{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0f0f79f6",
   "metadata": {},
   "source": [
    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/genomicsxai/alphagenome-pytorch/blob/main/examples/notebooks/variant_scoring.ipynb\">\n",
    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
    "</a>\n",
    "\n",
    "# AlphaGenome Variant Scoring Tutorial\n",
    "\n",
    "This notebook demonstrates how to score genetic variants using the AlphaGenome PyTorch model.\n",
    "\n",
    "## Prerequisites\n",
    "\n",
    "Before running this notebook, you need to:\n",
    "\n",
    "1. **Download required files** - See [variant_scoring/README.md](../../src/alphagenome_pytorch/variant_scoring/README.md) for detailed instructions:\n",
    "   - Model weights (`ag_pytorch_model.pt`)\n",
    "   - Track means (`track_means.pt`)\n",
    "   - Reference genome (hg38.fa with .fai index)\n",
    "   - Gene annotations (gencode.v46.annotation.parquet)\n",
    "   - PolyA annotations (gencode.v46.polyAs.linked.parquet) - optional\n",
    "   - Track metadata (track_metadata.parquet)\n",
    "\n",
    "2. **Set up annotation files** - Run the preprocessing scripts:\n",
    "   ```bash\n",
    "   # See README for download URLs and conversion commands\n",
    "   python scripts/convert_gtf_to_parquet.py --input gencode.v46.annotation.gtf --output gencode.v46.annotation.parquet\n",
    "   python scripts/preprocess_polya.py --metadata gencode.v46.metadata.PolyA_feature --gtf gencode.v46.annotation.parquet --output gencode.v46.polyAs.linked.parquet\n",
    "   ```\n",
    "\n",
    "3. **Update file paths** in the cells below to match your local setup."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8de9a680",
   "metadata": {},
   "source": [
    "## 1. Import Required Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f155e5ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from alphagenome_pytorch.model import AlphaGenome\n",
    "from alphagenome_pytorch.config import DtypePolicy\n",
    "from alphagenome_pytorch.variant_scoring import (\n",
    "    VariantScoringModel, Variant, Interval,\n",
    "    CenterMaskScorer, OutputType, AggregationType,\n",
    "    get_recommended_scorers,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b61ff637",
   "metadata": {},
   "source": [
    "## 2. Load AlphaGenome Model\n",
    "\n",
    "We load the model in **float32** precision for production use. This provides:\n",
    "- Cleaner signals with less quantization noise\n",
    "- Standard precision for downstream analysis\n",
    "\n",
    "**Note**: Use `compute_dtype=torch.bfloat16` only if you need exact parity with the JAX/API reference for testing or to fit the full model (such as 1MB context) on smaller hardware."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ad47f012",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓ Model loaded on GPU\n"
     ]
    }
   ],
   "source": [
    "TORCH_WEIGHTS_PATH = '../../ag_pytorch_model.pth\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "'\n",
    "\n",
    "\n",
    "# Initialize model in float32 (production use)\n",
    "model = AlphaGenome(num_organisms=2, dtype_policy=DtypePolicy.full_float32())\n",
    "checkpoint = torch.load(TORCH_WEIGHTS_PATH, map_location='cpu')\n",
    "model.load_state_dict(checkpoint['state_dict'])\n",
    "\n",
    "model.eval()\n",
    "\n",
    "# Move to GPU if available\n",
    "if torch.cuda.is_available():\n",
    "    model.cuda()\n",
    "    print(\"✓ Model loaded on GPU\")\n",
    "else:\n",
    "    print(\"✓ Model loaded on CPU\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb688d74",
   "metadata": {},
   "source": [
    "## 3. Create Variant Scoring Model\n",
    "\n",
    "The `VariantScoringModel` wraps the base model with variant scoring functionality.\n",
    "It requires:\n",
    "- **fasta_path**: Reference genome for sequence extraction\n",
    "- **gtf_path**: Gene annotations for gene-based scorers\n",
    "- **polya_path**: PolyA sites for PolyadenylationScorer (optional)\n",
