The goal of glyvis is to visualize everything in the glycoverse
ecosystem. Visualization is an essential part of data analysis. Human
beings are more sensitive to visual information than text and numbers.
Plotting helps us to understand the data better. glyvis provides a
unified interface for visualizing glycoverse data, including
statistical results, experiments, glycan biosynthesis pathways, and
more. It implements the autoplot() method for various glycoverse
data structures. Just autoplot() it!
We recommend installing the meta-package glycoverse, which includes this package and other core glycoverse packages.
If you don’t want to install all glycoverse packages, you can only install glyvis.
You can install the latest release of glyvis from r-universe (recommended):
# install.packages("pak")
pak::repo_add(glycoverse = "https://glycoverse.r-universe.dev")
pak::pkg_install("glyvis")Or from GitHub:
pak::pkg_install("glycoverse/glyvis@*release")Or install the development version (NOT recommended):
pak::pkg_install("glycoverse/glyvis")Note: Tips and troubleshooting for the meta-package glycoverse are also applicable here: Installation of glycoverse.
The main purpose of glyvis is to provide visualization for glystats
results. It implements autoplot() methods for each result class in
glystats, so that the users can visualize the results directly to get
a quick overview. It also provides some other visualization functions
for glycoverse data structures, such as glyexp::experiment(),
glyrepr::glycan_structure(), and others. This package is not intended
to produce publication-quality figures, but to provide a quick
exploration of the data.
library(glyexp)
library(glyclean)
#> Warning: 程序包'glyclean'是用R版本4.5.2 来建造的
#>
#> 载入程序包：'glyclean'
#> The following object is masked from 'package:stats':
#>
#> aggregate
library(glystats)
library(glyvis)
exp <- auto_clean(real_experiment)
#>
#> ── Normalizing data ──
#>
#> ℹ No QC samples found. Using default normalization method based on experiment type.
#> ℹ Experiment type is "glycoproteomics". Using `normalize_median()`.
#> ✔ Normalization completed.
#>
#> ── Removing variables with too many missing values ──
#>
#> ℹ No QC samples found. Using all samples.
#> ℹ Applying preset "discovery"...
#> ℹ Total removed: 24 (0.56%) variables.
#> ✔ Variable removal completed.
#>
#> ── Imputing missing values ──
#>
#> ℹ No QC samples found. Using default imputation method based on sample size.
#> ℹ Sample size <= 30, using `impute_sample_min()`.
#> ✔ Imputation completed.
#>
#> ── Aggregating data ──
#>
#> ℹ Aggregating to "gfs" level
#> ✔ Aggregation completed.
#>
#> ── Normalizing data again ──
#>
#> ℹ No QC samples found. Using default normalization method based on experiment type.
#> ℹ Experiment type is "glycoproteomics". Using `normalize_median()`.
#> ✔ Normalization completed.
#>
#> ── Correcting batch effects ──
#>
#> ℹ Batch column not found in sample_info. Skipping batch correction.
#> ✔ Batch correction completed.
pca_res <- gly_pca(exp)
autoplot(pca_res)