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    "# Hits, effects and cell loss\n",
    "\n",
    "This page asks which treatments moved away from the controls, which measurements changed, and whether a hit is\n",
    "a phenotype or cells that died. Hit calls come with a permutation p-value and feature effects with\n",
    "Mann-Whitney q-values.\n",
    "\n",
    "Potency has its own page, [Concentration response](dose_response.ipynb). The same questions asked of single\n",
    "cells are on [What the well median hides](../single_cells/heterogeneity.ipynb)."
   ]
  },
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    "import matplotlib.pyplot as plt\n",
    "import scanpy as sc\n",
    "\n",
    "import mantispy as mt\n",
    "\n",
    "# Palette shared with the mantispy plotters (mt.pl.hits, mt.pl.cytotoxicity):\n",
    "# crimson = significant / flagged, blue = ordinary / ok, grey = background.\n",
    "ACCENT = \"crimson\"  # significant / flagged / highlighted (mt.pl.hits \"hit\")\n",
    "ACCENT_DARK = \"#7a1b1f\"  # a second flagged compound, darker crimson\n",
    "NEUTRAL = \"tab:blue\"  # ordinary / not-flagged (mt.pl.cytotoxicity \"ok\")\n",
    "NULL = \"lightgrey\"  # not-significant / background (mt.pl.hits \"not called\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1534cf71",
   "metadata": {
    "execution": {
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   "cell_type": "markdown",
   "id": "c002",
   "metadata": {},
   "source": [
    "## A screen with replicates\n",
    "\n",
    "`pki` is the JUMP pilot's kinase inhibitor set: fifteen compounds in U2OS, eleven of them at three doses, with 32 to 64\n",
    "replicate wells per treatment {cite:p}`Chandrasekaran_2023`. That much replication is unusual, and it makes this\n",
    "a good place to see what a hit call does when the design is generous.\n",
    "\n",
    "The preparation is the one from [Normalize, select, aggregate](../profiles/normalize_and_select.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c003",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2026-10-21T01:02:37.910690Z",
     "shell.execute_reply": "2026-10-21T01:02:37.910089Z"
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    {
     "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'. 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",
      "  mt.pp.normalize(wells, by=\"Metadata_Plate\", reference=\"negcon\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 3072 × 850\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', 'degenerate_scale', 'selected'\n",
       "    uns: 'mantispy', 'pca'\n",
       "    obsm: 'X_pca'\n",
       "    varm: 'PCs'\n",
       "    layers: None (.X)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wells = mt.ds.pki()\n",
    "mt.pp.normalize(wells, by=\"Metadata_Plate\", reference=\"negcon\")\n",
    "mt.pp.feature_select(wells, na_cutoff=0.0)\n",
    "wells = mt.pp.subset_features(wells)\n",
    "sc.pp.pca(wells, n_comps=20)\n",
    "wells"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c004",
   "metadata": {},
   "source": [
    "## Calling hits\n",
    "\n",
    "{func}`~mantispy.tl.hit_calling` scores each group's median distance from the control centroid, measured in the\n",
    "controls' own covariance so that directions the controls already vary in count for less. The null is the other\n",
    "ways to draw a group of that size from the group and the held-out controls pooled.\n",
    "\n",
    "With 850 features and 832 control wells, half of them held out, the covariance would be singular, so the distance is\n",
    "read on a PCA representation. The function says so itself if you forget."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c005",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2026-10-21T01:02:37.918132Z",
     "iopub.status.idle": "2026-10-21T01:02:38.593507Z",
     "shell.execute_reply": "2026-10-21T01:02:38.591949Z"
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mt.tl.hit_calling(wells, groupby=\"Metadata_Perturbation\", use_rep=\"X_pca\", n_permutations=1000)\n",
