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Perform the Whole Analysis Pipeline

forge_analysis.Rd

This function performs a comprehensive analysis for group comparison.

Usage

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

Arguments

exp

A glyexp::experiment() object.

blueprint

A glysmith_blueprint object. Default is blueprint_default().

group_col

Column name of group information in the sample information. Used for various analyses. Default is "group".

Value

A glysmith_result object, with the following components:

  • exp: the experiment 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

exp <- glyexp::real_experiment2
result <- forge_analysis(exp)
#> ℹ Identification overview
#> ✔ Identification overview [127ms]
#> 
#> ℹ Preprocessing
#> 
#> ℹ Preprocessing

#> ── Removing variables with too many missing values ──
#> ℹ Preprocessing

#> 
#> ℹ Preprocessing

#> ℹ No QC samples found. Using all samples.
#> ℹ Preprocessing

#> ℹ Applying preset "discovery"...
#> ℹ Preprocessing

#> ℹ Total removed: 10 (14.93%) variables.
#> ℹ Preprocessing

#> ✔ Variable removal completed.
#> ℹ Preprocessing

#> 
#> ℹ Preprocessing

#> ── Normalizing data ──
#> ℹ Preprocessing

#> 
#> ℹ Preprocessing

#> ℹ No QC samples found. Using default normalization method based on experiment type.
#> ℹ Preprocessing

#> ℹ Experiment type is "glycomics". Using `normalize_median_quotient()` + `normalize_total_area()`.
#> ℹ Preprocessing

#> ✔ Normalization completed.
#> ℹ Preprocessing

#> 
#> ℹ Preprocessing

#> ── Normalizing data (Total Area) ──
#> ℹ Preprocessing

#> 
#> ℹ Preprocessing

#> ✔ Total area normalization completed.
#> ℹ Preprocessing

#> 
#> ℹ Preprocessing

#> ── Imputing missing values ──
#> ℹ Preprocessing

#> 
#> ℹ Preprocessing

#> ℹ No QC samples found. Using default imputation method based on sample size.
#> ℹ Preprocessing

#> ℹ Sample size > 100, using `impute_miss_forest()`.
#> ℹ Preprocessing

#> ✔ Imputation completed.
#> ℹ Preprocessing

#> 
#> ℹ Preprocessing

#> ── Correcting batch effects ──
#> ℹ Preprocessing

#> 
#> ℹ Preprocessing

#> ℹ Batch column  not found in sample_info. Skipping batch correction.
#> ℹ Preprocessing

#> ✔ Batch correction completed.
#> ℹ Preprocessing

#> ✔ Preprocessing [5.6s]
#> 
#> ℹ QC (post-preprocessing)
#> ✔ QC (post-preprocessing) [121ms]
#> 
#> ℹ Principal component analysis
#> ✔ Principal component analysis [297ms]
#> 
#> ℹ Differential expression analysis (limma)
#> ℹ Number of groups: 4
#> ℹ Differential expression analysis (limma)

#> ℹ Groups: "H", "M", "Y", and "C"
#> ℹ Differential expression analysis (limma)

#> ℹ Pairwise comparisons will be performed, with levels coming first as reference groups.
#> ℹ Differential expression analysis (limma)

#> ✔ Differential expression analysis (limma) [59ms]
#> 
#> ℹ Volcano plot
#> ✔ Volcano plot [526ms]
#> 
#> ℹ Heatmap of significant variables
#> ✔ Heatmap of significant variables [42ms]
#> 
#> ℹ Skipping `step_sig_enrich_go()` because input is not a glycoproteomics experiment and input has more than 2 groups.
#> ℹ Skipping `step_sig_enrich_kegg()` because input is not a glycoproteomics experiment and input has more than 2 groups.
#> ℹ Skipping `step_sig_enrich_reactome()` because input is not a glycoproteomics experiment and input has more than 2 groups.
#> ℹ Derived trait calculation
#> ✔ Derived trait calculation [2.1s]
#> 
#> ℹ Differential trait analysis (limma)
#> ℹ Number of groups: 4
#> ℹ Differential trait analysis (limma)

#> ℹ Groups: "H", "M", "Y", and "C"
#> ℹ Differential trait analysis (limma)

#> ℹ Pairwise comparisons will be performed, with levels coming first as reference groups.
#> ℹ Differential trait analysis (limma)

#> ✔ Differential trait analysis (limma) [56ms]
#> 
#> ℹ Heatmap of significant traits
#> ✔ Heatmap of significant traits [40ms]
#> 
print(result)
#> 
#> ── GlySmith Analysis Result 
#> Plots: 16, Tables: 7, Data: 7