{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8ab9d272",
   "metadata": {},
   "source": [
    "# Rohban ORF pathway screen\n",
    "\n",
    "{func}`~mantispy.ds.rohban` serves `cpg0017-rohban-pathways` {cite:p}`Rohban_2017`, an overexpression screen rather than a knockout or a small-molecule one. U2OS cells each overexpress one open reading frame, with roughly ten replicate wells per construct over five plates, so a hit is a gene whose gain of function moves the cells away from the control ORFs. The genes group into known signalling pathways, which is what makes it the pathway example. The object is well-level.\n",
    "\n",
    "**Use it if** your own data is an overexpression (gain-of-function) screen, or any well-level genetic screen where you want to read hits against pathway structure. [Interpreting a screen end to end](../tutorials/genetics/interpreting_a_screen.ipynb) and [Which measurements moved](../tutorials/phenotypes/which_features_moved.ipynb) both run on it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5602bdb2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T01:00:27.644784Z",
     "iopub.status.busy": "2026-10-21T01:00:27.644400Z",
     "iopub.status.idle": "2026-10-21T01:00:32.796180Z",
     "shell.execute_reply": "2026-10-21T01:00:32.794559Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "import scanpy as sc\n",
    "\n",
    "import mantispy as mt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "961d0d78",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T01:00:32.800370Z",
     "iopub.status.busy": "2026-10-21T01:00:32.800113Z",
     "iopub.status.idle": "2026-10-21T01:00:32.996953Z",
     "shell.execute_reply": "2026-10-21T01:00:32.995825Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 1918 × 3616\n",
       "    obs: 'Metadata_gene_name', 'Metadata_pert_name', 'Metadata_broad_sample', 'Metadata_cell_line', 'Metadata_ASSAY_WELL_ROLE', 'Metadata_GeneID', '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_Row', 'Metadata_Col', 'Metadata_CellCount', 'Metadata_SiteCount', 'Metadata_Control', 'Metadata_Control_Type', 'Metadata_Perturbation', 'Metadata_Perturbation_Type', 'Metadata_Gene', 'Metadata_Construct', 'Metadata_Allele'\n",
       "    var: 'object', 'feature_group', 'feature', 'channel', 'scale', 'angle', 'gray_levels', 'radial_bin', 'params', 'is_feature', 'feature_kind'\n",
       "    uns: 'mantispy'\n",
       "    layers: None (.X)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata = mt.ds.rohban()\n",
    "adata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72e44149",
   "metadata": {},
   "source": [
    "## Constructs, genes and controls\n",
    "\n",
    "`Metadata_Perturbation` is the ORF construct, the unit the screen varied, and several constructs can overexpress the same `Metadata_Gene`. `Metadata_Control` marks the control ORFs (Luciferase, LacZ and eGFP) that normalization reads against; the untreated `EMPTY` wells carry `Metadata_Perturbation == \"untreated\"` and are not flagged."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "42668183",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T01:00:33.001000Z",
     "iopub.status.busy": "2026-10-21T01:00:33.000844Z",
     "iopub.status.idle": "2026-10-21T01:00:33.006787Z",
     "shell.execute_reply": "2026-10-21T01:00:33.005772Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "wells            1918\n",
       "constructs        327\n",
       "genes             194\n",
       "plates              5\n",
       "control wells     120\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs = adata.obs\n",
    "pd.Series(\n",
    "    {\n",
    "        \"wells\": adata.n_obs,\n",
    "        \"constructs\": obs[\"Metadata_Perturbation\"].nunique(),\n",
    "        \"genes\": obs[\"Metadata_Gene\"].nunique(),\n",
    "        \"plates\": obs[\"Metadata_Plate\"].nunique(),\n",
    "        \"control wells\": int(obs[\"Metadata_Control\"].sum()),\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "908c7a18",
   "metadata": {},
   "source": [
    "## Cell count across the five plates\n",
    "\n",
    "Overexpression changes proliferation, which cell count picks up plate by plate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "193f4a32",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T01:00:33.009915Z",
     "iopub.status.busy": "2026-10-21T01:00:33.009780Z",
     "iopub.status.idle": "2026-10-21T01:00:33.694652Z",
     "shell.execute_reply": "2026-10-21T01:00:33.693569Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x640 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mt.pl.plate(adata, color=\"Metadata_CellCount\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "850e3aba",
   "metadata": {},
   "source": [
    "## Genes in profile space\n",
    "\n",
    "The gene-level consensus (one `modz` profile per gene) placed on its first two principal components shows the spread the pathways sit in. Each point is a gene, colored by how many replicate wells backed its consensus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b7f021ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-21T01:00:33.698554Z",
     "iopub.status.busy": "2026-10-21T01:00:33.698399Z",
     "iopub.status.idle": "2026-10-21T01:00:34.677299Z",
     "shell.execute_reply": "2026-10-21T01:00:34.676156Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        </script>\n",
       "        <script type=\"module\">import \"https://cdn.plot.ly/plotly-4.1.1.min\"</script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "genes = mt.ds.rohban(aggregated=True)\n",
    "scaled = genes.copy()\n",
    "sc.pp.scale(scaled)\n",
    "scaled.X = np.nan_to_num(scaled.X)\n",
    "sc.pp.pca(scaled, n_comps=10)\n",
    "coords = pd.DataFrame(scaled.obsm[\"X_pca\"][:, :2], columns=[\"PC1\", \"PC2\"])\n",
    "coords[\"gene\"] = genes.obs[\"Metadata_Gene\"].to_numpy()\n",
    "coords[\"replicates\"] = genes.obs[\"Metadata_ReplicateCount\"].to_numpy()\n",
    "fig = px.scatter(\n",
    "    coords,\n",
    "    x=\"PC1\",\n",
    "    y=\"PC2\",\n",
    "    color=\"replicates\",\n",
    "    hover_name=\"gene\",\n",
    "    title=\"Rohban genes in profile space\",\n",
    ")\n",
    "fig.show()"
   ]
  }
 ],
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