Package 'glystats'

Description

Filtering the experiment to keep only significant variables is a common task. This function provides a convenient way to do this. It supports results from all glystats DEA functions including gly_anova(), gly_ancova(), gly_kruskal(), gly_ttest(), gly_wilcox(), and gly_limma().

Usage

filter_sig_vars(
  exp,
  res,
  p_adj_cutoff = 0.05,
  p_val_cutoff = NULL,
  fc_cutoff = NULL,
  ...
)

## S3 method for class 'glystats_anova_res'
filter_sig_vars(
  exp,
  res,
  p_adj_cutoff = 0.05,
  p_val_cutoff = NULL,
  fc_cutoff = NULL,
  on = "main_test",
  comparison = NULL,
  ...
)

## S3 method for class 'glystats_ancova_res'
filter_sig_vars(
  exp,
  res,
  p_adj_cutoff = 0.05,
  p_val_cutoff = NULL,
  fc_cutoff = NULL,
  on = "main_test",
  comparison = NULL,
  ...
)

## S3 method for class 'glystats_kruskal_res'
filter_sig_vars(
  exp,
  res,
  p_adj_cutoff = 0.05,
  p_val_cutoff = NULL,
  fc_cutoff = NULL,
  on = "main_test",
  comparison = NULL,
  ...
)

## S3 method for class 'glystats_ttest_res'
filter_sig_vars(
  exp,
  res,
  p_adj_cutoff = 0.05,
  p_val_cutoff = NULL,
  fc_cutoff = NULL,
  ...
)

## S3 method for class 'glystats_wilcox_res'
filter_sig_vars(
  exp,
  res,
  p_adj_cutoff = 0.05,
  p_val_cutoff = NULL,
  fc_cutoff = NULL,
  ...
)

## S3 method for class 'glystats_limma_res'
filter_sig_vars(
  exp,
  res,
  p_adj_cutoff = 0.05,
  p_val_cutoff = NULL,
  fc_cutoff = NULL,
  comparison = NULL,
  ...
)

Arguments

Value

An new glyexp::experiment() object.

Examples

library(glyexp)
library(glyclean)

exp <- auto_clean(real_experiment) |>
  glyexp::slice_head_var(n = 10)
res <- gly_anova(exp)
sig_exp <- filter_sig_vars(exp, res)

Description

Syntax sugar for ⁠$tidy_result⁠ and ⁠$raw_result⁠ elements of a glystats result object. It's useful to be used in pipes.

Usage

get_tidy_result(res, which = NULL)

get_raw_result(res, which = NULL)

Arguments

Value

A tibble.

Examples

library(glyexp)
library(glyclean)
library(dplyr)

exp <- auto_clean(real_experiment) |>
  glyexp::slice_head_var(n = 10)

# Using a pipe
sig_res <- exp |>
  gly_anova() |>
  get_tidy_result("main_test") |>
  filter(p_adj < 0.05)

# Equivalent to
anova_res <- gly_anova(exp)
sig_res <- anova_res$tidy_result$main_test |>
  filter(p_adj < 0.05)

Description

Perform ANCOVA for glycomics or glycoproteomics data with sample-level covariates. The function supports parametric comparison of multiple groups while adjusting for covariates. For significant results, emmeans post-hoc comparisons (Tukey adjustment) are automatically performed. P-values are adjusted for multiple testing using the method specified by p_adj_method.

Usage

gly_ancova(
  exp,
  group_col = "group",
  covariate_cols = NULL,
  p_adj_method = "BH",
  add_info = TRUE,
  ...
)

gly_ancova_(expr_mat, groups, covariates, p_adj_method = "BH", ...)

Arguments

Details

The function performs log2 transformation on the expression data (log2(x + 1e-6)) before statistical testing. At least 2 groups and at least 1 covariate are required.

gly_ancova() is the top-level API that works with glyexp::experiment() objects and supports the add_info parameter for joining experiment metadata.

gly_ancova_() is the underlying API that works with matrices, factor vectors, and covariate data directly, providing more flexibility for users who don't use the glyexp package.

For any variable failed to fit a stats::aov() model, NAs will be assigned to the results in both main test and post-hoc test.