    "- **track_metadata**: Track names and metadata for tidy output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3521be18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓ Scoring model initialized\n"
     ]
    }
   ],
   "source": [
    "# Update these paths to match your local setup\n",
    "# See README for instructions on downloading and converting annotations\n",
    "scoring_model = VariantScoringModel(\n",
    "    model,\n",
    "    fasta_path=\"../../data/annotations/hg38.fa\",\n",
    "    gtf_path=\"../../data/annotations/gencode.v46.annotation.parquet\",\n",
    "    polya_path=\"../../data/annotations/gencode.v46.polyAs.linked.parquet\",  # Optional\n",
    "    default_organism=\"human\",\n",
    ")\n",
    "\n",
    "# Load track metadata for annotated results\n",
    "# These can be extracted from the model using scripts/extract_track_metadata.py\n",
    "scoring_model.load_all_metadata('../../track_metadata.parquet')\n",
    "print(\"✓ Scoring model initialized\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c88081db",
   "metadata": {},
   "source": [
    "## 4. Define Variant and Interval\n",
    "\n",
    "- **Variant**: Uses VCF-style 1-based coordinates (chrom:pos:ref>alt)\n",
    "- **Interval**: Model requires 131,072bp (128KB) centered on the variant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3c034745",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variant: chr22:36201698:A>C\n",
      "Interval: chr22:36136162-36267234\n",
      "Interval width: 131,072bp\n"
     ]
    }
   ],
   "source": [
    "# Define a test variant (chr22:36201698:A>C)\n",
    "variant = Variant.from_str('chr22:36201698:A>C')\n",
    "\n",
    "# Create interval centered on variant\n",
    "interval = Interval.centered_on('chr22', 36201698, width=\"100KB\")\n",
    "\n",
    "print(f\"Variant: {variant}\")\n",
    "print(f\"Interval: {interval}\")\n",
    "print(f\"Interval width: {interval.width:,}bp\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f104e3a8",
   "metadata": {},
   "source": [
    "## 5. Score Variant with Recommended Scorers\n",
    "\n",
    "The `get_recommended_scorers()` function returns 19 pre-configured scorers that match the official AlphaGenome API:\n",
    "\n",
    "- **CenterMaskScorer** (12 configs): ATAC, DNase, CAGE, PRO-cap, ChIP-TF, ChIP-histone\n",
    "- **GeneMaskLFCScorer** (1): RNA-seq log fold change\n",
    "- **GeneMaskActiveScorer** (1): RNA-seq active allele\n",
    "- **GeneMaskSplicingScorer** (2): Splice sites and usage\n",
    "- **SpliceJunctionScorer** (1): Junction disruption\n",
    "- **ContactMapScorer** (1): 3D chromatin contacts\n",
    "- **PolyadenylationScorer** (1): Polyadenylation QTLs (human only)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "adf34858",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✓ Scored variant with 19 scorers\n"
     ]
    }
   ],
   "source": [
    "# Score variant with all 19 recommended scorers\n",
    "scores = scoring_model.score_variant(\n",
    "    interval=interval,\n",
    "    variant=variant,\n",
    "    scorers=get_recommended_scorers('human'),\n",
    "    organism='human',\n",
    "    to_cpu=True,  # Move results to CPU for analysis\n",
    ")\n",
    "\n",
    "print(f\"✓ Scored variant with {len(scores)} scorers\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3a72aa2",
   "metadata": {},
   "source": [
    "## 6. Convert Results to Tidy DataFrame\n",
    "\n",
    "The `tidy_scores()` method converts results to a long-format DataFrame with:\n",
    "- One row per track\n",
    "- Variant and gene metadata\n",
    "- Track names and metadata (biosample, assay type, etc.)\n",
    "- Raw scores and quantile-normalized scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "62f387d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (38780, 21)\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>variant_id</th>\n",
       "      <th>scored_interval</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>gene_type</th>\n",
       "      <th>gene_strand</th>\n",
       "      <th>junction_Start</th>\n",
