{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "260fa1b6",
   "metadata": {},
   "source": [
    "# Multiple comparisons correction\n",
    "\n",
    "When you test colocalizations with 30 PET receptor maps, you're running 30 statistical tests at once. If you use a threshold of α=0.05, you'd expect 1–2 false positives by chance alone even if nothing is truly significant. This is the multiple comparisons problem.\n",
    "\n",
    "NiSpace provides several correction methods. This notebook explains what they do and when to use each one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "793abd10",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-01T13:03:52.145710Z",
     "iopub.status.busy": "2026-06-01T13:03:52.145589Z",
     "iopub.status.idle": "2026-06-01T13:03:52.468526Z",
     "shell.execute_reply": "2026-06-01T13:03:52.468163Z"
    }
   },
   "outputs": [],
   "source": [
    "import tqdm.notebook\n",
    "tqdm.notebook.tqdm = tqdm.tqdm\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ff323522",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-01T13:03:52.470302Z",
     "iopub.status.busy": "2026-06-01T13:03:52.470146Z",
     "iopub.status.idle": "2026-06-01T13:04:05.268910Z",
     "shell.execute_reply": "2026-06-01T13:04:05.268594Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:03:54 | nispace.datasets: Loading pet maps.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:03:54 | nispace.datasets: Loading integrated collection 'UniqueTracers' for dataset 'pet'.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:03:54 | nispace.datasets: Filtering maps by collection.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:03:54 | nispace.datasets: Loading data parcellated with 'Schaefer200Parcels7Networks'\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:03:54 | nispace.api: *** NiSpace.fit() - Data extraction and preparation. ***\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:03:54 | nispace.core.parcellation: Building multi-space Parcellation for 'Schaefer200Parcels7Networks' from library.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:03:54 | nispace.core.parcellation: Available spaces: MNI152NLin2009cAsym, MNI152NLin6Asym, fsaverage, fsLR\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:03:54 | nispace.core.parcellation: Parcellation 'Schaefer200Parcels7Networks': validation passed.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:03:54 | nispace.core.parcellation: Lazy-loading parcellation image for space 'MNI152NLin2009cAsym'.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:03:54 | nispace.core.parcellation: Parcellation 'Schaefer200Parcels7Networks': active space set to 'MNI152NLin2009cAsym'.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:03:54 | nispace.api: Checking input data for 'x' (should be, e.g., PET data):\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:03:54 | nispace.io: Input type: DataFrame, assuming parcellated data with shape (n_files/subjects/etc, n_parcels).\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING | 01/06/26 15:03:54 | nispace.io: Parcellated data contains nan values!\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:03:54 | nispace.api: Got 'x' data for 29 x 200 parcels.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:03:54 | nispace.api: Checking input data for 'y' (should be, e.g., subject data):\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:03:54 | nispace.io: Input type: list, assuming imaging data.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:03:54 | nispace.io: Background (bg) handling: ignoring bg: True (bg value: ['auto', 0.0]); dropping bg parcels: False\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:03:54 | nispace.io: Parcellating imaging data.\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Parcellating (4 proc):   0%|                                                                                                                                     | 0/1 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Parcellating (4 proc): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 126.57it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:03:58 | nispace.api: Got 'y' data for 1 x 200 parcels.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:03:58 | nispace.api: Z-standardizing 'X' data.