Post-hoc Test: emmeans pairwise comparisons with Tukey adjustment (emmeans::emmeans()) are performed for variables with significant main effects (p_adj < 0.05).

Value

A list containing two elements:

• tidy_result: A list containing:

  • main_test: A tibble with ANCOVA results containing the following columns:

    • variable: Variable name

    • term: ANCOVA term (usually "group")

    • df: Degrees of freedom

    • sumsq: Sum of squares

    • meansq: Mean squares

    • statistic: F-statistic

    • p_val: Raw p-value from ANCOVA

    • p_adj: Adjusted p-value (if p_adj_method is not NULL)

    • post_hoc: Significant group pairs from post-hoc test, in the format of "ref_vs_test".

  • post_hoc_test: A tibble with pairwise comparison results containing the following columns:

    • variable: Variable name

    • ref_group: Reference group

    • test_group: Test/treatment/case group

    • p_val: Adjusted p-value from emmeans pairwise test

    • p_adj: Adjusted p-value from emmeans pairwise test

• raw_result: A list containing:

  • main_test: A list of raw aov model objects.

  • post_hoc_test: A list of raw emmeans results.

Required packages

This function requires the emmeans package for post-hoc comparisons.

See Also

stats::aov(), emmeans::emmeans(), emmeans::contrast()

Description

Perform one-way ANOVA for glycomics or glycoproteomics data. The function supports parametric comparison of multiple groups. For significant results, Tukey's HSD post-hoc test is automatically performed. P-values are adjusted for multiple testing using the method specified by p_adj_method.

Usage

gly_anova(exp, group_col = "group", p_adj_method = "BH", add_info = TRUE, ...)

gly_anova_(expr_mat, groups, p_adj_method = "BH", ...)

Arguments

Details

The function performs log2 transformation on the expression data (log2(x + 1e-6)) before statistical testing. At least 2 groups are required in the grouping variable.

gly_anova() is the top-level API that works with glyexp::experiment() objects and supports the add_info parameter for joining experiment metadata.

gly_anova_() is the underlying API that works with matrices and factor vectors directly, providing more flexibility for users who don't use the glyexp package.

For any variable failed to fit a stats::aov() model, NAs will be assigned to the results in both main test and post-hoc test.

Post-hoc Test: Tukey's HSD test for pairwise comparisons (stats::TukeyHSD()) is performed for variables with significant main effects (p_adj < 0.05).

Value

A list containing three elements:

• tidy_result: A list containing:

  • main_test: A tibble with ANOVA results containing the following columns:

    • variable: Variable name

    • term: ANOVA term (usually "groups")

    • df: Degrees of freedom

    • sumsq: Sum of squares

    • meansq: Mean squares

    • statistic: F-statistic

    • p_val: Raw p-value from ANOVA

    • effect_size: Eta-squared

    • p_adj: Adjusted p-value (if p_adj_method is not NULL)

    • post_hoc: Significant group pairs from post-hoc test, in the format of "ref_vs_test".

  • post_hoc_test: A tibble with pairwise comparison results containing the following columns:

    • variable: Variable name

    • ref_group: Reference group

    • test_group: Test/treatment/case group

    • p_val: Raw p-value from Tukey's HSD test

    • p_adj: Adjusted p-value from Tukey's HSD test

• raw_result: A list containing:

  • main_test: A list of raw aov model objects.

  • post_hoc_test: A list of raw TukeyHSD objects. Post-hoc comparison labels follow the package direction, i.e. test_group - ref_group.

• meta_data (only for gly_anova()): A list containing metadata from the input experiment.

See Also

stats::aov(), stats::TukeyHSD()

Description

Perform pairwise correlation analysis on variables or samples in the expression data. The function calculates correlation coefficients and p-values for all pairs, with optional multiple testing correction.

Usage

gly_cor(exp, on = "variable", method = "pearson", p_adj_method = "BH", ...)

gly_cor_(
  expr_mat,
  on = "variable",
  method = "pearson",
  p_adj_method = "BH",
  ...
)

Arguments

Details

The function performs log2 transformation on the expression data (log2(x + 1e-6)) before correlation analysis. When on = "variable" (default), correlations are calculated between variables across samples. When on = "sample", correlations are calculated between samples across variables.

gly_cor() is the top-level API that works with glyexp::experiment() objects。

gly_cor_() is the underlying API that works with matrices directly, providing more flexibility for users who don't use the glyexp package.