       "      <th>junction_End</th>\n",
       "      <th>output_type</th>\n",
       "      <th>variant_scorer</th>\n",
       "      <th>...</th>\n",
       "      <th>track_strand</th>\n",
       "      <th>Assay title</th>\n",
       "      <th>ontology_curie</th>\n",
       "      <th>biosample_name</th>\n",
       "      <th>biosample_type</th>\n",
       "      <th>transcription_factor</th>\n",
       "      <th>histone_mark</th>\n",
       "      <th>gtex_tissue</th>\n",
       "      <th>raw_score</th>\n",
       "      <th>track_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr22:36201698:A&gt;C</td>\n",
       "      <td>chr22:36136162-36267234</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>atac</td>\n",
       "      <td>CenterMaskScorer(output=atac, width=501, agg=d...</td>\n",
       "      <td>...</td>\n",
       "      <td>.</td>\n",
       "      <td>ATAC-seq</td>\n",
       "      <td>CL:0000084</td>\n",
       "      <td>T-cell</td>\n",
       "      <td>primary_cell</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.062569</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr22:36201698:A&gt;C</td>\n",
       "      <td>chr22:36136162-36267234</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>atac</td>\n",
       "      <td>CenterMaskScorer(output=atac, width=501, agg=d...</td>\n",
       "      <td>...</td>\n",
       "      <td>.</td>\n",
       "      <td>ATAC-seq</td>\n",
       "      <td>CL:0000100</td>\n",
       "      <td>motor neuron</td>\n",
       "      <td>in_vitro_differentiated_cells</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.004393</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr22:36201698:A&gt;C</td>\n",
       "      <td>chr22:36136162-36267234</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>atac</td>\n",
       "      <td>CenterMaskScorer(output=atac, width=501, agg=d...</td>\n",
       "      <td>...</td>\n",
       "      <td>.</td>\n",
       "      <td>ATAC-seq</td>\n",
       "      <td>CL:0000236</td>\n",
       "      <td>B cell</td>\n",
       "      <td>primary_cell</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.007725</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr22:36201698:A&gt;C</td>\n",
       "      <td>chr22:36136162-36267234</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>atac</td>\n",
       "      <td>CenterMaskScorer(output=atac, width=501, agg=d...</td>\n",
       "      <td>...</td>\n",
       "      <td>.</td>\n",
       "      <td>ATAC-seq</td>\n",
       "      <td>CL:0000623</td>\n",
       "      <td>natural killer cell</td>\n",
       "      <td>primary_cell</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.057389</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr22:36201698:A&gt;C</td>\n",
       "      <td>chr22:36136162-36267234</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>atac</td>\n",
       "      <td>CenterMaskScorer(output=atac, width=501, agg=d...</td>\n",
       "      <td>...</td>\n",
       "      <td>.</td>\n",
       "      <td>ATAC-seq</td>\n",
       "      <td>CL:0000624</td>\n",
       "      <td>CD4-positive, alpha-beta T cell</td>\n",
       "      <td>primary_cell</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.059652</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           variant_id          scored_interval gene_id gene_name gene_type  \\\n",
       "0  chr22:36201698:A>C  chr22:36136162-36267234    None      None      None   \n",
       "1  chr22:36201698:A>C  chr22:36136162-36267234    None      None      None   \n",
       "2  chr22:36201698:A>C  chr22:36136162-36267234    None      None      None   \n",
       "3  chr22:36201698:A>C  chr22:36136162-36267234    None      None      None   \n",
       "4  chr22:36201698:A>C  chr22:36136162-36267234    None      None      None   \n",
       "\n",
       "  gene_strand  junction_Start  junction_End output_type  \\\n",
       "0        None             NaN           NaN        atac   \n",
       "1        None             NaN           NaN        atac   \n",
