Principal Component Analysis (PCA) Quiz

What does PCA stand for?

• Principal Component Analysis

• Pattern Component Analysis

• Primary Component Analysis

• Principle Component Analysis

Which of the following is NOT a use of PCA?

• Identifying biomarkers

• Determining blood type

• Clustering samples into groups

• Identifying patterns

What does PCA do to high dimensional data?

• Keeps it at the same dimension

• Complicates it further

• Discards all the attributes

• Simplifies it into lower dimensional data

In PCA, which principal component has a weight combination resulting in the largest spread of data points?

• PC1

• PC3

• PC2

• PC4

How many principal components would be made if there are 10 samples and 40 attributes?

• 40

• 10

• 20

• 30

What is the purpose of pre-processing the data in PCA?

• To make the data more comparable across attributes

• To make the data less comparable

• To discard the data

• To make the data more complex

What is the main reason why PCA is popular?

• It generates a large number of principal components

• It enables easy and visual discernment of group similarity or dissimilarity

• It only focuses on a few attributes

• It identifies attributes that are intuitive

What does PCA help to cluster or stratify?

• Groups into samples

• Principal components into groups

• Attributes into samples

• Samples into groups

What does PCA identify that may not be intuitive?

• Common attributes

• Biomarkers

• Obvious patterns

• Unimportant data

What do biostatisticians love about PCA?

• It only focuses on a few attributes

• It identifies attributes that are intuitive

• It enables easy and visual discernment of group similarity or dissimilarity

• It generates complex plots