Package 'glyexp'

Description

Getting a subset of an experiment object. Subsetting is first done on the expression matrix, then the sample information and variable information tibbles are filtered and ordered accordingly.

Syntax for [ is similar to subsetting a matrix, with some differences:

• Both row and column indices are required, i.e. exp[i] is not allowed, but exp[i, ] and exp[, j] are allowed.

• drop argument is not supported. Subsetting an experiment always returns an new experiment, even if it has only one sample or one variable.

• Renaming the subsetted experiment is no longer supported.

Assigning to a subset of an experiment is not allowed, i.e., ⁠exp[1, 1[ <- 0⁠ will raise an error. You can create a new experiment with new data if needed.

Usage

## S3 method for class 'glyexp_experiment'
x[i, j, ...]

## S3 replacement method for class 'glyexp_experiment'
x[i, j, ...] <- value

Arguments

Value

An experiment() object.

Examples

# Create a toy experiment for demonstration
exp <- toy_experiment

# Subsetting single samples
exp[, "S1"]
exp[, 1]

# Subsetting single variables
exp["V1", ]
exp[1, ]

# Subsetting multiple samples and variables
exp[c("V1", "V2"), c("S2", "S3")]
exp[c(1, 2), c(2, 3)]

# Create a copy
exp[, ]

Description

Arrange the sample or variable information tibble of an experiment().

The same syntax as dplyr::arrange() is used. For example, to arrange samples by the "group" column, use arrange_obs(exp, group). This actually calls dplyr::arrange() on the sample information tibble with the group column, and then updates the expression matrix accordingly to match the new order.

Usage

arrange_obs(exp, ...)

arrange_var(exp, ...)

Arguments

Value

An new experiment() object.

Examples

# Create a toy experiment for demonstration
exp <- toy_experiment |>
  mutate_var(type = c("Y", "X", "Z", "Y"))

# Arrange samples by group column
arranged_exp <- arrange_obs(exp, group)
get_sample_info(arranged_exp)
get_expr_mat(arranged_exp)

# Arrange variables by type column
arranged_exp <- arrange_var(exp, type)
get_var_info(arranged_exp)
get_expr_mat(arranged_exp)

# Arrange by multiple columns
arrange_obs(exp, group, sample)
get_sample_info(arranged_exp)
get_expr_mat(arranged_exp)

Description

Transforms a glycoproteomics-type experiment into a glycomics-type experiment by aggregating expression values by glycan structure (if available) or glycan composition.

This function implements the "pseudo-glycome" method described in doi:10.1038/s41467-026-68579-x, which aggregates glycoproteomic data by glycans to simulate glycome data when real glycome is unavailable.

Usage

as_pseudo_glycome(exp, aggr_method = c("sum", "mean", "median"))

Arguments

Details

Aggregation behavior:

• If glycan_structure column exists in var_info, aggregation is done by glycan structure (more specific)

• Otherwise, aggregation is done by glycan_composition

• Expression values are aggregated within each glycan group using the specified aggr_method

Limitation: Glycopeptides can have different ionization efficiencies, so the aggregation operation is not technically rigorous regardless of the method used. Use results with caution.

Value

A glycomics-type experiment() with aggregated expression values. The var_info will contain only glycan_composition and glycan_structure (if present in input) columns.

Examples

library(glyrepr)
as_pseudo_glycome(real_experiment)

Description

Convert an experiment() object to a SummarizedExperiment object. This function maps the experiment structure to SummarizedExperiment format:

• expr_mat becomes the main assay

• sample_info becomes colData

• var_info becomes rowData

• meta_data becomes metadata

Usage

as_se(exp, assay_name = "counts")

Arguments

Value

A SummarizedExperiment object.

Examples

# Convert toy experiment to SummarizedExperiment
se <- as_se(toy_experiment)
se

Description

Convert an experiment object to a tibble of "tidy" format. That is, each row is a unique combination of "sample" and "variable", with the observation (the abundance) in the "value" column. Additional columns in the sample and variable information are included. This format is also known as the "long" format.

Usually you don't want all columns in the sample information or variable information tibbles to be included in the output tibble, as this will make the output tibble very "wide". You can specify which columns to include in the output tibble by passing the column names to the sample_cols and var_cols arguments. <data-masking> syntax is used here. By default, all columns are included.