       "2        None             NaN           NaN        atac   \n",
       "3        None             NaN           NaN        atac   \n",
       "4        None             NaN           NaN        atac   \n",
       "\n",
       "                                      variant_scorer  ... track_strand  \\\n",
       "0  CenterMaskScorer(output=atac, width=501, agg=d...  ...            .   \n",
       "1  CenterMaskScorer(output=atac, width=501, agg=d...  ...            .   \n",
       "2  CenterMaskScorer(output=atac, width=501, agg=d...  ...            .   \n",
       "3  CenterMaskScorer(output=atac, width=501, agg=d...  ...            .   \n",
       "4  CenterMaskScorer(output=atac, width=501, agg=d...  ...            .   \n",
       "\n",
       "  Assay title ontology_curie                   biosample_name  \\\n",
       "0    ATAC-seq     CL:0000084                           T-cell   \n",
       "1    ATAC-seq     CL:0000100                     motor neuron   \n",
       "2    ATAC-seq     CL:0000236                           B cell   \n",
       "3    ATAC-seq     CL:0000623              natural killer cell   \n",
       "4    ATAC-seq     CL:0000624  CD4-positive, alpha-beta T cell   \n",
       "\n",
       "                  biosample_type transcription_factor histone_mark  \\\n",
       "0                   primary_cell                 None         None   \n",
       "1  in_vitro_differentiated_cells                 None         None   \n",
       "2                   primary_cell                 None         None   \n",
       "3                   primary_cell                 None         None   \n",
       "4                   primary_cell                 None         None   \n",
       "\n",
       "  gtex_tissue raw_score  track_index  \n",
       "0        None -0.062569            0  \n",
       "1        None  0.004393            1  \n",
       "2        None  0.007725            2  \n",
       "3        None -0.057389            3  \n",
       "4        None -0.059652            4  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert to tidy DataFrame\n",
    "df = scoring_model.tidy_scores(scores)\n",
    "\n",
    "print(f\"Shape: {df.shape}\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fcc582a",
   "metadata": {},
   "source": [
    "## 7. Explore Results\n",
    "\n",
    "Let's examine the scores by output type and scorer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "47e91b47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores by scorer:\n",
      "                                                    count         mean  \\\n",
      "variant_scorer                                                           \n",
      "CenterMaskScorer(output=atac, width=501, agg=ac...    256    33.684661   \n",
      "CenterMaskScorer(output=atac, width=501, agg=di...    256    -0.002459   \n",
      "CenterMaskScorer(output=cage, width=501, agg=ac...    640   205.459092   \n",
      "CenterMaskScorer(output=cage, width=501, agg=di...    640    -2.310507   \n",
      "CenterMaskScorer(output=chip_histone, width=200...   1152  2520.110189   \n",
      "CenterMaskScorer(output=chip_histone, width=200...   1152    -0.142199   \n",
      "CenterMaskScorer(output=chip_tf, width=501, agg...   1664   539.264916   \n",
      "CenterMaskScorer(output=chip_tf, width=501, agg...   1664     0.020639   \n",
      "CenterMaskScorer(output=dnase, width=501, agg=a...    384    75.993788   \n",
      "CenterMaskScorer(output=dnase, width=501, agg=d...    384     0.027010   \n",
      "CenterMaskScorer(output=procap, width=501, agg=...    128    18.222188   \n",
      "CenterMaskScorer(output=procap, width=501, agg=...    128    -0.034706   \n",
      "ContactMapScorer()                                     26     0.006099   \n",
      "GeneMaskActiveScorer(output=rna_seq, mode=exons...   9216     0.060194   \n",
      "GeneMaskLFCScorer(output=rna_seq, mode=exons, r...   9216    -0.071840   \n",
      "GeneMaskSplicingScorer(output=splice_sites_clas...     60     0.039123   \n",
      "GeneMaskSplicingScorer(output=splice_sites_usag...   8808     0.023144   \n",
