Package 'glysmith'

Description

A blueprint is a list of steps that are executed in order. Type step_ and TAB in RStudio to see all available steps.

Usage

blueprint(...)

Arguments

Value

A blueprint object.

Examples

blueprint(
  step_preprocess(),
  step_pca(),
  step_dea_limma(),  # this comma is ok
)

Description

This blueprint contains the following steps:

• step_ident_overview(): Summarize the experiment using glyexp::summarize_experiment().

• step_preprocess(): Preprocess the data using glyclean::auto_clean().

• step_plot_qc(when = "post"): Plot QC plots using glyclean::plot_qc().

• step_pca(): Principal component analysis using glystats::gly_pca(), and plot the PCA using glyvis::plot_pca().

• step_dea_limma(): Differential analysis using glystats::gly_limma().

• step_volcano(): Plot a volcano plot using glyvis::plot_volcano().

• step_heatmap(on = "sig_exp"): Plot a heatmap using glyvis::plot_heatmap().

• step_sig_enrich_go(): Perform GO enrichment analysis using glyfun::enrich_ora_go().

• step_sig_enrich_kegg(): Perform KEGG enrichment analysis using glyfun::enrich_ora_kegg().

• step_sig_enrich_reactome(): Perform Reactome enrichment analysis using glyfun::enrich_ora_reactome().

• step_derive_traits(): Derive traits using glydet::derive_traits().

• step_dea_limma(on = "trait_exp"): Differential trait analysis using glystats::gly_limma().

• step_heatmap(on = "sig_trait_exp"): Plot a heatmap using glyvis::plot_heatmap().

Usage

blueprint_default(preprocess = TRUE, enrich = TRUE, traits = TRUE)

Arguments

Value

A glysmith_blueprint object.

Examples

blueprint_default()

Description

Use br() to group steps that should run as an isolated branch with namespaced outputs prefixed by ⁠<name>__⁠.

Usage

br(name, ...)

Arguments

Value

A branch object used inside blueprint().

Examples

blueprint(
  step_preprocess(),
  br("limma",
    step_dea_limma(),
    step_volcano()
  ),
  br("ttest",
    step_dea_ttest(),
    step_volcano()
  )
)

Description

Helper functions to get processed experiment, plots, tables or data from a glysmith result object.

Usage

cast_exp(x)

cast_plot(x, name = NULL)

cast_table(x, name = NULL)

cast_data(x, name = NULL)

Arguments

Value

• cast_exp(): a processed glyexp::GlycomicSE() or glyexp::GlycoproteomicSE() object for new-container inputs. Legacy inputs retain their container class.

• cast_plot(): a ggplot2::ggplot().

• cast_table(): a tibble::tibble().

• cast_data(): can be any R object.

Examples

## Not run: 
library(glyexp)
exp <- real_experiment2
result <- forge_analysis(exp)
cast_exp(result)
cast_table(result)
cast_table(result, "summary")

## End(Not run)

Description

Checks whether the packages required by steps in a blueprint are installed. This does not install or check every package listed in Suggests; it only checks the packages declared by the steps in blueprint.

Usage

check_glysmith_deps(
  blueprint = blueprint_default(),
  action = c("ask", "error", "note")
)

Arguments

Value

Returns TRUE invisibly if all packages are installed. If action = "ask", may return TRUE after installation or FALSE if user declines.

Examples

## Not run: 
# Check dependencies required by the default blueprint
check_glysmith_deps()

# Check dependencies required by a custom blueprint
bp <- blueprint(
  step_ident_overview(),
  step_pca()
)
check_glysmith_deps(bp)

## End(Not run)

Description

This function performs a comprehensive analysis for group comparison.

Usage

forge_analysis(exp, blueprint = blueprint_default(), group_col = "group")

Arguments

Value

A glysmith_result object, with the following components:

• exp: the glyco data container after preprocessing.

• plots: a named list of ggplot objects.

• tables: a named list of tibbles.

• meta: a named list of metadata, containing:

  • explanation: a named character vector or list of explanations for each plot and table, with keys like tables$summary and plots$pca.

  • steps: a character vector of the steps of the analysis.

  • log: the messages and outputs from each step.

• blueprint: the blueprint used for the analysis.

Examples

## Not run: 
exp <- glyexp::real_experiment2
result <- forge_analysis(exp)
print(result)

## End(Not run)

Description

Ask a Large Language Model (LLM) to create a blueprint for glycomics or glycoproteomics data analysis. DeepSeek is used by default for backward compatibility. Other ellmer providers can be selected with provider, model, and provider-specific API key configuration.

Usage

inquire_blueprint(
  description,
  exp = NULL,
  group_col = "group",
  model = getOption("glysmith.ai_model", NULL),
  max_retries = 3,
  provider = getOption("glysmith.ai_provider", "deepseek"),
  api_key = getOption("glysmith.ai_api_key", NULL),
  base_url = getOption("glysmith.ai_base_url", NULL)
)

Arguments

Details

LLMs can be unstable. If you get an error, try again with another description. Make sure to examine the returned blueprint carefully to ensure it's what you want. You can also create parallel analysis branches with br("name", step_..., step_...), which will namespace outputs with the branch prefix. If the LLM needs required information to proceed, it may ask clarifying questions interactively and then retry with your answers. After a blueprint is generated, the description is printed and, in interactive sessions, you can press ENTER to accept it or type new requirements to refine the blueprint. This review step can repeat until you accept the plan.

Here are some examples that works:

• "I want to know what pathways are enriched for my differentially expressed glycoforms."

• "I want a heatmap and a pca plot. I have already performed preprocessing myself."

• "I have a glycomics dataset. I want to calculate derived traits and perform DEA on them."

Description

Ask a Large Language Model (LLM) to modify an existing blueprint for glycomics or glycoproteomics data analysis. DeepSeek is used by default for backward compatibility. Other ellmer providers can be selected with provider, model, and provider-specific API key configuration.

Usage

modify_blueprint(
  bp,
  description,
  qa_history = NULL,
  exp = NULL,
  group_col = "group",
  model = getOption("glysmith.ai_model", NULL),
  max_retries = 3,
  provider = getOption("glysmith.ai_provider", "deepseek"),
  api_key = getOption("glysmith.ai_api_key", NULL),
  base_url = getOption("glysmith.ai_base_url", NULL)
)

Arguments

Details

LLMs can be unstable. If you get an error, try again with another description. Make sure to examine the returned blueprint carefully to ensure it's what you want. This function is a companion of inquire_blueprint(). If the LLM needs required information to proceed, it may ask clarifying questions interactively and then retry with your answers.

Description

Generate a self-contained HTML report for a glysmith_result object. The report is rendered via rmarkdown::render() using an internal R Markdown template. If use_ai is TRUE, the report text will be polished, organized into sections, paired with plot descriptions, and summarized using the configured ellmer provider. DeepSeek is used by default for backward compatibility.

