Glycoproteomics and Glycomics Experiments • glyexp

Provides a tidy data framework for managing glycoproteomics and glycomics experimental data. The core feature is the ‘experiment()’ class, which serves as a unified data container integrating expression matrices, variable information (proteins, peptides, glycan compositions, etc.), and sample metadata (groups, batches, clinical variables, etc.). The package enforces data consistency, validates column types according to experiment types (glycomics, glycoproteomics, traitomics, traitproteomics), and provides dplyr-style data manipulation functions (filter, mutate, select, arrange, slice, join) for seamless data wrangling.

Installation

Install glycoverse

We recommend installing the meta-package glycoverse, which includes this package and other core glycoverse packages.

Install glyexp alone

If you don’t want to install all glycoverse packages, you can only install glyexp.

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

# install.packages("pak")
pak::repo_add(glycoverse = "https://glycoverse.r-universe.dev")
pak::pkg_install("glyexp")

Or from GitHub:

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

Or install the development version (NOT recommended):

pak::pkg_install("glycoverse/glyexp")

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

Documentation

🚀 Get started: Here
🔧 dplyr-style data manipulation: Here
📚 Reference: Here

Role in `glycoverse`

The experiment() class provides a consistent interface for glycoprotemics and glycomics data. All other packages in the glycoverse ecosystem know how to extract information from an experiment() object. So, put your data in an experiment() object and pass it around. Let other packages do the heavy lifting.

Example

library(glyexp)

# Create a toy experiment
a_little_toy <- toy_experiment
a_little_toy
#> 
#> ── Others Experiment ───────────────────────────────────────────────────────────
#> ℹ Expression matrix: 6 samples, 4 variables
#> ℹ Sample information fields: group <chr>, batch <dbl>
#> ℹ Variable information fields: protein <chr>, peptide <chr>, glycan_composition <chr>

get_expr_mat(a_little_toy)
#>    S1 S2 S3 S4 S5 S6
#> V1  1  5  9 13 17 21
#> V2  2  6 10 14 18 22
#> V3  3  7 11 15 19 23
#> V4  4  8 12 16 20 24

get_sample_info(a_little_toy)
#> # A tibble: 6 × 3
#>   sample group batch
#>   <chr>  <chr> <dbl>
#> 1 S1     A         1
#> 2 S2     A         2
#> 3 S3     A         1
#> 4 S4     B         2
#> 5 S5     B         1
#> 6 S6     B         2

get_var_info(a_little_toy)
#> # A tibble: 4 × 4
#>   variable protein peptide glycan_composition
#>   <chr>    <chr>   <chr>   <chr>             
#> 1 V1       PRO1    PEP1    H5N2              
#> 2 V2       PRO2    PEP2    H5N2              
#> 3 V3       PRO3    PEP3    H3N2              
#> 4 V4       PRO3    PEP4    H3N2

# Filter samples
a_little_toy |>
  filter_obs(group == "A") |>
  filter_var(protein == "PRO1")
#> 
#> ── Others Experiment ───────────────────────────────────────────────────────────
#> ℹ Expression matrix: 3 samples, 1 variables
#> ℹ Sample information fields: group <chr>, batch <dbl>
#> ℹ Variable information fields: protein <chr>, peptide <chr>, glycan_composition <chr>

glyexp