      "PolyadenylationScorer()                              1536     0.215987   \n",
      "SpliceJunctionScorer()                               1468     0.971068   \n",
      "\n",
      "                                                            std  \n",
      "variant_scorer                                                   \n",
      "CenterMaskScorer(output=atac, width=501, agg=ac...    61.648643  \n",
      "CenterMaskScorer(output=atac, width=501, agg=di...     0.073020  \n",
      "CenterMaskScorer(output=cage, width=501, agg=ac...   525.146884  \n",
      "CenterMaskScorer(output=cage, width=501, agg=di...     2.512695  \n",
      "CenterMaskScorer(output=chip_histone, width=200...  2778.759376  \n",
      "CenterMaskScorer(output=chip_histone, width=200...     0.438026  \n",
      "CenterMaskScorer(output=chip_tf, width=501, agg...   405.029899  \n",
      "CenterMaskScorer(output=chip_tf, width=501, agg...     0.096032  \n",
      "CenterMaskScorer(output=dnase, width=501, agg=a...   105.968748  \n",
      "CenterMaskScorer(output=dnase, width=501, agg=d...     0.102616  \n",
      "CenterMaskScorer(output=procap, width=501, agg=...   185.041655  \n",
      "CenterMaskScorer(output=procap, width=501, agg=...     0.144998  \n",
      "ContactMapScorer()                                     0.001699  \n",
      "GeneMaskActiveScorer(output=rna_seq, mode=exons...     0.195938  \n",
      "GeneMaskLFCScorer(output=rna_seq, mode=exons, r...     0.329138  \n",
      "GeneMaskSplicingScorer(output=splice_sites_clas...     0.178130  \n",
      "GeneMaskSplicingScorer(output=splice_sites_usag...     0.113591  \n",
      "PolyadenylationScorer()                                0.292362  \n",
      "SpliceJunctionScorer()                                 1.710794  \n"
     ]
    }
   ],
   "source": [
    "# View summary by scorer\n",
    "print(\"Scores by scorer:\")\n",
    "print(df.groupby('variant_scorer')['raw_score'].agg(['count', 'mean', 'std']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39b3529f",
   "metadata": {},
   "source": [
    "## 8. Visualize variant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9160d80f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/scratch/users/mkjellbe/micromamba/envs/alphagenome/lib/python3.12/site-packages/alphagenome/data/transcript.py:662: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  for chromosome, dfc in df.groupby('Chromosome')\n",
      "/scratch/users/mkjellbe/micromamba/envs/alphagenome/lib/python3.12/site-packages/alphagenome/data/transcript.py:662: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  for chromosome, dfc in df.groupby('Chromosome')\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 2000x1300 with 13 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from alphagenome_pytorch.variant_scoring import visualize_variant\n",
    "\n",
    "# Visualize variant effects (matching Colab defaults)\n",
    "visualize_variant(\n",
    "    scoring_model,\n",
    "    interval,\n",
    "    variant,\n",
    "    resolution=1, # 1bp resolution\n",
    "    # Ontology filtering\n",
    "    ontology_terms=['EFO:0001187', 'EFO:0002067', 'EFO:0002784'],  # HepG2, K562, GM12878\n",
    "    # Gene annotation\n",
    "    plot_gene_annotation=True,\n",
    "    plot_longest_transcript_only=True,\n",
    "    # Output types\n",
    "    plot_cage=True,\n",
    "    plot_rna_seq=True,\n",
    "    plot_splice_sites=True,\n",
    "    plot_atac=False,\n",
    "    plot_dnase=False,\n",
    "    plot_chip_histone=False,\n",
    "    plot_chip_tf=False,\n",
    "    plot_splice_site_usage=False,\n",
    "    # Strand filtering\n",
    "    filter_to_positive_strand=False,\n",
    "    filter_to_negative_strand=True,\n",
    "    # Plot options\n",
    "    plot_interval_width=43008,\n",
    "    plot_interval_shift=0,\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5199fbc7",
   "metadata": {},
   "source": [
    "## 8. Custom Scorer Example\n",
    "\n",