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:03:58 | nispace.api: *** NiSpace.colocalize() - Estimating X & Y colocalizations. ***\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:03:58 | nispace.api: Running 'spearman' colocalization.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:03:58 | nispace.api: Pre-ranking X and Y data.\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Colocalizing (spearman, 4 proc):   0%|                                                                                                                           | 0/1 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Colocalizing (spearman, 4 proc): 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 1686.49it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:04:01 | nispace.api: *** NiSpace.permute() - Estimate exact non-parametric p values. ***\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:04:01 | nispace.api: Permutation of: X maps.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:04:01 | nispace.api: Using default null method 'alexander_bloch' (parcellation null space: 'fsLR').\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:04:01 | nispace.core.parcellation: Lazy-loading parcellation image for space 'fsLR'.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:04:01 | nispace.api: Loading observed colocalizations (method = 'spearman').\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:04:01 | nispace.core.permute: No null maps found.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:04:01 | nispace.core.permute: Generating null maps (n = 1000, null_method = 'alexander_bloch').\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:04:01 | nispace.core.parcellation: Lazy-loading parcellation image for space 'fsaverage'.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:04:01 | nispace.nulls: Null map generation: Assuming n = 29 data vector(s) for n = 200 parcels.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:04:01 | nispace.nulls: Using provided precomputed spin matrix.\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Spin null maps:   0%|                                                                                                                                           | 0/29 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Spin null maps:  83%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▊                      | 24/29 [00:00<00:00, 234.22it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Spin null maps: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:00<00:00, 233.36it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:04:01 | nispace.nulls: Null data generation finished.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:04:01 | nispace.core.permute: Z-standardizing null maps.\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Processing null arrays (4 proc):   0%|                                                                                                                        | 0/1000 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Processing null arrays (4 proc):   1%|▉                                                                                                               | 8/1000 [00:00<01:46,  9.33it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Processing null arrays (4 proc):  26%|████████████████████████████▎                                                                                | 260/1000 [00:00<00:02, 366.13it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Processing null arrays (4 proc): 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:01<00:00, 978.64it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Null colocalizations (spearman, 4 proc):   0%|                                                                                                                | 0/1000 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Null colocalizations (spearman, 4 proc):  76%|████████████████████████████████████████████████████████████████████████████                        | 760/1000 [00:00<00:00, 6950.92it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Null colocalizations (spearman, 4 proc): 100%|███████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:00<00:00, 9067.92it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:04:05 | nispace.core.permute: Calculating exact p-values (tails = {'rho': 'upper'}).\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing 29 receptor maps simultaneously\n"
