plot_PCA.RdRuns PCA on normalized counts. Makes PCA and scree plots. Tests for significant clustering based on specific variables using PERMANOVA and Euclidean distances.
plot_PCA( x, norm_cts_type = "edger", assay_name = "norm_log_cpm", color_by = colnames(colData(x))[[1]], shape_by = colnames(colData(x))[[1]], adonis = TRUE, adonis_by = colnames(colData(x))[[1]] )
| x | A BbcSE object. |
|---|---|
| norm_cts_type | "edger" or "deseq2" |
| assay_name | Name of assay from the 'norm_cts' slot. Option was implemented with the idea of being able to plot batch-corrected data. |
| color_by | colData column to color by for PCA plot |
| shape_by | colData column to shape by for PCA plot |
| adonis | logical for whether vegan::adonis should be run. If TRUE, adonis() will be run with Euclidean distance calculated from the same normalized counts as used for PCA. |
| adonis_by | colData column to test using PERMANOVA. May be a vector of values; in this case, each variable is tested sequentially using adonis(). |
A list containing ggplot objects for 1. PCA 2. scree plot 3. prcomp()$x merged with meta data. Handy if visualizations of >PC2 are needed 4. a list of output from vegan::adonis()