    "You can create custom scorers with different parameters. Here's an example using ATAC-seq with a wider window:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b71c1d6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Custom scorer results: 256 tracks\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>variant_id</th>\n",
       "      <th>scored_interval</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>gene_name</th>\n",
       "      <th>gene_type</th>\n",
       "      <th>gene_strand</th>\n",
       "      <th>junction_Start</th>\n",
       "      <th>junction_End</th>\n",
       "      <th>output_type</th>\n",
       "      <th>variant_scorer</th>\n",
       "      <th>...</th>\n",
       "      <th>track_strand</th>\n",
       "      <th>Assay title</th>\n",
       "      <th>ontology_curie</th>\n",
       "      <th>biosample_name</th>\n",
       "      <th>biosample_type</th>\n",
       "      <th>transcription_factor</th>\n",
       "      <th>histone_mark</th>\n",
       "      <th>gtex_tissue</th>\n",
       "      <th>raw_score</th>\n",
       "      <th>track_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr22:36201698:A&gt;C</td>\n",
       "      <td>chr22:36136162-36267234</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>atac</td>\n",
       "      <td>CenterMaskScorer(output=atac, width=2001, agg=...</td>\n",
       "      <td>...</td>\n",
       "      <td>.</td>\n",
       "      <td>ATAC-seq</td>\n",
       "      <td>CL:0000084</td>\n",
       "      <td>T-cell</td>\n",
       "      <td>primary_cell</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.039216</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr22:36201698:A&gt;C</td>\n",
       "      <td>chr22:36136162-36267234</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>atac</td>\n",
       "      <td>CenterMaskScorer(output=atac, width=2001, agg=...</td>\n",
       "      <td>...</td>\n",
       "      <td>.</td>\n",
       "      <td>ATAC-seq</td>\n",
       "      <td>CL:0000100</td>\n",
       "      <td>motor neuron</td>\n",
       "      <td>in_vitro_differentiated_cells</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.001569</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr22:36201698:A&gt;C</td>\n",
       "      <td>chr22:36136162-36267234</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>atac</td>\n",
       "      <td>CenterMaskScorer(output=atac, width=2001, agg=...</td>\n",
       "      <td>...</td>\n",
       "      <td>.</td>\n",
       "      <td>ATAC-seq</td>\n",
       "      <td>CL:0000236</td>\n",
       "      <td>B cell</td>\n",
       "      <td>primary_cell</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.022250</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr22:36201698:A&gt;C</td>\n",
       "      <td>chr22:36136162-36267234</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>atac</td>\n",
       "      <td>CenterMaskScorer(output=atac, width=2001, agg=...</td>\n",
       "      <td>...</td>\n",
       "      <td>.</td>\n",
       "      <td>ATAC-seq</td>\n",
       "      <td>CL:0000623</td>\n",
       "      <td>natural killer cell</td>\n",
       "      <td>primary_cell</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.029534</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr22:36201698:A&gt;C</td>\n",
       "      <td>chr22:36136162-36267234</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>atac</td>\n",
       "      <td>CenterMaskScorer(output=atac, width=2001, agg=...</td>\n",
       "      <td>...</td>\n",
       "      <td>.</td>\n",
       "      <td>ATAC-seq</td>\n",
       "      <td>CL:0000624</td>\n",
       "      <td>CD4-positive, alpha-beta T cell</td>\n",
       "      <td>primary_cell</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.026243</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           variant_id          scored_interval gene_id gene_name gene_type  \\\n",
       "0  chr22:36201698:A>C  chr22:36136162-36267234    None      None      None   \n",
       "1  chr22:36201698:A>C  chr22:36136162-36267234    None      None      None   \n",
