glyhub collects glycomics and glycoproteomics data for the Glycoverse
ecosystem, using the
GlycomicSE
and
GlycoproteomicSE
containers from glyexp. The
package is experimental.
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:
NA in the assay.
Original values and processing masks remain in metadata; glyhub adds
no imputation or renormalization.2^source_value, while original log2
values remain in metadata(x)$raw_abundance. A source zero becomes
1; source missing values remain NA.-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.
Install the development version from GitHub:
pak::repo_add(glycoverse = "https://glycoverse.r-universe.dev")
pak::pkg_install("glycoverse/glyhub")
The Glycoverse repository provides dependencies such as glyexp. pak
also resolves required Bioconductor packages for your R version.
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")