Usage

polish_report(
  x,
  output_file,
  title = "GlySmith report",
  open = interactive(),
  use_ai = FALSE,
  ai_provider = getOption("glysmith.ai_provider", "deepseek"),
  ai_model = getOption("glysmith.ai_model", NULL),
  ai_api_key = getOption("glysmith.ai_api_key", NULL),
  ai_base_url = getOption("glysmith.ai_base_url", NULL)
)

Arguments

Value

The normalized path to the generated HTML file.

Examples

## Not run: 
library(glyexp)
exp <- real_experiment2
result <- forge_analysis(exp)
polish_report(result, tempfile(fileext = ".html"), open = FALSE)

## End(Not run)

Description

Save processed experiment, plots and tables of a glysmith result object to a directory. A README.md file will also be generated to describe the saved outputs.

Usage

quench_result(
  x,
  dir,
  plot_ext = "pdf",
  table_ext = "csv",
  plot_width = 5,
  plot_height = 5
)

Arguments

Examples

## Not run: 
library(glyexp)
exp <- real_experiment2
result <- forge_analysis(exp)
quench_result(result, tempdir())

## End(Not run)

Description

Adjust glycoform quantification values by correcting for protein abundance utilizing glyclean::adjust_protein(). Usually this step should be run after step_preprocess().

This step requires exp (experiment data).

Usage

step_adjust_protein(pro_expr_path = NULL, method = "ratio")

Arguments

Details

Data required:

• exp: The experiment to adjust

Data generated:

• unadj_exp: The original experiment (previous exp, saved for reference)

This step is special in that it silently overwrites the exp data with the adjusted experiment. This ensures that no matter if adjustment is performed or not, the "active" experiment is always under the key exp. The previous exp is saved as unadj_exp for reference.

Value

A glysmith_step object.

See Also

glyclean::adjust_protein()

Examples

fake_pro_expr_mat <- matrix(rnorm(100), nrow = 10, ncol = 10)
rownames(fake_pro_expr_mat) <- paste0("P", seq_len(10))
colnames(fake_pro_expr_mat) <- paste0("S", seq_len(10))
fake_pro_expr_path <- tempfile(fileext = ".rds")
saveRDS(fake_pro_expr_mat, fake_pro_expr_path)
step_adjust_protein(fake_pro_expr_path)

Description

Perform pairwise correlation analysis using glystats::gly_cor() and visualize the correlation matrix using glyvis::plot_corrplot(). This step calculates correlation coefficients and p-values for all pairs of variables or samples.

This step depends on the on parameter (default: exp).

• When on = "exp", requires exp (usually after step_preprocess()).

• When on = "sig_exp", requires sig_exp from one of step_dea_limma(), step_dea_ttest(), step_dea_wilcox(), step_dea_anova(), or step_dea_kruskal().

• When on = "trait_exp", requires trait_exp from step_derive_traits().

• When on = "sig_trait_exp", requires sig_trait_exp from DEA on traits.

• When on = "dynamic_motif_exp", requires dynamic_motif_exp from step_quantify_dynamic_motifs().

• When on = "sig_dynamic_motif_exp", requires sig_dynamic_motif_exp from DEA on motifs.

• When on = "branch_motif_exp", requires branch_motif_exp from step_quantify_branch_motifs().

• When on = "sig_branch_motif_exp", requires sig_branch_motif_exp from DEA on motifs.

Usage

step_correlation(
  on = "exp",
  on_cor = c("variable", "sample"),
  method = c("pearson", "spearman"),
  p_adj_method = "BH",
  plot_width = 7,
  plot_height = 7,
  ...
)

Arguments

Details

Data required:

• exp (if on = "exp"): The experiment to run correlation analysis on

• trait_exp (if on = "trait_exp"): The trait experiment to run correlation analysis on

• dynamic_motif_exp (if on = "dynamic_motif_exp"): The dynamic motif experiment to run correlation analysis on

• branch_motif_exp (if on = "branch_motif_exp"): The branch motif experiment to run correlation analysis on

Tables generated (with suffixes):

• correlation: A table containing pairwise correlation results with columns:

  • variable1, variable2 (or sample1, sample2 if on = "sample")

  • cor: Correlation coefficient

  • p_val: P-value from correlation test

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

Plots generated (with suffixes):

• correlation: A correlation matrix heatmap

Value

A glysmith_step object.

See Also

glystats::gly_cor(), glyvis::plot_corrplot()

Examples

step_correlation()
step_correlation(on = "sig_exp")
step_correlation(on_cor = "sample", method = "spearman")

Description

Perform survival analysis by fitting a Cox proportional hazards model using glystats::gly_cox() for each variable. This step identifies variables associated with survival outcomes.

This step depends on the on parameter (default: exp).

• When on = "exp", requires exp (usually after step_preprocess()).

• When on = "sig_exp", requires sig_exp from one of step_dea_limma(), step_dea_ttest(), step_dea_wilcox(), step_dea_anova(), or step_dea_kruskal().

• When on = "trait_exp", requires trait_exp from step_derive_traits().

• When on = "sig_trait_exp", requires sig_trait_exp from DEA on traits.

• When on = "dynamic_motif_exp", requires dynamic_motif_exp from step_quantify_dynamic_motifs().

• When on = "sig_dynamic_motif_exp", requires sig_dynamic_motif_exp from DEA on motifs.

• When on = "branch_motif_exp", requires branch_motif_exp from step_quantify_branch_motifs().

• When on = "sig_branch_motif_exp", requires sig_branch_motif_exp from DEA on motifs.

Usage

step_cox(
  on = "exp",
  time_col = "time",
  event_col = "event",
  p_adj_method = "BH",
  ...
)

Arguments

Details

Data required:

• exp (if on = "exp"): The experiment to run Cox regression on

• trait_exp (if on = "trait_exp"): The trait experiment to run Cox regression on

• dynamic_motif_exp (if on = "dynamic_motif_exp"): The dynamic motif experiment to run Cox regression on

• branch_motif_exp (if on = "branch_motif_exp"): The branch motif experiment to run Cox regression on

The experiment must contain survival data with time_col and event_col columns in the sample information.

Tables generated (with suffixes):

• cox: A table containing Cox regression results with 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)

Value

A glysmith_step object.

See Also

glystats::gly_cox(), survival::coxph()

Examples

step_cox()
step_cox(time_col = "survival_time", event_col = "death")
step_cox(on = "sig_exp", p_adj_method = "bonferroni")

Description

Run differential analysis using ANOVA via glystats::gly_anova(), then filter the experiment to keep only the differentially expressed variables using glystats::filter_sig_vars(). By default, this runs DEA on the main experiment (exp), but can be configured to run on derived traits (trait_exp) or other experiment objects.

This step depends on the on parameter (default: exp).

• When on = "exp", requires exp (usually after step_preprocess()).

• When on = "trait_exp", requires trait_exp from step_derive_traits().