     ]
    }
   ],
   "source": [
    "from nispace.datasets import fetch_reference\n",
    "from nispace.io import load_img\n",
    "from nispace.api import NiSpace\n",
    "\n",
    "# set up and run the pain analysis\n",
    "pet_maps = fetch_reference(\"pet\", parcellation=\"Schaefer200\",\n",
    "                           collection=\"UniqueTracers\", print_references=False)\n",
    "pain_map = load_img(\"neuroquery/pain.nii.gz\")\n",
    "\n",
    "nsp = NiSpace(x=pet_maps, y=pain_map, y_labels=\"Pain\",\n",
    "              parcellation=\"Schaefer200\", seed=42, n_proc=4)\n",
    "nsp.fit()\n",
    "nsp.colocalize(\"spearman\")\n",
    "nsp.permute(\"maps\", n_perm=1000, p_tails=\"upper\")\n",
    "\n",
    "p_values = nsp.get_p_values()\n",
    "print(f\"Testing {p_values.shape[1]} receptor maps simultaneously\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8917d2b9",
   "metadata": {},
   "source": [
    "## Methods overview\n",
    "\n",
    "NiSpace provides two categories of multiple comparison correction:\n",
    "\n",
    "- **Empirical** (use the permutation null distribution): Meff, maxT, step-maxT\n",
    "- **Classical** (operate on p-values directly): FDR (Benjamini-Hochberg), Bonferroni, and any statsmodels-compatible method\n",
    "\n",
    "The workflow is the same for all methods: call `correct_p()` to compute and store the corrected p-values, then retrieve them with `get_p_values()`:\n",
    "\n",
    "```python\n",
    "nsp.correct_p(mc_method=\"meff\")              # compute and store\n",
    "p_corr = nsp.get_p_values(mc_method=\"meff\")  # retrieve\n",
    "```\n",
    "\n",
    "| Method | Controls | When to use |\n",
    "|--------|----------|-------------|\n",
    "| Meff (Galwey) | FWER | **NiSpace default**; adapts to inter-map correlations |\n",
    "| maxT / step-maxT | FWER | When stats are comparable across maps (Spearman, Pearson, standardized beta) |\n",
    "| FDR-BH | FDR | When FWER control is too strict |\n",
    "| Bonferroni | FWER | Rarely — Meff is strictly better when tests are correlated |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "680ace3c",
   "metadata": {},
   "source": [
    "## Meff — the NiSpace default\n",
    "\n",
    "The 29 PET receptor maps are not independent — serotonin receptors, dopamine transporters, and cholinergic markers all tend to co-localize. Standard Bonferroni ignores this correlation structure and therefore over-corrects.\n",
    "\n",
    "Meff estimates the *effective number of independent tests* from the eigenvalue spectrum of the inter-map correlation matrix, then applies a Sidak correction using this smaller effective number. Because it adapts to the actual correlation structure of your X maps rather than assuming independence, it is the NiSpace default.\n",
    "\n",
    "Two variants are available — `\"meff\"` resolves to the Galwey method:\n",
    "- `\"meff\"` / `\"meff_galwey\"` — **default**; Galwey (2009), *Genet Epidemiol*\n",
    "- `\"meff_li_ji\"` — Li & Ji (2005), *Ann Hum Genet*; slightly different eigenvalue weighting\n",
    "\n",
    "Calling `correct_p()` with no arguments uses Meff (Galwey)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0646e1bc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-01T13:04:05.270630Z",
     "iopub.status.busy": "2026-06-01T13:04:05.270515Z",
     "iopub.status.idle": "2026-06-01T13:04:05.276432Z",
     "shell.execute_reply": "2026-06-01T13:04:05.276101Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uncorrected (p < 0.05): 2 significant\n",
      "Meff        (p < 0.05): 0 significant\n",
      "\n",
      "Meff: 12.3 effective independent tests out of 29 total\n"
     ]
    }
   ],
   "source": [
    "alpha = 0.05\n",
    "\n",
    "nsp.correct_p(mc_method=\"meff\")   # default; mc_method can be omitted\n",
    "p_meff = nsp.get_p_values(mc_method=\"meff\")\n",
    "\n",
    "print(f\"Uncorrected (p < {alpha}): {(p_values < alpha).sum(axis=1).values[0]} significant\")\n",
    "print(f\"Meff        (p < {alpha}): {(p_meff   < alpha).sum(axis=1).values[0]} significant\")\n",
    "\n",
