Retrieve MoTrPAC multi-omics from the openly licensed R data package and public bucket instead of the account-gated Data Hub API — transcriptomics, proteomics, phosphoproteomics, acetylome, metabolomics, ATAC-seq and RRBS across twenty rat tissues including heart, skeletal muscle, liver and adipose, in endurance-trained animals. Young-adult 6-month cohort, not an aged one.

MoTrPAC — rat endurance exercise training multi-omics

MoTrPAC (Molecular Transducers of Physical Activity Consortium, NIH Common Fund) ran a treadmill training intervention in Fischer 344 rats and then measured nine molecular assays across twenty tissues in the same animals. That combination is what makes it unusual: cardiac, skeletal-muscle, hepatic and adipose readouts from one cohort, one protocol, one set of sample identifiers, so a cross-organ pipeline can be validated without stitching cohorts together.

Two things decide whether it answers your question, and both are settled below before any code: which animals are in the open release, and which route actually serves the data — because the obvious one does not.

The open release is young adult, not aged

Every openly available omics object comes from PASS1B-06 — 6-month-old rats. Not a guess from the package name: PHENO carries the design columns, and on 2026-08-18 all 6,156 of its rows read key.agegroup == "6 months" and key.protocol == "phase 1b". The ## Try it block at the end asserts exactly this, so the claim re-checks itself.

The consortium did run an 18-month aged arm, and it is public — but only as physiology. In MotrpacRatTrainingPhysiologyData, VO2MAX$age and BODY_MASSES$age both take the values 6M and 18M (verified 2026-08-18): body composition, VO2max, run speed, muscle mass, fibre typing and plasma clinical analytes. No aged omics has been released on any open route. Checked, same date:

not in the R data package — PHENO has one age group
not on the public bucket — training-da/PROT/pass1b-06_… returns 200, the identical URL with pass1b-18 returns 404, and so does pass1a-06 (the acute-exercise arm)
not in GEO — a title search for MoTrPAC returns exactly four Rattus norvegicus series, GSE242358 and its three subseries, all titled …In 6 Months Old Rats

So: reach for this as an exercise-response and cross-tissue-integration resource. If your question is about age, the open omics cannot answer it, and the aged physiology lives in a differently licensed package (see the licence table). Say which one you used.

The design. Training ran for 1, 2, 4 or 8 weeks, both sexes, with a sedentary control group that was held for 8 weeks and serves as the reference for every timepoint — there is no per-timepoint control. Differential-analysis tables therefore carry comparison_group in 1w 2w 4w 8w and are computed separately per sex.

That grid is not filled in every tissue, and the holes are silent. Ovary and testes are one sex, so their tables carry four contrasts per feature, not eight. Vena cava has no female 1-week or 2-week samples at all: TRNSCRPT_VENACV_DA is 16,338 features × 6 contrasts, and METAB_VENACV_DA has 1,278 rows, which is not divisible by eight. Brown adipose metabolomics is ragged feature by feature — 47 features are missing female 1w and 47 are missing male 8w. All four measured at v2.1.0. Count the contrasts you have per feature; do not compute them from the design.

Do not route through the Data Hub API

The portal at motrpac-data.org is a single-page app, so its endpoints are invisible in the page source and turn up only in its JavaScript bundle. Both refuse anonymous callers, so neither belongs in a pipeline:

curl -s -o /dev/null -w "data_files  -> HTTP %{http_code}\n" "https://services.motrpac-data.org/v1/data_files"
curl -s "https://services.motrpac-data.org/v1/data_files" | grep -o UNAUTHENTICATED
curl -s -o /dev/null -w "search/api  -> HTTP %{http_code}\n" "https://search.motrpac-data.org/search/api"
curl -s "https://search.motrpac-data.org/search/api" | grep -o "Not authenticated"

Run 2026-08-18:

data_files  -> HTTP 401
UNAUTHENTICATED
search/api  -> HTTP 401
Not authenticated

The block greps rather than printing the bodies because the JSON field order is not stable — the same endpoint returned {"message":…,"code":401} and {"code":401,"message":…} on two calls minutes apart. The full first message is UNAUTHENTICATED — Method doesn't allow unregistered callers (callers without established identity). Please use API Key or other form of API consumer identity to call this API; the second is {"detail":"Not authenticated"}. Match on the status code, never on a serialised body.

No public registration route for such a key is advertised, so treat these as internal to the portal. Nothing in this skill needs them — every route below is anonymous.

What is open, and under exactly which terms

resource	licence	what it carries
`MotrpacRatTraining6moData`	code MIT, data CC BY 4.0 — its README states the two separately	206 objects — differential analyses, normalised matrices, sample metadata
`MotrpacRatTraining6mo`	MIT	companion analysis functions, R only
`MotrpacRatTraining6moWATData`	MIT	subcutaneous white adipose companion paper
`MotrpacRatTrainingPhysiologyData`	GPL-3	physiology for both the 6-month and 18-month arms
`motrpac-rat-training-6mo-extdata` bucket	no separate terms — same data as the package	full epigenomics, feature annotations, plain-text DA tables
GEO `GSE242358` (+ `GSE242354`, `GSE242355`, `GSE242357`)	GEO terms	raw RNA-seq (915), RRBS (416), ATAC-seq (416) samples

Two of these repositories report no licence through GitHub's licence API, which is what you see if you only check that field. Both actually carry one, and they are not the same one — R packages conventionally split the year and copyright holder into LICENSE and the licence text into LICENSE.md, which defeats the detector. Read both files:

MotrpacRatTraining6moWATData — LICENSE.md is the MIT text, DESCRIPTION says MIT + file LICENSE. Usable on the same terms as the main package.
MotrpacRatTrainingPhysiologyData — LICENSE.md is GPL-3, DESCRIPTION says GPL (>= 3) + file LICENSE. Anyone may download and use it; share-alike bites if you redistribute a derived dataset or package. This is the one holding the aged arm, so if you publish an aged-vs-adult comparison, check the obligation before you ship files.

Cite the version you used. The consortium's data use agreement asks for MoTrPAC to be acknowledged as the source including the dataset version number, which is a reasonable request and also the only way anyone can reproduce you — the objects change between releases.

Object names are the index

Everything in the data package follows {ASSAY}_{TISSUE}_{KIND}, and there is no search endpoint, so knowing the grammar is the query interface.

Nine assays, with the codes used in bucket paths:

abbrev	code	assay
`TRNSCRPT`	`transcript-rna-seq`	RNA-seq
`PROT`	`prot-pr`	global proteomics
`PHOSPHO`	`prot-ph`	phosphoproteomics
`ACETYL`	`prot-ac`	protein acetylation
`UBIQ`	`prot-ub`	protein ubiquitylation
`METAB`	`metab`	metabolomics, 13 platforms
`ATAC`	`epigen-atac-seq`	chromatin accessibility
`METHYL`	`epigen-rrbs`	DNA methylation, RRBS
`IMMUNO`	`immunoassay`	targeted multiplexed immunoassay

Twenty tissues: ADRNL BAT BLOOD COLON CORTEX HEART HIPPOC HYPOTH KIDNEY LIVER LUNG OVARY PLASMA SKMGN SKMVL SMLINT SPLEEN TESTES VENACV WATSC. SKMGN is gastrocnemius, SKMVL vastus lateralis, WATSC subcutaneous white adipose, BAT brown adipose.

Three naming traps, all confirmed against the objects themselves:

TISSUE_ABBREV is hyphenated and object names are not. The vector holds SKM-GN, SKM-VL, WAT-SC; the files are TRNSCRPT_SKMGN_DA.rda. Strip the hyphen when you build a filename, and keep it when you filter a tissue column.
VENACV maps to the tissue code t65-aorta. The label was renamed from aorta to vena cava; the code was not. Bucket URLs still need t65-aorta.
TISSUE_ABBREV_TO_CODE and ASSAY_ABBREV_TO_CODE are named character vectors, and pyreadr drops the names — you get the values in order with no keys. Order matches TISSUE_ABBREV / ASSAY_ABBREV exactly (checked in R), so zip them positionally, or read the mapping in R where the names survive.

