diff --git a/docs/tutorials/bludau-2021_tissue-specific-proteoform-inference-across-five-mouse-organs.ipynb b/docs/tutorials/bludau-2021_tissue-specific-proteoform-inference-across-five-mouse-organs.ipynb
index a85fa13..c796937 100644
--- a/docs/tutorials/bludau-2021_tissue-specific-proteoform-inference-across-five-mouse-organs.ipynb
+++ b/docs/tutorials/bludau-2021_tissue-specific-proteoform-inference-across-five-mouse-organs.ipynb
@@ -15,7 +15,22 @@
"\n",
"> Williams EG, Wu Y, Wolski W, Kim JY, Lan J, Hasan M, Halter C, Jha P, Ryu D, Auwerx J, and Aebersold R. **Quantifying and Localizing the Mitochondrial Proteome Across Five Tissues in A Mouse Population.** *Molecular & Cellular Proteomics*, 17(9):1766–1777, 2018. [doi:10.1074/mcp.RA118.000554](https://doi.org/10.1074/mcp.RA118.000554)\n",
"\n",
- "In this notebook, we show how the COPF implementation in ProteoPy can be used to perform the core proteoform inference workflow."
+ "In this notebook, we show how the COPF implementation in ProteoPy can be used to perform the core proteoform inference workflow.\n",
+ "\n",
+ "## What this notebook reproduces\n",
+ "\n",
+ "This is a faithful reproduction, not an approximation. Running it end to\n",
+ "end recovers **the same 63 proteins** that Bludau et al. report as\n",
+ "carrying multiple proteoform groups — the same count *and* the same\n",
+ "accessions, including the two the paper singles out, Ldb3 (Q9JKS4) and\n",
+ "Sorbs2 (Q3UTJ2) — together with the proteoform score plot of Figure 6B.\n",
+ "\n",
+ "ProteoPy's COPF implementation is itself tested against the intermediate\n",
+ "outputs of the original R code on this very dataset. Given the same\n",
+ "peptides, its correlations, dendrograms, cluster assignments and\n",
+ "proteoform scores agree with the reference to between 1e-14 and 1e-12,\n",
+ "which leaves the peptide set as the one thing this notebook has to get\n",
+ "right."
]
},
{
@@ -30,7 +45,24 @@
"cell_type": "code",
"execution_count": 1,
"id": "2bafc259",
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-03T06:49:28.120552Z",
+ "iopub.status.busy": "2026-08-03T06:49:28.120452Z",
+ "iopub.status.idle": "2026-08-03T06:49:29.520913Z",
+ "shell.execute_reply": "2026-08-03T06:49:29.520255Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/ifichtner/miniforge3/envs/proteoform-repro_exp-04/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ }
+ ],
"source": [
"import random\n",
"from pathlib import Path\n",
@@ -45,12 +77,15 @@
"# Set random seed for reproducibility\n",
"random.seed(42)\n",
"\n",
- "# Create a data directory in your current working directory \n",
+ "# Create a data directory in your current working directory\n",
"# to store files downloaded in this notebook.\n",
"cwd = Path(\".\").resolve()\n",
- "(cwd / 'data').mkdir(parents=True, exist_ok=True)"
- ],
- "outputs": []
+ "(cwd / \"data\").mkdir(parents=True, exist_ok=True)\n",
+ "\n",
+ "# Protein sequences, needed to locate each peptide in its protein.\n",
+ "# Shipped alongside this notebook so it runs straight after cloning.\n",
+ "FASTA_PATH = \"uniprot_mus-musculus_reviewed_bludau-2021.fasta\""
+ ]
},
{
"cell_type": "markdown",
@@ -72,13 +107,24 @@
"cell_type": "code",
"execution_count": 2,
"id": "d68b55d7",
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-03T06:49:29.522655Z",
+ "iopub.status.busy": "2026-08-03T06:49:29.522408Z",
+ "iopub.status.idle": "2026-08-03T06:50:12.106382Z",