• When on = "dynamic_motif_exp", requires dynamic_motif_exp from step_quantify_dynamic_motifs().

• When on = "branch_motif_exp", requires branch_motif_exp from step_quantify_branch_motifs().

Usage

step_dea_anova(
  on = "exp",
  p_adj_method = "BH",
  filter_p_adj_cutoff = 0.05,
  filter_p_val_cutoff = NULL,
  filter_fc_cutoff = NULL,
  filter_on = "main_test",
  filter_comparison = NULL,
  ...
)

Arguments

Details

Data required:

• Depends on on parameter (default: exp)

Data generated:

• dea_res: The DEA results (if on = "exp", default)

• dta_res: The DTA results (if on = "trait_exp")

• dynamic_dma_res: The DMA results (if on = "dynamic_motif_exp")

• branch_dma_res: The DMA results (if on = "branch_motif_exp")

• sig_exp: The filtered experiment (if on = "exp", default)

• sig_trait_exp: The filtered trait experiment (if on = "trait_exp")

• sig_dynamic_motif_exp: The filtered dynamic motif experiment (if on = "dynamic_motif_exp")

• sig_branch_motif_exp: The filtered branch motif experiment (if on = "branch_motif_exp")

Tables generated:

• dea_main_test, dea_post_hoc_test: Tables containing the results (if on = "exp", default)

• dta_main_test, dta_post_hoc_test: Tables containing the results (if on = "trait_exp")

• dynamic_dma_main_test, dynamic_dma_post_hoc_test: Tables containing the results (if on = "dynamic_motif_exp")

• branch_dma_main_test, branch_dma_post_hoc_test: Tables containing the results (if on = "branch_motif_exp")

Value

A glysmith_step object.

See Also

glystats::gly_anova()

Examples

step_dea_anova()
step_dea_anova(on = "trait_exp")  # Differential trait analysis

Description

Run differential analysis using Kruskal-Wallis analysis via glystats::gly_kruskal(), then filter the experiment to keep only the differentially expressed variables using glystats::filter_sig_vars(). By default, this runs DEA on the main experiment (exp), but can be configured to run on derived traits (trait_exp) or other experiment objects.

This step depends on the on parameter (default: exp).

• When on = "exp", requires exp (usually after step_preprocess()).

• When on = "trait_exp", requires trait_exp from step_derive_traits().

• When on = "dynamic_motif_exp", requires dynamic_motif_exp from step_quantify_dynamic_motifs().

• When on = "branch_motif_exp", requires branch_motif_exp from step_quantify_branch_motifs().

Usage

step_dea_kruskal(
  on = "exp",
  p_adj_method = "BH",
  filter_p_adj_cutoff = 0.05,
  filter_p_val_cutoff = NULL,
  filter_fc_cutoff = NULL,
  filter_on = "main_test",
  filter_comparison = NULL,
  ...
)

Arguments

Details

Data required:

• Depends on on parameter (default: exp)

Data generated:

• dea_res: The DEA results (if on = "exp", default)

• dta_res: The DTA results (if on = "trait_exp")

• dynamic_dma_res: The DMA results (if on = "dynamic_motif_exp")

• branch_dma_res: The DMA results (if on = "branch_motif_exp")

• sig_exp: The filtered experiment (if on = "exp", default)

• sig_trait_exp: The filtered trait experiment (if on = "trait_exp")

• sig_dynamic_motif_exp: The filtered dynamic motif experiment (if on = "dynamic_motif_exp")

• sig_branch_motif_exp: The filtered branch motif experiment (if on = "branch_motif_exp")

Tables generated:

• dea_main_test, dea_post_hoc_test: Tables containing the results (if on = "exp", default)

• dta_main_test, dta_post_hoc_test: Tables containing the results (if on = "trait_exp")

• dynamic_dma_main_test, dynamic_dma_post_hoc_test: Tables containing the results (if on = "dynamic_motif_exp")

• branch_dma_main_test, branch_dma_post_hoc_test: Tables containing the results (if on = "branch_motif_exp")

Value

A glysmith_step object.

See Also

glystats::gly_kruskal()

Examples

step_dea_kruskal()
step_dea_kruskal(on = "trait_exp")  # Differential trait analysis

Description

Run differential analysis using linear model-based analysis via glystats::gly_limma(), then filter the experiment to keep only the differentially expressed variables using glystats::filter_sig_vars(). By default, this runs DEA on the main experiment (exp), but can be configured to run on derived traits (trait_exp) or other experiment objects. This step is the recommended DEA method for all experiments, for both two-group and multi-group experiments.

This step depends on the on parameter (default: exp).

• When on = "exp", requires exp (usually after step_preprocess()).

• When on = "trait_exp", requires trait_exp from step_derive_traits().

• When on = "dynamic_motif_exp", requires dynamic_motif_exp from step_quantify_dynamic_motifs().

• When on = "branch_motif_exp", requires branch_motif_exp from step_quantify_branch_motifs().

Usage

step_dea_limma(
  on = "exp",
  p_adj_method = "BH",
  covariate_cols = NULL,
  subject_col = NULL,
  ref_group = NULL,
  contrasts = NULL,
  filter_p_adj_cutoff = 0.05,
  filter_p_val_cutoff = NULL,
  filter_fc_cutoff = NULL,
  ...
)

Arguments

Details

Data required:

• exp (if on = "exp"): The experiment to run DEA on

• trait_exp (if on = "trait_exp"): The trait experiment to run DEA on

• dynamic_motif_exp (if on = "dynamic_motif_exp"): The dynamic motif experiment to run DEA on

• branch_motif_exp (if on = "branch_motif_exp"): The branch motif experiment to run DEA on

Data generated:

• dea_res: The DEA (differential expression analysis) results (if on = "exp", default)

• dta_res: The DTA (differential trait analysis) results (if on = "trait_exp")

• dynamic_dma_res: The DMA results (if on = "dynamic_motif_exp")

• branch_dma_res: The DMA results (if on = "branch_motif_exp")

• sig_exp: The filtered experiment (if on = "exp", default)

• sig_trait_exp: The filtered trait experiment (if on = "trait_exp")

• sig_dynamic_motif_exp: The filtered dynamic motif experiment (if on = "dynamic_motif_exp")

• sig_branch_motif_exp: The filtered branch motif experiment (if on = "branch_motif_exp")

Tables generated:

• dea: A table containing the DEA (differential expression analysis) result (if on = "exp", default)

• dta: A table containing the DTA (differential trait analysis) result (if on = "trait_exp")

• dynamic_dma: A table containing the DMA result (if on = "dynamic_motif_exp")

• branch_dma: A table containing the DMA result (if on = "branch_motif_exp")

Value

A glysmith_step object.