Correlation Calculation: Correlation coefficients and p-values are calculated using Hmisc::rcorr() which is more efficient than pairwise stats::cor.test() calls.

Multiple Testing Correction: P-values are adjusted for multiple testing using the method specified by p_adj_method.

Value

A list with three elements:

• tidy_result: A tibble with correlation results containing the following columns:

  • variable1 or sample1: First element of the pair (depending on on parameter)

  • variable2 or sample2: Second element of the pair (depending on on parameter)

  • cor: Correlation coefficient

  • p_val: Raw p-value from correlation test

  • p_adj: Adjusted p-value (if p_adj_method is not NULL)

• raw_result: The raw rcorr object from Hmisc::rcorr()

• meta_data (only for gly_cor()): A list containing metadata from the input experiment The list has classes glystats_cor_res and glystats_res.

Required packages

This function requires the Hmisc package for efficient correlation calculation.

See Also

Hmisc::rcorr(), stats::cor()

Description

Fit a Cox proportional hazards model for each variable in the expression data, and extract p-values and hazard ratios from it.

Usage

gly_cox(
  exp,
  time_col = "time",
  event_col = "event",
  p_adj_method = "BH",
  add_info = TRUE,
  ...
)

gly_cox_(expr_mat, time, event, p_adj_method = "BH", ...)

Arguments

Details

The function fits an Cox proportional hazards model for each variable by:

coxph(Surv(time, event) ~ expression_value)

P-values are adjusted by Benjamini-Hochberg method by default.

Value

A list with three elements:

• tidy_result: A tibble with Cox model results containing the following columns:

  • variable: Variable name

  • coefficient: Regression coefficient (log hazard ratio)

  • std.error: Standard error of the coefficient

  • statistic: Wald test statistic

  • p_val: Raw p-value from Wald test

  • hr: Hazard ratio (exp(coefficient))

  • p_adj: Adjusted p-value (if p_adj_method is not NULL)

• raw_result: A list of raw coxph model objects.

• meta_data (only for gly_cox()): A list containing metadata from the input experiment

See Also

survival::coxph(), survival::Surv()

Description

Calculate fold change for a given expression matrix and group information. It could only be used for 2-group analysis. When you run this function, you will see message about "Ref Group" and "Test Group". "Ref Group" is the reference group, and "Test Group" is the test/treatment/case group.

Usage

gly_fold_change(exp, group_col = "group", add_info = TRUE)

gly_fold_change_(expr_mat, groups)

Arguments

Details

gly_fold_change() is the top-level API that works with glyexp::experiment() objects and supports the add_info parameter for joining experiment metadata.

gly_fold_change_() is the underlying API that works with matrices and factor vectors directly, providing more flexibility for users who don't use the glyexp package.

Value

A list with three elements:

• tidy_result: A tibble with fold change results containing the following columns:

  • variable: Variable name

  • log2fc: Log2 fold change (log2(group2_mean / group1_mean)) If more than two groups, two additional columns ref_group and test_group will be added.

• raw_result: The raw result (same as tidy_result for this function)

• meta_data (only for gly_fold_change()): A list containing metadata from the input experiment

Description

Perform hierarchical clustering on the expression data. The function uses stats::hclust() to perform clustering and provides tidy results including cluster assignments, dendrogram data for plotting, and merge heights.

Usage

gly_hclust(
  exp,
  on = "variable",
  k_values = c(2, 3, 4, 5),
  scale = TRUE,
  add_info = TRUE,
  ...
)

gly_hclust_(
  expr_mat,
  on = "variable",
  k_values = c(2, 3, 4, 5),
  scale = TRUE,
  ...
)

Arguments

Details

The function performs log2 transformation on the expression data (log2(x + 1e-6)) before clustering. When on = "variable" (default), variables are clustered based on their expression patterns across samples. When on = "sample", samples are clustered based on their expression profiles across variables.

gly_hclust() is the top-level API that works with glyexp::experiment() objects and supports the add_info parameter for joining experiment metadata.

gly_hclust_() is the underlying API that works with matrices directly, providing more flexibility for users who don't use the glyexp package.