    "mt.pl.hits(wells, label_top=6);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c006",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T01:02:38.596302Z",
     "iopub.status.busy": "2026-10-21T01:02:38.596146Z",
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     "shell.execute_reply": "2026-10-21T01:02:38.605715Z"
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_obs</th>\n",
       "      <th>distance</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>qvalue</th>\n",
       "      <th>is_hit</th>\n",
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       "      <th>BRD-U00086674-001-01-9@0.4</th>\n",
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       "      <th>BRD-K15179513-001-03-4@2.0</th>\n",
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       "      <td>True</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            n_obs   distance    pvalue    qvalue  is_hit\n",
       "group                                                                   \n",
       "BRD-U00086674-001-01-9@0.4     64  53.170085  0.000999  0.001225    True\n",
       "BRD-U00086675-001-01-9@0.4     64  40.179795  0.000999  0.001225    True\n",
       "BRD-K95785537-001-26-9@2.0     32  22.426508  0.000999  0.001225    True\n",
       "BRD-U00086675-001-01-9@0.2     64  21.439233  0.000999  0.001225    True\n",
       "BRD-U00086674-001-01-9@0.2     64  19.629812  0.000999  0.001225    True\n",
       "BRD-K15179513-001-03-4@2.0     32  19.386533  0.000999  0.001225    True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hits = wells.uns[\"mantispy\"][\"hits\"].set_index(\"group\")\n",
    "hits.sort_values(\"distance\", ascending=False).head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "85105ebd",
   "metadata": {
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     "iopub.status.idle": "2026-10-21T01:02:38.728674Z",
     "shell.execute_reply": "2026-10-21T01:02:38.727185Z"
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    {
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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Every treatment's hit distance, reusing the table hit_calling already wrote (no recompute).\n",
    "ranked = hits.sort_values(\"distance\", ascending=False)\n",
    "colours = [ACCENT if is_hit else NULL for is_hit in ranked[\"is_hit\"]]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 4))\n",
    "ax.bar(range(len(ranked)), ranked[\"distance\"], color=colours, width=1.0)\n",
    "ax.set_xticks([])\n",
    "ax.set_xlabel(\"treatment, ranked by distance\")\n",
    "ax.set_ylabel(\"distance from controls\")\n",
    "ax.set_title(\"Hit distances across all treatments\")\n",
    "handles = [\n",
    "    plt.Rectangle((0, 0), 1, 1, color=ACCENT),\n",
    "    plt.Rectangle((0, 0), 1, 1, color=NULL),\n",
    "]\n",
    "ax.legend(handles, [\"significant (is_hit)\", \"not significant\"], frameon=False, fontsize=8)\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8d6658d",
   "metadata": {},
   "source": [
    "A handful of treatments tower over the rest, and the coloured bars are the ones whose permutation q-value clears significance; everything in grey below them is within reach of a control-sized draw."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c007",
   "metadata": {},
   "source": [
    "## Which features moved\n",
    "\n",
    "A hit call says a treatment moved. It does not say what changed. {func}`~mantispy.tl.effect_size` gives a\n",
    "per-feature effect against the controls with Mann-Whitney p-values, and one BH correction over the whole table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c008",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T01:02:38.731311Z",
     "iopub.status.busy": "2026-10-21T01:02:38.731172Z",
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       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mt.tl.effect_size(wells, groupby=\"Metadata_Perturbation\", reference=\"negcon\")\n",