See Also

glystats::gly_limma()

Examples

step_dea_limma()
step_dea_limma(on = "trait_exp")  # Differential trait analysis
step_dea_limma(p_adj_method = "BH")

Description

Run differential analysis using t-test via glystats::gly_ttest(), then filter the experiment to keep only the differentially expressed variables using glystats::filter_sig_vars(). By default, this runs DEA on the main experiment (exp), but can be configured to run on derived traits (trait_exp) or other experiment objects. Only use this method for experiments with 2 groups.

This step depends on the on parameter (default: exp).

• When on = "exp", requires exp (usually after step_preprocess()).

• When on = "trait_exp", requires trait_exp from step_derive_traits().

• When on = "dynamic_motif_exp", requires dynamic_motif_exp from step_quantify_dynamic_motifs().

• When on = "branch_motif_exp", requires branch_motif_exp from step_quantify_branch_motifs().

Usage

step_dea_ttest(
  on = "exp",
  p_adj_method = "BH",
  ref_group = NULL,
  filter_p_adj_cutoff = 0.05,
  filter_p_val_cutoff = NULL,
  filter_fc_cutoff = NULL,
  ...
)

Arguments

Details

Data required:

• Depends on on parameter (default: exp)

Data generated:

• dea_res: The DEA results (if on = "exp", default)

• dta_res: The DTA results (if on = "trait_exp")

• dynamic_dma_res: The DMA results (if on = "dynamic_motif_exp")

• branch_dma_res: The DMA results (if on = "branch_motif_exp")

• sig_exp: The filtered experiment (if on = "exp", default)

• sig_trait_exp: The filtered trait experiment (if on = "trait_exp")

• sig_dynamic_motif_exp: The filtered dynamic motif experiment (if on = "dynamic_motif_exp")

• sig_branch_motif_exp: The filtered branch motif experiment (if on = "branch_motif_exp")

Tables generated:

• dea: A table containing the DEA result (if on = "exp", default)

• dta: A table containing the DTA result (if on = "trait_exp")

• dynamic_dma: A table containing the DMA result (if on = "dynamic_motif_exp")

• branch_dma: A table containing the DMA result (if on = "branch_motif_exp")

Value

A glysmith_step object.

See Also

glystats::gly_ttest()

Examples

step_dea_ttest()
step_dea_ttest(on = "trait_exp")  # Differential trait analysis

Description

Run differential analysis using Wilcoxon analysis via glystats::gly_wilcox(), then filter the experiment to keep only the differentially expressed variables using glystats::filter_sig_vars(). By default, this runs DEA on the main experiment (exp), but can be configured to run on derived traits (trait_exp) or other experiment objects. Only use this method for experiments with 2 groups.

This step depends on the on parameter (default: exp).

• When on = "exp", requires exp (usually after step_preprocess()).

• When on = "trait_exp", requires trait_exp from step_derive_traits().

• When on = "dynamic_motif_exp", requires dynamic_motif_exp from step_quantify_dynamic_motifs().

• When on = "branch_motif_exp", requires branch_motif_exp from step_quantify_branch_motifs().

Usage

step_dea_wilcox(
  on = "exp",
  p_adj_method = "BH",
  ref_group = NULL,
  filter_p_adj_cutoff = 0.05,
  filter_p_val_cutoff = NULL,
  filter_fc_cutoff = NULL,
  ...
)

Arguments

Details

Data required:

• Depends on on parameter (default: exp)

Data generated:

• dea_res: The DEA results (if on = "exp", default)

• dta_res: The DTA results (if on = "trait_exp")

• dynamic_dma_res: The DMA results (if on = "dynamic_motif_exp")

• branch_dma_res: The DMA results (if on = "branch_motif_exp")

• sig_exp: The filtered experiment (if on = "exp", default)

• sig_trait_exp: The filtered trait experiment (if on = "trait_exp")

• sig_dynamic_motif_exp: The filtered dynamic motif experiment (if on = "dynamic_motif_exp")

• sig_branch_motif_exp: The filtered branch motif experiment (if on = "branch_motif_exp")

Tables generated:

• dea: A table containing the DEA result (if on = "exp", default)

• dta: A table containing the DTA result (if on = "trait_exp")

• dynamic_dma: A table containing the DMA result (if on = "dynamic_motif_exp")

• branch_dma: A table containing the DMA result (if on = "branch_motif_exp")

Value

A glysmith_step object.

See Also

glystats::gly_wilcox()

Examples

step_dea_wilcox()
step_dea_wilcox(on = "trait_exp")  # Differential trait analysis

Description

Calculate glycan derived traits using glydet::derive_traits(). Advanced glycan structure analysis that summarizes structural properties of a glycome or each glycosite. Need glycan structure information.

This step requires exp (experiment data).

Usage

step_derive_traits(trait_fns = NULL, mp_fns = NULL, mp_cols = NULL)

Arguments

Details

Data required:

• exp: The experiment to calculate derived traits for

Data generated:

• trait_exp: The experiment with derived traits

Tables generated:

• derived_traits: A table containing the derived traits.

Value

A glysmith_step object.

See Also

glydet::derive_traits()

Examples

step_derive_traits()

Description

Create a heatmap plot using glyvis::plot_heatmap(). The heatmap visualizes expression values across samples.

This step depends on the on parameter (default: exp).

• When on = "exp", requires exp (usually after step_preprocess()).

• When on = "sig_exp", requires sig_exp from one of step_dea_limma(), step_dea_ttest(), step_dea_wilcox(), step_dea_anova(), or step_dea_kruskal().

• When on = "trait_exp", requires trait_exp from step_derive_traits().

• When on = "sig_trait_exp", requires sig_trait_exp from DEA on traits.

• When on = "dynamic_motif_exp", requires dynamic_motif_exp from step_quantify_dynamic_motifs().

• When on = "sig_dynamic_motif_exp", requires sig_dynamic_motif_exp from DEA on motifs.

• When on = "branch_motif_exp", requires branch_motif_exp from step_quantify_branch_motifs().

• When on = "sig_branch_motif_exp", requires sig_branch_motif_exp from DEA on motifs.

Usage

step_heatmap(on = "exp", plot_width = 7, plot_height = 7, ...)

Arguments

Details

Data required:

• Depends on on parameter (default: exp)

Plots generated:

• heatmap: A heatmap plot (if on = "exp")

• sig_heatmap: A heatmap plot (if on = "sig_exp")

• trait_heatmap: A heatmap plot (if on = "trait_exp")

• sig_trait_heatmap: A heatmap plot (if on = "sig_trait_exp")

• dynamic_motif_heatmap: A heatmap plot (if on = "dynamic_motif_exp")

• sig_dynamic_motif_heatmap: A heatmap plot (if on = "sig_dynamic_motif_exp")

• branch_motif_heatmap: A heatmap plot (if on = "branch_motif_exp")

• sig_branch_motif_heatmap: A heatmap plot (if on = "sig_branch_motif_exp")

Value

A glysmith_step object.