Usage

## S3 method for class 'glyexp_experiment'
as_tibble(
  x,
  sample_cols = tidyselect::everything(),
  var_cols = tidyselect::everything(),
  ...
)

Arguments

Value

A tibble.

Examples

library(tibble)

# Create a toy experiment for demonstration
toy_exp <- toy_experiment
toy_exp

# Convert the experiment to a tibble
as_tibble(toy_exp)

# specify columns to include
as_tibble(toy_exp, sample_cols = group, var_cols = c(protein, peptide))

Description

Retrieve the dimensions of an experiment object, i.e. the number of variables and samples.

Usage

## S3 method for class 'glyexp_experiment'
dim(x)

## S3 replacement method for class 'glyexp_experiment'
dim(x) <- value

Arguments

Value

A vector with two elements: the number of variables and the number of samples.

Examples

dim(real_experiment)

Description

The dimnames method for experiment() objects are the dimnames of their expression matrix.

Usage

## S3 method for class 'glyexp_experiment'
dimnames(x, ...)

Arguments

Value

A list with the dimnames of the expression matrix.

Examples

dimnames(real_experiment)

Description

The data container of a glycoproteomics or glycomics experiment. Expression matrix, sample information, and variable information are required then will be managed by the experiment object. It acts as the data core of the glycoverse ecosystem.

Usage

experiment(
  expr_mat,
  sample_info = NULL,
  var_info = NULL,
  exp_type = "others",
  glycan_type = NULL,
  coerce_col_types = TRUE,
  check_col_types = TRUE,
  ...
)

is_experiment(x)

Arguments

Value

A experiment(). If the input data is wrong, an error will be raised.

A logical value.

Requirements of the input data

Expression matrix:

• Must be a numeric matrix with variables as rows and samples as columns.

• The column names must correspond to sample IDs.

• The row names must correspond to variable IDs.

Sample information (sample_info):

• Must be a tibble with a column named "sample" (sample ID).

• Each value in "sample" must be unique.

• The set of "sample" values must match the column names of the expression matrix (order does not matter).

Variable information (var_info):

• Must be a tibble with a column named "variable" (variable ID).

• Each value in "variable" must be unique.

• The set of "variable" values must match the row names of the expression matrix (order does not matter).

The function will automatically reorder the expression matrix to match the order of "sample" and "variable" in the info tables.

Column requirements

Some columns are required compulsorily in the variable information tibble for a valid experiment. It depends on the experiment type.

• For "glycomics": glycan_composition.

• For "glycoproteomics": protein, protein_site, glycan_composition.

• For "traitomics": no required columns.

• For "traitproteomics": protein, protein_site.

• For "others": no required columns.

See the "Column conventions" section for detailed description of these columns.

The last two types of experiments are created by the glydet package. Normally you don't need to manually create them.

Column conventions

glycoverse has some conserved column names for sample_info and var_info to make everything work seamlessly. It's not mandatory, but following these conventions will make your life easier.

sample_info:

• group: factor, treatment/condition/grouping, used by many glystats and glyvis functions.

• batch: factor, batch information, used by glyclean::correct_batch_effect().

• bio_rep: factor, biological replicate, may be used in the future.

var_info:

• protein: character, protein Uniprot accession.

• protein_site: integer, glycosylation site position on protein.

• gene: character, gene symbol.

• peptide: character, peptide sequence.

• peptide_site: integer, glycosylation site position on peptide.

• glycan_composition: glyrepr::glycan_composition(), glycan composition.

• glycan_structure: glyrepr::glycan_structure(), glycan structure.

Meta data

Other meta data can be added to the meta_data attribute. meta_data is a list of additional information about the experiment. Two meta data fields are required:

• exp_type: "glycomics", "glycoproteomics", "traitomics", "traitproteomics", or "others"

• glycan_type: "N", "O-GalNAc", "O-GlcNAc", "O-Man", "O-Fuc", "O-Glc", or NULL

Other meta data will be added by other glycoverse packages for their own purposes.

Index columns

The index columns are the backbone that keep your data synchronized:

• The "sample" column in sample_info must match the column names of expr_mat.

• The "variable" column in var_info must match the row names of expr_mat.

These columns act as unique identifiers, ensuring that your expression matrix, sample information, and variable information always stay in sync, no matter how you filter, arrange, or subset your data.

