Experiment Types • glyexp

This vignette describes the four experiment types in glyexp: “glycomics”, “glycoproteomics”, “traitomics”, and “traitproteomics”. You can check your experiment type using get_exp_type().

library(glyexp)
library(glyrepr)

Why Experiment Types Matter

Experiment types refer to:

A label stored in the exp_type field of your experiment() object’s metadata
The expected structure of your variable information table

When your experiment has a specific type, glycoverse functions know what to expect from your data structure. These are guidelines rather than strict rules.

Note: If you’re not using glydet or glymotif, you can skip the traitomics and traitproteomics sections.

The Four Experiment Types

The glyexp package recognizes four distinct experiment types:

“glycomics”: Glycan-focused experiments
“glycoproteomics”: Glycans with protein context
“traitomics”: Derived traits from glycan data
“traitproteomics”: Derived traits at the protein level

The first two are standard glycobiology experiments. When you use glyread to import data, it automatically determines the type.

The last two are unique to the glycoverse ecosystem - they result from functions in glymotif or glydet.

Glycomics

“glycomics” is the most straightforward type. Each row in the variable information table represents a single glycan (or a spectrum match for MS data).

Expected columns in variable information table:

glycan_composition: Glycan composition as a glyrepr::glycan_composition() object
glycan_structure: (Optional) Glycan structure as a glyrepr::glycan_structure() object

get_var_info(real_experiment2)
#> # A tibble: 67 × 3
#>    variable                             glycan_composition      glycan_structure
#>    <glue>                               <comp>                  <struct>        
#>  1 Man(3)GlcNAc(3)                      Man(3)GlcNAc(3)         GlcNAc(?1-?)Man…
#>  2 Man(3)GlcNAc(7)                      Man(3)GlcNAc(7)         GlcNAc(?1-?)[Gl…
#>  3 Man(5)GlcNAc(2)                      Man(5)GlcNAc(2)         Man(?1-?)[Man(?…
#>  4 Man(4)Gal(2)GlcNAc(4)Neu5Ac(2)       Man(4)Gal(2)GlcNAc(4)N… Neu5Ac(?2-?)Gal…
#>  5 Man(3)Gal(1)GlcNAc(3)                Man(3)Gal(1)GlcNAc(3)   Gal(?1-?)GlcNAc…
#>  6 Man(3)Gal(2)GlcNAc(4)Fuc(2)          Man(3)Gal(2)GlcNAc(4)F… Gal(?1-?)GlcNAc…
#>  7 Man(3)GlcNAc(3)Fuc(1)                Man(3)GlcNAc(3)Fuc(1)   GlcNAc(?1-?)Man…
#>  8 Man(3)GlcNAc(4)                      Man(3)GlcNAc(4)         GlcNAc(?1-?)Man…
#>  9 Man(3)Gal(2)GlcNAc(5)Neu5Ac(1)       Man(3)Gal(2)GlcNAc(5)N… Neu5Ac(?2-?)Gal…
#> 10 Man(3)Gal(1)GlcNAc(5)Fuc(1)Neu5Ac(1) Man(3)Gal(1)GlcNAc(5)F… Neu5Ac(?2-?)Gal…
#> # ℹ 57 more rows

Glycoproteomics

“glycoproteomics” adds protein context. Each row represents a glycopeptide or glycoform, indicating not just what glycans are present but where they’re attached.

Variable information table should include:

protein: Protein UniProt accession (character string)
protein_site: Glycosylation site position on the protein (integer)
glycan_composition: Glycan composition as a glyrepr::glycan_composition() object
glycan_structure: (Optional) Glycan structure as a glyrepr::glycan_structure() object

get_var_info(real_experiment)
#> # A tibble: 4,262 × 8
#>    variable   peptide peptide_site protein protein_site gene  glycan_composition
#>    <chr>      <chr>          <int> <chr>          <int> <chr> <comp>            
#>  1 P08185-N1… NKTQGK             1 P08185           176 SERP… Hex(5)HexNAc(4)Ne…
#>  2 P04196-N3… HSHNNN…            5 P04196           344 HRG   Hex(5)HexNAc(4)Ne…
#>  3 P04196-N3… HSHNNN…            5 P04196           344 HRG   Hex(5)HexNAc(4)   
#>  4 P04196-N3… HSHNNN…            5 P04196           344 HRG   Hex(5)HexNAc(4)Ne…
#>  5 P10909-N2… HNSTGC…            2 P10909           291 CLU   Hex(6)HexNAc(5)   
#>  6 P04196-N3… HSHNNN…            5 P04196           344 HRG   Hex(5)HexNAc(4)Ne…
#>  7 P04196-N3… HSHNNN…            6 P04196           345 HRG   Hex(5)HexNAc(4)   
#>  8 P04196-N3… HSHNNN…            5 P04196           344 HRG   Hex(5)HexNAc(4)dH…
#>  9 P04196-N3… HSHNNN…            5 P04196           344 HRG   Hex(4)HexNAc(3)   
#> 10 P04196-N3… HSHNNN…            5 P04196           344 HRG   Hex(4)HexNAc(4)Ne…
#> # ℹ 4,252 more rows
#> # ℹ 1 more variable: glycan_structure <struct>

Traitomics

Both traitomics and traitproteomics emerge when you transform data using glydet::derive_traits() or glymotif::quantify_motifs().

When you apply derive_traits() to a glycomics experiment, you get a traitomics experiment. Instead of measuring individual glycans, you’re looking at derived traits - higher-level summaries of your glycan data.

The variable information table is flexible - no mandatory columns. Common columns include:

A trait column (from derive_traits())
A motif column (from quantify_motifs())

Traitproteomics

When you apply derive_traits() to a glycoproteomics experiment, you get a traitproteomics experiment. Instead of tracking individual glycans at each protein site, you measure derived traits at each site.

Essential columns:

protein: Protein UniProt accession (character string)
protein_site: Glycosylation site position (integer)

Plus:

A trait column (from derive_traits())
A motif column (from quantify_motifs())

In glycoproteomics, different sites can have different glycan repertoires. In traitproteomics, every site is scored on the same set of traits or motifs - standardized metrics across glycosylation sites.

Do You Need to Worry About Experiment Types?

Not really! Experiment types work behind the scenes. You’ll encounter them in:

Documentation - References to specific experiment types
Error messages - Like “Expecting a glycoproteomics experiment, but got a traitproteomics experiment”

Different functions have different expectations:

glymotif::quantify_motifs() requires glycomics or glycoproteomics data
glystats::gly_enrich_go() expects glycoproteomics or traitproteomics experiments

When to set experiment type: Manually when creating an experiment object using experiment(). Set the exp_type field to match your data structure.

Warning: Do not manually modify the exp_type field after creation. This breaks the contract between your data and glycoverse functions.