv2zHNoIprby\\u002fCCMSCAP3aD\\u002fuog5ber+4P4l5LMsItZ6\\u002fSZpA+p712b9ILaewKlvSP9vT6z6uKLo\\u002fBxJ4CWX0vz\\u002fsNpeF8+t+v9cZcmWnzdS\\u002fMPoeUiU8tT9EkMnLoMu6P2\\u002fk7jIgx9A\\u002f8eji4+rD4T\\u002fpinl89qq3P8sLIqv9rsW\\u002fwqXLLLe65b8xTk8ugqjRvwNwJFhmhbg\\u002fbjhdxVUDvT\\u002fuo6fxfGSLvyQSLXekp6C\\u002faZSC0+6Eo7+ezCCiwCu6P3o8DELgg7E\\u002fKAtrGF15qr\\u002fsWMv4xUC9P9dE2i81Z+O\\u002fFWJeUgPi5L\\u002fOvzp99buiP8k+\\u002fbn4gtu\\u002fXUOfE1hXp7+kSUixSjLkP49Z4lPEKtE\\u002fqdIpUFNt5L\\u002f6YEqNQrnhPw6wpsF1dL0\\u002fsTiucJ\\u002fswT8AAAAAAADwP75UYCoZzps\\u002fUgPdHMN2yr\\u002f35onpXe3JvwbT+hgsWeI\\u002fKt6xYdKivz+RnVvmOgO3P1Sr2Lm+4t0\\u002fj729aaSj4z\\u002faHRdlHKSVv1vp0IevecO\\u002fk1IiKymMzj\\u002ff7Q6Dy\\u002ffOPwTUMREx1+E\\u002f2esQylKPtD9Vl4hLz5uWv3LmubGYcds\\u002fGhmIWNSC3z9GdTTs8iLgP3EhGNf7Are\\u002fLl9W\\u002f0+g47\\u002f+l1uu1wXkv2kPgQ2JfOS\\u002fvOengonRtL8e+1MG8wTkv9q8\\u002fBNXi9U\\u002fS0OrmMyKzj82hrM8XWzkPzlKj6bG7uS\\u002f\\u002fuAFDtcG5b8amJR9npPjv+z3xRLGb+O\\u002fW8zeZUlb479YDIlIfsHjv95RcsYPneO\\u002f5vKijAfnpD+BVK0ybFXkv5wLdz+MBNs\\u002fubgfn+xw1D+mIhJJxMijP1\\u002fzn2rNoMo\\u002fU6DCX0164T9iUe3ippDQv558pFSum+U\\u002fBJhpvfWwyL8bwmEsKZ+tvzQg8\\u002fbj79s\\u002fI2aKZ4HVzr++VGAqGc6bPwAAAAAAAPA\\u002fDIUDRyW+0L+nXoIkJx\\u002fBv6JKool33cO\\u002fZfo41MG+3T\\u002fF5YOL8FrMv8dVAewPY8G\\u002frXKWqD5jzj+1IGWcff3nP79UAA2aLMO\\u002fFc3yWn4ryL9EFjq62WHAv024O5x9C8O\\u002fOHSDleCT3z8sKN2dVd3Iv0Sf\\u002f52M57S\\u002fsKt63dsMrr+dx3qqrWG6PwKpuM3gMec\\u002fx0RlUYoi0D8Jnru8mVvQP4hyTixv+8o\\u002f5Nb0kjev0L\\u002f5uiLK3GPPP7Gs7SaQta2\\u002fmOkLLP74tT9zjvT6AFXFv\\u002fELf6APxs8\\u002fysmHQ8Rbzz+6qSvGCsHRP0KQBDJVs9E\\u002f++BLehuh0T+v9itFrCHQP4TKX6GTsdA\\u002ft7Rf7XgdoT8XsKu1mBjQP+fsTKKN0Ze\\u002fuab6v\\u002fL5uj9X6f+bXJ2sv3Vn0VqENra\\u002fC5obqJDg1b9cFQiL3zPJvzI4DOfTDc6\\u002f2NY4fX4A1j\\u002fUFZojFhDQv8nMvG8SttS\\u002fMY5T3GZURL9SA90cw3bKvwyFA0clvtC\\u002fAAAAAAAA8D9dpQ2una7FP4G\\u002fftG8sce\\u002fhafdPyNy1L83E87peE+nvxuE5Kflpse\\u002fb6IepwnQzr+EhH3L7xbQv38Pv+KZF80\\u002fi0uAu69+pD83K0kZYfW5v5j8D7w7use\\u002fZHx+JXxG078218gh1Zakv9qV4hWs78C\\u002fmkL9hXZQy7\\u002fpctbRORHQvxbuSetoU9C\\u002fy\\u002fzkZmqKyj\\u002fKDC1mE97JPx2RwI0vbsI\\u002f3X6STMupxb+mzDrDWHzJP\\u002fYgp+ut9rK\\u002fgHaq2wpuwj+9VpH0ZEu\\u002fv67sGICDPsw\\u002fKJlKzYagyj8L3z52pJLMP+PMMXMER80\\u002ftiUlmruNzT9Xq55aWH7HP66weckC8Mg\\u002fxsViVejkjb9tmj\\u002ffi8\\u002fIP\\u002firQ0piMqY\\u002fOT\\u002fHwA5OwT8F9wMbRR+hvyiSavSl260\\u002fyf4YGp5ou7\\u002fJ6kQgc+DMvzl2t6rEH8m\\u002f2IGq\\u002fDf3zT8Bc+XiMvbMv15kyvsKwtC\\u002fu\\u002fCwk+ocuT\\u002f35onpXe3Jv6degiQnH8G\\u002fXaUNrp2uxT8AAAAAAADwPz1MGwtyu8m\\u002foSxIGAnHy798RDnwRd6Fv1ftt+kQnrC\\u002fyywhCBkww7+0aq1eUUPBv3aSCgLcq9K+5qLCouoekj9CFf9mn0u9PxW1q2CYU8G\\u002f3b4dSgNVyr\\u002ffXpDAhaGqv4KuFqnFZbm\\u002frO7Wa\\u002fxppL8icF5evuLAv83sIajKlsG\\u002fk2BplbiHkr8atDxwrYZ1P2A\\u002f5HYIo8g\\u002fwj1jis236j9wnhNvZm6BP60lXSYxyr6\\u002fHOfJ23no6L+VuKnoh+3Ov8la9gtoTaM\\u002f2flhajfpqj8LVdqgsne\\u002fv2h1MVJD5cG\\u002f0EQz6gluwr\\u002ftIRnJlSOKv7j7hO2\\u002fQ6q\\u002fhGB02LmItr\\u002fhlzLHDzmTPzMG9GlSi+K\\u002fgEl6A4\\u002fw6r9twQ6GXPzPv6qEGKDa3dy\\u002fKSUaIL3\\u002ftj\\u002fcOOtAUKHmPx0Pvf3w5rW\\u002f6ogtmB2M4b+j17lysE\\u002fqP70zXP0XV9s\\u002f1pDP7mIxo78G0\\u002foYLFniP6JKool33cO\\u002fgb9+0byxx789TBsLcrvJvwAAAAAAAPA\\u002fDXcN+YWO1T98C95P31mCvwLcDwFdp9U\\u002fKBytO3Oy4T\\u002fVQ7iM1ba9v6gQ+dybybu\\u002flpE4CTtN0j+3O1cGs7rUPxjPNZ7FyOk\\u002fE4C2ruDS0T\\u002f4xhDqnwawv9ZjWk\\u002fCFNY\\u002fG6yu+3Zw3j\\u002fEcWTBHfzgP8uIIXmo\\u002f8K\\u002fCPBwKkwS6r+e\\u002fcHC2+npvyA+hJxzeee\\u002fuvjdMxe83D\\u002fNq8vfhM7pv2E2XSWs19w\\u002fNlccppNDtr\\u002fvVZ7J7+PlP7gW2zcsUOm\\u002ftBTET4dH6b9j26\\u002fUAlzrv9JR8kfiW+u\\u002frBkhQb1G67\\u002f0e9aHmq7qvyhsdRGKAeu\\u002fkWRsa6vokL+UTv1FMhDqv+sX2BF5ktM\\u002f+gAo526avL8HF+KnmAXVvzbE9rbqpKg\\u002fFVivZnGf6D\\u002fcf5K9HjmYPx\\u002fN1CX9ntw\\u002f9xMZDxQ11b9\\u002fTx\\u002f8aL7aPw7lB6Ucees\\u002fHovrK7N12r8q3rFh0qK\\u002fP2X6ONTBvt0\\u002fhafdPyNy1L+hLEgYCcfLvw13DfmFjtU\\u002fAAAAAAAA8D+3+h\\u002fEqnnUv4x++4o5l7C\\u002fjOH1SluhyD\\u002f89HFBJiThP2CGsa4UWsm\\u002f8l+pTG5prL9HLuBz8Q9Wv8cbsJ43HNo\\u002fWMWeiWwH7D9BQiAT\\u002fvTRvyqB9tMrQow\\u002fD4Bc6eWAsD83HsCe\\u002