Object kinds you will meet: _DA (differential analysis), _NORM_DATA (normalised matrix), _RAW_COUNTS, _NORM_DATA_05FDR, _DA_METAREG (metabolomics meta-regression), _META (assay-level sample metadata), plus cross-cutting objects such as PHENO, OUTLIERS, TRAINING_REGULATED_FEATURES, RAT_TO_HUMAN_GENE and FEATURE_TO_GENE.

Coverage is ragged — build the grid before you plan

Nine assays times twenty tissues is 180 cells, and most are empty. Proteomics ran on seven tissues; acetylation and ubiquitylation on heart and liver only. Derive it rather than assuming — the unauthenticated GitHub contents API lists the package's data/ directory in one request:

import json, re, urllib.request
from collections import defaultdict

TAG = "v2.1.0"
REPO = "MoTrPAC/MotrpacRatTraining6moData"
entries = json.loads(urllib.request.urlopen(
    f"https://api.github.com/repos/{REPO}/contents/data?ref={TAG}", timeout=60).read())

objects = sorted(e["name"][:-4] for e in entries if e["name"].endswith(".rda"))
print(f"{len(objects)} data objects at {TAG}")

ASSAYS = ["ACETYL", "ATAC", "IMMUNO", "METAB", "METHYL", "PHOSPHO", "PROT", "TRNSCRPT", "UBIQ"]
TISSUES = ["ADRNL", "BAT", "BLOOD", "COLON", "CORTEX", "HEART", "HIPPOC", "HYPOTH",
           "KIDNEY", "LIVER", "LUNG", "OVARY", "PLASMA", "SKMGN", "SKMVL", "SMLINT",
           "SPLEEN", "TESTES", "VENACV", "WATSC"]

grid = defaultdict(set)
for o in objects:
    m = re.match(rf"({'|'.join(ASSAYS)})_({'|'.join(TISSUES)})_(.+){{BODY}}quot;, o)
    if m:
        grid[(m.group(1), m.group(2))].add(m.group(3))

kinds = sorted({k for v in grid.values() for k in v})
print("per-tissue object kinds:", kinds)
print()
print("assay      " + "".join(f"{t[:6]:>7}" for t in TISSUES))
for a in ASSAYS:
    row = f"{a:<11}"
    for t in TISSUES:
        ks = grid.get((a, t), set())
        row += f"{('DA' if any(k.startswith('DA') for k in ks) else ('N' if ks else '.')):>7}"
    print(row)
print("\nDA = differential analysis present · N = matrix only · . = assay not run there")

Run 2026-08-18 at tag v2.1.0:

206 data objects at v2.1.0
per-tissue object kinds: ['DA', 'DA_METAREG', 'NORM_DATA', 'NORM_DATA_05FDR', 'RAW_COUNTS']

assay        ADRNL    BAT  BLOOD  COLON CORTEX  HEART HIPPOC HYPOTH KIDNEY  LIVER   LUNG  OVARY PLASMA  SKMGN  SKMVL SMLINT SPLEEN TESTES VENACV  WATSC
ACETYL           .      .      .      .      .     DA      .      .      .     DA      .      .      .      .      .      .      .      .      .      .
ATAC             .      N      .      .      .      N      N      .      N      N      N      .      .      N      .      .      .      .      .      N
IMMUNO          DA     DA      .     DA     DA     DA     DA      .     DA     DA     DA     DA     DA     DA     DA     DA     DA     DA      .     DA
METAB           DA     DA      .     DA     DA     DA     DA     DA     DA     DA     DA     DA     DA     DA     DA     DA     DA     DA     DA     DA
METHYL           .      N      .      .      .      N      N      .      N      N      N      .      .      N      .      .      .      .      .      N
PHOSPHO          .      .      .      .     DA     DA      .      .     DA     DA     DA      .      .     DA      .      .      .      .      .     DA
PROT             .      .      .      .     DA     DA      .      .     DA     DA     DA      .      .     DA      .      .      .      .      .     DA
TRNSCRPT        DA     DA     DA     DA     DA     DA     DA     DA     DA     DA     DA     DA      .     DA     DA     DA     DA     DA     DA     DA
UBIQ             .      .      .      .      .     DA      .      .      .     DA      .      .      .      .      .      .      .      .      .      .

DA = differential analysis present · N = matrix only · . = assay not run there

No assay covers all twenty tissues. Metabolomics is the widest at nineteen — there is no metabolomics on blood, which was profiled by RNA-seq instead, and the bucket returns 404 for t30-blood-rna metab. Transcriptomics is also nineteen (no plasma), the immunoassay seventeen (no blood, hypothalamus or vena cava), proteomics and phosphoproteomics seven, ATAC and RRBS eight, acetylation and ubiquitylation two. The N row for ATAC and METHYL is not "matrix only" by accident — see Epigenomics in the package is an excerpt.

Use the contents API, not the recursive tree API, for this repository. The tree endpoint answered twice and then returned HTTP 504 repeatedly on 2026-08-17 — the repository is large enough to trip it. The contents endpoint returned all 206 entries in 0.8 s and pages at 1,000 entries, so one request covers this directory.

Get the files

The artifacts are R .rda objects, but you do not need R and you do not need to install the package. Each object is an individual file in the repository, so fetch the ones you want at a pinned tag and convert them where they land. pyreadr (pip install pyreadr) reads .rda into pandas without an R installation.

Pin the tag, not main. The latest release is v2.1.0 (2025-08-13) and the DESCRIPTION file in it still says Version: 2.0.0, so the package's own version string is not a usable identifier — cite the git tag or the Zenodo DOI instead.

import hashlib, json, os, urllib.request
import pandas as pd, pyreadr

TAG  = "v2.1.0"                      # pin it — main moves and carries no version bump
BASE = f"https://raw.githubusercontent.com/MoTrPAC/MotrpacRatTraining6moData/{TAG}/data"
OUT  = "Data/motrpac"
os.makedirs(OUT, exist_ok=True)

# one organ pair, three omes each, the animal-level phenotype table, and two objects
# that are not data frames — kept in the list deliberately, to exercise both failure modes
WANT = ["TRNSCRPT_HEART_DA", "PROT_HEART_DA", "METAB_HEART_DA",
        "TRNSCRPT_SKMGN_DA", "PROT_SKMGN_DA", "METAB_SKMGN_DA",
        "PHENO", "METAB_NORM_DATA_NESTED", "GRAPH_PW_ENRICH"]

manifest, needs_r = [], []
for name in WANT:
    rda = os.path.join(OUT, name + ".rda")
    if not os.path.exists(rda):
        urllib.request.urlretrieve(f"{BASE}/{name}.rda", rda)
    sha = hashlib.sha256(open(rda, "rb").read()).hexdigest()

    try:
        tables = pyreadr.read_r(rda)
    except pyreadr.custom_errors.LibrdataError as e:
        needs_r.append(name)          # some objects raise ...
        print(f"  {name:22} pyreadr raised — {e}")
        continue
    if not tables:                    # ... and others return an EMPTY dict, silently
        needs_r.append(name)
        print(f"  {name:22} not a data.frame — export it from R (see below)")
        continue

    for key, df in tables.items():
        csv = os.path.join(OUT, key + ".csv.gz")
        df.to_csv(csv, index=False, compression="gzip")
        manifest.append({"object": key, "url": f"{BASE}/{name}.rda", "tag": TAG,
                         "rda_sha256": sha, "rows": len(df), "cols": df.shape[1],
                         "csv": csv, "csv_bytes": os.path.getsize(csv)})
        print(f"  {key:26} {len(df):>9,} x {df.shape[1]:<3} -> {csv}")

with open(os.path.join(OUT, "manifest.json"), "w") as fh:
    json.dump({"source": "MotrpacRatTraining6moData", "tag": TAG,
               "licence": "code MIT, data CC BY 4.0",
               "cohort": "PASS1B-06 — 6-month-old F344 rats",
               "files": manifest, "needs_r_export": needs_r}, fh, indent=2)

print(f"\n{len(manifest)} tables written; {sum(m['csv_bytes'] for m in manifest):,} bytes of CSV")

Run 2026-08-18 at v2.1.0:

