{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cell-0",
   "metadata": {},
   "source": [
    "# Tutorial 8: Colocalization Analysis (2-way & 3-way)\n",
    "\n",
    "## Objective\n",
    "\n",
    "Beyond pairwise distance matrices (Tutorial 7), chromatin tracing data allows us to ask:\n",
    "*given a specific genomic locus (anchor), which other loci are physically close to it?*\n",
    "\n",
    "This tutorial covers two complementary analyses:\n",
    "\n",
    "1. **2-way colocalization** (`plot_4m`) — For a given anchor barcode, compute the frequency\n",
    "   at which each other barcode is within a distance cutoff. Produces a 1D profile (4M plot).\n",
    "2. **3-way colocalization** (`trace_3way_coloc`) — For a given anchor barcode, compute the\n",
    "   frequency at which **pairs** of other barcodes are *both* within the distance cutoff\n",
    "   simultaneously. Produces a 2D heatmap.\n",
    "\n",
    "Both scripts include **bootstrapping** to estimate statistical confidence (mean ± SEM).\n",
    "\n",
    "## Scientific context\n",
    "\n",
    "- **2-way (4M)**: reveals which loci interact with a chosen viewpoint — analogous to 4C/Capture-C.\n",
    "- **3-way**: identifies higher-order contacts where 3 loci converge simultaneously — evidence for\n",
    "  transcription factories, chromatin hubs, or phase-separated condensates.\n",
    "\n",
    "## Input\n",
    "\n",
    "We use the Pdx1-positive trace file from Tutorial 6:\n",
    "\n",
    "| File | Description |\n",
    "|------|-------------|\n",
    "| `Trace_3D_barcode_mask-mask0_ROI-16_Pdx1_Pdx1.ecsv` | Traces inside the Pdx1 mask |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cell-1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input trace: /mnt/grey/DATA/users/zidoum/traceraptops/jupyterlab_amel/output/merged_traces_split.ecsv\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import glob\n",
    "\n",
    "data_path = \"/mnt/grey/DATA/users/zidoum/traceraptops/jupyterlab_amel/output\"\n",
    "input_trace = f\"{data_path}/merged_traces_split.ecsv\"\n",
    "\n",
    "print(f\"Input trace: {input_trace}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-2",
   "metadata": {},
   "source": [
    "## Step 1: 2-way colocalization with `plot_4m`\n",
    "\n",
    "`plot_4m` computes, for a chosen **anchor** barcode, how frequently every other barcode is found\n",
    "within a distance cutoff. This is repeated over bootstrap iterations to obtain mean ± SEM.\n",
    "\n",
    "The result is a **4M plot**: a 1D profile where the X-axis is the barcode number and the\n",
    "Y-axis is the colocalization frequency with the anchor.\n",
    "\n",
    "### Parameters\n",
    "\n",
    "| Option | Default | Description |\n",
    "|--------|---------|-------------|\n",
    "| `--input` | — | Input trace file (ECSV) |\n",
    "| `--anchors` | — | One or more anchor barcode numbers (space-separated) |\n",
    "| `--cutoff` | `0.2` | Distance threshold in µm |\n",
    "| `--bootstrapping_cycles` | `10` | Number of bootstrap iterations |\n",
    "| `--output` | `colocalization_plot.png` | Base name for output plots |\n",
    "| `--x_min` / `--x_max` | auto | X-axis range for the plot |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2-way colocalization: anchor at barcode 10, distance cutoff 0.25 µm\n",
    "!plot_4m --input {input_trace} --anchors 10 --cutoff 0.25 --bootstrapping_cycles 50 --output {data_path}/coloc_4m.png"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-4",
   "metadata": {},
   "source": [
    "The output is saved as `coloc_4m_anchor_10.png` (the anchor ID is appended automatically)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cell-5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_file = f\"{data_path}/coloc_4m_anchor_10.png\"\n",
    "\n",
    "img = mpimg.imread(plot_file)\n",
    "fig, ax = plt.subplots(figsize=(12, 5))\n",
    "ax.imshow(img)\n",
    "ax.axis('off')\n",
    "ax.set_title(\"4M plot: colocalization with anchor barcode 10\", fontsize=14)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-6",
   "metadata": {},
   "source": [
    "**How to read the 4M plot:**\n",