fZvNPy9v\\u002f1xR4uI\\u002fLMENl\\u002fV81z8o9VD11cXXP6Rf9HH5Atc\\u002fIOnZeFs4pL82WYDyMD7XPxPdz8zGa8u\\u002fYecUeDZawb\\u002fwIi35J3LVv1K4kVpDLdc\\u002fXdRHc6h21z+5dWZ8yrTWP7em9FpFW9Y\\u002fsQj+nmww1j+CfG4g5tHXP23AyFLdmdc\\u002fcp\\u002fhSFPigb\\u002fizioKPrnXP6htb5OoedG\\u002fRd\\u002f\\u002fYvDBv7\\u002fMBApFFUfPP9LoL2a6GrG\\u002f5DIdr7pd17\\u002fwHexEGLXQP1YN1Ajyz8e\\u002fyxx9p0rav7+28wMqaPizvzMRTvf8adS\\u002fwNmAfEeR0T+RnVvmOgO3P8Xlg4vwWsy\\u002fNxPO6XhPp798RDnwRd6Fv3wL3k\\u002ffWYK\\u002ft\\u002fofxKp51L8AAAAAAADwP+RtrBXrOsU\\u002ffFDa07JfrD8pzKsm+QvQvwJg3EmRFLK\\u002fnMkZPLBPhj+XXcLOXKN2vy3zjrO\\u002fjmU\\u002fIJtTXTM\\u002f1L9yfvwslQ7OPyz\\u002frEc8P8I\\u002fbux+6\\u002f1Jtj8zeD\\u002fS50ubPxTkCGBSf9K\\u002fjifuOufwyj\\u002fQmEqbPljNP62HhFO40tQ\\u002faSWumFtF1z8ofLHg3eHLP5JtprEZ5sq\\u002fKYNQdttf3r8RGPVHvGrUvw628\\u002fTk0c0\\u002fpH5mcU0yzz+Rs2LZaeHCP+CxTH+hIME\\u002fkdl+JYZ3wD+B9WYdz2jMP+iR6Y7Clck\\u002f7VI3Bmporb+bemDVCsLNP0Z8NGemAuC\\u002fr9FzRZD33b8W37huOGCzPynihowKytS\\u002fohA+h0Zlwb8JdmnzBbvcP7CtJzPMyoy\\u002fjGhI55fc1r91FerFSeTVP6env6uQhKu\\u002fIlxhl4PazD9Uq9i5vuLdP8dVAewPY8G\\u002fG4Tkp+Wmx79X7bfpEJ6wvwLcDwFdp9U\\u002fjH77ijmXsL\\u002fkbawV6zrFPwAAAAAAAPA\\u002fHIdNosGd2T++lSyFxdzCv9vXVRTTjb2\\u002fxCxX0uLwvj8\\u002fBM6IRvTHP8c\\u002flYYfo9U\\u002fkNddD4Rdtb8VAUEmNaGdP+OKBQt6g9c\\u002fpPBHdZ\\u002f32z8lPU1Q16rVP8xin7S91MW\\u002fGbXZLFKtbL\\u002fuvbuaj+SKP8hUMgAO98Q\\u002fE+UusY3a4T+UU9RSCcWNP7zap73RpcG\\u002fPmrNr1mW478E9fCaCTHJv7DPrPx17Jg\\u002foOiR+s2aoz\\u002ffA47qk7W2v\\u002fEAiMHE\\u002fLq\\u002fXl8BitNFvL8S1pIM2CeGP7z+3YTFY5O\\u002f+Vuo5rQ4rL8o0QVYuR2YPx8DsCITfuC\\u002flu+RI4QN478qMOEGfB6YvwzYygDuwtW\\u002foz4jhQB0pT8+R5mv6PLhPygt3VJEEr0\\u002fmS+wEsiO4r+F9MZuOeXhPylg6YQFjcg\\u002fK7W1ETzYsj+Pvb1ppKPjP61ylqg+Y84\\u002fb6IepwnQzr\\u002fLLCEIGTDDvygcrTtzsuE\\u002fjOH1SluhyD98UNrTsl+sPxyHTaLBndk\\u002fAAAAAAAA8D\\u002farXdqYn60P+SQLHGFVMC\\u002fUHpELpwuyj+g2seFvaHOP7SD2Lhe5uA\\u002fouHq5OJBxj9ki7Xje9Kjv7ejFsHDq9Y\\u002f3vVSdSQG4T\\u002f5Kufls\\u002fviP1qwnAH3QJg\\u002fOeVh3y905r\\u002faMp9e3czmv8XYR5FXEue\\u002fEhfRwFKesb+0WXFgL7Lmv7xvKRDEQ9k\\u002f2E0wBfrAzz9u\\u002fsrt\\u002fdjmP\\u002f5xyhwGdee\\u002fU7tpAHiU578LjfnpOljmvw5Nd3EPPua\\u002fn7EeBBss5r8g4h3BHp\\u002fmvz\\u002fciMk+dOa\\u002f\\u002fDxXVEOVpz8iaCM4\\u002fBLnv\\u002fn0p9cced0\\u002f6wQbhYTZ1D\\u002fUxJwF5NiTv1PBdbI5o8s\\u002fXzZfOBnz4j+TrqFY+PjRv5WJhVXqQeU\\u002f5Rx5Rmffx7\\u002fZ8P5\\u002fC66ovxuQkOBcruE\\u002fQaMovhzh0r\\u002faHRdlHKSVv7UgZZx9\\u002fec\\u002fhIR9y+8W0L+0aq1eUUPBv9VDuIzVtr2\\u002f\\u002fPRxQSYk4T8pzKsm+QvQv76VLIXF3MK\\u002f2q13amJ+tD8AAAAAAADwP\\u002ft0SemwBcS\\u002fEFAp7zz0yr+6YMuF6pe5vyqr+GCHDsG\\u002fo+Rr0GLR4T9OqoHmSbvNv8HUoyjh57u\\u002fZq+ql5P3tr+lUhEjPTvQP4UngIKIpuk\\u002fTH\\u002fZGF4Vwj88IqRNmjDCP2PEJZc9SsA\\u002fyl5qwYnxwb+WYSdhGZbCP0qWo7C4u6+\\u002fedj0TMYNpz9gEQ\\u002fvHzK3v7nXOvkAvME\\u002fav9gnDPawT\\u002ftSxCxPZLEP8DAWNBDOsQ\\u002flo3lEpfgwz\\u002fpUrUxBqrDP\\u002f9McLL57sM\\u002fkGr1DDp0nT8LPnXyzHfCP1PLR3a8IKK\\u002fR6AfA69zqz9fCwd08mFrv575LP1zeqq\\u002fv5y\\u002fAFBgzL8vNrQfu+20v8kgOQMsd8e\\u002fmRH4mVatyj9DG0MwAt3Dvxhh3E+svMi\\u002fbMc2gimtvL9b6dCHr3nDv79UAA2aLMO\\u002ffw+\\u002f4pkXzT92kgoC3KvSvqgQ+dybybu\\u002fYIaxrhRayb8CYNxJkRSyv9vXVRTTjb2\\u002f5JAscYVUwL\\u002f7dEnpsAXEvwAAAAAAAPA\\u002fuWzSYtgAvT\\u002fk1Nx5QPyov5qEbByGsbi\\u002f2jY\\u002fFaIuxr\\u002fC5KHhK1Cev+Vk32fZNr2\\u002f8aQYbbADwL\\u002fparN+9ym\\u002fv1L\\u002fkdwC3cS\\u002fl22FuefqyT8CpsgHYu7LPy7EFLKO29E\\u002fkExv+iHP0z9WgR\\u002fgeIDKP2In6faEfcG\\u002fc7uEqzcR2L8T7zg7eynRvwO2aK\\u002fYsss\\u002fLLabGPMbzT9ReZ5WglzDP7ONE0mWAcI\\u002fn5leJh6RwT8VZNsuylDKPxfz3wfdS8g\\u002ffF6l2Kdzrr8+ZzFVphTMP9SdG5Fg2Ne\\u002fmvjwM0862b\\u002f44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      "text/plain": [
       "<Figure size 1200x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shape = ((screen.var[\"object\"] == \"Cells\") & (screen.var[\"feature_group\"] == \"AreaShape\")).to_numpy()\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
    "mt.pl.feature_correlation(screen[:, shape].copy(), key=None, ax=axes[0])\n",
    "axes[0].set_title(f\"before: {int(shape.sum())} features\")\n",
    "kept = shape & screen.var[\"selected\"].to_numpy()\n",
