Curated Glycomics and Glycoproteomics Data Resources • glyhub

glyhub collects glycomics and glycoproteomics data for the Glycoverse ecosystem, using the GlycomicSE and GlycoproteomicSE containers from glyexp. The package is experimental.

Available data

glyhub provides 97 curated datasets as GlycomicSE or GlycoproteomicSE objects:

Source	Datasets	Coverage
GlycoPOST	75	47 public projects covering released N- and O-glycans, glycosphingolipid-derived glycans, glycosaminoglycan disaccharides and glycopeptides.
CPTAC	8	N-glycoproteomics from eight cancer cohorts: CCRCC, COAD, HGSC, HNSCC, LSCC, LUAD, PDAC and UCEC.
PRIDE, jPOST, iProX and MassIVE	14	Published N- and O-glycoproteomics studies, including a human tissue atlas, mouse brain, cancer, DQGlyco and Glyco-DIA experiments.

Use glyhub_catalog() to browse dataset identifiers, experiment and glycan types, structure availability and quantification methods. Each dataset’s help page describes its biological system, experimental design, source tables, units and limitations. Data are downloaded on demand from a versioned release and cached locally; see the loading examples below.

Objects retain source feature annotations in rowData, measurement conditions and observation labels in colData, and original numerical values and processing records in metadata. Compatible GlycoPOST tables from the same study and glycan class are combined using supported feature identities, with source-coverage masks for unmeasured combinations. Structures are encoded only when supported by explicit evidence; unresolved annotations remain uncertain. Inclusion may cover only part of a study: GPST000602, for example, contains annotated peaks with spectrum-specific feature identities rather than an aligned feature matrix.

Abundance scales and missing-value rules depend on the source:

GlycoPOST: zero-valued abundances become NA in the assay. Original values and processing masks remain in metadata; glyhub adds no imputation or renormalization.
CPTAC: assays contain 2^source_value, while original log2 values remain in metadata(x)$raw_abundance. A source zero becomes 1; source missing values remain NA.
Other published studies: source numerical values and zeros are retained, except for the documented -1 to NA convention in glyhub_MSV000089484_N_HCC. Some source tables already contain normalization, imputation or feature selection, as documented in the dataset help.

Consult ?glyhub_data_conventions and the dataset help before comparing assays. raw_abundance preserves the selected source matrix and may itself contain author-processed values. Source-data licenses and citation requirements are recorded separately from the package’s code license.

Installation

The glyhub package is not a core glycoverse package. You need to install it individually even if you have installed the meta-package glycoverse.

You can install the latest release of glyhub from r-universe (recommended):

pak::repo_add(glycoverse = "https://glycoverse.r-universe.dev")
pak::pkg_install("glyhub")

Or from GitHub:

pak::pkg_install("glycoverse/glyhub@*release")

Or install the development version (NOT recommended):

pak::pkg_install("glycoverse/glyhub")

Note: Tips and troubleshooting for the meta-package glycoverse are also applicable here: Installation of glycoverse.

The Glycoverse repository provides dependencies such as glyexp. pak also resolves required Bioconductor packages for your R version.

Example

The datasets are stored in the Glyhub data release. The package includes the catalog and loader code, with no bundled data objects.

head(glyhub::glyhub_catalog()[, c("dataset", "source", "bytes")])
#> # A tibble: 6 × 3
#>   dataset                     source     bytes
#>   <chr>                       <chr>      <int>
#> 1 glyhub_GPST000738_N_B4GalT1 GlycoPOST  85324
#> 2 glyhub_GPST000738_N_ST6GAL1 GlycoPOST  86980
#> 3 glyhub_GPST000643_O         GlycoPOST 136920
#> 4 glyhub_GPST000643_N         GlycoPOST  10320
#> 5 glyhub_GPST000213_N         GlycoPOST 273048
#> 6 glyhub_GPST000213_O         GlycoPOST  46920

Use either the common loader or a dataset’s own function. Both share the same verified cache and return the same object:

x <- glyhub::glyhub_load("glyhub_CPTAC_CCRCC")
x <- glyhub::glyhub_CPTAC_CCRCC()

x <- glyhub::glyhub_GPST000738_N_B4GalT1()
x <- glyhub::glyhub_load("glyhub_GPST000738_N_B4GalT1", offline = TRUE)

# Original CPTAC source archive; returns a verified local file path.
raw_file <- glyhub::glyhub_CPTAC_CCRCC(kind = "raw")
# Equivalent: glyhub::glyhub_load("glyhub_CPTAC_CCRCC", kind = "raw")