Save GlySmith Result — quench

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

x: A glysmith result object.
dir: The directory to save the result.
plot_ext: The extension of the plot files. Either "pdf", "png" or "svg". Default is "pdf".
table_ext: The extension of the table files. Either "csv" or "tsv". Default is "csv".
plot_width: The width of the plot in inches. Default is 5.
plot_height: The height of the plot in inches. Default is 5.

Examples

library(glyexp)
exp <- real_experiment2
result <- forge_analysis(exp)
#> ℹ Identification overview
#> ✔ Identification overview [101ms]
#> 
#> 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) [96ms]
#> 
#> ℹ Principal component analysis
#> ✔ Principal component analysis [304ms]
#> 
#> 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) [57ms]
#> 
#> ℹ Volcano plot
#> ✔ Volcano plot [516ms]
#> 
#> 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 [2s]
#> 
#> 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) [94ms]
#> 
#> ℹ Heatmap of significant traits
#> ✔ Heatmap of significant traits [42ms]
#> 
quench_result(result, tempdir())
#> ℹ Directory already exists. Overwrite? [y/N] 
#> Warning: Ignoring empty aesthetic: `width`.
#> ✔ Result saved to /tmp/RtmpBgHsNs