Distance Calculation: Distance is calculated using stats::dist() with the specified method.

Clustering Method: Hierarchical clustering is performed using stats::hclust() with the specified method.

Cluster Assignment: The dendrogram is cut at different heights to produce cluster assignments for the specified k values using stats::cutree().

Value

A list containing:

• tidy_result: A list of tibbles with clustering results:

  • clusters: Cluster assignments containing the following columns:

    • variable or sample: Variable or sample name (depending on on parameter)

    • cluster_k2, cluster_k3, etc.: Cluster assignments for different k values

  • dendrogram: Dendrogram segment data for plotting (if ggdendro is available) containing:

    • x, y, xend, yend: Segment coordinates for plotting

  • heights: Merge heights and steps containing the following columns:

    • merge_step: Step number in the clustering process

    • height: Height at which clusters are merged

    • n_clusters: Number of clusters remaining after this merge

  • labels: Labels and their positions (if ggdendro is available) containing:

    • x, y: Position coordinates

    • label: Label text

• raw_result: The raw hclust object from stats::hclust()

• meta_data (only for gly_hclust()): A list containing metadata from the input experiment

Required packages

This function only uses base R packages and does not require additional dependencies. For enhanced dendrogram plotting capabilities, the ggdendro package is recommended but not required.

See Also

stats::hclust(), stats::dist(), stats::cutree()

Description

Perform k-means clustering on the expression data. The function uses stats::kmeans() to perform clustering and provides tidy results with cluster assignments.

Usage

gly_kmeans(
  exp,
  on = "variable",
  centers = 3,
  scale = TRUE,
  add_info = TRUE,
  ...
)

gly_kmeans_(expr_mat, on = "variable", centers = 3, scale = TRUE, ...)

Arguments

Details

The function performs log2 transformation on the expression data (log2(x + 1e-6)) before clustering. When on = "variable" (default), variables are clustered based on their expression patterns across samples. When on = "sample", samples are clustered based on their expression profiles across variables.

gly_kmeans() is the top-level API that works with glyexp::experiment() objects and supports the add_info parameter for joining experiment metadata.

gly_kmeans_() is the underlying API that works with matrices directly, providing more flexibility for users who don't use the glyexp package.

Data Preparation: Data is log2-transformed and optionally scaled before clustering.

Clustering Method: K-means clustering is performed using stats::kmeans() with the specified parameters.

Value

A list with three elements:

• tidy_result: A tibble with cluster assignments containing the following columns:

  • variable or sample: Variable or sample name (depending on on parameter)

  • cluster: Cluster assignment

• raw_result: The raw kmeans object from stats::kmeans().

• meta_data (only for gly_kmeans()): A list containing metadata from the input experiment

Required packages

This function only uses base R packages and does not require additional dependencies.

See Also

stats::kmeans()

Description

Perform Kruskal-Wallis test for glycomics or glycoproteomics data. The function supports non-parametric comparison of multiple groups. For significant results, Dunn's post-hoc test is automatically performed. P-values are adjusted for multiple testing using the method specified by p_adj_method.

Usage

gly_kruskal(
  exp,
  group_col = "group",
  p_adj_method = "BH",
  add_info = TRUE,
  ...
)

gly_kruskal_(expr_mat, groups, p_adj_method = "BH", ...)

Arguments

Details

The function performs log2 transformation on the expression data (log2(x + 1e-6)) before statistical testing. At least 2 groups are required in the grouping variable.

gly_kruskal() is the top-level API that works with glyexp::experiment() objects and supports the add_info parameter for joining experiment metadata.

gly_kruskal_() is the underlying API that works with matrices and factor vectors directly, providing more flexibility for users who don't use the glyexp package.

For any variable failed to fit a stats::kruskal.test() model, NAs will be assigned to the results in both main test and post-hoc test.

Post-hoc Test: Dunn's test with Holm correction for multiple comparisons (FSA::dunnTest()) is performed for variables with significant main effects (p_adj < 0.05).