    "mt.pl.feature_correlation(screen[:, kept].copy(), key=None, ax=axes[1])\n",
    "axes[1].set_title(f\"after: {int(kept.sum())} features\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c021",
   "metadata": {},
   "source": [
    "The panel titles give the shape measurements of the whole cell before and after selection. The large block of features that move together\n",
    "collapses to a few, and what remains correlates below the cutoff. Strongly negative pairs survive, because the cut\n",
    "uses the signed correlation, as pycytominer's does. `pl.feature_groups` shows which feature families lost\n",
    "features to selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c022",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T02:26:49.303633Z",
     "iopub.status.busy": "2026-11-11T02:26:49.303483Z",
     "iopub.status.idle": "2026-11-11T02:26:50.252043Z",
     "shell.execute_reply": "2026-11-11T02:26:50.251409Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n",
    "mt.pl.feature_groups(screen, ax=axes[0])\n",
    "axes[0].set_title(\"before\")\n",
    "mt.pl.feature_groups(screen, key=\"selected\", ax=axes[1])\n",
    "axes[1].set_title(\"after\")\n",
    "axes[0].get_legend().remove()  # both panels share the channels; one legend on the right is enough\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c022a",
   "metadata": {},
   "source": [
    "### Removing multivariate redundancy\n",
    "\n",
    "`correlation_threshold` is pairwise: it drops one feature from each pair correlated above the cutoff. A\n",
    "feature that is a linear combination of *several* others, a texture that tracks the sum of two neighbouring\n",
    "scales, say, need not correlate strongly with any single one, so the pairwise cut keeps it even though it\n",
    "carries no new information.\n",
    "\n",
    "`decorrelate` removes exactly that. A rank-revealing, column-pivoted QR {cite:p}`Businger_1965,Golub_2013`\n",
    "orders the features so that each is the one least explained by those already kept, and drops a feature once\n",
    "the features kept before it predict it at multiple correlation `decorr_threshold`. One decomposition does it,\n",
    "cheap enough to run at several strengths, and it keeps original features rather than projecting to components.\n",
    "It is experimental and not part of pycytominer, so it is opt-in via `decorrelate=True`.\n",
    "\n",
    "How hard to decorrelate is a judgement call, so rather than trust one number, run it at a few thresholds and\n",
    "score each against the biological outcome you care about. Below: information retained, technical replicate\n",
    "retrieval, and biological activity retrieval (perturbation vs. control) {cite:p}`Kalinin_2025`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c022b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T02:26:50.253603Z",
     "iopub.status.busy": "2026-11-11T02:26:50.253456Z",
     "iopub.status.idle": "2026-11-11T02:27:21.719526Z",
     "shell.execute_reply": "2026-11-11T02:27:21.718879Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<path>:96: UserWarning: 1 selected feature(s) hold values more than outlier_cutoff=500 robust standard deviations from their own median (Nuclei_ObjectSkeleton_NumberNonTrunkBranches_mito_skel). They can dominate distances and PCA. Add 'drop_outliers' to operations to drop them, or check where the extreme values come from; on single cells, before aggregation, since a median profile hides a few extreme cells.\n",
      "  func(target, **params, **extra)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<path>:96: UserWarning: 1 selected feature(s) hold values more than outlier_cutoff=500 robust standard deviations from their own median (Nuclei_ObjectSkeleton_NumberNonTrunkBranches_mito_skel). They can dominate distances and PCA. Add 'drop_outliers' to operations to drop them, or check where the extreme values come from; on single cells, before aggregation, since a median profile hides a few extreme cells.\n",