Examples

# The minimum required input is an expression matrix.
expr_mat <- matrix(runif(9), nrow = 3, ncol = 3)
colnames(expr_mat) <- c("S1", "S2", "S3")
rownames(expr_mat) <- c("V1", "V2", "V3")
experiment(expr_mat)

# Or with more detailed information.
sample_info <- tibble::tibble(sample = c("S1", "S2", "S3"), group = c("A", "B", "A"))
var_info <- tibble::tibble(
  variable = c("V1", "V2", "V3"),
  glycan_composition = glyrepr::glycan_composition(c(Hex = 1))
)
experiment(expr_mat, sample_info, var_info, exp_type = "glycomics", glycan_type = "N")

Description

Getting a subset of an experiment() by filtering samples or variables.

The same syntax as dplyr::filter() is used. For example, to get a subset of an experiment keeping only "HC" samples, use filter_obs(exp, group == "HC"). This actually calls dplyr::filter() on the sample information tibble with condition group == "HC", and then updates the expression matrix accordingly.

Usage

filter_obs(exp, ..., .drop_levels = TRUE)

filter_var(exp, ..., .drop_levels = TRUE)

Arguments

Details

One difference between filter_obs() or filter_var() and dplyr::filter is that, when filtering on factor columns, the unused levels are automatically dropped by default. This behavior can be turnt off by setting .drop_levels to FALSE.

Value

An new experiment() object.

Examples

# Create a toy experiment for demonstration
exp <- toy_experiment |>
  mutate_var(type = c("X", "X", "Y", "Y"))

# Filter samples
sub_exp_1 <- filter_obs(exp, group == "A")
get_sample_info(sub_exp_1)
get_expr_mat(sub_exp_1)

# Filter variables
sub_exp_2 <- filter_var(exp, type == "X")
get_var_info(sub_exp_2)
get_expr_mat(sub_exp_2)

# Use pipe
sub_exp_3 <- exp |>
  filter_obs(group == "A") |>
  filter_var(type == "X")
get_sample_info(sub_exp_3)
get_var_info(sub_exp_3)
get_expr_mat(sub_exp_3)

Description

Convert a SummarizedExperiment object to an experiment() object. This function maps the SummarizedExperiment structure to experiment format:

• The main assay becomes expr_mat

• colData becomes sample_info

• rowData becomes var_info

• metadata becomes meta_data

Usage

from_se(se, assay_name = NULL, exp_type = NULL, glycan_type = NULL)

Arguments

Value

An experiment() object.

Examples

# Convert SummarizedExperiment back to experiment
se <- as_se(toy_experiment)
exp_back <- from_se(se, exp_type = "glycomics", glycan_type = "N")
exp_back

Description

A matrix of expression values with samples as columns and variables as rows.

Usage

get_expr_mat(exp)

Arguments

Value

A matrix of expression values.

Examples

get_expr_mat(real_experiment)[1:5, 1:5]

Description

Meta data is some descriptions about the experiment, like the experiment type ("glycomics" or "glycoproteomics"), or the glycan type ("N", "O-GalNAc", "O-GlcNAc", "O-Man", "O-Fuc", or "O-Glc").

Usage

get_meta_data(exp, x = NULL)

get_exp_type(exp)

get_glycan_type(exp)

Arguments

Value

The value of the meta data field. If the field does not exist, NULL will be returned.

Examples

get_meta_data(real_experiment)
get_exp_type(real_experiment)
get_glycan_type(real_experiment)

Description

A tibble of sample information, with the first column being "sample".

Usage

get_sample_info(exp)

Arguments

Value

A tibble of sample information.

Examples

get_sample_info(real_experiment)

Description

A tibble of variable information, with the first column being "variable".

Usage

get_var_info(exp)

Arguments

Value

A tibble of variable information.

Examples

get_var_info(real_experiment)

Description

These functions allow you to join additional data to the sample information or variable information of an experiment(). They work similarly to dplyr::left_join(), dplyr::inner_join(), dplyr::semi_join(), and dplyr::anti_join(), but are designed to work with experiment objects.

After joining, the expr_mat is automatically updated to reflect any changes in the number of samples or variables.