See Also

glyvis::plot_heatmap()

Examples

step_heatmap()
step_heatmap(on = "sig_exp")
step_heatmap(on = "trait_exp")

Description

Summarize the experiment using glyexp::summarize_experiment(). This is usually the first step, BEFORE step_preprocess(). Very light-weight to run, so always include it.

This step requires exp (experiment data).

Usage

step_ident_overview(count_struct = NULL)

Arguments

Details

Data required:

• exp: The experiment to summarize

Tables generated:

• summary: A table containing the identification overview of the experiment

Value

A glysmith_step object.

See Also

glyexp::summarize_experiment()

Examples

step_ident_overview()

Description

Infer glycan structures from the glycan_composition column in var_info. This step uses glyanno::comp_to_struc() with a structure database from glydb::glydb_structures() and keeps only variables with an inferred structure.

This step requires exp (experiment data).

Usage

step_infer_structure(species = NULL, structure_level = "topological")

Arguments

Details

Data required:

• exp: The experiment whose glycan structures should be inferred

Data generated:

• uninferred_exp: The original experiment before structure inference

Tables generated:

• inferred_structures: A table containing the inferred structure for each original variable and whether inference succeeded.

Value

A glysmith_step object.

See Also

glyanno::comp_to_struc(), glydb::glydb_structures()

Examples

step_infer_structure()
step_infer_structure(species = "Homo sapiens")

Description

Create a logo plot for glycosylation sites using glyvis::plot_logo(). The logo plot visualizes the amino acid sequence patterns around glycosylation sites. This step is only applicable for glycoproteomics experiments.

This step depends on the on parameter (default: exp).

• When on = "exp", requires exp (experiment data).

• When on = "sig_exp", requires sig_exp from one of step_dea_limma(), step_dea_ttest(), step_dea_wilcox(), step_dea_anova(), or step_dea_kruskal().

Usage

step_logo(
  on = "exp",
  n_aa = 5L,
  fasta = NULL,
  plot_width = 5,
  plot_height = 3,
  ...
)

Arguments

Details

Data required:

• Depends on on parameter (default: exp)

Plots generated:

• logo: A logo plot (if on = "exp")

• sig_logo: A logo plot (if on = "sig_exp")

Value

A glysmith_step object.

See Also

glyvis::plot_logo()

Examples

step_logo()
step_logo(fasta = "proteins.fasta")
step_logo(on = "sig_exp")

Description

Perform OPLS-DA using glystats::gly_oplsda() and plot it with glyvis::plot_oplsda(). OPLS-DA separates variation into predictive (related to group) and orthogonal (unrelated) components. This step only works with binary classification (exactly 2 groups).

This step depends on the on parameter (default: exp).

• When on = "exp", requires exp (usually after step_preprocess()).

• When on = "sig_exp", requires sig_exp from one of step_dea_limma(), step_dea_ttest(), step_dea_wilcox(), step_dea_anova(), or step_dea_kruskal().

• When on = "trait_exp", requires trait_exp from step_derive_traits().

• When on = "sig_trait_exp", requires sig_trait_exp from DEA on traits.

• When on = "dynamic_motif_exp", requires dynamic_motif_exp from step_quantify_dynamic_motifs().

• When on = "sig_dynamic_motif_exp", requires sig_dynamic_motif_exp from DEA on motifs.

• When on = "branch_motif_exp", requires branch_motif_exp from step_quantify_branch_motifs().

• When on = "sig_branch_motif_exp", requires sig_branch_motif_exp from DEA on motifs.

Usage

step_oplsda(
  on = "exp",
  pred_i = 1,
  ortho_i = NA,
  scale = TRUE,
  plot_width = 5,
  plot_height = 5,
  ...
)

Arguments

Details

Data required:

• exp (if on = "exp"): The experiment to run OPLS-DA on

• trait_exp (if on = "trait_exp"): The trait experiment to run OPLS-DA on

• dynamic_motif_exp (if on = "dynamic_motif_exp"): The dynamic motif experiment to run OPLS-DA on

• branch_motif_exp (if on = "branch_motif_exp"): The branch motif experiment to run OPLS-DA on

Tables generated (with suffixes):

• oplsda_samples: A table containing the OPLS-DA scores for each sample

• oplsda_variables: A table containing the OPLS-DA loadings for each variable

• oplsda_variance: A table containing the explained variance for each component

• oplsda_vip: A table containing the Variable Importance in Projection (VIP) scores

• oplsda_perm_test: A table containing permutation test results

Plots generated (with suffixes):

• oplsda_scores: An OPLS-DA score plot colored by group

• oplsda_loadings: An OPLS-DA loading plot

• oplsda_variance: An OPLS-DA variance (scree) plot

• oplsda_vip: An OPLS-DA VIP score plot

Value

A glysmith_step object.

See Also

glystats::gly_oplsda(), glyvis::plot_oplsda()

Examples

step_oplsda()
step_oplsda(pred_i = 1, ortho_i = 1)

Description

Run PCA using glystats::gly_pca() and plot it with glyvis::plot_pca(). Loading plot for glycoproteomics data can be crowded with too many variables. Ignore the resulting plot if it is not informative.

This step depends on the on parameter (default: exp).

• When on = "exp", requires exp (usually after step_preprocess()).

• When on = "sig_exp", requires sig_exp from one of step_dea_limma(), step_dea_ttest(), step_dea_wilcox(), step_dea_anova(), or step_dea_kruskal().

• When on = "trait_exp", requires trait_exp from step_derive_traits().

• When on = "sig_trait_exp", requires sig_trait_exp from DEA on traits.

• When on = "dynamic_motif_exp", requires dynamic_motif_exp from step_quantify_dynamic_motifs().

• When on = "sig_dynamic_motif_exp", requires sig_dynamic_motif_exp from DEA on motifs.

• When on = "branch_motif_exp", requires branch_motif_exp from step_quantify_branch_motifs().

• When on = "sig_branch_motif_exp", requires sig_branch_motif_exp from DEA on motifs.

Usage

step_pca(
  on = "exp",
  center = TRUE,
  scale = TRUE,
  loadings = FALSE,
  screeplot = TRUE,
  plot_width = 5,
  plot_height = 5,
  ...
)

Arguments

Details

Data required:

• exp (if on = "exp"): The experiment to run PCA on

• trait_exp (if on = "trait_exp"): The trait experiment to run PCA on

• dynamic_motif_exp (if on = "dynamic_motif_exp"): The dynamic motif experiment to run PCA on

• branch_motif_exp (if on = "branch_motif_exp"): The branch motif experiment to run PCA on

Tables generated (with suffixes):

• pca_samples: A table containing the PCA scores for each sample

• pca_variables: A table containing the PCA loadings for each variable

• pca_eigenvalues: A table containing the PCA eigenvalues

Plots generated (with suffixes):

• pca_scores: A PCA score plot colored by group (always generated)

• pca_loadings: A PCA loading plot (if loadings = TRUE)

• pca_screeplot: A PCA screeplot (if screeplot = TRUE)

Value

A glysmith_step object.