    "strongest = hits.drop(index=\"DMSO\")[\"distance\"].idxmax()\n",
    "\n",
    "mt.pl.feature_volcano(wells, group=strongest);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "406055f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T01:02:39.239127Z",
     "iopub.status.busy": "2026-10-21T01:02:39.238972Z",
     "iopub.status.idle": "2026-10-21T01:02:39.474382Z",
     "shell.execute_reply": "2026-10-21T01:02:39.473232Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x550 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Reuse the effect-size table tl.effect_size already wrote; do not recompute.\n",
    "effects = wells.uns[\"mantispy\"][\"effect\"]\n",
    "top_hits = hits.drop(index=\"DMSO\").sort_values(\"distance\", ascending=False).head(8).index.tolist()\n",
    "\n",
    "grid = effects[effects[\"group\"].isin(top_hits)].pivot(index=\"feature\", columns=\"group\", values=\"effect\")[top_hits]\n",
    "top_features = grid.abs().max(axis=1).sort_values(ascending=False).head(18).index\n",
    "grid = grid.loc[top_features]\n",
    "\n",
    "_COMPARTMENTS = (\"Cytoplasm_\", \"Nuclei_\", \"Cells_\")\n",
    "_CHANNELS = (\"DNA\", \"ER\", \"RNA\", \"AGP\", \"Mito\", \"Brightfield\")\n",
    "\n",
    "\n",
    "def short(name):\n",
    "    \"\"\"Shorten a CellProfiler feature name to its family and channel, dropping the compartment prefix and trailing indices.\"\"\"\n",
    "    label = name\n",
    "    for prefix in _COMPARTMENTS:\n",
    "        if label.startswith(prefix):\n",
    "            label = label[len(prefix) :]\n",
    "            break\n",
    "    parts = label.split(\"_\")\n",
    "    channels = [p for p in parts if p in _CHANNELS]\n",
    "    if channels:\n",
    "        family = parts[: parts.index(channels[0])]\n",
    "        return \"_\".join(family) + \" \" + \" \".join(channels)\n",
    "    while parts and parts[-1].isdigit():\n",
    "        parts.pop()\n",
    "    return \"_\".join(parts)\n",
    "\n",
    "\n",
    "def tick(group):\n",
    "    \"\"\"Shorten a treatment label to its first two compound tokens plus the dose.\"\"\"\n",
    "    head, dose = group.split(\"@\")\n",
    "    return \"-\".join(head.split(\"-\")[:2]) + \"@\" + dose\n",
    "\n",
    "\n",
    "vmax = float(np.nanmax(np.abs(grid.to_numpy())))\n",
    "fig, ax = plt.subplots(figsize=(7, 5.5))\n",
    "im = ax.imshow(grid.to_numpy(), aspect=\"auto\", cmap=\"RdBu_r\", vmin=-vmax, vmax=vmax)\n",
    "ax.set_xticks(range(grid.shape[1]))\n",
    "ax.set_xticklabels([tick(g) for g in grid.columns], rotation=90, fontsize=7)\n",
    "ax.set_yticks(range(grid.shape[0]))\n",
    "ax.set_yticklabels([short(f) for f in grid.index], fontsize=7)\n",
    "fig.colorbar(im, ax=ax, label=\"signed effect size (Cohen's d)\")\n",
    "ax.set_title(\"Shared feature responses across the top hits\")\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bf16abe",
   "metadata": {},
   "source": [
    "Columns that share a red-and-blue pattern move the same features in the same direction, which is the raw material for asking whether two compounds share a mechanism in [Mechanism of action](mechanism_of_action.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c009",
   "metadata": {},
   "source": [
    "Each point is one CellProfiler measurement. The named ones are where this compound's effect lives, and they are\n",
    "the handle for the next question, whether two compounds move the same features, which is\n",
    "[Mechanism of action](mechanism_of_action.ipynb).\n",
    "\n",
    "## One profile per perturbation\n",
    "\n",
    "Replicate wells of a treatment are combined into a signature with {func}`~mantispy.tl.consensus`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c010",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T01:02:39.479048Z",
     "iopub.status.busy": "2026-10-21T01:02:39.478896Z",
     "iopub.status.idle": "2026-10-21T01:02:39.600023Z",
     "shell.execute_reply": "2026-10-21T01:02:39.599023Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3072 wells -> 38 signatures\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 38 × 850\n",