Value

A list containing three elements:

• tidy_result: A list containing:

  • main_test: A tibble with Kruskal-Wallis test results containing the following columns:

    • variable: Variable name

    • statistic: Kruskal-Wallis test statistic

    • p_val: Raw p-value from Kruskal-Wallis test

    • parameter: Degrees of freedom

    • method: Statistical method used

    • effect_size: Epsilon-squared

    • p_adj: Adjusted p-value (if p_adj_method is not NULL)

    • post_hoc: Significant group pairs from post-hoc test, in the format of "ref_vs_test".

  • post_hoc_test: A tibble with pairwise comparison results containing the following columns:

    • variable: Variable name

    • ref_group: Reference group

    • test_group: Test/treatment/case group

    • p_val: Raw p-value from Dunn's test

    • p_adj: Adjusted p-value from Dunn's test

• raw_result: A list containing:

  • main_test: A list of raw kruskal.test objects.

  • post_hoc_test: A list of raw dunnTest objects. Post-hoc comparison labels and direction-sensitive statistics follow the package direction, i.e. test_group - ref_group.

• meta_data: A list containing metadata from the input experiment.

Required packages

This function requires the FSA package for Dunn's post-hoc test.

See Also

stats::kruskal.test(), FSA::dunnTest()

Description

Perform differential expression analysis using linear models with empirical Bayes moderation from the limma package. Supports both two-group and multi-group comparisons.

Usage

gly_limma(
  exp,
  group_col = "group",
  covariate_cols = NULL,
  subject_col = NULL,
  p_adj_method = "BH",
  ref_group = NULL,
  contrasts = NULL,
  add_info = TRUE,
  ...
)

gly_limma_(
  expr_mat,
  groups,
  p_adj_method = "BH",
  ref_group = NULL,
  contrasts = NULL,
  covariates = NULL,
  subjects = NULL,
  ...
)

Arguments

Details

The function performs log2 transformation on the expression data (log2(x + 1e-6)) before statistical testing. The analysis uses linear models with empirical Bayes moderation to improve statistical power, especially for small sample sizes.

gly_limma() is the top-level API that works with glyexp::experiment() objects and supports the add_info parameter for joining experiment metadata.

gly_limma_() is the underlying API that works with matrices and factor vectors directly, providing more flexibility for users who don't use the glyexp package.

For two or more groups, all pairwise comparisons are automatically generated (one contrast for two groups) and performed using contrast matrices unless custom contrasts are specified.

If covariates are provided, they are added as additional terms in the design matrix and are not part of the group contrasts.

If subjects are provided, they are included as a blocking factor in the design matrix using the formula ~ 0 + group + subject (plus any covariates).

When specifying custom contrasts, use either "group1-group2" or "group1_vs_group2" format. If group names contain hyphens and you use the first format, an error will be raised suggesting to use the second format.

Value

A list with three elements:

• tidy_result: A tibble with limma results containing the following columns:

  • variable: Variable name

  • log2fc: Log2 fold change

  • AveExpr: Average expression level

  • t: t-statistic

  • p_val: Raw p-value

  • p_adj: Adjusted p-value (if p_adj_method is not NULL)

  • b: B-statistic (log-odds of differential expression)

  • ref_group: Reference group

  • test_group: Test group

• raw_result: The raw limma fit object(s).

• meta_data (only for gly_limma()): A list containing metadata from the input experiment.

Required packages

This function requires the following packages to be installed:

• limma for linear model fitting and empirical Bayes moderation

See Also

limma::lmFit(), limma::eBayes(), limma::makeContrasts()

Description

Perform orthogonal partial least squares discriminant analysis on the expression data. The function uses ropls::opls() to perform OPLS-DA and returns tidy results.

Usage

gly_oplsda(
  exp,
  group_col = "group",
  pred_i = 1,
  ortho_i = NA,
  scale = TRUE,
  add_info = TRUE,
  ...
)

gly_oplsda_(expr_mat, groups, pred_i = 1, ortho_i = NA, scale = TRUE, ...)

Arguments

Details

gly_oplsda() is the top-level API that works with glyexp::experiment() objects and supports the add_info parameter for joining experiment metadata.

gly_oplsda_() is the underlying API that works with matrices and factor vectors directly, providing more flexibility for users who don't use the glyexp package.