Important Notes:

• The relationship parameter is locked to "many-to-one" to ensure that the number of observations never increases, which would violate the experiment object assumptions.

• right_join() and full_join() are not supported as they could add new observations to the experiment.

Usage

left_join_obs(exp, y, by = NULL, ...)

inner_join_obs(exp, y, by = NULL, ...)

semi_join_obs(exp, y, by = NULL, ...)

anti_join_obs(exp, y, by = NULL, ...)

left_join_var(exp, y, by = NULL, ...)

inner_join_var(exp, y, by = NULL, ...)

semi_join_var(exp, y, by = NULL, ...)

anti_join_var(exp, y, by = NULL, ...)

Arguments

Value

A new experiment() object with updated sample or variable information.

Examples

library(dplyr)
library(tibble)

# Create a toy experiment
exp <- toy_experiment

# Create additional sample information to join
extra_sample_info <- tibble(
  sample = c("S1", "S2", "S3", "S4"),
  age = c(25, 30, 35, 40),
  treatment = c("A", "B", "A", "B")
)

# Left join to sample information
exp_with_extra <- left_join_obs(exp, extra_sample_info, by = "sample")
get_sample_info(exp_with_extra)

# Inner join (only keeps matching samples)
exp_inner <- inner_join_obs(exp, extra_sample_info, by = "sample")
get_sample_info(exp_inner)
get_expr_mat(exp_inner) # Note: expr_mat is updated too

# Create additional variable information to join
extra_var_info <- tibble(
  protein = c("P1", "P2", "P3"),
  pathway = c("A", "B", "A"),
  importance = c(0.8, 0.6, 0.9)
)

# Left join to variable information
exp_with_var_extra <- left_join_var(exp, extra_var_info, by = "protein")
get_var_info(exp_with_var_extra)

Description

Merges two experiment() objects into a single object.

Expression matrix: Expression matrices are merged by matching variables. For variables that are present in only one of the experiments, the corresponding values in the expression matrix are set to NA.

Sample information: Column names and column types must be identical (order does not matter). No overlap is allowed for sample names.

Variable information: Column names and column types must be identical (order does not matter). Variable names are ignored. The identity of variables is determined by all other columns in the variable information. If row A in the first var_info is identical (order does not matter) to row B in the second var_info, they are considered the same variable. Therefore, please make sure that each row has a unique combination of values (except for variable), otherwise the function cannot determine which variable in x is identical to which variable in y. To ensure uniqueness, you can use glyclean::aggregate().

Metadata: Metadata is taken from the first experiment. This is the only place where the order of x and y matters.

Usage

## S3 method for class 'glyexp_experiment'
merge(x, y, ...)

Arguments

Value

A new experiment() object

Variable matching

In the variable information tibble, the variable column is just arbitrary unique identifier, while the real identity of a variable is determined by all other columns. For example, if the variable information tibble has the following columns:

variable glycan_composition protein protein_site
V1       Hex(2)HexNAc(1)    P1      2
V2       Hex(2)HexNAc(1)    P2      3
V3       Hex(2)HexNAc(2)    P2      3
V4       Hex(2)HexNAc(2)    P2      3

We know that V1, V2, and V3 are all different variables, but V3 and V4 are the same variable (they have the same glycan composition, protein, and protein site).

During the merge, the function will use all columns in the variable information tibble except for variable to match variables. This means that if the combination of all other columns is not unique, the function cannot determine the identity of the variables.

To ensure uniqueness, you can use glyclean::aggregate(), which merges the expression levels of variables with the same identity.

After the merge, a new variable column is added to the variable information tibble, and used as the rownames of the expression matrix.

Variable and sample orders

The order of variables and samples in the merged experiment is deterministic.

For samples, as no overlapping is allowed for sample names, the new sample names are the union of the sample names in x and y, orders reserved.

For variables, the new variables are:

1. First, all variables in x, with the same order as in x.

2. Then, all variables in y while not in x, with the same order as in y.

Note that for variables, we refer to the identity of variables, not the variable names in the variable column.

Examples

# Merging is most useful with experiments from different batches.
# Here we just demonstrate the usage.

# Create experiments to be merged
exp1 <- toy_experiment
exp2 <- toy_experiment |>
  mutate_obs(sample = paste0("S", 7:12))
exp3 <- toy_experiment |>
  mutate_obs(sample = paste0("S", 13:18))

# Merge two experiments
merge(exp1, exp2)

# Merge multiple experiments
Reduce(merge, list(exp1, exp2, exp3))

Description

Mutate the sample or variable information tibble of an experiment().