    "# how many effective tests did Meff find?\n",
    "from nispace.stats.misc import compute_meff\n",
    "n_tests = p_meff.shape[1]\n",
    "meff_value = compute_meff(np.array(nsp.get_x()))\n",
    "print(f\"\\nMeff: {meff_value:.1f} effective independent tests out of {n_tests} total\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "277236b3",
   "metadata": {},
   "source": [
    "## maxT and step-maxT\n",
    "\n",
    "maxT (Westfall & Young, 1993) controls FWER by using the permutation null distribution directly. Rather than looking at each test's null distribution independently, it builds a joint null distribution from the *maximum* test statistic across all reference maps in each permutation. This naturally accounts for inter-map correlations without any distributional assumptions.\n",
    "\n",
    "**Key constraint:** maxT assumes test statistics are on a *comparable scale* across X maps. This holds for:\n",
    "- Spearman and Pearson correlations — always in [−1, 1] ✓\n",
    "- Standardized regression coefficients (X was z-scored) ✓\n",
    "- **Not** for unstandardized regression coefficients — NiSpace emits a warning if this assumption may be violated\n",
    "\n",
    "Because `permute()` already stores the null distributions, calling `correct_p(\"maxT\")` or `correct_p(\"step_maxT\")` is computationally free. Step-maxT is a stepwise variant that tests maps in decreasing order of their observed statistic and achieves higher power than basic maxT."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1faab05c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-01T13:04:05.277928Z",
     "iopub.status.busy": "2026-06-01T13:04:05.277814Z",
     "iopub.status.idle": "2026-06-01T13:04:05.282905Z",
     "shell.execute_reply": "2026-06-01T13:04:05.282598Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maxT      (p < 0.05): 0 significant\n",
      "step-maxT (p < 0.05): 0 significant\n"
     ]
    }
   ],
   "source": [
    "nsp.correct_p(mc_method=\"maxT\")\n",
    "p_maxt = nsp.get_p_values(mc_method=\"maxT\")\n",
    "\n",
    "nsp.correct_p(mc_method=\"step_maxT\")\n",
    "p_step_maxt = nsp.get_p_values(mc_method=\"step_maxT\")\n",
    "\n",
    "print(f\"maxT      (p < {alpha}): {(p_maxt      < alpha).sum(axis=1).values[0]} significant\")\n",
    "print(f\"step-maxT (p < {alpha}): {(p_step_maxt < alpha).sum(axis=1).values[0]} significant\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0a0ba9e",
   "metadata": {},
   "source": [
    "## FDR correction (Benjamini-Hochberg)\n",
    "\n",
    "FDR correction controls the *expected proportion of false positives among rejected hypotheses* — the false discovery rate — rather than the probability of any false positive. It is less conservative than FWER methods and is widely used in neuroimaging.\n",
    "\n",
    "Note that FDR control has a different interpretation from FWER: it says \"among the significant results, I expect about X% to be false\" rather than \"I control the probability of making any error at all.\" Whether this is appropriate depends on your analysis goals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "11ab15bd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-01T13:04:05.284393Z",
     "iopub.status.busy": "2026-06-01T13:04:05.284276Z",
     "iopub.status.idle": "2026-06-01T13:04:05.286887Z",
     "shell.execute_reply": "2026-06-01T13:04:05.286561Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FDR-BH (p < 0.05): 0 significant\n"
     ]
    }
   ],
   "source": [
    "nsp.correct_p(mc_method=\"fdr_bh\")\n",
    "p_fdr = nsp.get_p_values(mc_method=\"fdr_bh\")\n",
    "print(f\"FDR-BH (p < {alpha}): {(p_fdr < alpha).sum(axis=1).values[0]} significant\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4023bba1",
   "metadata": {},
   "source": [
    "## Bonferroni correction\n",
    "\n",
    "Bonferroni multiplies each p-value by the total number of tests (capped at 1), assuming all tests are independent. It is the most conservative option. In practice, the reference maps in NiSpace are correlated, so Meff (which accounts for this) is almost always a better choice — it controls FWER with higher power."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6c7f2c93",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-01T13:04:05.288546Z",