+ "shell.execute_reply": "2026-08-03T06:50:12.105715Z"
+ }
+ },
"outputs": [],
"source": [
"# Define paths to the data files. These are the same paths that will be used in the download function below.\n",
"intensities_path = \"data/williams-2018_mouse-tissue_intensities.tsv\"\n",
- "sample_annotation_path = \"data/williams-2018_mouse-tissue_sample_annotation.tsv\"\n",
- "peptide_annotation_path = \"data/williams-2018_mouse-tissue_peptide_annotation.tsv\"\n",
+ "sample_annotation_path = (\n",
+ " \"data/williams-2018_mouse-tissue_sample_annotation.tsv\"\n",
+ ")\n",
+ "peptide_annotation_path = (\n",
+ " \"data/williams-2018_mouse-tissue_peptide_annotation.tsv\"\n",
+ ")\n",
"\n",
"pr.download.williams_2018(\n",
" intensities_path=intensities_path,\n",
@@ -100,17 +146,24 @@
"cell_type": "code",
"execution_count": 3,
"id": "0ede6e75",
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-03T06:50:12.108553Z",
+ "iopub.status.busy": "2026-08-03T06:50:12.108430Z",
+ "iopub.status.idle": "2026-08-03T06:50:12.283511Z",
+ "shell.execute_reply": "2026-08-03T06:50:12.282400Z"
+ }
+ },
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "sample_id\tpeptide_id\tintensity\n",
- "C57_Brain\tAAAAAAAAAAAAAAAGAAGK\t26302.0\n",
- "DBA_Brain\tAAAAAAAAAAAAAAAGAAGK\t15460.0\n",
- "45_Brain\tAAAAAAAAAAAAAAAGAAGK\t24631.0\n",
- "66_Brain\tAAAAAAAAAAAAAAAGAAGK\t26554.0\n"
+ "sample_id\tpeptide_id\tintensity\r\n",
+ "C57_Brain\tAAAAAAAAAAAAAAAGAAGK\t26302.0\r\n",
+ "DBA_Brain\tAAAAAAAAAAAAAAAGAAGK\t15460.0\r\n",
+ "45_Brain\tAAAAAAAAAAAAAAAGAAGK\t24631.0\r\n",
+ "66_Brain\tAAAAAAAAAAAAAAAGAAGK\t26554.0\r\n"
]
}
],
@@ -123,17 +176,24 @@
"cell_type": "code",
"execution_count": 4,
"id": "cfbf3627",
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-03T06:50:12.286257Z",
+ "iopub.status.busy": "2026-08-03T06:50:12.286158Z",
+ "iopub.status.idle": "2026-08-03T06:50:12.476164Z",
+ "shell.execute_reply": "2026-08-03T06:50:12.474900Z"
+ }
+ },
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "sample_id\ttissue\tmouse_id\n",
- "C57_Brain\tBrain\tC57\n",
- "DBA_Brain\tBrain\tDBA\n",
- "45_Brain\tBrain\t45\n",
- "66_Brain\tBrain\t66\n"
+ "sample_id\ttissue\tmouse_id\r\n",
+ "C57_Brain\tBrain\tC57\r\n",
+ "DBA_Brain\tBrain\tDBA\r\n",
+ "45_Brain\tBrain\t45\r\n",
+ "66_Brain\tBrain\t66\r\n"
]
}
],
@@ -146,17 +206,24 @@
"cell_type": "code",
"execution_count": 5,
"id": "b6d872a8",
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-03T06:50:12.478082Z",
+ "iopub.status.busy": "2026-08-03T06:50:12.477874Z",
+ "iopub.status.idle": "2026-08-03T06:50:12.650799Z",
+ "shell.execute_reply": "2026-08-03T06:50:12.649620Z"
+ }
+ },
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "peptide_id\tprotein_id\tgene_id\n",
- "AAAAAAAAAAAAAAAGAAGK\tP55012\tSlc12a2\n",
- "AAAAADLANR\tQ9JHS4\tClpx\n",
- "AAAADGEPLHNEEER\tQ80WW9\tDdrgk1\n",
- "AAAAEGARPLER\tQ80UM7\tMogs\n"
+ "peptide_id\tprotein_id\tgene_id\r\n",
+ "AAAAAAAAAAAAAAAGAAGK\tP55012\tSlc12a2\r\n",
+ "AAAAADLANR\tQ9JHS4\tClpx\r\n",
+ "AAAADGEPLHNEEER\tQ80WW9\tDdrgk1\r\n",
+ "AAAAEGARPLER\tQ80UM7\tMogs\r\n"
]
}
],
@@ -170,14 +237,21 @@
"id": "sa0gtgqxej",
"metadata": {},
"source": [
- "With the three files on disk, `pr.read.long()` reads the long-format intensities, pivots them into a dense matrix, and merges the sample and peptide annotations into a single AnnData object. Setting `fill_na=0` replaces any missing intensity values with zero."
