# Principal Component Analysis

# Principal Component Analysis

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Performs Principal Component Analysis on Phenotypes. The script generates 5 files: pca_summary.txt by which we get information on the importance of components. The first row denotes the components that the data were analyzed, second row denotes the standard deviation third row the Proportion of Variance and the last one the Cumulative Proportion , pca_scores.txt assigns a score of each of the component to each of the samples, loadings.txt which describes the variation each of the factors,e.g. markers e.g. phenotype, contributes to each of the components, pca.pdf graphically represents the the contribution of each of the components and Rplots.pdf actually contains two graphs the first one represents graphically the outcome of pca_scores and the second includes also the direction of each of the loadings..Â

## Quick Start

- To use
**Principal Component Analysis**, import your data in cvs format. The first row is the names of the markers or Phenotypes. The first column is the names of the samples and all the rest Phenotypes or markers **Resources: DE**

## Test Data

Test data for this app appears directly in the Discovery Environment in the Data window under _Community Data -> iplantcollaborative -> example_data ->_PCA.

#### Input File(s)

Use example_datamod.csv from the directory above as test input.

#### Output File(s)

Expect as output 5 files named pca_summary.txt, pca_scores.txt, loadings.txt, pca.pdf and Rplots.pdf . For the test case, the output files you will find in the example_data called PCA