See Also

glystats::gly_pca(), glyvis::plot_pca()

Examples

step_pca()

Description

Generate quality control plots for the experiment using glyclean plotting functions. This step can be used before AND after step_preprocess() to generate QC plots at different stages.

This step requires exp (experiment data).

Usage

step_plot_qc(
  when = "post",
  batch_col = "batch",
  rep_col = NULL,
  plot_width = 7,
  plot_height = 5
)

Arguments

Details

Data required:

• exp: The experiment to plot QC for

Plots generated:

• qc_missing_heatmap: Missing value heatmap

• qc_missing_samples_bar: Missing value bar plot on samples

• qc_missing_variables_bar: Missing value bar plot on variables

• qc_tic_bar: Total intensity count bar plot

• qc_rank_abundance: Rank abundance plot

• qc_int_boxplot: Intensity boxplot

• qc_rle: RLE plot

• qc_cv_dent: CV density plot

• qc_batch_pca: PCA score plot colored by batch (if batch_col provided)

• qc_rep_scatter: Replicate scatter plots (if rep_col provided)

When when = "pre", plots are prefixed with qc_pre_ to distinguish from post-QC plots. When when = "post" or NULL, plots use the standard qc_ prefix.

Value

A glysmith_step object.

See Also

glyclean::plot_missing_heatmap(), glyclean::plot_tic_bar(), and other glyclean plotting functions.

Examples

step_plot_qc(when = "pre")
step_plot_qc(when = "post")

Description

Perform PLS-DA using glystats::gly_plsda() and plot it with glyvis::plot_plsda(). PLS-DA is a supervised method that finds components maximizing covariance between predictors and the response variable (group membership).

This step depends on the on parameter (default: exp).

• When on = "exp", requires exp (usually after step_preprocess()).

• When on = "sig_exp", requires sig_exp from one of step_dea_limma(), step_dea_ttest(), step_dea_wilcox(), step_dea_anova(), or step_dea_kruskal().

• When on = "trait_exp", requires trait_exp from step_derive_traits().

• When on = "sig_trait_exp", requires sig_trait_exp from DEA on traits.

• When on = "dynamic_motif_exp", requires dynamic_motif_exp from step_quantify_dynamic_motifs().

• When on = "sig_dynamic_motif_exp", requires sig_dynamic_motif_exp from DEA on motifs.

• When on = "branch_motif_exp", requires branch_motif_exp from step_quantify_branch_motifs().

• When on = "sig_branch_motif_exp", requires sig_branch_motif_exp from DEA on motifs.

Usage

step_plsda(
  on = "exp",
  ncomp = 2,
  scale = TRUE,
  plot_width = 5,
  plot_height = 5,
  ...
)

Arguments

Details

Data required:

• exp (if on = "exp"): The experiment to run PLS-DA on

• trait_exp (if on = "trait_exp"): The trait experiment to run PLS-DA on

• dynamic_motif_exp (if on = "dynamic_motif_exp"): The dynamic motif experiment to run PLS-DA on

• branch_motif_exp (if on = "branch_motif_exp"): The branch motif experiment to run PLS-DA on

Tables generated (with suffixes):

• plsda_samples: A table containing the PLS-DA scores for each sample

• plsda_variables: A table containing the PLS-DA loadings for each variable

• plsda_variance: A table containing the explained variance for each component

• plsda_vip: A table containing the Variable Importance in Projection (VIP) scores

• plsda_perm_test: A table containing permutation test results

Plots generated (with suffixes):

• plsda_scores: A PLS-DA score plot colored by group

• plsda_loadings: A PLS-DA loading plot

• plsda_variance: A PLS-DA variance (scree) plot

• plsda_vip: A PLS-DA VIP score plot

Value

A glysmith_step object.

See Also

glystats::gly_plsda(), glyvis::plot_plsda()

Examples

step_plsda()
step_plsda(ncomp = 3)

Description

Preprocess the experiment using glyclean::auto_clean(). This step can be omitted if the experiment is already preprocessed.

This step requires exp (experiment data).

Usage

step_preprocess(
  batch_col = "batch",
  normalize_to_try = NULL,
  impute_to_try = NULL,
  remove_preset = "discovery",
  batch_prop_threshold = 0.3,
  check_batch_confounding = TRUE,
  batch_confounding_threshold = 0.4,
  rep_col = NULL
)

Arguments

Details

Data required:

• exp: The experiment to preprocess

Data generated:

• raw_exp: The raw experiment (previous exp, saved for reference)

This step is special in that it silently overwrites the exp data with the preprocessed experiment. This ensures that no matter if preprocessing is performed or not, the "active" experiment is always under the key exp. The previous exp is saved as raw_exp for reference.

Value

A glysmith_step object.

See Also

glyclean::auto_clean()

Examples

step_preprocess()
step_preprocess(remove_preset = "discovery")

Description

Quantify N-glycan branch motifs using glydet::quantify_motifs() with glymotif::branch_motifs(). This extracts specific N-glycan branching patterns (bi-antennary, tri-antennary, etc.). Only works with N-glycans.

This step requires exp (experiment data).

Usage

step_quantify_branch_motifs(method = "relative")

Arguments

Details

Data required:

• exp: The experiment to quantify motifs for (must be N-glycans)

Data generated:

• branch_motif_exp: The experiment with quantified branch motifs

Tables generated:

• branch_motifs: A table containing the quantified branch motifs.

Value

A glysmith_step object.

See Also

glydet::quantify_motifs(), glymotif::branch_motifs()

Examples

step_quantify_branch_motifs()

Description

Quantify glycan motifs using glydet::quantify_motifs() with glymotif::dynamic_motifs(). This extracts all possible motifs from glycan structures. Works with any glycan type.

This step requires exp (experiment data).

Usage

step_quantify_dynamic_motifs(max_size = 3, method = "relative")

Arguments

Details

Data required:

• exp: The experiment to quantify motifs for

Data generated:

• dynamic_motif_exp: The experiment with quantified motifs

Tables generated:

• dynamic_motifs: A table containing the quantified motifs.

Value

A glysmith_step object.

See Also

glydet::quantify_motifs(), glymotif::dynamic_motifs()

Examples

step_quantify_dynamic_motifs()

Description

Perform ROC analysis using glystats::gly_roc(), extract top 10 variables with highest AUC, and plot ROC curves for these variables using glyvis::plot_roc().

This step requires exp (experiment data).

Usage

step_roc(pos_class = NULL, plot_width = 5, plot_height = 5)

Arguments

Details

Data required:

• exp: The experiment to perform ROC analysis on

Tables generated:

• roc_auc: A table containing the ROC AUC values for all variables

Plots generated:

• roc_curves: ROC curves for the top 10 variables

Value

A glysmith_step object.

