calc_pca | R Documentation |

The `calc_pca()`

function performs principal components analysis of the gene count
vectors across all samples.

A corresponding `autoplot()`

method then can visualize the results.

```
calc_pca(object, assay_name = "counts", n_top = NULL)
```

`object` |
( |

`assay_name` |
( |

`n_top` |
( |

PCA should be performed after filtering out low quality genes and samples, as well as normalization of counts.

In addition, genes with constant counts across all samples are excluded from the analysis internally in

`calc_pca()`

. Centering and scaling is also applied internally.Plots can be obtained with the

`ggplot2::autoplot()`

function with the corresponding method from the`ggfortify`

package to plot the results of a principal components analysis saved in a`HermesDataPca`

object. See`ggfortify::autoplot.prcomp()`

for details.

A HermesDataPca object which is an extension of the stats::prcomp class.

Afterwards correlations between principal components
and sample variables can be calculated, see `pca_cor_samplevar`

.

```
object <- hermes_data %>%
add_quality_flags() %>%
filter() %>%
normalize()
result <- calc_pca(object, assay_name = "tpm")
summary(result)
result1 <- calc_pca(object, assay_name = "tpm", n_top = 500)
summary(result1)
# Plot the results.
autoplot(result)
autoplot(result, x = 2, y = 3)
autoplot(result, variance_percentage = FALSE)
autoplot(result, label = TRUE, label.repel = TRUE)
```

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