     "iopub.status.busy": "2026-06-01T13:04:05.288411Z",
     "iopub.status.idle": "2026-06-01T13:04:05.291059Z",
     "shell.execute_reply": "2026-06-01T13:04:05.290772Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bonferroni (p < 0.05): 0 significant\n"
     ]
    }
   ],
   "source": [
    "nsp.correct_p(mc_method=\"bonferroni\")\n",
    "p_bonf = nsp.get_p_values(mc_method=\"bonferroni\")\n",
    "print(f\"Bonferroni (p < {alpha}): {(p_bonf < alpha).sum(axis=1).values[0]} significant\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c294199c",
   "metadata": {},
   "source": [
    "## Full comparison\n",
    "\n",
    "Let's put all correction methods side by side:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a61d1da8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-01T13:04:05.292662Z",
     "iopub.status.busy": "2026-06-01T13:04:05.292545Z",
     "iopub.status.idle": "2026-06-01T13:04:05.302016Z",
     "shell.execute_reply": "2026-06-01T13:04:05.301728Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_raw</th>\n",
       "      <th>p_meff</th>\n",
       "      <th>p_maxt</th>\n",
       "      <th>p_step_maxt</th>\n",
       "      <th>p_fdr</th>\n",
       "      <th>p_bonf</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>set</th>\n",
       "      <th>map</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Noradrenaline/Acetylcholine</th>\n",
       "      <th>target-VAChT_tracer-feobv_n-18_dx-hc_pub-aghourian2017</th>\n",
       "      <td>0.435971</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.219690</td>\n",
       "      <td>0.163</td>\n",
       "      <td>0.163</td>\n",
       "      <td>0.44950</td>\n",
       "      <td>0.580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Opioids/Endocannabinoids</th>\n",
       "      <th>target-KOR_tracer-ly2795050_n-28_dx-hc_pub-vijay2018</th>\n",
       "      <td>0.339014</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.320682</td>\n",
       "      <td>0.407</td>\n",
       "      <td>0.385</td>\n",
       "      <td>0.44950</td>\n",
       "      <td>0.899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Noradrenaline/Acetylcholine</th>\n",
       "      <th>target-NET_tracer-mrb_n-10_dx-hc_pub-hesse2017</th>\n",
       "      <td>0.309315</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.578821</td>\n",
       "      <td>0.493</td>\n",
       "      <td>0.464</td>\n",
       "      <td>0.54375</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Histamine</th>\n",
       "      <th>target-H3_tracer-gsk189254_n-8_dx-hc_pub-gallezot2017</th>\n",
       "      <td>0.304823</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.616060</td>\n",
       "      <td>0.508</td>\n",
       "      <td>0.464</td>\n",
       "      <td>0.54375</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glutamate</th>\n",
       "      <th>target-mGluR5_tracer-abp688_n-73_dx-hc_pub-smart2019</th>\n",
       "      <td>0.219451</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.893923</td>\n",
       "      <td>0.731</td>\n",
       "      <td>0.633</td>\n",
       "      <td>0.81200</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serotonin</th>\n",
       "      <th>target-5HTT_tracer-dasb_n-18_dx-hc_pub-savli2012</th>\n",
       "      <td>0.176491</td>\n",
       "      <td>0.227</td>\n",
       "      <td>0.957637</td>\n",
       "      <td>0.812</td>\n",
       "      <td>0.734</td>\n",
       "      <td>0.81200</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Opioids/Endocannabinoids</th>\n",
       "      <th>target-MOR_tracer-carfentanil_n-204_dx-hc_pub-kantonen2020</th>\n",
       "      <td>0.151909</td>\n",
       "      <td>0.254</td>\n",
       "      <td>0.972622</td>\n",
       "      <td>0.853</td>\n",
       "      <td>0.783</td>\n",
       "      <td>0.81200</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dopamine</th>\n",
       "      <th>target-DAT_tracer-fpcit_n-174_dx-hc_pub-dukart2018</th>\n",
       "      <td>0.129229</td>\n",
       "      <td>0.272</td>\n",
       "      <td>0.979716</td>\n",
       "      <td>0.891</td>\n",
       "      <td>0.830</td>\n",
       "      <td>0.81200</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Opioids/Endocannabinoids</th>\n",