      "  func(target, **params, **extra)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<path>:96: UserWarning: 1 selected feature(s) hold values more than outlier_cutoff=500 robust standard deviations from their own median (Nuclei_ObjectSkeleton_NumberNonTrunkBranches_mito_skel). They can dominate distances and PCA. Add 'drop_outliers' to operations to drop them, or check where the extreme values come from; on single cells, before aggregation, since a median profile hides a few extreme cells.\n",
      "  func(target, **params, **extra)\n"
     ]
    },
    {
     "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>features</th>\n",
       "      <th>information kept (R2)</th>\n",
       "      <th>replicate mAP</th>\n",
       "      <th>activity mAP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>exact</th>\n",
       "      <td>854</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.219</td>\n",
       "      <td>0.632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decorrelate 0.99</th>\n",
       "      <td>518</td>\n",
       "      <td>0.987</td>\n",
       "      <td>0.216</td>\n",
       "      <td>0.614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decorrelate 0.95</th>\n",
       "      <td>273</td>\n",
       "      <td>0.917</td>\n",
       "      <td>0.211</td>\n",
       "      <td>0.605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>decorrelate 0.9</th>\n",
       "      <td>181</td>\n",
       "      <td>0.861</td>\n",
       "      <td>0.212</td>\n",
       "      <td>0.597</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  features  information kept (R2)  replicate mAP  activity mAP\n",
       "exact                  854                  1.000          0.219         0.632\n",
       "decorrelate 0.99       518                  0.987          0.216         0.614\n",
       "decorrelate 0.95       273                  0.917          0.211         0.605\n",
       "decorrelate 0.9        181                  0.861          0.212         0.597"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import RidgeCV\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "\n",
    "selected = mt.pp.subset_features(screen)  # the pycytominer set\n",
    "reference = np.nan_to_num(np.asarray(selected.X, dtype=np.float64))\n",
    "reference = (reference - reference.mean(0)) / (reference.std(0) + 1e-9)\n",
    "rng = np.random.default_rng(0)\n",
    "\n",
    "\n",
    "def information_kept(kept, n=30):\n",
    "    \"\"\"Cross-validated R2 predicting held-out pycytominer features from the kept subset.\"\"\"\n",
    "    mask = selected.var_names.isin(kept.var_names)\n",
    "    block = reference[:, mask]\n",
    "    scores = []\n",
    "    for c in rng.choice(selected.n_vars, size=n, replace=False):\n",
    "        y = reference[:, c]\n",
    "        pred = cross_val_predict(RidgeCV(), block, y, cv=5)\n",
    "        scores.append(1.0 - ((y - pred) ** 2).sum() / ((y - y.mean()) ** 2).sum())\n",
    "    return round(float(np.mean(scores)), 3)\n",
    "\n",
    "\n",
    "def activity(kept, mode):\n",
    "    \"\"\"Mean mAP over the treated wells: replicate (technical) or activity (biological) retrieval.\"\"\"\n",
    "    adata = kept.copy()\n",
    "    mt.tl.map(adata, mode=mode, null_size=1000, seed=0, **({\"reference\": None} if mode == \"replicability\" else {}))\n",
    "    treated = adata.obs[\"Metadata_Control_Type\"].to_numpy() != \"negcon\"\n",
    "    return round(float(adata.obs[\"map\"].to_numpy()[treated].mean()), 3)\n",
    "\n",
    "\n",
    "# Sweep decorrelate at a few thresholds and score each selected set against the metric that matters.\n",
    "sweep = mt.pp.decorr_threshold_sweep(\n",
    "    selected,\n",
    "    [0.99, 0.95, 0.9],\n",
    "    {\n",
    "        \"information kept (R2)\": information_kept,\n",
    "        \"replicate mAP\": lambda kept: activity(kept, \"replicability\"),\n",
    "        \"activity mAP\": lambda kept: activity(kept, \"activity\"),\n",
    "    },\n",
    "    operations=(),\n",
    ")\n",
    "\n",
    "baseline = {\n",
    "    \"n_kept\": selected.n_vars,\n",
    "    \"information kept (R2)\": 1.0,\n",
    "    \"replicate mAP\": activity(selected, \"replicability\"),\n",
    "    \"activity mAP\": activity(selected, \"activity\"),\n",