       "    obs: 'Metadata_Perturbation', 'Metadata_ReplicateCount', 'Metadata_plate_map_name', 'Metadata_broad_sample', 'Metadata_mg_per_ml', 'Metadata_mmoles_per_liter', 'Metadata_solvent', 'Metadata_Site_Count', 'Metadata_Barcode', 'Metadata_Supplier', 'Metadata_Supplier_Catalog', 'Metadata_pert_type', 'Metadata_control_type', 'Metadata_Control_Type', 'Metadata_Control', 'Metadata_Compound', 'Metadata_Concentration', 'Metadata_ConcentrationUnit', 'Metadata_MOA', 'Metadata_Perturbation_Type', 'hits_distance', 'hits_qvalue'\n",
       "    var: 'object', 'feature_group', 'feature', 'channel', 'scale', 'angle', 'gray_levels', 'radial_bin', 'params', 'is_feature', 'feature_kind', 'degenerate_scale', 'selected'\n",
       "    uns: 'mantispy'\n",
       "    layers: None (.X)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signatures = mt.tl.consensus(wells, by=\"Metadata_Perturbation\", method=\"median\")\n",
    "print(f\"{wells.n_obs} wells -> {signatures.n_obs} signatures\")\n",
    "signatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4deec3a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T01:02:39.602070Z",
     "iopub.status.busy": "2026-10-21T01:02:39.601923Z",
     "iopub.status.idle": "2026-10-21T01:02:57.458905Z",
     "shell.execute_reply": "2026-10-21T01:02:57.457339Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How consistent are a treatment's replicate wells? percent_replicating takes the median pairwise\n",
    "# correlation within each perturbation's replicates and compares it to a non-replicate null.\n",
    "treated = wells[~wells.obs[\"Metadata_Control\"].to_numpy(dtype=bool)].copy()\n",
    "mt.tl.percent_replicating(treated, groupby=\"Metadata_Perturbation\", metric=\"pearson\")\n",
    "agreement = treated.uns[\"mantispy\"][\"percent_replicating\"][\"median_replicate_correlation\"]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "ax.hist(agreement, bins=20, color=ACCENT)\n",
    "ax.set_xlabel(\"median replicate correlation\")\n",
    "ax.set_ylabel(\"treatments\")\n",
    "ax.set_title(\"How consistent are replicate wells\")\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98ca2977",
   "metadata": {},
   "source": [
    "Most treatments' replicates agree closely, so a plain median loses little; the low-correlation tail is where replicates disagree and where `modz`, by down-weighting off-consensus wells, earns its keep."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c011",
   "metadata": {},
   "source": [
    "`median` is the default and is what pycytominer does by default. `modz` weights each replicate by how well it\n",
    "agrees with the others, which helps when replicates are few and noisy."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c012",
   "metadata": {},
   "source": [
    "## Is it a hit, or did the cells die?\n",
    "\n",
    "A compound that kills four fifths of the cells leaves a well median computed from a fifth as\n",
    "many cells, and that median moves away from the controls for reasons unrelated to the biology\n",
    "being screened. It looks like a strong hit.\n",
    "\n",
    "The cell count is also a baseline to beat. {cite:t}`Seal_2025` found that across three\n",
    "bioactivity benchmarks a model given only the cell count often matched one given the whole\n",
    "Cell Painting profile, and {cite:t}`Ewald_2026` found that profiles predicted LDH release no\n",
    "better than cell count, plate and well position.\n",
    "\n",
    "{func}`~mantispy.tl.cytotoxicity` reads the two together: each group's viability, its cells per field\n",
    "against the controls', beside its median distance from the controls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c013",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T01:02:57.462985Z",
     "iopub.status.busy": "2026-10-21T01:02:57.462648Z",
     "iopub.status.idle": "2026-10-21T01:02:57.483917Z",
