---
myst:
  html_meta:
    description: Principal components of samples in CytePool, as DESeq2's plotPCA computes them from the genes of largest variance, for a matrix of log-expression values or a cell type of a pseudobulk cube, with their coordinates, shares of variance and loadings, and their figure.
---

# Principal components

{func}`~cytepool.principal_components.principal_components` places samples on the
principal components of their most variable genes, as DESeq2's `plotPCA` does: the
`top` genes of largest variance over the samples, each centred, decomposed by singular
values. It takes a samples x genes matrix of log-expression values or a cell type of a
cube, whose samples are prepared as edgeR prepares counts for `plotMDS`. The result, a
{class}`~cytepool.principal_components.PrincipalComponents`, holds the samples'
coordinates, each component's share of the variance and the loadings of the genes, and
{func}`~cytepool.plotting.plot_pca` draws it.

```{eval-rst}
.. automodule:: cytepool.principal_components
```