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Get Started with glyvis

glyvis.Rmd

“The simple graph has brought more information to the data analyst’s mind than any other device.” — John Tukey

Data visualization isn’t just another step in your analysis pipeline—it’s where insights come alive. As visual creatures, we process charts and graphs far more intuitively than walls of numbers and text. glyvis brings this visual power to the glycoverse, offering lightning-fast and effortless visualization for your glycomics data. Built as the perfect companion to glystats, it transforms complex statistical results into clear, compelling visuals.

library(glyvis)
library(glyexp)
library(glyclean)
#> 
#> Attaching package: 'glyclean'
#> The following object is masked from 'package:stats':
#> 
#>     aggregate
library(glystats)

Let’s dive in with some real data to see glyvis in action. We’ll work with the real_experiment dataset bundled with glyexp— a compelling real-world N-glycoproteomics study featuring 12 patients across four distinct liver conditions: healthy controls (H), hepatitis (M), cirrhosis (Y), and hepatocellular carcinoma (C), with 3 samples representing each condition. To get our data analysis-ready, we’ll use glyclean::auto_clean() for streamlined preprocessing.

exp <- auto_clean(real_experiment)
#> ℹ Normalizing data (Median)
#> ✔ Normalizing data (Median) [266ms]
#> 
#> ℹ Removing variables with >50% missing values
#> ✔ Removing variables with >50% missing values [28ms]
#> 
#> ℹ Imputing missing values
#> ℹ Sample size <= 30, using sample minimum imputation
#> ℹ Imputing missing values✔ Imputing missing values [31ms]
#> 
#> ℹ Aggregating data
#> ✔ Aggregating data [780ms]
#> 
#> ℹ Normalizing data again
#> ✔ Normalizing data again [18ms]

The Dual Nature of glyvis

Think of glyvis as a versatile artist with two distinct painting styles:

  • autoplot() — The intelligent assistant that automatically crafts suitable plots from your glystats results
  • plot_xxx() — The precision toolkit for creating specific visualizations exactly as you envision them

Let’s see this in action. To create a PCA plot, we can take the direct route with plot_pca() on our exp data:

Alternatively, we can take the analytical pathway: first conducting PCA analysis with glystats::gly_pca(), then letting autoplot() work its magic on the statistical results.

pca_res <- gly_pca(exp)
autoplot(pca_res)

While the first approach gets you there quickly, the second pathway unlocks a world of possibilities with your results. You gain access to the underlying statistical objects for advanced analyses, and can craft custom ggplot2 masterpieces tailored for publications.

The beauty of autoplot() lies in its versatility—it speaks fluent glystats across nearly every analysis type. Explore the complete reference to discover the full spectrum of autoplot() capabilities and specialized plot_xxx() functions.

A Philosophy on Aesthetics

Let’s set expectations straight: glyvis isn’t your publication graphics department. Creating truly stunning, publication-ready figures is an art form that demands thoughtful customization. Every compelling visualization emerges from careful consideration of countless decisions:

  • Focus: What story does your data want to tell?
  • Scale: How much visual real estate will make your message shine without overwhelming?
  • Layout: How can multiple plots dance together harmoniously?
  • Palette: Which colors will captivate while staying true to your data?
  • Annotation: What labels and text will guide your reader’s eye?
  • Polish: The devil’s in the details—legends, fonts, ticks, axes, grids…

These creative choices flow from your intimate knowledge of the data and its scientific context. glyvis doesn’t presume to make these artistic decisions for you.

Instead, think of glyvis as your data exploration companion. It excels at what it was born to do: transforming glystats results into instant, informative visuals. It’s your first glimpse into the data’s soul, helping you spot patterns and generate hypotheses at the speed of thought.