  TRNSCRPT_HEART_DA            115,560 x 22  -> Data/motrpac/TRNSCRPT_HEART_DA.csv.gz
  PROT_HEART_DA                 73,472 x 18  -> Data/motrpac/PROT_HEART_DA.csv.gz
  METAB_HEART_DA                11,440 x 27  -> Data/motrpac/METAB_HEART_DA.csv.gz
  TRNSCRPT_SKMGN_DA            110,192 x 22  -> Data/motrpac/TRNSCRPT_SKMGN_DA.csv.gz
  PROT_SKMGN_DA                 47,992 x 18  -> Data/motrpac/PROT_SKMGN_DA.csv.gz
  METAB_SKMGN_DA                 9,880 x 27  -> Data/motrpac/METAB_SKMGN_DA.csv.gz
  PHENO                          6,156 x 509 -> Data/motrpac/PHENO.csv.gz
  METAB_NORM_DATA_NESTED not a data.frame — export it from R (see below)
  GRAPH_PW_ENRICH        pyreadr raised — Invalid file, or file has unsupported features

7 tables written; 26,874,057 bytes of CSV

Write the manifest. The .rda checksum plus the tag is what lets a later rebuild be compared against this one, and the objects do change between releases — CSVs with no provenance stamp are not reproducible. Swap to_csv for df.to_parquet(...) if pyarrow is installed and you would rather keep dtypes.

Both guards are load-bearing, and they catch different things. For an object that is not a data frame, pyreadr either returns an empty dict with no exception and no warning, or raises LibrdataError. The first is the dangerous one: list(tables.values())[0] becomes an IndexError far from the cause, and a bare loop over .items() silently writes nothing and reports success. The last two entries in WANT are there to make sure both branches are exercised rather than assumed.

A route with no R and no pyreadr at all

The same differential analyses are on a public Google Cloud Storage bucket as tab-separated text, which is the shortest path if you only want the training-effect results. The grammar, with three exceptions:

https://storage.googleapis.com/motrpac-rat-training-6mo-extdata/training-da/
    {ASSAY}/pass1b-06_{tissue_code}_{assay_code}_training-dea-fdr.txt

METAB sits one level deeper, and the two sub-paths are different analyses that pair with different package objects. METAB/meta-regression/…_metab-meta-reg_… collapses a metabolite measured on several platforms into one row — 1,234 rows for heart — and is the file that matches METAB_HEART_DA_METAREG. METAB/redundant/…_metab_… keeps one row per feature per platform, with dataset, is_targeted and site columns — 1,430 rows for heart — and is the file that matches plain METAB_HEART_DA. Crossing the pair is the easy mistake: METAB_HEART_DA against the meta-regression file agrees on neither the row count nor the FDR. A metabolite can appear more than once in the redundant file, so a naive count of significant hits double-counts. The plain METAB/…_metab_… path does not exist and returns 404.
UBIQ uses prot-ub-protein-corrected, not the prot-ub from the code table.
IMMUNO is one pooled file for every tissue, with no tissue in the filename; filter it on tissue_abbreviation. That request answers 200 for a tissue the immunoassay never ran on — VENACV, HYPOTH and BLOOD are simply absent from the file, and the filter returns an empty frame that a loop will happily write to disk. Every other assay answers 404 for a combination that does not exist. Check the row count, not the status code.

The TISSUE map below is the whole of TISSUE_ABBREV_TO_CODE. Carrying a handful of entries is a trap of its own — with the five obvious ones, three of the seven proteomics tissues and fourteen of the nineteen metabolomics tissues raise KeyError rather than fetching anything, and nothing about VENACV suggests t65-aorta or OVARY suggests t64-ovaries.

import io, os, urllib.error, urllib.request, gzip
import pandas as pd

DA   = "https://storage.googleapis.com/motrpac-rat-training-6mo-extdata/training-da"
OUT  = "Data/motrpac/tsv"
os.makedirs(OUT, exist_ok=True)

# TISSUE_ABBREV_TO_CODE in full — five entries is not enough, and there is no way to guess
# t65-aorta from VENACV or t64-ovaries from OVARY
TISSUE = {"ADRNL": "t60-adrenal",   "BAT": "t69-brown-adipose", "BLOOD": "t30-blood-rna",
          "COLON": "t61-colon",     "CORTEX": "t53-cortex",     "HEART": "t58-heart",
          "HIPPOC": "t52-hippocampus", "HYPOTH": "t54-hypothalamus", "KIDNEY": "t59-kidney",
          "LIVER": "t68-liver",     "LUNG": "t66-lung",         "OVARY": "t64-ovaries",
          "PLASMA": "t31-plasma",   "SKM-GN": "t55-gastrocnemius",
          "SKM-VL": "t56-vastus-lateralis", "SMLINT": "t67-small-intestine",
          "SPLEEN": "t62-spleen",   "TESTES": "t63-testes",     "VENACV": "t65-aorta",
          "WAT-SC": "t70-white-adipose"}
ASSAY  = {"ACETYL": "prot-ac", "ATAC": "epigen-atac-seq", "IMMUNO": "immunoassay",
          "METAB": "metab", "METHYL": "epigen-rrbs", "PHOSPHO": "prot-ph",
          "PROT": "prot-pr", "TRNSCRPT": "transcript-rna-seq", "UBIQ": "prot-ub"}

def da_url(assay, tissue, metareg=False):
    """Default is the REDUNDANT table, because that is what METAB_*_DA pairs with.
    Pass metareg=True only when joining METAB_*_DA_METAREG — the two differ by 196
    rows for heart, and crossing them drops 314 of 1,430 keys with FDR off by 0.27."""
    code = ASSAY[assay]
    if assay == "IMMUNO":                       # one pooled file, all tissues
        return f"{DA}/IMMUNO/pass1b-06_immunoassay_training-dea-fdr.txt"
    if assay == "METAB":
        # metareg pairs with METAB_*_DA_METAREG; redundant pairs with plain METAB_*_DA
        sub, code = ("meta-regression", "metab-meta-reg") if metareg else ("redundant", "metab")
        return f"{DA}/METAB/{sub}/pass1b-06_{TISSUE[tissue]}_{code}_training-dea-fdr.txt"
    if assay == "UBIQ":
        code = "prot-ub-protein-corrected"
    return f"{DA}/{assay}/pass1b-06_{TISSUE[tissue]}_{code}_training-dea-fdr.txt"

want = [("PROT", "HEART"), ("PROT", "CORTEX"), ("PHOSPHO", "KIDNEY"), ("ACETYL", "LIVER"),
        ("UBIQ", "LIVER"), ("TRNSCRPT", "OVARY"), ("TRNSCRPT", "VENACV"),
        ("METAB", "HEART"), ("METAB", "VENACV"),
        ("IMMUNO", "HEART"), ("IMMUNO", "VENACV"), ("PROT", "ADRNL")]

for assay, tissue in want:
    url = da_url(assay, tissue)
    try:
        raw = urllib.request.urlopen(url, timeout=180).read()
    except urllib.error.HTTPError as e:
        print(f"  {assay:9} {tissue:7} HTTP {e.code}  {url.rsplit('/', 1)[1]}")
        continue
    df = pd.read_csv(io.BytesIO(raw), sep="\t")
    if assay == "IMMUNO":
        # The pooled file hyphenates (SKM-GN), the coverage grid above does not (SKMGN).
        # Matching literally reported SKM-GN, SKM-VL and WAT-SC as never assayed when the
        # assay ran on all three, at 53, 39 and 53 rows.
        norm = lambda x: str(x).replace("-", "").upper()
        df = df[df["tissue_abbreviation"].map(norm) == norm(tissue)]
        if df.empty:                            # 200 + zero rows is the failure mode here
            print(f"  {assay:9} {tissue:7} HTTP 200 but 0 rows. IMMUNO ran on 14 of the 20 "
                  f"tissues; confirm against the coverage grid before concluding it did not "
                  f"run here")
            continue
    key = ["feature_ID", "panel"] if assay == "IMMUNO" else \
          ["feature_ID", "dataset"] if assay == "METAB" else ["feature_ID"]
    # METAB's two sub-paths collide on this filename, so record which one produced it.
    tag = "_metareg" if assay == "METAB" and "meta-regression" in url else ""
    dest = os.path.join(OUT, f"{assay}_{tissue.replace('-', '')}{tag}_training-dea-fdr.tsv.gz")
    with gzip.open(dest, "wt") as fh:
        df.to_csv(fh, sep="\t", index=False)
    print(f"  {assay:9} {tissue:7} {len(df):>8,} rows x {df.shape[1]:<3} "
          f"key {'+'.join(key):18} unique {df.drop_duplicates(key).shape[0]:>8,} -> {os.path.basename(dest)}")