See Also

glystats::gly_roc(), glyvis::plot_roc()

Examples

step_roc()

Description

Create boxplots for the most significant variables from DEA analysis using glyvis::plot_boxplot(). The function selects the top n_top variables with the lowest adjusted p-values from the DEA results and plots their expression values grouped by sample groups.

This step depends on the on parameter (default: sig_exp).

• When on = "sig_exp", requires sig_exp from one of step_dea_limma(), step_dea_ttest(), step_dea_wilcox(), step_dea_anova(), or step_dea_kruskal().

• When on = "sig_trait_exp", requires sig_trait_exp from DEA on traits.

• When on = "sig_dynamic_motif_exp", requires sig_dynamic_motif_exp from DEA on motifs.

• When on = "sig_branch_motif_exp", requires sig_branch_motif_exp from DEA on motifs.

The number of variables is limited to a maximum of 25, as enforced by glyvis::plot_boxplot().

Usage

step_sig_boxplot(
  on = "sig_exp",
  n_top = 25,
  panel_width = 1.5,
  panel_height = 1.2,
  min_width = 5,
  min_height = 3,
  max_width = 14,
  max_height = 12,
  ...
)

Arguments

Details

Data required:

• Depends on on parameter:

  • sig_exp (default): Significant experiment from DEA

  • sig_trait_exp: Significant trait experiment from DTA

  • sig_dynamic_motif_exp: Significant dynamic motif experiment from DMA

  • sig_branch_motif_exp: Significant branch motif experiment from DMA

Plots generated:

• sig_boxplot: A boxplot of significant variables (if on = "sig_exp")

• sig_trait_boxplot: A boxplot of significant traits (if on = "sig_trait_exp")

• sig_dynamic_motif_boxplot: A boxplot of significant dynamic motifs (if on = "sig_dynamic_motif_exp")

• sig_branch_motif_boxplot: A boxplot of significant branch motifs (if on = "sig_branch_motif_exp")

Value

A glysmith_step object.

See Also

glyvis::plot_boxplot()

Examples

step_sig_boxplot()
step_sig_boxplot(n_top = 12)
step_sig_boxplot(on = "sig_trait_exp")

Description

Perform Disease Ontology enrichment analysis on differentially expressed variables using glyfun::enrich_ora_do().

This step requires dea_res (differential analysis result from DEA). Run one of step_dea_limma(), step_dea_ttest(), or step_dea_wilcox() before this step. Only execute for glycoproteomics experiments with exactly 2 groups. If used for glycomics experiments, the step will be skipped. Use all genes in OrgDb as the background.

Usage

step_sig_enrich_do(
  universe = "all",
  plot_type = "dotplot",
  plot_width = 7,
  plot_height = 7,
  ...
)

Arguments

Details

Data required:

• exp: The experiment to perform Disease Ontology enrichment analysis for

• dea_res: The result from DEA, generated by step_dea_xxx().

Tables generated:

• do_enrich: A table containing the Disease Ontology enrichment results.

Value

A glysmith_step object.

See Also

glyfun::enrich_ora_do()

Examples

step_sig_enrich_do()
step_sig_enrich_do(plot_type = "barplot")

Description

Perform GO enrichment analysis on differentially expressed variables using glyfun::enrich_ora_go().

This step requires dea_res (differential analysis result from DEA). Run one of step_dea_limma(), step_dea_ttest(), or step_dea_wilcox() before this step. Only execute for glycoproteomics experiments with exactly 2 groups. If used for glycomics experiments, the step will be skipped. Use all genes in OrgDb as the background.

Usage

step_sig_enrich_go(
  universe = "all",
  plot_type = "dotplot",
  plot_width = 7,
  plot_height = 7,
  ...
)

Arguments

Details

Data required:

• exp: The experiment to perform GO enrichment analysis for

• dea_res: The result from DEA, generated by step_dea_xxx().

Tables generated:

• go_enrich: A table containing the GO enrichment results.

Value

A glysmith_step object.

See Also

glyfun::enrich_ora_go()

Examples

step_sig_enrich_go()
step_sig_enrich_go(plot_type = "barplot")

Description

Perform KEGG enrichment analysis on differentially expressed variables using glyfun::enrich_ora_kegg().

This step requires dea_res (differential analysis result from DEA). Run one of step_dea_limma(), step_dea_ttest(), or step_dea_wilcox() before this step. Only execute for glycoproteomics experiments with exactly 2 groups. If used for glycomics experiments, the step will be skipped. Use all genes in OrgDb as the background.

Usage

step_sig_enrich_kegg(
  universe = "all",
  plot_type = "dotplot",
  plot_width = 7,
  plot_height = 7,
  ...
)

Arguments

Details

Data required:

• exp: The experiment to perform KEGG enrichment analysis for

• dea_res: The result from DEA, generated by step_dea_xxx().

Tables generated:

• kegg_enrich: A table containing the KEGG enrichment results.

Value

A glysmith_step object.

See Also

glyfun::enrich_ora_kegg()

Examples

step_sig_enrich_kegg()
step_sig_enrich_kegg(plot_type = "barplot")

Description

Perform NCG enrichment analysis on differentially expressed variables using glyfun::enrich_ora_ncg().

This step requires dea_res (differential analysis result from DEA). Run one of step_dea_limma(), step_dea_ttest(), or step_dea_wilcox() before this step. Only execute for glycoproteomics experiments with exactly 2 groups. If used for glycomics experiments, the step will be skipped. Use all genes in OrgDb as the background.

Usage

step_sig_enrich_ncg(
  universe = "all",
  plot_type = "dotplot",
  plot_width = 7,
  plot_height = 7,
  ...
)

Arguments

Details

Data required:

• exp: The experiment to perform NCG enrichment analysis for

• dea_res: The result from DEA, generated by step_dea_xxx().

Tables generated:

• ncg_enrich: A table containing the NCG enrichment results.

Value

A glysmith_step object.

See Also

glyfun::enrich_ora_ncg()

Examples

step_sig_enrich_ncg()
step_sig_enrich_ncg(plot_type = "barplot")

Description

Perform Reactome enrichment analysis on differentially expressed variables using glyfun::enrich_ora_reactome().

This step requires dea_res (differential analysis result from DEA). Run one of step_dea_limma(), step_dea_ttest(), or step_dea_wilcox() before this step. Only execute for glycoproteomics experiments with exactly 2 groups. If used for glycomics experiments, the step will be skipped. Use all genes in OrgDb as the background.

Usage

step_sig_enrich_reactome(
  universe = "all",
  plot_type = "dotplot",
  plot_width = 7,
  plot_height = 7,
  ...
)

Arguments

Details

Data required:

• exp: The experiment to perform Reactome enrichment analysis for

• dea_res: The result from DEA, generated by step_dea_xxx().

Tables generated:

• reactome_enrich: A table containing the Reactome enrichment results.

Value

A glysmith_step object.