     "shell.execute_reply": "2026-10-21T01:02:57.482420Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>BRD-U00086675-001-01-9@0.2</td>\n",
       "      <td>64</td>\n",
       "      <td>0.497</td>\n",
       "      <td>21.439</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>BRD-U00086675-001-01-9@0.4</td>\n",
       "      <td>64</td>\n",
       "      <td>0.331</td>\n",
       "      <td>40.180</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>BRD-U00086676-001-01-9@0.004</td>\n",
       "      <td>64</td>\n",
       "      <td>0.968</td>\n",
       "      <td>10.195</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>BRD-U00086676-001-01-9@0.01</td>\n",
       "      <td>64</td>\n",
       "      <td>0.973</td>\n",
       "      <td>11.281</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>BRD-U00086676-001-01-9@0.04</td>\n",
       "      <td>64</td>\n",
       "      <td>0.931</td>\n",
       "      <td>13.462</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>BRD-U00086677-001-01-9@0.004</td>\n",
       "      <td>64</td>\n",
       "      <td>1.013</td>\n",
       "      <td>4.250</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>BRD-U00086677-001-01-9@0.01</td>\n",
       "      <td>64</td>\n",
       "      <td>1.008</td>\n",
       "      <td>4.275</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>BRD-U00086677-001-01-9@0.04</td>\n",
       "      <td>64</td>\n",
       "      <td>0.997</td>\n",
       "      <td>4.722</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>BRD-U00086678-001-01-9@0.004</td>\n",
       "      <td>64</td>\n",
       "      <td>0.979</td>\n",
       "      <td>4.854</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>BRD-U00086678-001-01-9@0.01</td>\n",
       "      <td>64</td>\n",
       "      <td>0.996</td>\n",
       "      <td>4.676</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>BRD-U00086678-001-01-9@0.04</td>\n",
       "      <td>64</td>\n",
       "      <td>1.005</td>\n",
       "      <td>5.306</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>BRD-U00086679-001-01-9@0.2</td>\n",
       "      <td>64</td>\n",
       "      <td>0.990</td>\n",
       "      <td>4.503</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>BRD-U00086679-001-01-9@0.4</td>\n",
       "      <td>64</td>\n",
       "      <td>0.991</td>\n",
       "      <td>4.789</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>BRD-U00086679-001-01-9@1.0</td>\n",
       "      <td>64</td>\n",
       "      <td>1.044</td>\n",
       "      <td>5.121</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>BRD-U00086680-001-01-9@0.04</td>\n",
       "      <td>64</td>\n",
       "      <td>1.000</td>\n",
       "      <td>4.234</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>BRD-U00086680-001-01-9@0.2</td>\n",
       "      <td>64</td>\n",
       "      <td>1.000</td>\n",
       "      <td>6.361</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>BRD-U00086680-001-01-9@0.4</td>\n",
       "      <td>64</td>\n",
       "      <td>1.005</td>\n",
       "      <td>7.345</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>BRD-U00086681-001-01-9@0.04</td>\n",
       "      <td>64</td>\n",
       "      <td>1.005</td>\n",
       "      <td>4.149</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>BRD-U00086681-001-01-9@0.2</td>\n",
       "      <td>64</td>\n",
       "      <td>0.994</td>\n",
       "      <td>4.777</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>BRD-U00086681-001-01-9@0.4</td>\n",
       "      <td>64</td>\n",
       "      <td>1.049</td>\n",
       "      <td>4.897</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>BRD-U00086682-001-01-9@0.04</td>\n",
       "      <td>64</td>\n",
       "      <td>1.030</td>\n",
       "      <td>6.415</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>BRD-U00086682-001-01-9@0.2</td>\n",
       "      <td>64</td>\n",
       "      <td>0.983</td>\n",
       "      <td>8.326</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>BRD-U00086682-001-01-9@0.4</td>\n",
       "      <td>64</td>\n",