    "- Each point is a barcode; the Y-value is the fraction of traces where that barcode is within 0.25 µm of the anchor.\n",
    "- Error bars show the bootstrap SEM.\n",
    "- The **red dashed line** marks the anchor barcode itself.\n",
    "- Barcodes **near the anchor** on the genome (close barcode numbers) are expected to have higher frequencies.\n",
    "- A barcode **far on the genome** but with high frequency suggests a **long-range contact**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-7",
   "metadata": {},
   "source": [
    "### Multiple anchors\n",
    "\n",
    "You can compute 4M profiles for several anchors in a single run.\n",
    "Each anchor produces its own plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cell-8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------- Running plot_4m.py --------\n",
      "Compute barcode interaction frequencies with bootstrapping.\n",
      "\n",
      "\n",
      "$ Processing trace file: /mnt/grey/DATA/users/zidoum/traceraptops/jupyterlab_amel/output/merged_traces_split.ecsv\n",
      "$ Importing table from pyHiM format\n",
      "Successfully loaded trace table: /mnt/grey/DATA/users/zidoum/traceraptops/jupyterlab_amel/output/merged_traces_split.ecsv\n",
      "\n",
      "$ Processing anchors: [3, 10, 20]\n"
     ]
    }
   ],
   "source": [
    "# Multiple anchors in one run\n",
    "!plot_4m --input {input_trace} --anchors 3 10 20 --cutoff 0.25 --bootstrapping_cycles 50 --output {data_path}/coloc_4m_multi.png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cell-9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display all anchor plots side by side\n",
    "anchors = [3, 10, 20]\n",
    "fig, axes = plt.subplots(1, 3, figsize=(24, 5))\n",
    "\n",
    "for ax, anchor in zip(axes, anchors):\n",
    "    plot_file = f\"{data_path}/coloc_4m_multi_anchor_{anchor}.png\"\n",
    "    img = mpimg.imread(plot_file)\n",
    "    ax.imshow(img)\n",
    "    ax.axis('off')\n",
    "    ax.set_title(f\"Anchor {anchor}\", fontsize=13)\n",
    "\n",
    "fig.suptitle(\"4M profiles for three anchors\", fontsize=15)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-10",
   "metadata": {},
   "source": [
    "## Step 2: 3-way colocalization with `trace_3way_coloc`\n",
    "\n",
    "`trace_3way_coloc` extends the analysis to **three-body contacts**.\n",
    "Given an anchor barcode, it checks every pair of other barcodes (i, j) and asks:\n",
    "*in what fraction of traces are anchor, i, and j all within the distance cutoff simultaneously?*\n",
    "\n",
    "The result is a **symmetric heatmap** where each cell (i, j) shows the 3-way\n",
    "colocalization frequency. A second heatmap shows the bootstrap SEM.\n",
    "\n",
    "### Parameters\n",
    "\n",
    "| Option | Default | Description |\n",
    "|--------|---------|-------------|\n",
    "| `--input` | — | Input trace file (ECSV) |\n",
    "| `--anchors` | — | One or more anchor barcode numbers |\n",
    "| `--cutoff` | `0.2` | Distance threshold in µm |\n",
    "| `--bootstrapping_cycles` | `10` | Number of bootstrap iterations |\n",
    "| `--output` | `threeway_coloc_plot.png` | Base name for output files |\n",
    "| `--vmin` / `--vmax` | auto | Colormap range |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------- Running trace_3way_coloc.py --------\n",
      "Compute three-way co-localization frequencies with bootstrapping.\n",
      "\n",
      ">> Processing file: /mnt/grey/DATA/users/zidoum/traceraptops/jupyterlab_amel/output/merged_traces_split.ecsv\n",
      "$ Importing table from pyHiM format\n",
      "Successfully loaded trace table: /mnt/grey/DATA/users/zidoum/traceraptops/jupyterlab_amel/output/merged_traces_split.ecsv\n",
      "Using distance cutoff: 0.25 µm\n",
      "Performing 50 bootstrap iterations\n",
      "\n",
      "Running analysis for anchor: 10\n"
     ]
    }
   ],
   "source": [
    "# 3-way colocalization: anchor at barcode 10\n",
    "!trace_3way_coloc --input {input_trace} --anchors 10 --cutoff 0.25 --bootstrapping_cycles 50 --output {data_path}/coloc_3way.png"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-12",
   "metadata": {},
   "source": [
    "This produces:\n",
    "\n",
    "| Output file | Description |\n",
