The goal of glyexp is to manage data in glycoproteomics and glycomics experiments in a tidy way.
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")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>