Run 2026-08-18:

  PROT      HEART      9,184 rows x 12  key feature_ID         unique    9,184 -> PROT_HEART_training-dea-fdr.tsv.gz
  PROT      CORTEX    11,108 rows x 12  key feature_ID         unique   11,108 -> PROT_CORTEX_training-dea-fdr.tsv.gz
  PHOSPHO   KIDNEY    30,144 rows x 12  key feature_ID         unique   30,144 -> PHOSPHO_KIDNEY_training-dea-fdr.tsv.gz
  ACETYL    LIVER      9,750 rows x 12  key feature_ID         unique    9,750 -> ACETYL_LIVER_training-dea-fdr.tsv.gz
  UBIQ      LIVER      9,344 rows x 12  key feature_ID         unique    9,344 -> UBIQ_LIVER_training-dea-fdr.tsv.gz
  TRNSCRPT  OVARY     17,035 rows x 18  key feature_ID         unique   17,035 -> TRNSCRPT_OVARY_training-dea-fdr.tsv.gz
  TRNSCRPT  VENACV    16,338 rows x 18  key feature_ID         unique   16,338 -> TRNSCRPT_VENACV_training-dea-fdr.tsv.gz
  METAB     HEART      1,430 rows x 22  key feature_ID+dataset unique    1,430 -> METAB_HEART_training-dea-fdr.tsv.gz
  METAB     VENACV       213 rows x 26  key feature_ID+dataset unique      213 -> METAB_VENACV_training-dea-fdr.tsv.gz
  IMMUNO    HEART         39 rows x 18  key feature_ID+panel   unique       39 -> IMMUNO_HEART_training-dea-fdr.tsv.gz
  IMMUNO    VENACV  HTTP 200 but 0 rows. IMMUNO ran on 14 of the 20 tissues; confirm against the coverage grid before concluding it did not run here
  PROT      ADRNL   HTTP 404  pass1b-06_t60-adrenal_prot-pr_training-dea-fdr.txt

The last two lines are the two ways a combination can be absent, and they do not look alike: a 404 carrying a GCS NoSuchKey body for every assay served as its own per-tissue file, and a 200 carrying nothing for the immunoassay.

This route is plain text and needs nothing but a HTTP client — but it is unversioned. There is no tag, no checksum and no release note on the bucket. Use the package route when reproducibility matters and this one when you want a quick answer or a file too large to have been shipped inside the package.

Two tables, two different questions

The most consequential thing to get right. For the same tissue and assay you have two tables and they answer different questions:

	rows	what it tests
`PROT_HEART_DA.rda`	73,472	timewise contrasts — each trained timepoint against the sedentary control, separately per sex
`…_training-dea-fdr.txt`	9,184	one row per feature — an F-test of the whole training model `~1+group` against `~1`, with per-sex F statistics and a combined p and FDR

9,184 features × 2 sexes × 4 timepoints = 73,472 exactly, and the feature_ID sets are identical. The link between them is selection_fdr in the _DA table, which equals adj_p_value in the text table to within 1.1e-16 (measured, all 9,184 features).

Which means: adj_p_value inside a _DA row is not the training-effect FDR. It is the adjusted p for that one sex-by-timepoint contrast. Selecting training-regulated features on it, rather than on selection_fdr, is a different and much weaker analysis than the one the consortium published.

`feature_ID` is the key for seven assays out of nine

The arithmetic above holds for PROT, PHOSPHO, ACETYL, UBIQ and TRNSCRPT, where a feature is measured once, and for the two epigenomic tables on the bucket (heart ATAC: 728,326 regions × 2 sexes × 4 timepoints). It does not hold for METAB or IMMUNO, where the same metabolite or analyte is measured on several platforms and the row key is feature_ID plus the platform column. The bucket calls that column dataset in the metabolomics files and panel in the immunoassay file; the package calls it dataset in both.

Measured at v2.1.0, heart:

table	rows	unique `feature_ID`	unique `feature_ID` + platform
`PROT_HEART_DA`	73,472	9,184	—
`METAB_HEART_DA`	11,440	1,309	1,430
`IMMUNO_HEART_DA`	312	37	39

11,440 = 1,430 × 2 × 4 and 312 = 39 × 2 × 4; neither works from the feature_ID count. And the join to the bucket does not merely lose rows, it invents them — merging METAB_HEART_DA to METAB/redundant/… on feature_ID alone returns 1,724 rows out of a 1,430-row table, with the wrong FDR attached to the cross-platform ones (max |Δ| 0.88). On ["feature_ID", "dataset"] it returns 1,430, one-to-one, max |Δ| 9.9e-17. Verified the same way on plasma, vena cava and hypothalamus metabolomics and on heart and plasma immunoassay, and asserted in ## Try it.

`TRAINING_REGULATED_FEATURES` is per feature and platform, and not always eight rows

TRAINING_REGULATED_FEATURES is the consortium's selection at 5% FDR, expanded back to one row per sex per timepoint. Three things about it that a heart-proteomics example hides — all measured across its 88 assay-by-tissue cells at v2.1.0:

The unit is (feature_ID, platform), with platform NA for the seven single-platform assays, a metab-* or panel name for the others, and meta-reg for a metabolite pooled across platforms. Heart metabolomics is 561 distinct feature_ID but 568 feature-platform pairs.
The row count per unit is n_sexes × n_timepoints present, so 8 in most cells, 4 in ovary and testes, 6 in vena cava, and both 6 and 8 inside one cell in brown adipose metabolomics. Never derive it from a constant.
For metabolomics the reference table is the meta-regression file, not the redundant one. Heart: 568 pairs in the selection, 568 rows at adj_p_value < 0.05 in METAB/meta-regression/…, and 603 in METAB/redundant/…. Check against the wrong file and 35 features look like a discrepancy in the consortium's selection.

For heart proteomics — one platform, both sexes, all four timepoints — it collapses to the simple case: 693 features, 5,544 rows, exactly the count of adj_p_value < 0.05 in the text table. That cell is the exception that reads like the rule.

A cell can also be legitimately empty. The immunoassay measured 46 analytes in ovary and none reached 5% FDR, so TRAINING_REGULATED_FEATURES has no rows for IMMUNO/OVARY — an empty selection there is the right answer, not a failed download.

Epigenomics in the package is an excerpt

ATAC and METHYL ship only _NORM_DATA_05FDR objects, and the suffix is doing more work than it looks. ATAC_HEART_NORM_DATA_05FDR has 75 rows. The full heart ATAC differential analysis, which lives only on the bucket, has 5,826,608 rows — 728,326 regions × 2 sexes × 4 timepoints — of which exactly 75 pass 5% FDR. The .rda was parsed on 2026-08-17; the equivalent training-dea-fdr.txt was streamed again on 2026-08-18 and holds 728,326 rows with exactly 75 at adj_p_value < 0.05, and 728,326 × 8 = 5,826,608. The two agree, which is what confirms the suffix means "the training-regulated selection" and not "a convenience subset" — and 75 is also the number of ATAC/HEART features in TRAINING_REGULATED_FEATURES, at eight rows each.