       "      <th>target-CB1_tracer-omar_n-77_dx-hc_pub-normandin2015</th>\n",
       "      <td>0.102278</td>\n",
       "      <td>0.325</td>\n",
       "      <td>0.991982</td>\n",
       "      <td>0.924</td>\n",
       "      <td>0.868</td>\n",
       "      <td>0.81200</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GABA</th>\n",
       "      <th>target-GABAa5_tracer-ro154513_n-10_dx-hc_pub-lukow2022</th>\n",
       "      <td>0.111743</td>\n",
       "      <td>0.334</td>\n",
       "      <td>0.993200</td>\n",
       "      <td>0.911</td>\n",
       "      <td>0.858</td>\n",
       "      <td>0.81200</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                     rho  \\\n",
       "set                         map                                                            \n",
       "Noradrenaline/Acetylcholine target-VAChT_tracer-feobv_n-18_dx-hc_pub-aghour...  0.435971   \n",
       "Opioids/Endocannabinoids    target-KOR_tracer-ly2795050_n-28_dx-hc_pub-vija...  0.339014   \n",
       "Noradrenaline/Acetylcholine target-NET_tracer-mrb_n-10_dx-hc_pub-hesse2017      0.309315   \n",
       "Histamine                   target-H3_tracer-gsk189254_n-8_dx-hc_pub-gallez...  0.304823   \n",
       "Glutamate                   target-mGluR5_tracer-abp688_n-73_dx-hc_pub-smar...  0.219451   \n",
       "Serotonin                   target-5HTT_tracer-dasb_n-18_dx-hc_pub-savli2012    0.176491   \n",
       "Opioids/Endocannabinoids    target-MOR_tracer-carfentanil_n-204_dx-hc_pub-k...  0.151909   \n",
       "Dopamine                    target-DAT_tracer-fpcit_n-174_dx-hc_pub-dukart2018  0.129229   \n",
       "Opioids/Endocannabinoids    target-CB1_tracer-omar_n-77_dx-hc_pub-normandin...  0.102278   \n",
       "GABA                        target-GABAa5_tracer-ro154513_n-10_dx-hc_pub-lu...  0.111743   \n",
       "\n",
       "                                                                                p_raw  \\\n",
       "set                         map                                                         \n",
       "Noradrenaline/Acetylcholine target-VAChT_tracer-feobv_n-18_dx-hc_pub-aghour...  0.020   \n",
       "Opioids/Endocannabinoids    target-KOR_tracer-ly2795050_n-28_dx-hc_pub-vija...  0.031   \n",
       "Noradrenaline/Acetylcholine target-NET_tracer-mrb_n-10_dx-hc_pub-hesse2017      0.068   \n",
       "Histamine                   target-H3_tracer-gsk189254_n-8_dx-hc_pub-gallez...  0.075   \n",
       "Glutamate                   target-mGluR5_tracer-abp688_n-73_dx-hc_pub-smar...  0.167   \n",
       "Serotonin                   target-5HTT_tracer-dasb_n-18_dx-hc_pub-savli2012    0.227   \n",
       "Opioids/Endocannabinoids    target-MOR_tracer-carfentanil_n-204_dx-hc_pub-k...  0.254   \n",
       "Dopamine                    target-DAT_tracer-fpcit_n-174_dx-hc_pub-dukart2018  0.272   \n",
       "Opioids/Endocannabinoids    target-CB1_tracer-omar_n-77_dx-hc_pub-normandin...  0.325   \n",
       "GABA                        target-GABAa5_tracer-ro154513_n-10_dx-hc_pub-lu...  0.334   \n",
       "\n",
       "                                                                                  p_meff  \\\n",
       "set                         map                                                            \n",
       "Noradrenaline/Acetylcholine target-VAChT_tracer-feobv_n-18_dx-hc_pub-aghour...  0.219690   \n",
       "Opioids/Endocannabinoids    target-KOR_tracer-ly2795050_n-28_dx-hc_pub-vija...  0.320682   \n",
       "Noradrenaline/Acetylcholine target-NET_tracer-mrb_n-10_dx-hc_pub-hesse2017      0.578821   \n",
       "Histamine                   target-H3_tracer-gsk189254_n-8_dx-hc_pub-gallez...  0.616060   \n",
       "Glutamate                   target-mGluR5_tracer-abp688_n-73_dx-hc_pub-smar...  0.893923   \n",
       "Serotonin                   target-5HTT_tracer-dasb_n-18_dx-hc_pub-savli2012    0.957637   \n",
       "Opioids/Endocannabinoids    target-MOR_tracer-carfentanil_n-204_dx-hc_pub-k...  0.972622   \n",
       "Dopamine                    target-DAT_tracer-fpcit_n-174_dx-hc_pub-dukart2018  0.979716   \n",
       "Opioids/Endocannabinoids    target-CB1_tracer-omar_n-77_dx-hc_pub-normandin...  0.991982   \n",
       "GABA                        target-GABAa5_tracer-ro154513_n-10_dx-hc_pub-lu...  0.993200   \n",
       "\n",