       "2  chr22:36201698:A>C  chr22:36136162-36267234    None      None      None   \n",
       "3  chr22:36201698:A>C  chr22:36136162-36267234    None      None      None   \n",
       "4  chr22:36201698:A>C  chr22:36136162-36267234    None      None      None   \n",
       "\n",
       "  gene_strand junction_Start junction_End output_type  \\\n",
       "0        None           None         None        atac   \n",
       "1        None           None         None        atac   \n",
       "2        None           None         None        atac   \n",
       "3        None           None         None        atac   \n",
       "4        None           None         None        atac   \n",
       "\n",
       "                                      variant_scorer  ... track_strand  \\\n",
       "0  CenterMaskScorer(output=atac, width=2001, agg=...  ...            .   \n",
       "1  CenterMaskScorer(output=atac, width=2001, agg=...  ...            .   \n",
       "2  CenterMaskScorer(output=atac, width=2001, agg=...  ...            .   \n",
       "3  CenterMaskScorer(output=atac, width=2001, agg=...  ...            .   \n",
       "4  CenterMaskScorer(output=atac, width=2001, agg=...  ...            .   \n",
       "\n",
       "  Assay title ontology_curie                   biosample_name  \\\n",
       "0    ATAC-seq     CL:0000084                           T-cell   \n",
       "1    ATAC-seq     CL:0000100                     motor neuron   \n",
       "2    ATAC-seq     CL:0000236                           B cell   \n",
       "3    ATAC-seq     CL:0000623              natural killer cell   \n",
       "4    ATAC-seq     CL:0000624  CD4-positive, alpha-beta T cell   \n",
       "\n",
       "                  biosample_type transcription_factor histone_mark  \\\n",
       "0                   primary_cell                 None         None   \n",
       "1  in_vitro_differentiated_cells                 None         None   \n",
       "2                   primary_cell                 None         None   \n",
       "3                   primary_cell                 None         None   \n",
       "4                   primary_cell                 None         None   \n",
       "\n",
       "  gtex_tissue raw_score  track_index  \n",
       "0        None -0.039216            0  \n",
       "1        None -0.001569            1  \n",
       "2        None  0.022250            2  \n",
       "3        None -0.029534            3  \n",
       "4        None -0.026243            4  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a custom scorer\n",
    "custom_scorer = CenterMaskScorer(\n",
    "    requested_output=OutputType.ATAC,\n",
    "    width=2001,  # Wider window (2kb centered on variant)\n",
    "    aggregation_type=AggregationType.DIFF_LOG2_SUM,  # Log fold change\n",
    "    resolution=1,  # 1bp resolution for fine detail\n",
    ")\n",
    "\n",
    "# Score with custom scorer\n",
    "custom_scores = scoring_model.score_variant(\n",
    "    interval=interval,\n",
    "    variant=variant,\n",
    "    scorers=[custom_scorer],\n",
    "    organism='human',\n",
    "    to_cpu=True,\n",
    ")\n",
    "\n",
    "# Convert to DataFrame\n",
    "custom_df = scoring_model.tidy_scores(custom_scores)\n",
    "print(f\"Custom scorer results: {custom_df.shape[0]} tracks\")\n",
    "custom_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e16bb7e",
   "metadata": {},
   "source": [
    "## 9. Gene-Based Scoring\n",
    "\n",
    "Gene-based scorers return one score per gene overlapping the interval:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2389376f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 12 genes in interval\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>gene_name</th>\n",
       "      <th>gene_id</th>\n",
       "      <th>raw_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MTCO1P20</td>\n",
       "      <td>ENSG00000233764</td>\n",
       "      <td>0.005431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>768</th>\n",
       "      <td>MTCO2P20</td>\n",
       "      <td>ENSG00000231576</td>\n",