So any epigenome-wide question — your own multiple-testing correction, a background set, an enrichment against all accessible regions — needs the bucket. Objects are readable anonymously; the bucket is not listable, so a wrong name gives 404 rather than an empty result, and the naming convention is the only index you get:

import urllib.error, urllib.request

EXT = "https://storage.googleapis.com/motrpac-rat-training-6mo-extdata"

probes = [("epigen-rda",    "ATAC_HEART_DA"),
          ("epigen-rda",    "ATAC_HEART_NORM_DATA"),
          ("epigen-rda",    "METHYL_HEART_DA"),
          ("epigen-rda",    "METHYL_SKMGN_DA"),
          ("epigen-rda",    "ATAC_ADRNL_DA"),          # ATAC was never run on adrenal
          ("feature-annot", "ATAC_FEATURE_ANNOT"),
          ("feature-annot", "METHYL_FEATURE_ANNOT"),
          ("feature-annot", "PROT_FEATURE_ANNOT")]     # documented as .rda; only .txt exists

for folder, obj in probes:
    url = f"{EXT}/{folder}/{obj}.rda"
    try:
        with urllib.request.urlopen(urllib.request.Request(url, method="HEAD"), timeout=45) as r:
            print(f"  {obj:24} {int(r.headers['Content-Length']):>13,} bytes")
    except urllib.error.HTTPError as e:
        print(f"  {obj:24} HTTP {e.code:<9} {folder}/{obj}.rda does not exist")

try:
    urllib.request.urlopen(f"https://storage.googleapis.com/storage/v1/b/"
                           f"motrpac-rat-training-6mo-extdata/o", timeout=45)
    print("\nbucket listing: allowed")
except urllib.error.HTTPError as e:
    print(f"\nbucket listing: HTTP {e.code} — anonymous callers cannot enumerate the bucket, "
          "so the naming convention above is the only index")

Run 2026-08-18:

  ATAC_HEART_DA              250,595,765 bytes
  ATAC_HEART_NORM_DATA        32,863,319 bytes
  METHYL_HEART_DA            256,313,831 bytes
  METHYL_SKMGN_DA            291,527,234 bytes
  ATAC_ADRNL_DA            HTTP 404       epigen-rda/ATAC_ADRNL_DA.rda does not exist
  ATAC_FEATURE_ANNOT          29,078,861 bytes
  METHYL_FEATURE_ANNOT       160,193,038 bytes
  PROT_FEATURE_ANNOT       HTTP 404       feature-annot/PROT_FEATURE_ANNOT.rda does not exist

bucket listing: HTTP 401 — anonymous callers cannot enumerate the bucket, so the naming convention above is the only index

Two findings in that output worth carrying:

A 404 means the assay was never run on that tissue, and there is no way to tell the two apart without the coverage grid — ATAC_ADRNL_DA is absent because ATAC was not done on adrenal, exactly as the grid shows. Build the grid first; probing names blindly cannot distinguish "not measured" from "wrong filename". Objects that do exist are a quarter of a gigabyte each and use the unhyphenated tissue token. pyreadr reads them — the 5,826,608-row ATAC_HEART_DA was parsed for the numbers above — but it holds the whole frame in memory, so pull the equivalent training-dea-fdr.txt and stream it if that is a problem.
Some .rda feature-annotation URLs printed in the package's own documentation are dead, and the extension is split by assay. All seven were probed both ways on 2026-08-18: ATAC and METHYL exist only as .rda (29 MB and 160 MB); PROT, PHOSPHO, UBIQ, ACETYL and TRNSCRPT exist only as .txt (158 MB, 108 MB, 3.9 MB, 3.2 MB, 3.1 MB) and 404 as .rda — which is the extension the package documentation prints for them. Change the extension before concluding a file is gone.

Objects pyreadr cannot read

Most of the 206 objects are data frames or character vectors and convert cleanly. These do not, checked one by one on 2026-08-18 with pyreadr 0.5.3:

object	how it fails
`METAB_NORM_DATA_NESTED`	empty dict
`IMMUNO_NORM_DATA_NESTED`	empty dict
`GRAPH_COMPONENTS`	empty dict
`REPFDR_INPUTS`, `REPFDR_RES`	empty dict
`GENE_UNIVERSES`	empty dict
`PATHWAY_PARENTS`	empty dict
`GRAPH_PW_ENRICH`	raises `LibrdataError`

METAB_NORM_DATA_NESTED is 13 metabolomics platforms, each a list of tissues, each a data frame — a shape a pandas dict cannot express. Flat equivalents exist for two of them (METAB_NORM_DATA_FLAT, IMMUNO_NORM_DATA_FLAT); reach for those first, but read Joining a matrix to the animals before you do — they are keyed on pid, not viallabel, and the nested originals are not. Note that GRAPH_STATES does read fine (34,244 × 10), so do not assume everything with GRAPH in the name needs R. When there is no flat version, export once from R and work in whatever you like afterwards:

tag  <- "v2.1.0"
base <- sprintf("https://raw.githubusercontent.com/MoTrPAC/MotrpacRatTraining6moData/%s/data", tag)
out  <- "Data/motrpac/nested"
dir.create(out, recursive = TRUE, showWarnings = FALSE)

name <- "METAB_NORM_DATA_NESTED"
rda  <- file.path(out, paste0(name, ".rda"))
if (!file.exists(rda)) download.file(file.path(base, paste0(name, ".rda")), rda,
                                     mode = "wb", quiet = TRUE)

env <- new.env()
obj <- get(load(rda, envir = env)[1], envir = env)

n <- 0
for (platform in names(obj)) {
  for (tissue in names(obj[[platform]])) {
    df <- obj[[platform]][[tissue]]
    f  <- file.path(out, sprintf("%s__%s__%s.csv", name, platform, gsub("-", "", tissue)))
    write.csv(cbind(feature = rownames(df), df), f, row.names = FALSE)
    n <- n + 1
  }
}
cat(sprintf("%d platforms, %d tables written to %s\n", length(obj), n, out))
cat("platforms:", paste(names(obj), collapse = ", "), "\n")

Run 2026-08-18 under R 4.4.1:

13 platforms, 113 tables written to Data/motrpac/nested
platforms: metab-t-amines, metab-t-acoa, metab-t-nuc, metab-t-oxylipneg, metab-t-ka, metab-t-etamidpos, metab-t-tca, metab-u-lrppos, metab-u-lrpneg, metab-u-hilicpos, metab-u-rppos, metab-u-rpneg, metab-u-ionpneg

metab-t-* are targeted platforms and metab-u-* untargeted; the same metabolite can appear on several, which is what the meta-regression objects exist to reconcile. Note that load() returns the object name, not the object — assigning x <- load(f) gives you a string, which is a routine way to lose an hour.

Joining a matrix to the animals

Per-tissue _NORM_DATA objects are wide: the four identifier columns feature, feature_ID, tissue, assay, then one column per sample, headed by viallabel. That is the key into PHENO, and it is what makes cross-assay and cross-tissue integration possible at all — the same animal's pid appears under a different vial label in every assay.

The two flat tables are keyed differently, and merging them on viallabel fails silently. METAB_NORM_DATA_FLAT and IMMUNO_NORM_DATA_FLAT hold every tissue in one object, so their sample columns cannot be vial labels — they are 8-digit pid, the animal, where the per-tissue matrices carry 11-digit vial labels. Measured at v2.1.0: none of the 54 metabolomics column names appears in PHENO$viallabel and all 54 appear in PHENO$pid. Merge them on viallabel and every phenotype column comes back NA for all 778,626 rows, with no error — the shape is right and the content is empty. They also add a fifth identifier column dataset, which has to go into id_vars or the platform label becomes a sample. METAB_NORM_DATA_NESTED does not share the problem: its per-platform, per-tissue frames are vial-label-headed like everything else.