Value

A list containing:

• tidy_result: A list of tibbles with OPLS-DA results:

  • samples: OPLS-DA scores for each sample containing the following columns:

    • sample: Sample name

    • group: Group assignment

    • p1, p2, etc.: Predictive component scores

    • o1, o2, etc.: Orthogonal component scores

  • variables: OPLS-DA loadings for each variable containing the following columns:

    • variable: Variable name

    • p1, p2, etc.: Predictive component loadings

    • o1, o2, etc.: Orthogonal component loadings

    • pcorr1, pcorr2, etc.: Correlation between each variable and the corresponding predictive component

  • variance: OPLS-DA explained variance containing the following columns:

    • component: Component name (p1, o1, etc.)

    • prop_var_explained: Proportion of variance explained by each component

    • cumulative_prop_var: Cumulative proportion of variance explained

  • vip: Variable Importance in Projection scores containing the following columns:

    • variable: Variable name

    • vip: VIP score

  • perm_test: Permutation test results containing the following columns:

    • model: Model type ("Original" for the original model, "Permutation" for permuted models)

    • perm_id: Permutation ID (0 for original model, 1+ for permutations)

    • Additional columns from the permutation test matrix (e.g., R2X, R2Y, Q2, etc.)

• raw_result: The raw ropls opls object from ropls::opls()

• meta_data (only for gly_oplsda()): A list containing metadata from the input experiment

Required packages

This function requires the following packages to be installed:

• ropls for OPLS-DA analysis

See Also

ropls::opls()

Description

Perform principal component analysis on the expression data. The function uses prcomp() to perform PCA and broom::tidy() to tidy the results. If scale = TRUE, constant variables (zero variance) will be removed before PCA.

Usage

gly_pca(exp, center = TRUE, scale = TRUE, add_info = TRUE, ...)

gly_pca_(expr_mat, center = TRUE, scale = TRUE, ...)

Arguments

Details

The function performs log transformation on the expression data (log(x + 1)) before PCA analysis.

gly_pca() is the top-level API that works with glyexp::experiment() objects and supports the add_info parameter for joining experiment metadata.

gly_pca_() is the underlying API that works with matrices directly, providing more flexibility for users who don't use the glyexp package.

Value

A list containing:

• tidy_result: A list of tibbles with PCA results:

  • samples: PCA scores for each sample containing the following columns:

    • sample: Sample name

    • PC: Principal component name (PC1, PC2, etc.)

    • value: Score value for the principal component

  • variables: PCA loadings for each variable containing the following columns:

    • variable: Variable name

    • PC: Principal component name (PC1, PC2, etc.)

    • value: Loading value for the principal component

  • eigenvalues: PCA eigenvalues containing the following columns:

    • PC: Principal component name (PC1, PC2, etc.)

    • std.dev: Standard deviation

    • percent: Percentage of variance explained

    • cumulative: Cumulative percentage of variance explained

• raw_result: The raw prcomp object from stats::prcomp()

• meta_data (only for gly_pca()): A list containing metadata from the input experiment

Required packages

This function only uses base R packages and does not require additional dependencies.

See Also

stats::prcomp()

Description

Perform partial least squares discriminant analysis on the expression data. The function uses ropls::opls() to perform PLS-DA and returns tidy results.

Usage

gly_plsda(
  exp,
  group_col = "group",
  ncomp = 2,
  scale = TRUE,
  add_info = TRUE,
  ...
)

gly_plsda_(expr_mat, groups, ncomp = 2, scale = TRUE, ...)

Arguments

Details

gly_plsda() is the top-level API that works with glyexp::experiment() objects and supports the add_info parameter for joining experiment metadata.

gly_plsda_() is the underlying API that works with matrices and factor vectors directly, providing more flexibility for users who don't use the glyexp package.

Value

A list containing:

• tidy_result: A list of tibbles with PLS-DA results:

  • samples: PLS-DA scores for each sample containing the following columns:

    • sample: Sample name

    • group: Group assignment

    • p1, p2, etc.: PLS-DA component scores

  • variables: PLS-DA loadings for each variable containing the following columns:

    • variable: Variable name

    • p1, p2, etc.: PLS-DA component loadings

    • pcorr1, pcorr2, etc.: Correlation between each variable and component

  • variance: PLS-DA explained variance containing the following columns:

    • component: Component name (p1, p2, etc.)

