A brief tutorial encompasing the use of PCA Maker, a web application in R made with Shiny.
The purpose of this post is present a brief tutorial about how to install and use PCA Maker. Some concepts about PCA are covered in other posts on this blog, where you can also found useful links about the topic:
Principal Component Analysis through Singular Value Decomposition Principal Component Analysis (PCA) from Scratch Principal Component Analysis to Many Responses What is PCA Maker?...
Principal Component Analysis through Singular Value Decomposition
How to perform PCA trough singular value decomposition using R.
What is singular value decomposition? Singular value decomposition (SVD) is a factorization of a real or complex matrix which generalizes the eigendecomposition of a square normal matrix with an orthonormal eigenbasis to any m x n matrix:
Where M is m x n, U is m x m, S is m x n, and V is n x n....
Principal Component Analysis (PCA) from Scratch
How to perform PCA step by step using R and basic linear algebra functions and operations.
What is PCA? PCA is an exploratory data analysis based in dimensions reduction. The general idea is to reduce the dataset to have fewer dimensions and at the same time preserve as much information as possible.
PCA allows us to make visual representations in two dimensions and check for groups or differences in the data related to different states, treatments, etc....
Principal Component Analysis to Many Responses
How to perform a principal component analysis on metabolomic data using R. This includes visual representations like a scree plot and a scatter plot for PC1 and PC2.
1 Problem You have obtained the relative quantities for 43 metabolites in Arabidopsis thaliana under salt stress conditions at different times. Saline stress is a important factor limiting plant growth and you want to study how the level of primary metabolites changes under these conditions....