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Omics data visualization in R and Python
  • Introduction
    • From Authors
    • Virtual environments - let's begin
    • Getting started with Python
    • Getting started with R
    • Example data sets
  • PERFORMING FUNDAMENTAL OPERATIONS ON OMICs DATA USING R
    • Fundamental data structures
    • Loading data into R
    • Preferred formats in metabolomics and lipidomics analysis
    • Preprocess data type using Tidyverse package
    • Useful R tricks and features in OMICs mining
      • Application of pipe (%>%) functions
      • Changing data frames format with pivot_longer()
      • Data wrangling syntaxes useful in OMICs mining
      • Writing functions in R
      • The 'for' loop in R (advanced)
  • PERFORMING FUNDAMENTAL OPERATIONS ON OMICs DATA USING PYTHON
    • Fundamental data structures
    • Loading data into Python
  • Missing values handling in R
    • Missing values – Introduction
    • Detecting missing values (DataExplorer R package)
    • Filtering out columns containing mostly NAs
    • Data imputation by different available R libraries
      • Basic data imputation in R with dplyr and tidyr (tidyverse)
      • Data imputation using recipes library (tidymodels)
      • Replacing NAs via k-nearest neighbor (kNN) model (VIM library)
      • Replacing NAs via random forest (RF) model (randomForest library)
  • Missing values handling in Python
    • Detecting missing values
    • Filtering out columns containing mostly NAs
    • Data imputation
  • Data transformation, scaling, and normalization in R
    • Data normalization in R - fundamentals
    • Data normalization to the internal standards (advanced)
    • Batch effect corrections in R (advanced)
    • Data transformation and scaling - introduction
    • Data transformation and scaling using different available R packages
      • Data transformation and scaling using mutate()
      • Data transformation and scaling using recipes R package
      • Data Normalization – bestNormalize R package
  • Data transformation, scaling, and normalization in Python
    • Data Transformation and scaling in Python
  • Metabolites and lipids descriptive statistical analysis in R
    • Computing descriptive statistics in R
    • Using gtsummary to create publication-ready tables
    • Basic plotting in R
      • Bar charts
      • Box plots
      • Histograms
      • Density plots
      • Scatter plots
      • Dot plots with ggplot2 and tidyplots
      • Correlation heat maps
    • Customizing ggpubr and ggplot2 charts in R
    • Creating interactive plots with ggplotly
    • GGally for quick overviews
  • Metabolites and lipids descriptive statistics analysis in Python
    • Basic plotting
    • Scatter plots and linear regression
    • Correlation analysis
  • Metabolites and lipids univariate statistics in R
    • Two sample comparisons in R
    • Multi sample comparisons in R
    • Adjustments of p-values for multiple comparisons
    • Effect size computation and interpretation
    • Graphical representation of univariate statistics
      • Results of tests as annotations in the charts
      • Volcano plots
      • Lipid maps and acyl-chain plots
  • Metabolites and lipids univariate statistical analysis in Python
    • Two sample comparisons in Python
    • Multi-sample comparisons in Python
    • Statistical annotations on plots
  • Metabolites and lipids multivariate statistical analysis in R
    • Principal Component Analysis (PCA)
    • t-Distributed Stochastic Neighbor Embedding (t-SNE)
    • Uniform Manifold Approximation and Projection (UMAP)
    • Partial Least Squares (PLS)
    • Orthogonal Partial Least Squares (OPLS)
    • Hierarchical Clustering (HC)
      • Dendrograms
      • Heat maps with clustering
      • Interactive heat maps
  • Metabolites and lipids multivariate statistical analysis in Python
    • Principal Component Analysis
    • t-Distributed Stochastic Neighbor Embedding
    • Uniform Manifold Approximation and Projection
    • PLS Discriminant Analysis
    • Clustered heatmaps
  • OMICS IN MACHINE LEARNING APPROACHES IN R AND PYTHON
    • Application of selected models to OMICs data
    • OMICs machine learning – Examples
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  1. Metabolites and lipids descriptive statistical analysis in R

Basic plotting in R

Metabolites and lipids descriptive statistical analysis in R

R is an excellent solution for producing graphics depicting descriptive statistics. Among the R packages, there are various solutions suitable for beginners, e.g., dlookr, DataExplorer, ggpubr, or ggstatsplot, which create charts via uncomplicated functions. More experienced users select libraries that offer a high level of flexibility, like ggplot2 or plotly. However, in some cases, the code can become too complex for a beginner.

Here, we will show you how to prepare essential charts in R, which are frequently used in lipidomics and metabolomics studies. We will start every subchapter with the simplest method, progressing toward ggplot2 or plotly solution. We will begin with setting the working directory (wd) and the installation of all necessary packages:

# Set your working directory through setwd("dir").
# If your files with data are on the D drive, in the "Data analysis folder," then use:
setwd("D:/Data analysis")

# Installing packages for the data visualization:
# 1. ggpubr
install.packages("ggpubr")

# 2. ggstatsplot
install.packages("ggstatsplot")

# 3. dlookr -- already installed if you follow the handbook
install.packages("devtools")
devtools::install_github("choonghyunryu/dlookr")

# 4. DataExplorer -- already installed if you follow the handbook
install.packages("DataExplorer")

# 5. plotly
install.packages("plotly")

# 6. ggplot2 -- it is a part of the tidyverse collection!
install.packages("ggplot2")

Here, we keep using the lipidomics data set we shared at the beginning of the Gitbook. Now, we are ready to prepare the first plots in R.

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Last updated 9 months ago