    "}\n",
    "table = pd.concat([pd.DataFrame([baseline]), sweep.drop(columns=\"threshold\")], ignore_index=True)\n",
    "table.index = [\"exact\", \"decorrelate 0.99\", \"decorrelate 0.95\", \"decorrelate 0.9\"]\n",
    "table = table.rename(columns={\"n_kept\": \"features\"})[\n",
    "    [\"features\", \"information kept (R2)\", \"replicate mAP\", \"activity mAP\"]\n",
    "]\n",
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c022c",
   "metadata": {},
   "source": [
    "`decorrelate` is a dial, and there is no free lunch: a lower threshold removes more features, but because the\n",
    "redundant ones still carry a sliver of unique signal, retrieval falls with them. The technical replicate\n",
    "retrieval is nearly flat here; the biological activity retrieval is the one that pays, gently at 0.99, where\n",
    "the set shrinks by about 40%, and more by 0.9. On a screen with real mechanism-of-action structure the trade is often\n",
    "kinder still, because dropping redundant, noisy features can tighten same-MOA neighbourhoods, but pki has too\n",
    "few compounds per MOA to show that here.\n",
    "\n",
    "So the recommendation is the workflow above rather than a fixed cutoff: sweep a few thresholds, compare against\n",
    "the metric that matters for your screen, and keep the smallest set that still holds it. `decorr_threshold=0.99`\n",
    "is a sensible place to start."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c023",
   "metadata": {},
   "source": [
    "### The blocklist\n",
    "\n",
    "The default blocklist is pycytominer's, copied unchanged with its source recorded in the file. It lists features\n",
    "known to be unreliable: twenty Manders and twenty rank-weighted colocalization coefficients, plus the three\n",
    "highest nuclear granularity bands in each channel. `MeasureColocalization` writes Manders, RWC and Costes\n",
    "coefficients, and only the first two are on the list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c024",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T02:27:21.721182Z",
     "iopub.status.busy": "2026-11-11T02:27:21.720799Z",
     "iopub.status.idle": "2026-11-11T02:27:21.725999Z",
     "shell.execute_reply": "2026-11-11T02:27:21.725530Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'on the list': 55,\n",
       " 'in this screen': 55,\n",
       " 'examples': ['Nuclei_Correlation_Manders_AGP_DNA',\n",
       "  'Nuclei_Correlation_Manders_AGP_ER',\n",
       "  'Nuclei_Correlation_Manders_AGP_Mito']}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blocked = mt.pp.feature_blocklist()\n",
    "{\"on the list\": len(blocked), \"in this screen\": int(screen.var_names.isin(blocked).sum()), \"examples\": blocked[:3]}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cscale0",
   "metadata": {},
   "source": [
    "## Scaling correlation to large screens\n",
    "\n",
    "`correlation_threshold` compares every pair of features, so its cost grows with the square of the feature count;\n",
    "on a screen with tens of thousands of features the exact pass takes minutes.\n",
    "\n",
    "mantispy keeps the exact pass as the default, matching pycytominer {cite:p}`Serrano_2025`. Passing `corr_window`\n",
    "switches to a faster two-pass approximation: pass one sorts features by name (a CellProfiler family sorts\n",
    "together) and prunes redundancy only within a sliding window, which is cheap and whittles the list down; pass two\n",
    "runs the exact all-pairs correlation on the survivors, a much smaller set, so it catches the cross-family\n",
    "redundancy the windows could not see while its quadratic cost stays small. A window of 500 is a good default.\n",
    "\n",
    "Be clear about the deviation from the standard: the fast pass does not reproduce the exact pass feature for\n",
    "feature. Correlated features are near-duplicates, so which member survives is close to arbitrary, and the fast\n",
    "pass keeps a different set. What matters is whether the reduced space keeps the same information and the same\n",