    "|-------------|-------------|\n",
    "| `coloc_3way_<trace_basename>_anchor_10.png` | 3-way frequency heatmap |\n",
    "| `coloc_3way_<trace_basename>_anchor_10.npy` | Frequency matrix (NumPy) |\n",
    "| `coloc_3way_<trace_basename>_anchor_10_sem.png` | Bootstrap SEM heatmap |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-13",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find and display the 3-way heatmap (frequency and SEM)\n",
    "freq_matches = sorted(glob.glob(f\"{data_path}/coloc_3way_*_anchor_10.png\"))\n",
    "sem_matches = sorted(glob.glob(f\"{data_path}/coloc_3way_*_anchor_10_sem.png\"))\n",
    "\n",
    "if freq_matches and sem_matches:\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(20, 8))\n",
    "\n",
    "    img_freq = mpimg.imread(freq_matches[0])\n",
    "    axes[0].imshow(img_freq)\n",
    "    axes[0].axis('off')\n",
    "    axes[0].set_title(\"3-way colocalization frequency\", fontsize=14)\n",
    "\n",
    "    img_sem = mpimg.imread(sem_matches[0])\n",
    "    axes[1].imshow(img_sem)\n",
    "    axes[1].axis('off')\n",
    "    axes[1].set_title(\"Bootstrap SEM\", fontsize=14)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-14",
   "metadata": {},
   "source": [
    "**How to read the 3-way heatmap:**\n",
    "- Axes show barcode IDs (excluding the anchor).\n",
    "- Cell (i, j) = fraction of traces where anchor, barcode i, and barcode j are **all three** within the distance cutoff.\n",
    "- **Black crosshair** marks the anchor barcode position.\n",
    "- High values off the diagonal reveal **three-body hubs** where three distant loci converge.\n",
    "- The SEM heatmap helps identify which entries are statistically robust (low SEM)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-15",
   "metadata": {},
   "source": [
    "## Step 3: Varying the distance cutoff\n",
    "\n",
    "The distance cutoff has a strong effect on colocalization frequencies.\n",
    "A small cutoff (e.g. 0.15 µm) captures only very tight contacts, while a larger one (e.g. 0.35 µm)\n",
    "includes more diffuse proximity. Try different values to see how the patterns change:\n",
    "\n",
    "```bash\n",
    "# Tight cutoff\n",
    "plot_4m --input Trace.ecsv --anchors 10 --cutoff 0.15 --output coloc_tight.png\n",
    "\n",
    "# Relaxed cutoff\n",
    "plot_4m --input Trace.ecsv --anchors 10 --cutoff 0.35 --output coloc_relaxed.png\n",
    "```\n",
    "\n",
    "A robust interaction should be visible across a range of cutoffs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-16",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "### Workflow\n",
    "\n",
    "```\n",
    "trace file (.ecsv)\n",
    "       │\n",
    "       ├─── plot_4m               →  1D profile (anchor vs each barcode)\n",
    "       │\n",
    "       └─── trace_3way_coloc      →  2D heatmap (anchor vs pairs) + SEM\n",
    "```\n",
    "\n",
    "### Commands reference\n",
    "\n",
    "```bash\n",
    "# 2-way (4M profile)\n",
    "plot_4m --input Trace.ecsv --anchors 10 --cutoff 0.25 --bootstrapping_cycles 50 --output 4m.png\n",
    "\n",
    "# 3-way (heatmap)\n",
    "trace_3way_coloc --input Trace.ecsv --anchors 10 --cutoff 0.25 --bootstrapping_cycles 50 --output 3way.png\n",
    "```\n",
    "\n",
    "### Key differences\n",
    "\n",
    "| | `plot_4m` (2-way) | `trace_3way_coloc` (3-way) |\n",
    "|---|---|---|\n",
    "| Question | Is barcode X close to anchor? | Are barcodes X **and** Y both close to anchor? |\n",
    "| Output | 1D line plot | 2D symmetric heatmap |\n",
    "| Complexity | O(n) per trace | O(n²) per trace |\n",
    "| Detects | Pairwise interactions | Higher-order hubs |\n",
    "\n",
    "### Notes\n",
    "\n",
    "- **Bootstrap cycles**: 10 is fast for exploration; use 100–1000 for publication.\n",
    "- **Multiple anchors**: both scripts accept `--anchors 3 10 20` to process several viewpoints in one run.\n",
    "- **Large datasets**: 3-way analysis is computationally heavier than 2-way. For many barcodes, consider running on a filtered/split subset.\n",
    "\n",
    "**Next:** [Tutorial 9 — Compare datasets](tutorial_09_compare_datasets.ipynb)"
   ]
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