       "                                                                                p_maxt  \\\n",
       "set                         map                                                          \n",
       "Noradrenaline/Acetylcholine target-VAChT_tracer-feobv_n-18_dx-hc_pub-aghour...   0.163   \n",
       "Opioids/Endocannabinoids    target-KOR_tracer-ly2795050_n-28_dx-hc_pub-vija...   0.407   \n",
       "Noradrenaline/Acetylcholine target-NET_tracer-mrb_n-10_dx-hc_pub-hesse2017       0.493   \n",
       "Histamine                   target-H3_tracer-gsk189254_n-8_dx-hc_pub-gallez...   0.508   \n",
       "Glutamate                   target-mGluR5_tracer-abp688_n-73_dx-hc_pub-smar...   0.731   \n",
       "Serotonin                   target-5HTT_tracer-dasb_n-18_dx-hc_pub-savli2012     0.812   \n",
       "Opioids/Endocannabinoids    target-MOR_tracer-carfentanil_n-204_dx-hc_pub-k...   0.853   \n",
       "Dopamine                    target-DAT_tracer-fpcit_n-174_dx-hc_pub-dukart2018   0.891   \n",
       "Opioids/Endocannabinoids    target-CB1_tracer-omar_n-77_dx-hc_pub-normandin...   0.924   \n",
       "GABA                        target-GABAa5_tracer-ro154513_n-10_dx-hc_pub-lu...   0.911   \n",
       "\n",
       "                                                                                p_step_maxt  \\\n",
       "set                         map                                                               \n",
       "Noradrenaline/Acetylcholine target-VAChT_tracer-feobv_n-18_dx-hc_pub-aghour...        0.163   \n",
       "Opioids/Endocannabinoids    target-KOR_tracer-ly2795050_n-28_dx-hc_pub-vija...        0.385   \n",
       "Noradrenaline/Acetylcholine target-NET_tracer-mrb_n-10_dx-hc_pub-hesse2017            0.464   \n",
       "Histamine                   target-H3_tracer-gsk189254_n-8_dx-hc_pub-gallez...        0.464   \n",
       "Glutamate                   target-mGluR5_tracer-abp688_n-73_dx-hc_pub-smar...        0.633   \n",
       "Serotonin                   target-5HTT_tracer-dasb_n-18_dx-hc_pub-savli2012          0.734   \n",
       "Opioids/Endocannabinoids    target-MOR_tracer-carfentanil_n-204_dx-hc_pub-k...        0.783   \n",
       "Dopamine                    target-DAT_tracer-fpcit_n-174_dx-hc_pub-dukart2018        0.830   \n",
       "Opioids/Endocannabinoids    target-CB1_tracer-omar_n-77_dx-hc_pub-normandin...        0.868   \n",
       "GABA                        target-GABAa5_tracer-ro154513_n-10_dx-hc_pub-lu...        0.858   \n",
       "\n",
       "                                                                                  p_fdr  \\\n",
       "set                         map                                                           \n",
       "Noradrenaline/Acetylcholine target-VAChT_tracer-feobv_n-18_dx-hc_pub-aghour...  0.44950   \n",
       "Opioids/Endocannabinoids    target-KOR_tracer-ly2795050_n-28_dx-hc_pub-vija...  0.44950   \n",
       "Noradrenaline/Acetylcholine target-NET_tracer-mrb_n-10_dx-hc_pub-hesse2017      0.54375   \n",
       "Histamine                   target-H3_tracer-gsk189254_n-8_dx-hc_pub-gallez...  0.54375   \n",
       "Glutamate                   target-mGluR5_tracer-abp688_n-73_dx-hc_pub-smar...  0.81200   \n",
       "Serotonin                   target-5HTT_tracer-dasb_n-18_dx-hc_pub-savli2012    0.81200   \n",
       "Opioids/Endocannabinoids    target-MOR_tracer-carfentanil_n-204_dx-hc_pub-k...  0.81200   \n",
       "Dopamine                    target-DAT_tracer-fpcit_n-174_dx-hc_pub-dukart2018  0.81200   \n",
       "Opioids/Endocannabinoids    target-CB1_tracer-omar_n-77_dx-hc_pub-normandin...  0.81200   \n",
       "GABA                        target-GABAa5_tracer-ro154513_n-10_dx-hc_pub-lu...  0.81200   \n",
       "\n",
       "                                                                                p_bonf  \n",
       "set                         map                                                         \n",
       "Noradrenaline/Acetylcholine target-VAChT_tracer-feobv_n-18_dx-hc_pub-aghour...   0.580  \n",
       "Opioids/Endocannabinoids    target-KOR_tracer-ly2795050_n-28_dx-hc_pub-vija...   0.899  \n",
       "Noradrenaline/Acetylcholine target-NET_tracer-mrb_n-10_dx-hc_pub-hesse2017       1.000  \n",
