Principal Components Analysis Calculation

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.

Usage

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

Arguments

object: (AnyHermesData)
input.
assay_name: (string)
name of the assay to use.
n_top: (count or NULL)
filter criteria based on number of genes with maximum variance.

Value

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

Details

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.

Examples

object <- hermes_data %>%
  add_quality_flags() %>%
  filter() %>%
  normalize()

result <- calc_pca(object, assay_name = "tpm")
summary(result)
#> Importance of first k=18 (out of 19) components:
#>                            PC1     PC2     PC3      PC4      PC5     PC6
#> Standard deviation     22.9971 18.7315 16.3042 13.47009 13.05843 11.4881
#> Proportion of Variance  0.2212  0.1467  0.1112  0.07589  0.07132  0.0552
#> Cumulative Proportion   0.2212  0.3679  0.4791  0.55500  0.62632  0.6815
#>                             PC7     PC8     PC9    PC10    PC11    PC12    PC13
#> Standard deviation     10.60653 9.67291 9.29607 8.97324 8.54474 8.11786 7.70847
#> Proportion of Variance  0.04705 0.03913 0.03614 0.03368 0.03054 0.02756 0.02485
#> Cumulative Proportion   0.72857 0.76770 0.80384 0.83752 0.86805 0.89562 0.92047
#>                           PC14    PC15    PC16    PC17    PC18
#> Standard deviation     7.20798 6.91976 6.11309 5.77360 4.42960
#> Proportion of Variance 0.02173 0.02003 0.01563 0.01394 0.00821
#> Cumulative Proportion  0.94220 0.96222 0.97785 0.99179 1.00000

result1 <- calc_pca(object, assay_name = "tpm", n_top = 500)
summary(result1)
#> Importance of first k=18 (out of 19) components:
#>                            PC1    PC2     PC3     PC4     PC5     PC6     PC7
#> Standard deviation     11.2652 9.6518 6.89353 6.34548 5.29254 5.20812 4.70857
#> Proportion of Variance  0.2538 0.1863 0.09504 0.08053 0.05602 0.05425 0.04434
#> Cumulative Proportion   0.2538 0.4401 0.53517 0.61570 0.67172 0.72597 0.77031
#>                           PC8     PC9   PC10    PC11   PC12    PC13    PC14
#> Standard deviation     4.2012 4.06409 3.7884 3.51647 3.2405 3.07753 3.04570
#> Proportion of Variance 0.0353 0.03303 0.0287 0.02473 0.0210 0.01894 0.01855
#> Cumulative Proportion  0.8056 0.83864 0.8673 0.89208 0.9131 0.93202 0.95058
#>                           PC15    PC16    PC17    PC18
#> Standard deviation     2.87077 2.65226 2.46289 1.83598
#> Proportion of Variance 0.01648 0.01407 0.01213 0.00674
#> Cumulative Proportion  0.96706 0.98113 0.99326 1.00000

# Plot the results.
autoplot(result)

autoplot(result, x = 2, y = 3)

autoplot(result, variance_percentage = FALSE)

autoplot(result, label = TRUE, label.repel = TRUE)