The same syntax as dplyr::mutate() is used. For example, to add a new column to the sample information tibble, use mutate_obs(exp, new_column = value). This actually calls dplyr::mutate() on the sample information tibble with new_column = value.

If the sample column in sample_info or the variable column in var_info is to be modified, the new column must be unique, otherwise an error is thrown. The column names or row names of expr_mat will be updated accordingly.

Usage

mutate_obs(exp, ...)

mutate_var(exp, ...)

Arguments

Value

An new experiment() object.

Examples

# Create a toy experiment for demonstration
exp <- toy_experiment |>
  mutate_var(type = c("X", "X", "Y", "Y"))

# Add a new column to sample information tibble or variable information tibble
exp |>
  mutate_obs(new_column = c(1, 2, 3, 4, 5, 6)) |>
  get_sample_info()

exp |>
  mutate_var(new_column = c("A", "A", "B", "B")) |>
  get_var_info()

# Modify existing columns
exp |>
  mutate_obs(group = dplyr::if_else(group == "A", "good", "bad")) |>
  get_sample_info()

exp |>
  mutate_var(type = dplyr::if_else(type == "X", "good", "bad")) |>
  get_var_info()

# Modify the `sample` column in sample information tibble
new_exp <- mutate_obs(exp, sample = c("SI", "SII", "SIII", "SIV", "SV", "SVI"))
get_sample_info(new_exp)
get_expr_mat(new_exp)

# Modify the `variable` column in variable information tibble
new_exp <- mutate_var(exp, variable = c("VI", "VII", "VIII", "VIV"))
get_var_info(new_exp)
get_expr_mat(new_exp)

Description

Getting the number of samples or variables of an experiment(). Syntax sugar for ncol(exp$expr_mat) and nrow(exp$expr_mat).

Usage

n_samples(exp)

n_variables(exp)

Arguments

Value

An integer with the number of samples or variables.

Examples

exp <- toy_experiment
n_samples(exp)
n_variables(exp)

Description

A real glycoproteomics experiment object. This dataset is derived from the unpublished ProteomeXchange dataset PXD063749. It contains serum N-glycoproteome profiles from 12 patients with different liver conditions: healthy (H), hepatitis (M), cirrhosis (Y), and hepatocellular carcinoma (C). Glycopeptide identification and annotation were performed using pGlyco3 (https://github.com/pFindStudio/pGlyco3), and quantification was carried out with pGlycoQuant (https://github.com/Power-Quant/pGlycoQuant). The raw data were imported with glyread::read_pglyco3_pglycoquant().

Usage

real_experiment

Format

An experiment() object with 4262 variables and 12 samples.

Variable information:

• peptide: peptide sequence

• peptide_site: site position on the peptide

• protein: protein accession

• protein_site: site position on the protein

• gene: gene symbol

• glycan_composition: glycan composition (glyrepr::glycan_composition())

• glycan_structure: glycan structure (glyrepr::glycan_structure())

Sample information:

• sample: sample ID

• group: disease group, one of "H" (healthy), "M" (hepatitis), "Y" (cirrhosis), and "C" (hepatocellular carcinoma)

Description

A real glycomics experiment object. This dataset is derived from the unpublished glycoPOST dataset GPST000313. It contains serum N-glycome profiles from 144 samples with different liver conditions: healthy (H), hepatitis (M), cirrhosis (Y), and hepatocellular carcinoma (C).

Usage

real_experiment2

Format

An experiment() object with 67 variables and 144 samples.

Variable information:

• glycan_composition: glycan composition (glyrepr::glycan_composition())

• glycan_structure: glycan structure (glyrepr::glycan_structure())

Sample information:

• sample: sample ID

• group: disease group, one of "H" (healthy), "M" (hepatitis), "Y" (cirrhosis), and "C" (hepatocellular carcinoma)

Description

These two functions provide a way to rename columns in the sample or variable information tibble of an experiment().