       "      <td>0.941</td>\n",
       "      <td>16.747</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>DMSO</td>\n",
       "      <td>832</td>\n",
       "      <td>1.000</td>\n",
       "      <td>3.928</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           group  n_obs  viability  distance  suspect\n",
       "0     BRD-K15179513-001-03-4@2.0     32      0.783    19.387    False\n",
       "1     BRD-K15819326-001-01-6@2.0     32      0.840    17.506    False\n",
       "2     BRD-K40109029-001-06-9@2.0     32      0.831    15.117    False\n",
       "3     BRD-K95785537-001-26-9@2.0     32      0.763    22.427    False\n",
       "4     BRD-U00086672-001-01-9@0.2     64      1.030     4.100    False\n",
       "5     BRD-U00086672-001-01-9@0.4     64      1.012     4.626    False\n",
       "6     BRD-U00086672-001-01-9@1.0     64      0.983     5.709    False\n",
       "7     BRD-U00086673-001-01-9@0.2     64      0.998     4.844    False\n",
       "8     BRD-U00086673-001-01-9@0.4     64      0.988     5.023    False\n",
       "9     BRD-U00086673-001-01-9@1.0     64      0.972     5.707    False\n",
       "10   BRD-U00086674-001-01-9@0.04     64      0.973     6.414    False\n",
       "11    BRD-U00086674-001-01-9@0.2     64      0.676    19.630     True\n",
       "12    BRD-U00086674-001-01-9@0.4     64      0.266    53.170     True\n",
       "13   BRD-U00086675-001-01-9@0.04     64      0.935     6.864    False\n",
       "14    BRD-U00086675-001-01-9@0.2     64      0.497    21.439     True\n",
       "15    BRD-U00086675-001-01-9@0.4     64      0.331    40.180     True\n",
       "16  BRD-U00086676-001-01-9@0.004     64      0.968    10.195    False\n",
       "17   BRD-U00086676-001-01-9@0.01     64      0.973    11.281    False\n",
       "18   BRD-U00086676-001-01-9@0.04     64      0.931    13.462    False\n",
       "19  BRD-U00086677-001-01-9@0.004     64      1.013     4.250    False\n",
       "20   BRD-U00086677-001-01-9@0.01     64      1.008     4.275    False\n",
       "21   BRD-U00086677-001-01-9@0.04     64      0.997     4.722    False\n",
       "22  BRD-U00086678-001-01-9@0.004     64      0.979     4.854    False\n",
       "23   BRD-U00086678-001-01-9@0.01     64      0.996     4.676    False\n",
       "24   BRD-U00086678-001-01-9@0.04     64      1.005     5.306    False\n",
       "25    BRD-U00086679-001-01-9@0.2     64      0.990     4.503    False\n",
       "26    BRD-U00086679-001-01-9@0.4     64      0.991     4.789    False\n",
       "27    BRD-U00086679-001-01-9@1.0     64      1.044     5.121    False\n",
       "28   BRD-U00086680-001-01-9@0.04     64      1.000     4.234    False\n",
       "29    BRD-U00086680-001-01-9@0.2     64      1.000     6.361    False\n",
       "30    BRD-U00086680-001-01-9@0.4     64      1.005     7.345    False\n",
       "31   BRD-U00086681-001-01-9@0.04     64      1.005     4.149    False\n",
       "32    BRD-U00086681-001-01-9@0.2     64      0.994     4.777    False\n",
       "33    BRD-U00086681-001-01-9@0.4     64      1.049     4.897    False\n",
       "34   BRD-U00086682-001-01-9@0.04     64      1.030     6.415    False\n",
       "35    BRD-U00086682-001-01-9@0.2     64      0.983     8.326    False\n",
       "36    BRD-U00086682-001-01-9@0.4     64      0.941    16.747    False\n",
       "37                          DMSO    832      1.000     3.928    False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mt.tl.cytotoxicity(wells)\n",
    "wells.uns[\"mantispy\"][\"cytotoxicity\"].round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c014",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T01:02:57.486396Z",
     "iopub.status.busy": "2026-10-21T01:02:57.486258Z",
     "iopub.status.idle": "2026-10-21T01:02:57.554658Z",
     "shell.execute_reply": "2026-10-21T01:02:57.553121Z"
    }
   },
   "outputs": [
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     "metadata": {},
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   ],