    • prop_var_explained: Proportion of variance explained by each component

    • cumulative_prop_var: Cumulative proportion of variance explained

  • vip: Variable Importance in Projection scores containing the following columns:

    • variable: Variable name

    • vip: VIP score

  • perm_test: Permutation test results containing the following columns:

    • model: Model type ("Original" for the original model, "Permutation" for permuted models)

    • perm_id: Permutation ID (0 for original model, 1+ for permutations)

    • Additional columns from the permutation test matrix (e.g., R2X, R2Y, Q2, etc.)

• raw_result: The raw ropls opls object from ropls::opls()

• meta_data (only for gly_plsda()): A list containing metadata from the input experiment

Required packages

This function requires the following packages to be installed:

• ropls for PLS-DA analysis

See Also

ropls::opls()

Description

Perform Receiver Operating Characteristic (ROC) analysis for binary classification of glycomics or glycoproteomics data. The function calculates ROC curves and Area Under the Curve (AUC) values for each variable to assess their discriminatory power between two groups.

Usage

gly_roc(exp, group_col = "group", pos_class = NULL, add_info = TRUE)

gly_roc_(expr_mat, groups, pos_class = NULL)

Arguments

Details

For each variable, a ROC curve is computed using the expression values as predictor and the binary group labels as response.

The function requires exactly 2 groups in the specified grouping variable. If more than 2 groups are present, an error will be thrown.

gly_roc() is the top-level API that works with glyexp::experiment() objects and supports the add_info parameter for joining experiment metadata.

gly_roc_() is the underlying API that works with matrices and factor vectors directly, providing more flexibility for users who don't use the glyexp package.

Underlying Function:

• ROC analysis is performed using pROC::roc()

• Coordinates are extracted using pROC::coords()

Value

A list with three elements:

• tidy_result: A list containing two tibbles:

  • auc: A tibble containing AUC values for each variable with the following columns:

    • variable: Variable name

    • auc: Area Under the Curve value

    • auc_ci_low: Lower bound of 95% confidence interval for AUC

    • auc_ci_high: Upper bound of 95% confidence interval for AUC

  • coords: A tibble containing ROC curve coordinates with the following columns:

    • variable: Variable name

    • threshold: Threshold value for classification

    • specificity: Specificity (True Negative Rate)

    • sensitivity: Sensitivity (True Positive Rate)

• raw_result: A list of pROC objects

• meta_data (only for gly_roc()): A list containing metadata from the input experiment The list has classes glystats_roc_res and glystats_res.

Required packages

This function requires the pROC package to be installed for ROC curve computation.

See Also

pROC::roc(), pROC::coords()

Description

Perform t-SNE dimensionality reduction on the expression data. The function uses Rtsne::Rtsne() to perform t-SNE analysis.

Usage

gly_tsne(exp, dims = 2, perplexity = 30, add_info = TRUE, ...)

gly_tsne_(expr_mat, dims = 2, perplexity = 30, ...)

Arguments

Details

gly_tsne() is the top-level API that works with glyexp::experiment() objects and supports the add_info parameter for joining experiment metadata.

gly_tsne_() is the underlying API that works with matrices directly, providing more flexibility for users who don't use the glyexp package.

Value

A list with three elements:

• tidy_result: A tibble with t-SNE coordinates containing the following columns:

  • sample: Sample name

  • tsne1: First t-SNE dimension

  • tsne2: Second t-SNE dimension

• raw_result: The raw Rtsne object

• meta_data (only for gly_tsne()): A list containing metadata from the input experiment The list has classes glystats_tsne_res and glystats_res.

Required packages

This function requires the Rtsne package to be installed for t-SNE analysis.

See Also

Rtsne::Rtsne()

Description

Perform two-sample t-test for glycomics or glycoproteomics data. The function supports Student's t-test for comparing two groups. P-values are adjusted for multiple testing using the method specified by p_adj_method.

Usage

gly_ttest(
  exp,
  group_col = "group",
  p_adj_method = "BH",
  ref_group = NULL,
  add_info = TRUE,
  ...
)

gly_ttest_(expr_mat, groups, p_adj_method = "BH", ref_group = NULL, ...)