The same syntax as dplyr::rename() is used. For example, to rename the "group" column in the sample information tibble to "condition", use rename_obs(exp, condition = group). Note that you can't rename the "sample" column in the sample information tibble, as well as the "variable" column in the variable information tibble. These two columns are used to link the sample or variable information tibble to the expression matrix.

Usage

rename_obs(exp, ...)

rename_var(exp, ...)

Arguments

Value

An new experiment() object.

Examples

toy_exp <- toy_experiment
toy_exp

# Rename columns in sample information tibble
rename_obs(toy_exp, condition = group)

# Rename columns in variable information tibble
rename_var(toy_exp, composition = glycan_composition)

Description

Getting the names of samples or variables of an experiment(). Syntax sugar for colnames(exp$expr_mat) and rownames(exp$expr_mat).

Usage

samples(exp)

variables(exp)

Arguments

Value

A character vector of sample or variable names.

Examples

exp <- toy_experiment
samples(exp)
variables(exp)

Description

These two functions provide a way to trimming down the sample or variable information tibble of an experiment() to only the columns of interest.

The same syntax as dplyr::select() is used. For example, to get a new experiment() with only the "sample" and "group" columns in the sample information tibble, use select_obs(exp, group). Note that you don't need to (and you can't) explicitly select or deselect the sample column in sample_info. The same applies to the variable column in var_info. Whatever the selection expression is, the sample or variable column will always be kept.

Usage

select_obs(exp, ...)

select_var(exp, ...)

Arguments

Details

When using select_var() with dplyr, you may encounter package conflicts. dplyr also has a function called select_var() that has been deprecated for over two years. If you encounter package conflicts, use the following code to resolve them:

conflicted::conflicts_prefer(glyexp::select_var)

Value

An new experiment() object.

Examples

toy_exp <- toy_experiment

toy_exp_2 <- toy_exp |>
  select_obs(group) |>
  select_var(protein, peptide)

get_sample_info(toy_exp_2)
get_var_info(toy_exp_2)

Description

Set meta data values for the experiment, like the experiment type ("glycomics" or "glycoproteomics"), or the glycan type ("N", "O-GalNAc", "O-GlcNAc", "O-Man", "O-Fuc", or "O-Glc").

Usage

set_meta_data(exp, x, value)

set_exp_type(exp, value)

set_glycan_type(exp, value)

Arguments

Value

The modified experiment() object.

Description

Slice the sample or variable information tibble of an experiment().

These functions provide row-wise slicing operations similar to dplyr's slice functions. They select rows by position or based on values in specified columns, and update the expression matrix accordingly to match the new selection.

• slice_obs() and slice_var(): Select rows by position

• slice_head_obs() and slice_head_var(): Select first n rows

• slice_tail_obs() and slice_tail_var(): Select last n rows

• slice_sample_obs() and slice_sample_var(): Select random n rows

• slice_max_obs() and slice_max_var(): Select rows with highest values

• slice_min_obs() and slice_min_var(): Select rows with lowest values

Usage

slice_obs(exp, ...)

slice_var(exp, ...)

slice_head_obs(exp, n, prop)

slice_head_var(exp, n, prop)

slice_tail_obs(exp, n, prop)

slice_tail_var(exp, n, prop)

slice_sample_obs(exp, n, prop, weight_by = NULL, replace = FALSE)

slice_sample_var(exp, n, prop, weight_by = NULL, replace = FALSE)

slice_max_obs(exp, order_by, ..., n, prop, with_ties = TRUE, na_rm = FALSE)

slice_max_var(exp, order_by, ..., n, prop, with_ties = TRUE, na_rm = FALSE)

slice_min_obs(exp, order_by, ..., n, prop, with_ties = TRUE, na_rm = FALSE)

slice_min_var(exp, order_by, ..., n, prop, with_ties = TRUE, na_rm = FALSE)

Arguments

Value

A new experiment() object.

Examples

# Create a toy experiment for demonstration
exp <- toy_experiment |>
  mutate_obs(score = c(10, 20, 30, 15, 25, 35)) |>
  mutate_var(value = c(5, 10, 15, 8))

# Select specific rows by position
slice_obs(exp, 1, 3, 5)

# Select first 3 samples
slice_head_obs(exp, n = 3)

# Select last 2 variables
slice_tail_var(exp, n = 2)

# Select 2 random samples
slice_sample_obs(exp, n = 2)

# Select samples with highest scores
slice_max_obs(exp, order_by = score, n = 2)

# Select variables with lowest values
slice_min_var(exp, order_by = value, n = 2)

Description

Devides an experiment() into a list of experiment() objects by f.