   "source": [
    "mt.pl.cytotoxicity(wells);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "50ba81b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T01:02:57.557415Z",
     "iopub.status.busy": "2026-10-21T01:02:57.557262Z",
     "iopub.status.idle": "2026-10-21T01:02:57.816748Z",
     "shell.execute_reply": "2026-10-21T01:02:57.814995Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Join the cytotoxicity table to each treatment's compound and dose, then follow the flagged compounds down the ladder.\n",
    "cyto = wells.uns[\"mantispy\"][\"cytotoxicity\"]\n",
    "meta = wells.obs.groupby(\"Metadata_Perturbation\", observed=True)[\n",
    "    [\"Metadata_Compound\", \"Metadata_Concentration\"]\n",
    "].first()\n",
    "viability_table = cyto.join(meta, on=\"group\")\n",
    "flagged = viability_table.loc[viability_table[\"suspect\"], \"Metadata_Compound\"].unique()\n",
    "\n",
    "# These are the same two compounds mt.pl.cytotoxicity draws as crimson \"suspect\" points above.\n",
    "# Colour them in crimson too, the more-toxic one (lowest surviving fraction) in ACCENT and the\n",
    "# second in a darker crimson, so the reader connects the two figures.\n",
    "toxicity = viability_table[viability_table[\"Metadata_Compound\"].isin(flagged)]\n",
    "order = toxicity.groupby(\"Metadata_Compound\", observed=True)[\"viability\"].min().sort_values().index\n",
    "palette = dict(zip(order, [ACCENT, ACCENT_DARK], strict=False))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 4))\n",
    "for compound in flagged:\n",
    "    block = viability_table[viability_table[\"Metadata_Compound\"] == compound].sort_values(\n",
    "        \"Metadata_Concentration\", key=lambda s: s.astype(float)\n",
    "    )\n",
    "    dose = block[\"Metadata_Concentration\"].astype(float)\n",
    "    label = \"-\".join(str(compound).split(\"-\")[:2])\n",
    "    ax.plot(dose, block[\"viability\"], marker=\"o\", color=palette[compound], label=label)\n",
    "ax.axhline(1.0, color=\"0.7\", lw=1, ls=\"--\")\n",
    "ax.set_xscale(\"log\")\n",
    "ax.set_xlabel(\"dose (Metadata_Concentration)\")\n",
    "ax.set_ylabel(\"viability (cells per field vs controls)\")\n",
    "ax.set_title(\"Viability falls at the top doses\")\n",
    "ax.legend(frameon=False, fontsize=8)\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d749a378",
   "metadata": {},
   "source": [
    "Both flagged compounds keep a control-sized complement of cells at their lowest dose and lose a growing share of it at the two highest, which is the confound `tl.cytotoxicity` marks: a large distance at high dose is partly cells dying rather than phenotype."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c015",
   "metadata": {},
   "source": [
    "Two compounds are flagged, each at its two highest doses, which keep between 27% and 68% of the\n",
    "controls' cells. The strongest, `BRD-U00086674` at 0.4, keeps 27% of its cells and sits further from the controls\n",
    "than any other treatment. At its lowest dose, 0.04, its cells survive and it sits close to the controls.\n",
    "\n",
    "Both conditions have to hold. Cell loss alone is a phenotype (a compound that arrests growth is a real finding),\n",
    "and a large distance alone is a hit. Only the combination is suspect. `tl.cytotoxicity` is a diagnostic and does\n",
    "not correct anything."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c016",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "- A hit call says a treatment moved away from the controls. {func}`~mantispy.tl.effect_size` says which\n",
    "  features moved, and {func}`~mantispy.tl.consensus` gives one signature per treatment.\n",
    "- Cell loss and phenotype are confounded by construction. Flag the combination with\n",
    "  {func}`~mantispy.tl.cytotoxicity` instead of correcting it away.\n",
    "\n",
    "Next: [Mechanism of action](mechanism_of_action.ipynb), which asks whether two treatments moved the same way."
   ]
  }
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