So decide the key from the header rather than assuming it, and collapse PHENO to one row per animal when it is pid — PHENO has 6,156 rows for 147 rats, one per vial.

import os, re, urllib.request
import pandas as pd, pyreadr

TAG  = "v2.1.0"
BASE = f"https://raw.githubusercontent.com/MoTrPAC/MotrpacRatTraining6moData/{TAG}/data"
OUT  = "Data/motrpac"
os.makedirs(OUT, exist_ok=True)

def fetch(name):
    p = os.path.join(OUT, name + ".rda")
    if not os.path.exists(p):
        urllib.request.urlretrieve(f"{BASE}/{name}.rda", p)
    tables = pyreadr.read_r(p)
    assert tables, f"{name} is not a data.frame — pyreadr returns {{}}, not an error"
    return list(tables.values())[0]

pheno = fetch("PHENO")
WANT  = ["pid", "sex", "key.anirandgroup", "key.agegroup", "key.intervention", "key.sacrificetime"]

def to_long(name):
    mat  = fetch(name)
    ids  = [c for c in mat.columns if not re.fullmatch(r"\d+", str(c))]
    samp = [c for c in mat.columns if re.fullmatch(r"\d+", str(c))]
    key  = "viallabel" if len(str(samp[0])) == 11 else "pid"      # 11 digits vs 8
    long = mat.melt(id_vars=ids, var_name=key, value_name="value").astype({key: str})
    ann  = pheno[[key] + [c for c in WANT if c != key]].astype({key: str}).drop_duplicates(key)
    out  = long.merge(ann, on=key, how="left")
    print(f"  {name:26} {mat.shape[0]:>6,} x {mat.shape[1]:<3} | id cols {ids}")
    print(f"  {'':26} {len(samp):>3} sample columns keyed on {key:9} | long {len(out):>9,} rows "
          f"| unmatched {int(out['sex'].isna().sum())}")
    return out

heart = to_long("TRNSCRPT_HEART_NORM_DATA")
to_long("PROT_HEART_NORM_DATA")
flat  = to_long("METAB_NORM_DATA_FLAT")
to_long("IMMUNO_NORM_DATA_FLAT")

print("\nage groups     :", sorted(heart["key.agegroup"].dropna().unique()))
print("design cells, heart transcriptomics:")
print(heart.drop_duplicates("viallabel").groupby(["key.anirandgroup", "sex"]).size().unstack(fill_value=0))
print("\ndesign cells, metabolomics (animal level, all tissues):")
print(flat.drop_duplicates("pid").groupby(["key.anirandgroup", "sex"]).size().unstack(fill_value=0))

dest = os.path.join(OUT, "TRNSCRPT_HEART_long.csv.gz")
heart.to_csv(dest, index=False, compression="gzip")
print("\nwrote", dest, f"{os.path.getsize(dest):,} bytes")

Run 2026-08-18:

  TRNSCRPT_HEART_NORM_DATA   14,445 x 54  | id cols ['feature', 'feature_ID', 'tissue', 'assay']
                              50 sample columns keyed on viallabel | long   722,250 rows | unmatched 0
  PROT_HEART_NORM_DATA        9,184 x 64  | id cols ['feature', 'feature_ID', 'tissue', 'assay']
                              60 sample columns keyed on viallabel | long   551,040 rows | unmatched 0
  METAB_NORM_DATA_FLAT       14,419 x 59  | id cols ['feature', 'feature_ID', 'tissue', 'assay', 'dataset']
                              54 sample columns keyed on pid       | long   778,626 rows | unmatched 0
  IMMUNO_NORM_DATA_FLAT         720 x 64  | id cols ['feature', 'feature_ID', 'tissue', 'assay', 'dataset']
                              59 sample columns keyed on pid       | long    42,480 rows | unmatched 0

age groups     : ['6 months']
design cells, heart transcriptomics:
sex                                female  male
key.anirandgroup
Eight-week program Control Group        5     5
Eight-week program Training Group       5     5
Four-week program                       5     5
One-week program                        5     5
Two-week program                        5     5

design cells, metabolomics (animal level, all tissues):
sex                                female  male
key.anirandgroup
Eight-week program Control Group        6     5
Eight-week program Training Group       6     5
Four-week program                       6     6
One-week program                        5     5
Two-week program                        5     5

wrote Data/motrpac/TRNSCRPT_HEART_long.csv.gz 5,473,930 bytes

Five animals per sex per group in heart transcriptomics — six in some metabolomics cells. This is a small design, and the number that matters for power is that cell size, not the 14,445 features. Sample counts differ by assay on the same tissue — PROT_HEART_NORM_DATA is 9,184 × 64, so 60 sample columns against transcriptomics' 50 — so count them per object rather than carrying one number across a manuscript. The 0 in the unmatched column is the check that matters; it is what the viallabel assumption turns into 778,626.

Check OUTLIERS yourself; the matrices are not consistently cleaned. It lists 79 sample-level exclusions with reasons, across all eight assays that have any. Verified 2026-08-18: the flagged liver transcriptomics sample (reason PC2) is still a column in TRNSCRPT_LIVER_NORM_DATA, while the flagged liver proteomics sample — a suspected sex mismatch or sample swap — is not in PROT_LIVER_NORM_DATA. Drop them explicitly if you re-derive anything, and prefer TRAINING_REGULATED_NORM_DATA_NO_OUTLIERS over TRAINING_REGULATED_NORM_DATA when you want the cleaned version of that object.

Installing the R packages, and why it usually fails

If you are working in R and want the packages rather than individual objects, know that remotes::install_github("MoTrPAC/MotrpacRatTraining6moData") frequently does not work. The repository is roughly 400 MB and GitHub's tarball endpoint times out generating the archive — intermittently, which is the worst kind: https://api.github.com/repos/MoTrPAC/MotrpacRatTraining6moData/tarball/v2.1.0 returned HTTP 504 on both attempts made 2026-08-17 and HTTP 200 on 2026-08-18. The package's own README calls it intermittent too. The codeload archive is a different endpoint and answered 200 on both days:

options(timeout = 3600)
dir.create("motrpac_pkg", showWarnings = FALSE)
tarball <- "motrpac_pkg/MotrpacRatTraining6moData-v2.1.0.tar.gz"
download.file(
  "https://codeload.github.com/MoTrPAC/MotrpacRatTraining6moData/tar.gz/refs/tags/v2.1.0",
  tarball, mode = "wb")
install.packages(tarball, repos = NULL, type = "source")
library(MotrpacRatTraining6moData)
data(PHENO); dim(PHENO)

This is the one block in this skill not executed to completion. The codeload URL was confirmed to answer HTTP 200 on both dates and had transferred 215 MB after 420 seconds without finishing, so the download works and is simply slow; install.packages() on the result was not reached. Everything else here was run verbatim. The citable alternative is the Zenodo snapshot of the same tag, DOI 10.5281/zenodo.16851449, a 399 MB zip.

Either way, prefer the per-object route above unless you specifically want the analysis package's functions — pulling 400 MB to read one 460 kB table is a poor trade, and it is the reason this skill is built the way it is.

Limits worth stating in a write-up

Rat, not human. MoTrPAC has a human arm, and none of the routes in this skill serve any of it: the packages are rat-only, the bucket paths are all pass1b-06, and the four GEO series are Rattus norvegicus. Do not plan around obtaining human MoTrPAC data here. Map with RAT_TO_HUMAN_GENE — 21,461 rows joining rat symbol, RGD, NCBI, Ensembl and UniProt identifiers to a human ortholog symbol — and say in the text that you did, because one-to-many and absent orthologs are silent otherwise.
The annotation is old. Transcript features are built against Ensembl release 95 on Rnor_6.0, superseded by mRatBN7.2. Coordinates from ATAC and METHYL features, which are genomic intervals, are on that build and must be lifted before they meet anything modern.
Sex is a factor, not a nuisance. Every differential analysis is per-sex by design, and the published headline was how much of the response differs between sexes. Pooling the sexes discards the finding. Two tissues are single-sex by anatomy (ovary, testes) and one — vena cava — is missing its female 1-week and 2-week samples, so a sex-difference statement there rests on 4w and 8w only.
Timepoints are training duration, not time of day. comparison_group counts weeks of training. Circadian information is separate, in PHENO columns including key.sacrificetime.
IMMUNO is a targeted panel, tens of analytes, not a discovery assay — 720 rows for 17 tissues in one pooled file, 26 to 60 per tissue across six panels. Absence of a protein there means it was not on the panel, and absence of a tissue means the assay was not run there, which the file expresses as nothing at all rather than as an error.
One intervention. Progressive treadmill endurance training. Nothing here speaks to resistance training, and the acute-exercise arm is not in this release.

Try it

A self-contained check on the two open routes, on the claim that decides whether this resource fits an aging question, and on the four places where a technique that works on heart proteomics silently produces a wrong answer somewhere else. Public data, no account, no key.