       "Histamine                   target-H3_tracer-gsk189254_n-8_dx-hc_pub-gallez...   1.000  \n",
       "Glutamate                   target-mGluR5_tracer-abp688_n-73_dx-hc_pub-smar...   1.000  \n",
       "Serotonin                   target-5HTT_tracer-dasb_n-18_dx-hc_pub-savli2012     1.000  \n",
       "Opioids/Endocannabinoids    target-MOR_tracer-carfentanil_n-204_dx-hc_pub-k...   1.000  \n",
       "Dopamine                    target-DAT_tracer-fpcit_n-174_dx-hc_pub-dukart2018   1.000  \n",
       "Opioids/Endocannabinoids    target-CB1_tracer-omar_n-77_dx-hc_pub-normandin...   1.000  \n",
       "GABA                        target-GABAa5_tracer-ro154513_n-10_dx-hc_pub-lu...   1.000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_results = pd.DataFrame({\n",
    "    \"rho\":         nsp.get_colocalizations().T[\"Pain\"],\n",
    "    \"p_raw\":       p_values.T[\"Pain\"],\n",
    "    \"p_meff\":      p_meff.T[\"Pain\"],\n",
    "    \"p_maxt\":      p_maxt.T[\"Pain\"],\n",
    "    \"p_step_maxt\": p_step_maxt.T[\"Pain\"],\n",
    "    \"p_fdr\":       p_fdr.T[\"Pain\"],\n",
    "    \"p_bonf\":      p_bonf.T[\"Pain\"],\n",
    "}).sort_values(\"p_raw\")\n",
    "all_results.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1322c2b8",
   "metadata": {},
   "source": [
    "## Plotting with correction\n",
    "\n",
    "`nsp.plot()` can incorporate any of the correction methods. Pass `mc_method` to display corrected significance annotations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "97955bcd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-06-01T13:04:05.303403Z",
     "iopub.status.busy": "2026-06-01T13:04:05.303292Z",
     "iopub.status.idle": "2026-06-01T13:04:05.726748Z",
     "shell.execute_reply": "2026-06-01T13:04:05.726425Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mINFO | 01/06/26 15:04:05 | nispace.plotting: Significance annotation: 2/29 p_uncorrected < 0.05, 0/29 p_meff < 0.05\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x730 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 500x730 with 1 Axes>,\n",
       " <Axes: title={'center': \"$Spearman's\\\\ Rho$ colocalization\\n(permutation of $X\\\\ maps$)\"}, xlabel='$Rho$'>,\n",
       " <seaborn._core.plot.Plotter at 0x15e727f40>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nsp.plot(mc_method=\"meff\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c85e009",
   "metadata": {},
   "source": [
    "## Which method should I use?\n",
    "\n",
    "- **Meff (default)**: The NiSpace default and the right starting point for most analyses. It accounts for the actual correlation structure of your reference maps and is more powerful than Bonferroni without any additional assumptions.\n",
    "- **maxT / step-maxT**: Excellent alternatives when your colocalization method produces statistics on a comparable scale — that is, Spearman or Pearson correlations, or regression with z-scored X. Computationally free once `permute()` has run. Step-maxT has more power than basic maxT.\n",
    "- **FDR-BH**: Appropriate when you're comfortable with FDR control rather than FWER — common in exploratory neuroimaging but note that the interpretation differs.\n",
    "- **Bonferroni**: Rarely justified; Meff is strictly better when your tests are correlated, which they typically are for any standard reference dataset.\n",
    "\n",
    "## Summary\n",
    "\n",
    "| Method | Call | Controls |\n",
    "|--------|------|----------|\n",
    "| Meff — **NiSpace default** | `nsp.correct_p()` | FWER |\n",
    "| maxT | `nsp.correct_p(mc_method=\"maxT\")` | FWER |\n",
    "| step-maxT | `nsp.correct_p(mc_method=\"step_maxT\")` | FWER |\n",
    "| FDR (Benjamini-Hochberg) | `nsp.correct_p(mc_method=\"fdr_bh\")` | FDR |\n",
    "| Bonferroni | `nsp.correct_p(mc_method=\"bonferroni\")` | FWER |\n",
    "\n",
    "Retrieve corrected p-values with `nsp.get_p_values(mc_method=...)` after calling `correct_p()`.\n",
    "\n",
    "Next: [Notebook 7](intro07_visualization.ipynb) covers how to customize `NiSpace.plot()` and `NiSpace.plot_brain()` to produce publication-quality figures."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "nsp309",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.20"
  },
  "nbsphinx": {
   "execute": "never"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}