    "biology. The next cells measure exactly that, across strategies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cscale1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T02:27:21.727121Z",
     "iopub.status.busy": "2026-11-11T02:27:21.726979Z",
     "iopub.status.idle": "2026-11-11T02:29:01.629193Z",
     "shell.execute_reply": "2026-11-11T02:29:01.628659Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<path>:8: UserWarning: 133 of 5839 features have no reference values to centre on in 8 group(s) of 'Metadata_Plate' among the rows selected by reference='negcon': BR00122970 (104 reference rows), BR00122971 (104 reference rows), BR00122972 (104 reference rows), BR00122973 (104 reference rows), BR00122974 (104 reference rows) and 3 more. Every reference value of those features is missing or infinite there. Their centre is set to 0 where it is missing and their scale to 1, so the values measured outside the reference rows pass through unnormalized instead of becoming NaN, and are not comparable across groups. 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().\n",
      "  mt.pp.normalize(base, method=\"mad_robustize\", by=\"Metadata_Plate\")\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<path>:96: UserWarning: 1 selected feature(s) hold values more than outlier_cutoff=500 robust standard deviations from their own median (Nuclei_ObjectSkeleton_NumberNonTrunkBranches_mito_skel). They can dominate distances and PCA. Add 'drop_outliers' to operations to drop them, or check where the extreme values come from; on single cells, before aggregation, since a median profile hides a few extreme cells.\n",
      "  func(target, **params, **extra)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<path>:96: UserWarning: 1 selected feature(s) hold values more than outlier_cutoff=500 robust standard deviations from their own median (Nuclei_ObjectSkeleton_NumberBranchEnds_mito_skel). They can dominate distances and PCA. Add 'drop_outliers' to operations to drop them, or check where the extreme values come from; on single cells, before aggregation, since a median profile hides a few extreme cells.\n",
      "  func(target, **params, **extra)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<path>:96: UserWarning: 1 selected feature(s) hold values more than outlier_cutoff=500 robust standard deviations from their own median (Nuclei_ObjectSkeleton_NumberBranchEnds_mito_skel). They can dominate distances and PCA. Add 'drop_outliers' to operations to drop them, or check where the extreme values come from; on single cells, before aggregation, since a median profile hides a few extreme cells.\n",
      "  func(target, **params, **extra)\n"
     ]
    },
    {
     "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>features kept</th>\n",
       "      <th>time (s)</th>\n",
       "      <th>overlap with exact (Jaccard)</th>\n",
       "      <th>information kept (R2 vs exact)</th>\n",
       "      <th>replicate mAP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>exact</th>\n",
       "      <td>870</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fast (window 500)</th>\n",
       "      <td>1002</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.560</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fast (window 1500)</th>\n",
       "      <td>1010</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.561</td>\n",
       "      <td>0.992</td>\n",
       "      <td>0.363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    features kept  time (s)  overlap with exact (Jaccard)  \\\n",
       "exact                         870       3.2                         1.000   \n",
       "fast (window 500)            1002       0.8                         0.560   \n",
       "fast (window 1500)           1010       1.1                         0.561   \n",
       "\n",
       "                    information kept (R2 vs exact)  replicate mAP  \n",
       "exact                                        1.000          0.362  \n",
       "fast (window 500)                            0.995          0.363  \n",
       "fast (window 1500)                           0.992          0.363  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Normalize a copy so the downstream mAP is meaningful, then compare selection strategies on it.\n",