Arguments

Details

The function performs log2 transformation on the expression data (log2(x + 1e-6)) before statistical testing. Exactly 2 groups are required in the grouping variable.

gly_ttest() is the top-level API that works with glyexp::experiment() objects and supports the add_info parameter for joining experiment metadata.

gly_ttest_() is the underlying API that works with matrices and factor vectors directly, providing more flexibility for users who don't use the glyexp package.

Value

A list with three elements:

• tidy_result: A tibble with t-test results containing the following columns:

  • variable: Variable name

  • estimate: Difference in group means (group2 - group1)

  • estimate1: Mean of group 1

  • estimate2: Mean of group 2

  • statistic: t-statistic

  • p_val: Raw p-value from t-test

  • parameter: Degrees of freedom

  • conf_low: Lower bound of 95% confidence interval

  • conf_high: Upper bound of 95% confidence interval

  • effect_size: Cohen's d

  • method: Statistical method used

  • alternative: Alternative hypothesis

  • p_adj: Adjusted p-value (if p_adj_method is not NULL)

  • log2fc: Log2 fold change (log2(group2_mean / group1_mean))

• raw_result: A list of t.test model objects

• meta_data (only for gly_ttest()): A list containing metadata from the input experiment The list has classes glystats_ttest_res and glystats_res.

See Also

stats::t.test()

Description

Perform UMAP dimensionality reduction on the expression data. The function uses uwot::umap() to perform UMAP analysis.

Usage

gly_umap(exp, n_neighbors = 15, n_components = 2, add_info = TRUE, ...)

gly_umap_(expr_mat, n_neighbors = 15, n_components = 2, ...)

Arguments

Details

gly_umap() is the top-level API that works with glyexp::experiment() objects and supports the add_info parameter for joining experiment metadata.

gly_umap_() is the underlying API that works with matrices directly, providing more flexibility for users who don't use the glyexp package.

Value

A list with three elements:

• tidy_result: A tibble with UMAP coordinates containing the following columns:

  • sample: Sample name

  • umap1: First UMAP dimension

  • umap2: Second UMAP dimension

  • umap3, umap4, etc.: Additional UMAP dimensions (if n_components > 2)

• raw_result: The raw UMAP result matrix

• meta_data (only for gly_umap()): A list containing metadata from the input experiment The list has classes glystats_umap_res and glystats_res.

Required packages

This function requires the uwot package to be installed for UMAP analysis.

See Also

uwot::umap()

Description

Perform Wilcoxon rank-sum test (Mann-Whitney U test) for glycomics or glycoproteomics data. The function supports non-parametric comparison of two groups. P-values are adjusted for multiple testing using the method specified by p_adj_method.

Usage

gly_wilcox(
  exp,
  group_col = "group",
  p_adj_method = "BH",
  ref_group = NULL,
  add_info = TRUE,
  ...
)

gly_wilcox_(expr_mat, groups, p_adj_method = "BH", ref_group = NULL, ...)

Arguments

Details

The function performs log2 transformation on the expression data (log2(x + 1e-6)) before statistical testing. Exactly 2 groups are required in the grouping variable.

gly_wilcox() is the top-level API that works with glyexp::experiment() objects and supports the add_info parameter for joining experiment metadata.

gly_wilcox_() is the underlying API that works with matrices and factor vectors directly, providing more flexibility for users who don't use the glyexp package.

Value

A list with three elements:

• tidy_result: A tibble with Wilcoxon test results containing the following columns:

  • variable: Variable name

  • statistic: Wilcoxon test statistic

  • p_val: Raw p-value from Wilcoxon test

  • effect_size: Rank-biserial correlation

  • method: Statistical method used

  • alternative: Alternative hypothesis

  • p_adj: Adjusted p-value (if p_adj_method is not NULL)

  • log2fc: Log2 fold change (log2(group2_mean / group1_mean)) Additional columns from experiment metadata may be included if add_info = TRUE.

• raw_result: A list of wilcox.test model objects

• meta_data (only for gly_wilcox()): A list containing metadata from the input experiment The list has classes glystats_wilcox_res and glystats_res.

See Also

stats::wilcox.test()