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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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On this page
  • Required packages
  • Loading the data
  • Correlation plots
  1. Metabolites and lipids descriptive statistics analysis in Python

Correlation analysis

PreviousScatter plots and linear regressionNextTwo sample comparisons in R

Last updated 6 months ago

Required packages

The required packages for this section are pandas and seaborn. These can be installed with the following command in the command window (Windows) / terminal (Mac).

pip install pandas seaborn

Loading the data

Like in the other sections we will use the lipidomics demo dataset:

import pandas as pd
import numpy as np
df = pd.read_excel("Lipidomics_dataset.xlsx", decimal=",")
df.set_index("Sample Name", inplace=True)

Correlation plots

Creating a basic correlation plot is again very simple with Pandas and Seaborn:

import seaborn as sns
import matplotlib.pyplot as plt
sns.heatmap(df.corr(numeric_only=True));
plt.show()

We can just call the heatmap funtion from seaborn, and call the corr funtion on the DataFrame (with numeric_only=True to ignore non numeric the Label column in our Dataframe) and pass the calculated correlation matrix as an argument to the heatmap function. The results will look something like this:

There are several problems with this plot that we'll address one by one. For starters there are many more lipids in this plot than there is space on the axis for the axis labels. We can fix this by setting the canvas size and the axis labels size on the ax object that we import from matplotlib (you may need to further adjust the figsize and labelsize parameters):

fig, ax = plt.subplots(figsize=(20,20))
ax.tick_params(axis='both', which='major', labelsize=6)
sns.heatmap(df.corr(numeric_only=True), ax=ax);
plt.show()

Next, correlation values are located in the interval [-1;1], so it would make more sense to have a color scalebar that is white at zero, diverges to a different color for positive and negative values, and is set to -1 for the minimum value and +1 for the maximum:

sns.heatmap(df.corr(numeric_only=True), ax=ax, cmap='vlag',vmin=-1, vmax=1);
fig, ax = plt.subplots(figsize=(20,20))
ax.tick_params(axis='both', which='major', labelsize=6)
plt.show()

Finally, we'll remove the redundant symmetry. We'll create a mask by first creating a matrix of ones with the same dimensions as the lipid DataFrame (using the numpy ones_like funtion, and we'll set the data type to bool) and then we'll set the values above the diagonal to zero with the numpy triu function:

mask = np.triu(np.ones_like(df.corr(numeric_only=True), dtype=bool))

we'll then pass in this mask to the mask parameter of the heatmap funtion:

sns.heatmap(df.corr(numeric_only=True), ax=ax, cmap='vlag',vmin=-1, vmax=1);
fig, ax = plt.subplots(figsize=(20,20))
ax.tick_params(axis='both', which='major', labelsize=6)
plt.show()

Finally, in correlation maps with fewer variables, it may be interesting to set add "annot=True" to the heatmap parameters, as this will show the actual correlation values on the map.

348KB
Lipidomics_dataset.xlsx