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

You can install the latest release of glyexp from GitHub with:

# install.packages("remotes")
remotes::install_github("glycoverse/glyexp@*release")

Or install the development version:

remotes::install_github("glycoverse/glyexp")

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)
library(magrittr)

# Create a toy experiment
a_little_toy <- toy_experiment
a_little_toy
#> 
#> ── 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    N3N2              
#> 4 V4       PRO3    PEP4    N3N2

# Filter samples
a_little_toy %>% 
  filter_obs(group == "A") %>%
  filter_var(protein == "PRO1")
#> 
#> ── 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>