    "base = screen.copy()\n",
    "mt.pp.normalize(base, method=\"mad_robustize\", by=\"Metadata_Plate\")\n",
    "matrix = np.nan_to_num(np.asarray(base.X, dtype=np.float64))\n",
    "matrix = (matrix - matrix.mean(0)) / (matrix.std(0) + 1e-9)\n",
    "rng = np.random.default_rng(0)\n",
    "\n",
    "\n",
    "def reconstruct_r2(target, predictors, n=30):\n",
    "    \"\"\"Cross-validated R2 predicting each target feature from the predictor feature set.\"\"\"\n",
    "    # cross-validated R2 predicting each target feature from the predictor set: is the information kept\n",
    "    cols = rng.choice(target, size=min(n, len(target)), replace=False)\n",
    "    block = matrix[:, predictors]\n",
    "    scores = []\n",
    "    for c in cols:\n",
    "        y = matrix[:, c]\n",
    "        tot = ((y - y.mean()) ** 2).sum()\n",
    "        if tot == 0:\n",
    "            scores.append(1.0)\n",
    "            continue\n",
    "        pred = cross_val_predict(RidgeCV(), block, y, cv=5)\n",
    "        scores.append(1.0 - ((y - pred) ** 2).sum() / tot)\n",
    "    return float(np.mean(scores))\n",
    "\n",
    "\n",
    "def select(**kwargs):\n",
    "    \"\"\"Run feature_select with these kwargs; return the boolean selection mask and the runtime.\"\"\"\n",
    "    adata = base.copy()\n",
    "    start = time.perf_counter()\n",
    "    mt.pp.feature_select(adata, operations=(\"drop_degenerate\", \"correlation_threshold\"), **kwargs)\n",
    "    return adata.var[\"selected\"].to_numpy(dtype=bool), time.perf_counter() - start\n",
    "\n",
    "\n",
    "def replicate_map(mask):\n",
    "    \"\"\"Mean replicate-retrieval mAP on the subset of features flagged by mask.\"\"\"\n",
    "    # downstream biology: do a compound's replicate wells retrieve each other in the reduced space\n",
    "    adata = base[:, mask].copy()\n",
    "    mt.tl.map(adata, mode=\"replicability\", null_size=1000, reference=None, seed=0)\n",
    "    return float(adata.obs[\"map\"].mean())\n",
    "\n",
    "\n",
    "strategies = {\"exact\": {}, \"fast (window 500)\": {\"corr_window\": 500}, \"fast (window 1500)\": {\"corr_window\": 1500}}\n",
    "masks = {name: select(**kw) for name, kw in strategies.items()}\n",
    "exact_mask = masks[\"exact\"][0]\n",
    "exact_idx = np.flatnonzero(exact_mask)\n",
    "\n",
    "rows = []\n",
    "for name, (mask, seconds) in masks.items():\n",
    "    idx = np.flatnonzero(mask)\n",
    "    rows.append(\n",
    "        {\n",
    "            \"features kept\": int(mask.sum()),\n",
    "            \"time (s)\": round(seconds, 1),\n",
    "            \"overlap with exact (Jaccard)\": round((exact_mask & mask).sum() / (exact_mask | mask).sum(), 3),\n",
    "            \"information kept (R2 vs exact)\": 1.0 if name == \"exact\" else round(reconstruct_r2(exact_idx, idx), 3),\n",
    "            \"replicate mAP\": round(replicate_map(mask), 3),\n",
    "        }\n",
    "    )\n",
    "pd.DataFrame(rows, index=list(strategies))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cscale2",
   "metadata": {},
   "source": [
    "In the table above, the fast selections overlap the exact one only about half at the feature level\n",
    "(this is the deviation from pycytominer, and it is real), yet they reconstruct the exact features at an R2 near 1\n",
    "and reach the same replicate mAP. Different features, same information, same biology. Window size trades a little\n",
    "speed for a slightly larger, still-equivalent set. The exact row here keeps 870 features and a replicate\n",
    "mAP of 0.362, unlike the 854 and 0.219 of the decorrelate table, because that table normalizes against the\n",
    "negative controls and this one does not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cscale3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T02:29:01.630842Z",
     "iopub.status.busy": "2026-11-11T02:29:01.630685Z",
     "iopub.status.idle": "2026-11-11T02:29:22.035957Z",
     "shell.execute_reply": "2026-11-11T02:29:22.035452Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'features': 20000,\n",