Data — six objects from MotrpacRatTraining6moData at tag v2.1.0 (code MIT, data CC BY 4.0), plus four plain-text tables from the public bucket:

https://raw.githubusercontent.com/MoTrPAC/MotrpacRatTraining6moData/v2.1.0/data/PHENO.rda
https://raw.githubusercontent.com/MoTrPAC/MotrpacRatTraining6moData/v2.1.0/data/PROT_HEART_DA.rda
https://raw.githubusercontent.com/MoTrPAC/MotrpacRatTraining6moData/v2.1.0/data/METAB_HEART_DA.rda
https://raw.githubusercontent.com/MoTrPAC/MotrpacRatTraining6moData/v2.1.0/data/METAB_VENACV_DA.rda
https://raw.githubusercontent.com/MoTrPAC/MotrpacRatTraining6moData/v2.1.0/data/METAB_NORM_DATA_FLAT.rda
https://raw.githubusercontent.com/MoTrPAC/MotrpacRatTraining6moData/v2.1.0/data/TRAINING_REGULATED_FEATURES.rda
https://storage.googleapis.com/motrpac-rat-training-6mo-extdata/training-da/PROT/pass1b-06_t58-heart_prot-pr_training-dea-fdr.txt
https://storage.googleapis.com/motrpac-rat-training-6mo-extdata/training-da/METAB/redundant/pass1b-06_t58-heart_metab_training-dea-fdr.txt
https://storage.googleapis.com/motrpac-rat-training-6mo-extdata/training-da/METAB/meta-regression/pass1b-06_t58-heart_metab-meta-reg_training-dea-fdr.txt
https://storage.googleapis.com/motrpac-rat-training-6mo-extdata/training-da/IMMUNO/pass1b-06_immunoassay_training-dea-fdr.txt

About 20 MB in total, no licence acceptance and no account. Heart proteomics is the well-behaved case; heart and vena cava metabolomics and the pooled immunoassay are the ones that break a heart-proteomics-shaped pipeline. Last confirmed reachable 2026-08-18. Needs pip install pyreadr pandas.

import io, os, re, urllib.request
import numpy as np, pandas as pd, pyreadr

TAG  = "v2.1.0"
PKG  = f"https://raw.githubusercontent.com/MoTrPAC/MotrpacRatTraining6moData/{TAG}/data"
BUCK = "https://storage.googleapis.com/motrpac-rat-training-6mo-extdata/training-da"
os.makedirs("motrpac_check", exist_ok=True)

def rda(name):
    p = os.path.join("motrpac_check", name + ".rda")
    if not os.path.exists(p):
        urllib.request.urlretrieve(f"{PKG}/{name}.rda", p)
    tables = pyreadr.read_r(p)
    assert tables, f"{name} is not a data.frame — pyreadr returns {{}}, not an error"
    return list(tables.values())[0]

def txt(path):
    return pd.read_csv(io.BytesIO(urllib.request.urlopen(f"{BUCK}/{path}", timeout=180).read()), sep="\t")

pheno = rda("PHENO")
trf   = rda("TRAINING_REGULATED_FEATURES")
trf["platform"] = trf["platform"].fillna("NA")          # NA for the 7 single-platform assays

# 1. what cohort is this? the answer is in PHENO, not in the file names
print("PHENO rows / age groups / protocols :", len(pheno),
      sorted(pheno["key.agegroup"].unique()), sorted(pheno["key.protocol"].unique()))
assert sorted(pheno["key.agegroup"].unique()) == ["6 months"]

# 2. a _DA table is sex x timepoint contrasts — and feature_ID is the key only for the
#    seven single-platform assays
prot = rda("PROT_HEART_DA")
ptsv = txt("PROT/pass1b-06_t58-heart_prot-pr_training-dea-fdr.txt")
nfeat, nsex, ntime = (prot["feature_ID"].nunique(), prot["sex"].nunique(),
                      prot["comparison_group"].nunique())
print("PROT_HEART_DA rows                  :", len(prot),
      f"= {nfeat} features x {nsex} sexes x {ntime} timepoints")
assert len(prot) == nfeat * nsex * ntime == 73472
m = prot[["feature_ID", "selection_fdr"]].drop_duplicates().merge(
        ptsv[["feature_ID", "adj_p_value"]], on="feature_ID")
print("PROT/HEART matched to bucket TSV    :", len(m),
      f"| max |Δ FDR| = {float(np.nanmax(np.abs(m.selection_fdr - m.adj_p_value))):.1e}")
assert len(m) == len(ptsv) == nfeat

# 3. METAB and IMMUNO break that: the row key is feature_ID + dataset, because the same
#    metabolite is measured on several platforms. Merging on feature_ID alone INFLATES.
metab = rda("METAB_HEART_DA")
red   = txt("METAB/redundant/pass1b-06_t58-heart_metab_training-dea-fdr.txt")
naive = metab[["feature_ID", "selection_fdr"]].drop_duplicates().merge(
            red[["feature_ID", "adj_p_value"]], on="feature_ID")
right = metab[["feature_ID", "dataset", "selection_fdr"]].drop_duplicates().merge(
            red[["feature_ID", "dataset", "adj_p_value"]], on=["feature_ID", "dataset"])
print("METAB/HEART bucket rows             :", len(red),
      f"| merge on feature_ID -> {len(naive)} rows, max |Δ FDR| "
      f"{float(np.nanmax(np.abs(naive.selection_fdr - naive.adj_p_value))):.1e}"
      f" | merge on feature_ID+dataset -> {len(right)} rows, max |Δ FDR| "
      f"{float(np.nanmax(np.abs(right.selection_fdr - right.adj_p_value))):.1e}")
assert len(naive) > len(red) and len(right) == len(red) == 1430
assert float(np.nanmax(np.abs(right.selection_fdr - right.adj_p_value))) < 1e-12
assert metab["feature_ID"].nunique() == 1309 < 1430
assert len(metab) == 1430 * nsex * ntime

# 4. the design is not a full grid everywhere: female vena cava has no 1w or 2w sample
ven = rda("METAB_VENACV_DA")
cells = ven.groupby("sex")["comparison_group"].unique().apply(sorted).to_dict()
print("METAB_VENACV_DA rows                :", len(ven), "| timepoints per sex:", cells)
assert len(ven) % (2 * 4) != 0 and cells["female"] == ["4w", "8w"]

# 5. IMMUNO is one pooled file for every tissue — HTTP 200 for a tissue it never ran on
imm = txt("IMMUNO/pass1b-06_immunoassay_training-dea-fdr.txt")
print("IMMUNO pooled rows / tissues        :", len(imm), imm["tissue_abbreviation"].nunique(),
      "| VENACV rows:", int((imm["tissue_abbreviation"] == "VENACV").sum()),
      "| OVARY rows:", int((imm["tissue_abbreviation"] == "OVARY").sum()),
      "at FDR<0.05:", int((imm[imm.tissue_abbreviation == "OVARY"].adj_p_value < 0.05).sum()))
assert imm["tissue_abbreviation"].nunique() == 17
assert (imm["tissue_abbreviation"] == "VENACV").sum() == 0

# 6. the two _FLAT matrices are keyed on pid (8 digits), not viallabel (11)
flat = rda("METAB_NORM_DATA_FLAT")
samp = [str(c) for c in flat.columns if re.fullmatch(r"\d+", str(c))]
print("METAB_NORM_DATA_FLAT sample columns :", len(samp),
      "| in PHENO.viallabel:", len(set(samp) & set(pheno.viallabel.astype(str))),
      "| in PHENO.pid:", len(set(samp) & set(pheno.pid.astype(str))))
assert set(samp) & set(pheno.viallabel.astype(str)) == set()
assert set(samp) <= set(pheno.pid.astype(str))

