Creating Experiments

This vignette describes how to create an experiment object from scratch.

Note: If you’re using glyread to import your data, sample information is already handled, and you can focus on understanding the structure.

Required Components

Creating an experiment object requires three key components:

• Expression Matrix: Numeric data with variables as rows and samples as columns
• Sample Information: A DataFrame containing sample metadata (groups, batches, etc.)
• Variable Information: A DataFrame describing variables (proteins, peptides, glycan compositions, etc.)

You also need to specify experiment metadata.

Now let’s make a glycomic experiment together.

Step 1: Sample Information (Column Data)

The sample information table records metadata for each sample. Conditions, groupings, batches… Anything describe your samples can be put here.

We recommend you at least include a group column as a factor for your conditions, which will be used throughout glyclean, glystats, and glyvis.

Let’s build our sample information table:

col_data <- DataFrame(
  group = factor(c("A", "A", "A", "B", "B", "B"), levels = c("A", "B")),
  batch = factor(c(1, 2, 1, 2, 1, 2), levels = c(1, 2)),
  row.names = c("S1", "S2", "S3", "S4", "S5", "S6")
)
col_data
#> DataFrame with 6 rows and 2 columns
#>       group    batch
#>    <factor> <factor>
#> S1        A        1
#> S2        A        2
#> S3        A        1
#> S4        B        2
#> S5        B        1
#> S6        B        2

Step 2: Variable Information (Row Data)

The variable information table describes what each measurement represents. If you use the glyread package, the variable information table will be automatically generated. You can also provide your own variable information table.

For GlycomicSE, at least the following columns should be present:

• glycan_composition: Required. Glycan composition as a glyrepr::glycan_composition() object

For GlycoproteomicSE, at least the following columns should be present:

• protein: Required. UniProt accession (character)
• protein_site: Required. Glycosylation site position on the protein (integer)
• glycan_composition: Required. Glycan composition as a glyrepr::glycan_composition() object

If glycan structures are available, an additional glycan_structure column as a glyrepr::glycan_structure() object can also be provided in the data frame.

Let’s create our variable information table:

row_data <- DataFrame(
  glycan_composition = glyrepr::glycan_composition(
    c(GalNAc = 1),
    c(Gal = 1, GalNAc = 1),
    c(Gal = 1, GalNAc = 1, GlcNAc = 1)
  ),
  glycan_structure = glyrepr::as_glycan_structure(c(
    "GalNAc(a1-",
    "Gal(b1-3)GalNAc(a1-",
    "Gal(b1-3)[GlcNAc(a1-6)]GalNAc(a1-"
  )),
  row.names = c("V1", "V2", "V3")
)
row_data
#> DataFrame with 3 rows and 2 columns
#>        glycan_composition       glycan_structure
#>     <glyrepr_composition>    <glyrepr_structure>
#> V1              GalNAc(1)             GalNAc(a1-
#> V2        Gal(1)GalNAc(1)    Gal(b1-3)GalNAc(a1-
#> V3 Gal(1)GlcNAc(1)GalNA.. Gal(b1-3)[GlcNAc(a1-..

Step 3: Expression Matrix

The expression matrix contains your actual measurements. Layout: variables as rows, samples as columns.

No transformation (like log-transformation) is needed - glycoverse functions handle this internally if required.

Matching Requirements

Row names must match row names of the variable information table, and column names must match the row names of the sample information table, in the same order.

Let’s create our expression matrix:

# Create a simple matrix with 3 variables and 6 samples
expr_mat <- matrix(
  rnorm(18, mean = 10, sd = 2), # Some realistic-looking data
  nrow = 3, 
  ncol = 6
)
rownames(expr_mat) <- rownames(row_data)
colnames(expr_mat) <- rownames(col_data)
expr_mat
#>           S1        S2       S3        S4        S5       S6
#> V1  7.199913  9.988857 6.356365  9.434589 14.130050 6.273977
#> V2 10.510634 11.243105 9.505349  8.892601  6.738021 8.955975
#> V3  5.125473 12.296823 9.511601 11.257964 11.024854 9.894796

Step 4: Creating the Experiment

Now assemble the experiment object:

exp <- GlycomicSE(expr_mat, colData = col_data, rowData = row_data, metadata = list(glycan_type = "N"))
exp
#> 
#> ── GlycomicSE ──────────────────────────────────────────────────────────────────
#> ℹ Abundance assay: 6 samples, 3 variables
#> ℹ Glycan type: N
#> ℹ Row data fields: glycan_composition <comp>, glycan_structure <struct>
#> ℹ Column data fields: group <fct>, batch <fct>
#> ℹ Metadata fields: glycan_type <chr>

Note that you should also provided a list as the metadata. The glycan_type field is required, and you can provide others to serve your old need.