       "      <td>0.004852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1536</th>\n",
       "      <td>MTATP6P20</td>\n",
       "      <td>ENSG00000237948</td>\n",
       "      <td>0.001218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2304</th>\n",
       "      <td>MTCO3P20</td>\n",
       "      <td>ENSG00000225557</td>\n",
       "      <td>0.002390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3072</th>\n",
       "      <td>MTCYBP34</td>\n",
       "      <td>ENSG00000237129</td>\n",
       "      <td>0.003161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3840</th>\n",
       "      <td>MTND1P10</td>\n",
       "      <td>ENSG00000229088</td>\n",
       "      <td>-0.000389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4608</th>\n",
       "      <td>ENSG00000288778</td>\n",
       "      <td>ENSG00000288778</td>\n",
       "      <td>0.040432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5376</th>\n",
       "      <td>APOL1</td>\n",
       "      <td>ENSG00000100342</td>\n",
       "      <td>0.025417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6144</th>\n",
       "      <td>ENSG00000279805</td>\n",
       "      <td>ENSG00000279805</td>\n",
       "      <td>0.001093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6912</th>\n",
       "      <td>APOL3</td>\n",
       "      <td>ENSG00000128284</td>\n",
       "      <td>0.002691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7680</th>\n",
       "      <td>APOL4</td>\n",
       "      <td>ENSG00000100336</td>\n",
       "      <td>0.002197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8448</th>\n",
       "      <td>APOL2</td>\n",
       "      <td>ENSG00000128335</td>\n",
       "      <td>0.011930</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            gene_name          gene_id  raw_score\n",
       "0            MTCO1P20  ENSG00000233764   0.005431\n",
       "768          MTCO2P20  ENSG00000231576   0.004852\n",
       "1536        MTATP6P20  ENSG00000237948   0.001218\n",
       "2304         MTCO3P20  ENSG00000225557   0.002390\n",
       "3072         MTCYBP34  ENSG00000237129   0.003161\n",
       "3840         MTND1P10  ENSG00000229088  -0.000389\n",
       "4608  ENSG00000288778  ENSG00000288778   0.040432\n",
       "5376            APOL1  ENSG00000100342   0.025417\n",
       "6144  ENSG00000279805  ENSG00000279805   0.001093\n",
       "6912            APOL3  ENSG00000128284   0.002691\n",
       "7680            APOL4  ENSG00000100336   0.002197\n",
       "8448            APOL2  ENSG00000128335   0.011930"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from alphagenome_pytorch.variant_scoring.scorers import GeneMaskLFCScorer\n",
    "\n",
    "# Create gene-level scorer\n",
    "gene_scorer = GeneMaskLFCScorer(OutputType.RNA_SEQ)\n",
    "\n",
    "# Score variant\n",
    "gene_scores = scoring_model.score_variant(\n",
    "    interval=interval,\n",
    "    variant=variant,\n",
    "    scorers=[gene_scorer],\n",
    "    organism='human',\n",
    "    to_cpu=True,\n",
    ")\n",
    "\n",
    "# Convert to DataFrame and view genes\n",
    "gene_df = scoring_model.tidy_scores(gene_scores)\n",
    "print(f\"Found {gene_df['gene_id'].nunique()} genes in interval\")\n",
    "gene_df[['gene_name', 'gene_id', 'raw_score']].drop_duplicates('gene_id')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1808af82",
   "metadata": {},
   "source": [
    "## Next Steps\n",
    "\n",
    "- **Batch scoring**: Score multiple variants by looping over variant list\n",
    "- **In-silico mutagenesis**: Use `score_ism_variants()` to score all possible SNVs in a window\n",
    "- **Visualization**: See the variant scoring README for plotting utilities\n",
    "- **Custom analysis**: Filter by effect size, biosample type, or genomic context\n",
    "\n",
    "For more details, see:\n",
    "- [Variant Scoring README](../../src/alphagenome_pytorch/variant_scoring/README.md)\n",
    "- [In-Silico Mutagenesis Notebook](./in_silico_mutagenesis.ipynb)"
   ]
  }
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