       " 'exact': '18.4s, 1500 kept',\n",
       " 'fast (window 500)': '1.1s, 1500 kept',\n",
       " 'speedup': '16.3x'}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from anndata import AnnData\n",
    "\n",
    "rng = np.random.default_rng(0)\n",
    "n_cells, n_features, n_drivers = 2000, 20_000, 1500\n",
    "drivers = rng.standard_normal((n_cells, n_drivers), dtype=np.float32)\n",
    "assign = np.sort(rng.integers(0, n_drivers, size=n_features))\n",
    "X = drivers[:, assign] + rng.standard_normal((n_cells, n_features), dtype=np.float32) * 0.05\n",
    "names = [f\"Cells_Texture_Driver{assign[i]:05d}_{i:06d}\" for i in range(n_features)]\n",
    "big = AnnData(X, var=pd.DataFrame(index=names))\n",
    "\n",
    "\n",
    "def timed(**kwargs):\n",
    "    \"\"\"Run feature_select on the synthetic screen; return the runtime and the features kept.\"\"\"\n",
    "    adata = big.copy()\n",
    "    start = time.perf_counter()\n",
    "    mt.pp.feature_select(adata, operations=(\"correlation_threshold\",), **kwargs)\n",
    "    return time.perf_counter() - start, int(adata.var[\"selected\"].sum())\n",
    "\n",
    "\n",
    "exact_time, exact_kept = timed()\n",
    "fast_time, fast_kept = timed(corr_window=500)\n",
    "{\n",
    "    \"features\": n_features,\n",
    "    \"exact\": f\"{exact_time:.1f}s, {exact_kept} kept\",\n",
    "    \"fast (window 500)\": f\"{fast_time:.1f}s, {fast_kept} kept\",\n",
    "    \"speedup\": f\"{exact_time / fast_time:.1f}x\",\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cscale4",
   "metadata": {},
   "source": [
    "Guidance: use the exact default for faithful pycytominer parity, and reach for `corr_window=500` when a screen has\n",
    "many thousands of features and the correlation step dominates. The saving grows with the feature count, and the\n",
    "reduced space carries the same information and the same replicate signal."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c025",
   "metadata": {},
   "source": [
    "## Looking at the plates\n",
    "\n",
    "Plate layout artifacts are easiest to see on a plate map. `pl.plate` draws any feature or `obs` column; here, how\n",
    "many cells each well of the eight plates held."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c026",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T02:29:22.037654Z",
     "iopub.status.busy": "2026-11-11T02:29:22.037487Z",
     "iopub.status.idle": "2026-11-11T02:29:23.060676Z",
     "shell.execute_reply": "2026-11-11T02:29:23.060128Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x640 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mt.pl.plate(screen, color=\"Metadata_CellCount\", groupby=\"Metadata_Plate\", ncols=4);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c027",
   "metadata": {},
   "source": [
    "## Handing off\n",
    "\n",
    "`get.to_dataframe` returns the flat, metadata-first table that pycytominer {cite:p}`Serrano_2025`, cytominer-eval\n",
    "and most published scripts expect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c028",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-11T02:29:23.062184Z",
     "iopub.status.busy": "2026-11-11T02:29:23.062040Z",
     "iopub.status.idle": "2026-11-11T02:29:23.113929Z",
     "shell.execute_reply": "2026-11-11T02:29:23.113441Z"
    }
   },
   "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>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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "\n",
       "    Metadata_mmoles_per_liter Metadata_solvent Metadata_Plate  \n",
       "0                         NaN             DMSO     BR00122970  \n",
       "1                         NaN             DMSO     BR00122970  \n",
       "10                        NaN             DMSO     BR00122970  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = mt.get.to_dataframe(mt.pp.subset_features(screen))\n",
    "frame.iloc[:3, :6]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c029",
   "metadata": {},
   "source": [
    "Next: [Plate artifacts and corrections](artifacts.ipynb)."
   ]
  }
 ],
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