See Also

glyfun::enrich_ora_reactome()

Examples

step_sig_enrich_reactome()
step_sig_enrich_reactome(plot_type = "barplot")

Description

Perform WikiPathways enrichment analysis on differentially expressed variables using glyfun::enrich_ora_wp().

This step requires dea_res (differential analysis result from DEA). Run one of step_dea_limma(), step_dea_ttest(), or step_dea_wilcox() before this step. Only execute for glycoproteomics experiments with exactly 2 groups. If used for glycomics experiments, the step will be skipped. Use all genes in OrgDb as the background.

Usage

step_sig_enrich_wp(
  universe = "all",
  plot_type = "dotplot",
  plot_width = 7,
  plot_height = 7,
  ...
)

Arguments

Details

Data required:

• exp: The experiment to perform WikiPathways enrichment analysis for

• dea_res: The result from DEA, generated by step_dea_xxx().

Tables generated:

• wp_enrich: A table containing the WikiPathways enrichment results.

Value

A glysmith_step object.

See Also

glyfun::enrich_ora_wp()

Examples

step_sig_enrich_wp()
step_sig_enrich_wp(plot_type = "barplot")

Description

Subset the experiment to specific groups using the group column in sample information. This is useful when downstream steps require exactly two groups for comparison. Usually run after step_preprocess() and before DEA or enrichment steps.

This step requires exp (experiment data).

Usage

step_subset_groups(groups = NULL)

Arguments

Details

Data required:

• exp: The experiment to subset

Data generated:

• full_exp: The original experiment before subsetting

This step overwrites exp in the context with the subset experiment.

Value

A glysmith_step object.

Examples

step_subset_groups(groups = c("H", "C"))

Description

Perform t-SNE analysis using glystats::gly_tsne() and plot a t-SNE plot using glyvis::plot_tsne(). Note that the result of t-SNE largely depends on the perplexity parameter. Usually it's a trial-and-error process to find the best value iteratively. If you are not satisfied with the result, manually call glyvis::plot_tsne() with different perplexity values to find the best one.

This step depends on the on parameter (default: exp).

• When on = "exp", requires exp (usually after step_preprocess()).

• When on = "sig_exp", requires sig_exp from one of step_dea_limma(), step_dea_ttest(), step_dea_wilcox(), step_dea_anova(), or step_dea_kruskal().

• When on = "trait_exp", requires trait_exp from step_derive_traits().

• When on = "sig_trait_exp", requires sig_trait_exp from DEA on traits.

• When on = "dynamic_motif_exp", requires dynamic_motif_exp from step_quantify_dynamic_motifs().

• When on = "sig_dynamic_motif_exp", requires sig_dynamic_motif_exp from DEA on motifs.

• When on = "branch_motif_exp", requires branch_motif_exp from step_quantify_branch_motifs().

• When on = "sig_branch_motif_exp", requires sig_branch_motif_exp from DEA on motifs.

Usage

step_tsne(
  on = "exp",
  dims = 2,
  perplexity = 30,
  plot_width = 5,
  plot_height = 5,
  ...
)

Arguments

Details

Data required:

• exp (if on = "exp"): The experiment to perform t-SNE on

• trait_exp (if on = "trait_exp"): The trait experiment to perform t-SNE on

• dynamic_motif_exp (if on = "dynamic_motif_exp"): The dynamic motif experiment to perform t-SNE on

• branch_motif_exp (if on = "branch_motif_exp"): The branch motif experiment to perform t-SNE on

Data generated (with suffixes):

• tsne: The t-SNE result

Plots generated (with suffixes):

• tsne: The t-SNE plot

Value

A glysmith_step object.

See Also

glystats::gly_tsne(), glyvis::plot_tsne()

Examples

step_tsne()
step_tsne(perplexity = 30)

Description

Perform UMAP analysis using glystats::gly_umap() and plot a UMAP plot using glyvis::plot_umap(). Note that the result of UMAP largely depends on the n_neighbors parameter. Usually it's a trial-and-error process to find the best value iteratively. If you are not satisfied with the result, manually call glyvis::plot_umap() with different n_neighbors values to find the best one.

This step depends on the on parameter (default: exp).

• When on = "exp", requires exp (usually after step_preprocess()).

• When on = "sig_exp", requires sig_exp from one of step_dea_limma(), step_dea_ttest(), step_dea_wilcox(), step_dea_anova(), or step_dea_kruskal().

• When on = "trait_exp", requires trait_exp from step_derive_traits().

• When on = "sig_trait_exp", requires sig_trait_exp from DEA on traits.

• When on = "dynamic_motif_exp", requires dynamic_motif_exp from step_quantify_dynamic_motifs().

• When on = "sig_dynamic_motif_exp", requires sig_dynamic_motif_exp from DEA on motifs.

• When on = "branch_motif_exp", requires branch_motif_exp from step_quantify_branch_motifs().

• When on = "sig_branch_motif_exp", requires sig_branch_motif_exp from DEA on motifs.

Usage

step_umap(
  on = "exp",
  n_neighbors = 15,
  n_components = 2,
  plot_width = 5,
  plot_height = 5,
  ...
)

Arguments

Details

Data required:

• exp (if on = "exp"): The experiment to perform UMAP on

• trait_exp (if on = "trait_exp"): The trait experiment to perform UMAP on

• dynamic_motif_exp (if on = "dynamic_motif_exp"): The dynamic motif experiment to perform UMAP on

• branch_motif_exp (if on = "branch_motif_exp"): The branch motif experiment to perform UMAP on

Data generated (with suffixes):

• umap: The UMAP result

Plots generated (with suffixes):

• umap: The UMAP plot

Value

A glysmith_step object.

See Also

glystats::gly_umap(), glyvis::plot_umap()

Examples

step_umap()
step_umap(n_neighbors = 15)

Description

Create a volcano plot from DEA results using glyvis::plot_volcano().

This step requires one of the DEA steps to be run:

• step_dea_limma() (multi-group comparison is also supported)

• step_dea_ttest()

• step_dea_wilcox()

Usage

step_volcano(
  log2fc_cutoff = 1,
  p_cutoff = 0.05,
  p_col = "p_adj",
  plot_width = 5,
  plot_height = 6,
  ...
)

Arguments

Details

Data required:

• dea_res: The DEA results from glystats::gly_limma()

Plots generated:

• volcano: A volcano plot

Value

A glysmith_step object.

See Also

glyvis::plot_volcano()

Examples

step_volcano()
step_volcano(log2fc_cutoff = 2)

Description

• write_blueprint() saves a blueprint to a RDS file.

• read_blueprint() loads a blueprint from a RDS file.

Usage

write_blueprint(bp, file)

read_blueprint(file)

Arguments

Value

Invisibly returns the blueprint object.

Examples

bp <- blueprint(
  step_preprocess(),
  step_pca(),
  step_dea_limma(),
)
write_blueprint(bp, tempfile(fileext = ".rds"))