+ "With the three files on disk, `pr.read.long()` reads the long-format intensities, pivots them into a dense matrix, and merges the sample and peptide annotations into a single AnnData object."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "981576e9",
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-03T06:50:12.653447Z",
+ "iopub.status.busy": "2026-08-03T06:50:12.653260Z",
+ "iopub.status.idle": "2026-08-03T06:50:13.534886Z",
+ "shell.execute_reply": "2026-08-03T06:50:13.534351Z"
+ }
+ },
"outputs": [
{
"data": {
@@ -193,14 +267,13 @@
}
],
"source": [
- "# Create a peptide level AnnData object from the \n",
- "# downloaded data files and fill missing values with 0.\n",
+ "# Create a peptide-level AnnData object from the downloaded files.\n",
+ "# `fill_na` is deliberately left unset: see above.\n",
"adata = pr.read.long(\n",
" intensities=intensities_path,\n",
" sample_annotation=sample_annotation_path,\n",
" var_annotation=peptide_annotation_path,\n",
" level=\"peptide\",\n",
- " fill_na=0,\n",
")\n",
"\n",
"adata"
@@ -210,7 +283,14 @@
"cell_type": "code",
"execution_count": 7,
"id": "795e2296",
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-03T06:50:13.537666Z",
+ "iopub.status.busy": "2026-08-03T06:50:13.537554Z",
+ "iopub.status.idle": "2026-08-03T06:50:13.550863Z",
+ "shell.execute_reply": "2026-08-03T06:50:13.550489Z"
+ }
+ },
"outputs": [
{
"name": "stdout",
@@ -228,101 +308,17 @@
"cell_type": "code",
"execution_count": 8,
"id": "95d976af",
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-03T06:50:13.552010Z",
+ "iopub.status.busy": "2026-08-03T06:50:13.551908Z",
+ "iopub.status.idle": "2026-08-03T06:50:13.557794Z",
+ "shell.execute_reply": "2026-08-03T06:50:13.557519Z"
+ }
+ },
"outputs": [
{
"data": {
- "application/vnd.microsoft.datawrangler.viewer.v0+json": {
- "columns": [
- {
- "name": "index",
- "rawType": "object",
- "type": "string"
- },
- {
- "name": "sample_id",
- "rawType": "object",
- "type": "string"
- },
- {
- "name": "tissue",
- "rawType": "category",
- "type": "unknown"
- },
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"text/html": [
"\n",
"\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " protein_id | \n",
+ " gene_id | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | VLPGPSQPR | \n",
+ " Q9JKS4 | \n",
+ " Ldb3 | \n",
+ "
\n",
+ " \n",
+ " | VGIFPISYVEK | \n",
+ " Q3UTJ2 | \n",
+ " Sorbs2 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
"