Brain tumor plasma metabolomics (Godlewski et al., 2023)

Browse datasets

datamonger:godlewski2023@1 · Registry 1.3.0. Default version in this registry.

Lossless CSV conversion of complete Supplementary Table S2: 235 plasma samples, 138 metabolite measurements, Sample Name identifiers, and the original six Sample type labels (glioma grades 1-4, meningioma, and healthy control). Preserves every source value, column name, and row and column order. Identifiers are excluded from model features. Contains 94 glioma samples although the paper reports 95. No SLOPE sample selection or binary label mapping is applied. The study authors normalized, filtered, and imputed the measurements before publishing the table.

Dimensions and tasks

Predictor counts follow each task's feature selection and exclude targets and identifiers. Sample counts are reported separately for each split.

Retrieve

These examples pin both the dataset version and the registry's exact index. An uncached dataset requires network access to its source. The CLI retrieves verified artifacts. The language clients also decode them.

from datamonger import Registry, fetch_data

registry = Registry(
    release="1.3.0",
    index_sha256="94639f8d711d64895689d6da87add0f0f56574070262ec2a04f1f1ad43279f85",
    index_url="https://github.com/jolars/datamonger/releases/download/registry-v1.3.0/index.json",
)
data = fetch_data("datamonger:godlewski2023@1", registry=registry)

library(datamonger)

registry <- registry_selector(
  release = "1.3.0",
  index_sha256 = "94639f8d711d64895689d6da87add0f0f56574070262ec2a04f1f1ad43279f85",
  index_url = "https://github.com/jolars/datamonger/releases/download/registry-v1.3.0/index.json"
)
data <- fetch_data("datamonger:godlewski2023@1", registry = registry)

using Datamonger

registry = registry_selector(
    "1.3.0",
    "94639f8d711d64895689d6da87add0f0f56574070262ec2a04f1f1ad43279f85",
    "https://github.com/jolars/datamonger/releases/download/registry-v1.3.0/index.json",
)
data = fetch_data("datamonger:godlewski2023@1"; registry=registry)

cat > datamonger-registry.json <<'JSON'
{
  "index_sha256": "94639f8d711d64895689d6da87add0f0f56574070262ec2a04f1f1ad43279f85",
  "index_url": "https://github.com/jolars/datamonger/releases/download/registry-v1.3.0/index.json",
  "release": "1.3.0",
  "schema_version": 1
}
JSON
datamonger --selector datamonger-registry.json fetch 'datamonger:godlewski2023@1'

Representation and variables

Tabular data with a dense representation. Decoder: delimited-text version 1.

Showing 30 of 140 components. The complete registry record contains every variable name and task binding.

Preprocessing

Upstream

Status: documented.

The table is processed metabolomics data, not raw spectra. The paper describes further model-specific processing for its experiments; those operations are not reproduced here.

normalization. Authors processed, quantified, and normalized the spectra in MetIDQ according to the Biocrates kit manual.

Parameters: software: MetIDQ Oxygen DB110-3005.

missing-value-removal. Authors retained metabolites present in at least 80% of samples, yielding 138 of 188 measured metabolites.

Parameters: axis: columns; minimum_presence: 80%.

imputation. Authors substituted missing measurements with half the metabolite's limit of detection in each batch.

Parameters: method: half limit of detection per batch.

Evidence: https://pmc.ncbi.nlm.nih.gov/articles/PMC10329700/, https://www.nature.com/articles/s41598-023-38243-1

Datamonger

Status: none.

Only lossless XLSX-to-CSV conversion and gzip compression. Retains all samples, original labels, identifiers, measurements, names, and ordering; no statistical transformations are applied.

Evidence: curation/godlewski2023/1/prepare.py, curation/godlewski2023/1/DATASET.md

Provenance and citation

Provider: Datamonger; derived from Godlewski et al., Scientific Reports. Upstream name: Supplementary Table S2. Retrieved: 2026-10-20.

Original source

Authors: Adrian Godlewski, Marcin Czajkowski, Patrycja Mojsak, Tomasz Pienkowski, Wioleta Gosk, Tomasz Lyson, Zenon Mariak, Joanna Reszec, Marcin Kondraciuk, Karol Kaminski, Marek Kretowski, Marcin Moniuszko, Adam Kretowski, Michal Ciborowski.

Godlewski A et al. (2023). A comparison of different machine-learning techniques for the selection of a panel of metabolites allowing early detection of brain tumors. Scientific Reports 13:11044. https://doi.org/10.1038/s41598-023-38243-1. Supplementary Table S2. Datamonger portable representation, version 1: https://doi.org/10.5281/zenodo.23498714.

License

CC-BY-4.0

License information

The article licenses its material under CC BY 4.0, subject to separately credited third-party material. Its data-availability statement identifies the supplements as part of the work. No separate restriction or third-party credit appears in Table S2. This automated source review applies the article license to that table and its lossless CSV conversion, retaining attribution and documenting the format change. See curation/godlewski2023/1/RIGHTS.md.

Artifacts

Total download: 77.0 KiB. Sizes describe the distributed files, including any compression.

data: mirror

Preservation deposit

Versions in this registry

1 (default)

Registry record

Download the complete registry record, registry selector, or full registry index.

Index SHA-256: 94639f8d711d64895689d6da87add0f0f56574070262ec2a04f1f1ad43279f85.