Usage

## S3 method for class 'glyexp_experiment'
split(x, f, drop = FALSE, where = "var_info", ...)

Arguments

Value

A named list of experiment() objects.

Examples

split(toy_experiment, group, where = "sample_info")

Description

Converts meaningless variable IDs (like "GP1", "V1") into meaningful, human-readable IDs based on the experiment type and available columns.

The format of the new IDs depends on the exp_type if format is not specified:

• glycomics: {glycan_composition}, e.g., "Hex(5)HexNAc(2)"

• glycoproteomics: {protein}-{protein_site}-{glycan_composition}

• traitomics: {motif} or {trait} depending on which column is present

• traitproteomics: {protein}-{protein_site}-{motif} or {protein}-{protein_site}-{trait}

If duplicate IDs are generated (e.g., same composition with multiple PSMs), a unique integer suffix is appended using the unique_suffix pattern.

Usage

standardize_variable(exp, format = NULL, unique_suffix = "-{N}")

Arguments

Value

The experiment with standardized variable IDs.

Examples

# Glycomics example
expr_mat <- matrix(1:4, nrow = 2)
rownames(expr_mat) <- c("V1", "V2")
colnames(expr_mat) <- c("S1", "S2")
sample_info <- tibble::tibble(sample = c("S1", "S2"))
var_info <- tibble::tibble(
  variable = c("V1", "V2"),
  glycan_composition = glyrepr::glycan_composition(c(Hex = 5, HexNAc = 2))
)
exp <- experiment(expr_mat, sample_info, var_info,
  exp_type = "glycomics", glycan_type = "N"
)
standardize_variable(exp)

# Glycoproteomics example
expr_mat <- matrix(1:4, nrow = 2)
rownames(expr_mat) <- c("GP1", "GP2")
colnames(expr_mat) <- c("S1", "S2")
sample_info <- tibble::tibble(sample = c("S1", "S2"))
var_info <- tibble::tibble(
  variable = c("GP1", "GP2"),
  protein = c("P12345", "P12345"),
  protein_site = c(32L, 45L),
  glycan_composition = glyrepr::glycan_composition(c(Hex = 5, HexNAc = 2))
)
exp <- experiment(expr_mat, sample_info, var_info,
  exp_type = "glycoproteomics", glycan_type = "N"
)
standardize_variable(exp)

# Custom format example
standardize_variable(exp, format = "{protein}-{glycan_composition}")

Description

This function summarizes the number of glycan compositions, glycan structures, glycopeptides, peptides, glycoforms, glycoproteins, and glycosites in an experiment().

Usage

summarize_experiment(x, count_struct = NULL)

Arguments

Details

The following columns are required in the variable information tibble:

• composition: glycan_composition

• structure: glycan_structure

• peptide: peptide

• glycopeptide: glycan_composition or glycan_structure, peptide, peptide_site

• glycoform: glycan_composition or glycan_structure, protein or proteins, protein_site or protein_sites

• protein: protein or proteins

• glycosite: protein or proteins, protein_site or protein_sites

You can use count_struct parameter to control how to count glycopeptides and glycoforms, either by glycan structures or by glycan compositions.

Value

A tibble with columns item and n summarizing the results. The items include:

• total_composition: The number of glycan compositions.

• total_structure: The number of glycan structures.

• total_peptide: The number of peptides.

• total_glycopeptide: The number of unique combinations of peptides, sites, and glycans.

• total_glycoform: The number of unique combinations of proteins, sites, and glycans.

• total_protein: The number of proteins.

• total_glycosite: The number of unique combinations of proteins and sites.

In addition, ⁠_per_sample⁠ items are calculated as the average number of detected items per sample. For example, composition_per_sample is the average number of glycan compositions detected per sample.

Examples

exp <- real_experiment
summarize_experiment(exp)

Description

A toy experiment object for new users to play with to get familiar with experiment() objects.

Usage

toy_experiment

Format

toy_experiment

An experiment() object with 4 variables and 6 samples. var_info contains fields: protein, peptide, and glycan_composition. sample_info contains fields: group and batch.