# 7. TRAINING_REGULATED_FEATURES: the unit is (feature_ID, platform), and the row count per
#    unit is n_sexes x n_timepoints present — 8, 6 or 4, not always 8
mreg = txt("METAB/meta-regression/pass1b-06_t58-heart_metab-meta-reg_training-dea-fdr.txt")
for assay, tissue, expect in [("PROT", "HEART", 8), ("TRNSCRPT", "OVARY", 4),
                              ("METAB", "VENACV", 6), ("IMMUNO", "HEART", 8)]:
    sel = trf[(trf.assay == assay) & (trf.tissue == tissue)]
    per = sorted(int(x) for x in sel.groupby(["feature_ID", "platform"]).size().unique())
    print(f"TRF {assay:8} {tissue:6} {sel.feature_ID.nunique():>5} features, "
          f"{sel.drop_duplicates(['feature_ID','platform']).shape[0]:>5} feature-platform pairs, "
          f"{len(sel):>6} rows, {per} rows each")
    assert per == [expect]
    assert len(sel) == sel.drop_duplicates(["feature_ID", "platform"]).shape[0] * expect
sel = trf[(trf.assay == "METAB") & (trf.tissue == "HEART")]
print("METAB/HEART: TRF pairs / meta-reg FDR<0.05 / redundant FDR<0.05 :",
      sel.drop_duplicates(["feature_ID", "platform"]).shape[0],
      int((mreg.adj_p_value < 0.05).sum()), int((red.adj_p_value < 0.05).sum()))
assert sel.drop_duplicates(["feature_ID", "platform"]).shape[0] == int((mreg.adj_p_value < 0.05).sum())
sel = trf[(trf.assay == "PROT") & (trf.tissue == "HEART")]
assert sel.feature_ID.nunique() == int((ptsv.adj_p_value < 0.05).sum()) == 693

print("\nage groups in the open omics release:", sorted(pheno["key.agegroup"].unique()))

What it printed on 2026-08-18:

PHENO rows / age groups / protocols : 6156 ['6 months'] ['phase 1b']
PROT_HEART_DA rows                  : 73472 = 9184 features x 2 sexes x 4 timepoints
PROT/HEART matched to bucket TSV    : 9184 | max |Δ FDR| = 1.1e-16
METAB/HEART bucket rows             : 1430 | merge on feature_ID -> 1724 rows, max |Δ FDR| 8.8e-01 | merge on feature_ID+dataset -> 1430 rows, max |Δ FDR| 9.9e-17
METAB_VENACV_DA rows                : 1278 | timepoints per sex: {'female': ['4w', '8w'], 'male': ['1w', '2w', '4w', '8w']}
IMMUNO pooled rows / tissues        : 720 17 | VENACV rows: 0 | OVARY rows: 46 at FDR<0.05: 0
METAB_NORM_DATA_FLAT sample columns : 54 | in PHENO.viallabel: 0 | in PHENO.pid: 54
TRF PROT     HEART    693 features,   693 feature-platform pairs,   5544 rows, [8] rows each
TRF TRNSCRPT OVARY    896 features,   896 feature-platform pairs,   3584 rows, [4] rows each
TRF METAB    VENACV    23 features,    23 feature-platform pairs,    138 rows, [6] rows each
TRF IMMUNO   HEART      4 features,     5 feature-platform pairs,     40 rows, [8] rows each
METAB/HEART: TRF pairs / meta-reg FDR<0.05 / redundant FDR<0.05 : 568 568 603

age groups in the open omics release: ['6 months']

Expect

Invariants — these hold regardless of release, and a failure means this skill is wrong, not that upstream moved:

PHENO has exactly one age group. Any second value means an aged arm has been released into the omics package and every "young adult only" statement above needs revisiting — which is a good outcome, not a bug, but it must be noticed.
A _DA table has n_keys × n_sexes × n_timepoints_present rows, where the key is feature_ID for the single-platform assays and feature_ID + dataset for METAB and IMMUNO. Treating it as one row per feature_ID is the most common way to misread these objects, and on metabolomics it does not even produce a whole number.
n_timepoints_present is not always four and n_sexes is not always two. Ovary and testes are single-sex; vena cava has no female 1w or 2w. Count them, never assume them.
selection_fdr in the _DA table equals adj_p_value in the matching bucket table for every row when joined on the full key — and the matching table for plain METAB_*_DA is METAB/redundant/…, not the meta-regression file. Joined on feature_ID alone, metabolomics returns more rows than the bucket table has, with max |Δ FDR| near 1 rather than near zero. That inflation is the assertion here.
TRAINING_REGULATED_FEATURES for a tissue and assay contains exactly the (feature_ID, platform) pairs at adj_p_value < 0.05 in the matching bucket table — the meta-regression one for metabolomics — at n_sexes × n_timepoints rows each. For heart proteomics that collapses to features at eight rows each; nowhere else is it safe to assume either.
The immunoassay's pooled file answers 200 with zero rows for a tissue it never ran on. Every other assay answers 404. Assert on the row count.
Sample columns in METAB_NORM_DATA_FLAT and IMMUNO_NORM_DATA_FLAT are pid, and are disjoint from PHENO$viallabel. A viallabel merge on them returns a full-size frame with every phenotype column NA.
pyreadr returns a truthy dict for every object here. The assertion inside rda() is what stops an empty dict from becoming a confusing IndexError later.

Observed 2026-08-18 against tag v2.1.0 — these move when the consortium re-releases, so a mismatch is drift to investigate rather than a failure:

PHENO 6,156 rows · age groups ['6 months'] · protocols ['phase 1b']
PROT_HEART_DA 73,472 rows = 9,184 features × 2 sexes × 4 timepoints; 9,184 matched to the bucket table, max |Δ FDR| 1.1e-16
METAB_HEART_DA 1,430 feature-platform keys, 1,309 distinct feature_ID; the naive join returns 1,724 rows against a 1,430-row bucket table
METAB_VENACV_DA 1,278 rows, female 4w 8w only
immunoassay 720 rows over 17 tissues; ovary 46 analytes, none at 5% FDR
METAB_NORM_DATA_FLAT 54 pid columns, none of them a vial label
training-regulated: heart proteins 693 / 5,544 rows · ovary transcripts 896 / 3,584 · vena cava metabolites 23 / 138 · heart immunoassay 4 features but 5 feature-panel pairs / 40 rows · heart metabolites 568 pairs, matching the 568 at FDR < 0.05 in the meta-regression file and not the 603 in the redundant one

Sources

MoTrPAC — https://www.motrpac.org/
Data Hub (portal, browsable by hand) — https://motrpac-data.org/
Data package — https://github.com/MoTrPAC/MotrpacRatTraining6moData
Analysis package — https://github.com/MoTrPAC/MotrpacRatTraining6mo
Physiology package, includes the 18-month arm, GPL-3 — https://github.com/MoTrPAC/MotrpacRatTrainingPhysiologyData
White adipose companion package, MIT — https://github.com/MoTrPAC/MotrpacRatTraining6moWATData
Zenodo snapshots of the data package — https://doi.org/10.5281/zenodo.7877121
GEO SuperSeries — https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE242358
MoTrPAC Study Group (2024) Nature 629, 174-183 — https://doi.org/10.1038/s41586-023-06877-w
Sanford et al. (2020) Cell 181, 1464-1474 — https://doi.org/10.1016/j.cell.2020.06.004
Schenk et al. (2024) Function 5, zqae014 — adult and aged rat physiology — https://doi.org/10.1093/function/zqae014
Amar et al. (2024) Cell Metabolism 36, 1411-1429 — mitochondrial response across tissues — https://doi.org/10.1016/j.cmet.2023.12.021
Many et al. (2024) Nature Metabolism 6, 963-979 — white adipose companion — https://doi.org/10.1038/s42255-023-00959-9

Licences, precisely: the main data package states MIT for its code and CC BY 4.0 for its data; the analysis and white adipose packages are MIT; the physiology package is GPL-3. Acknowledge MoTrPAC as the source and name the dataset version you used — the consortium's data use agreement asks for both, and nothing else makes the analysis reproducible.

motrpac

MoTrPAC — rat endurance exercise training multi-omics

The open release is young adult, not aged

Do not route through the Data Hub API

What is open, and under exactly which terms

Object names are the index

Coverage is ragged — build the grid before you plan

Get the files

A route with no R and no pyreadr at all

Two tables, two different questions

feature_ID is the key for seven assays out of nine

TRAINING_REGULATED_FEATURES is per feature and platform, and not always eight rows

Epigenomics in the package is an excerpt

Objects pyreadr cannot read

Joining a matrix to the animals

Installing the R packages, and why it usually fails

Limits worth stating in a write-up

Try it

Sources

`feature_ID` is the key for seven assays out of nine

`TRAINING_REGULATED_FEATURES` is per feature and platform, and not always eight rows