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Calculate Principal Components Analysis (PCA)

get_PCs.Rd

Performs a principal component analysis (PCA) on the samples within a SummarizedExperiment object.

Usage

get_PCs(object, condition = NA)

# S4 method for class 'SummarizedExperiment'
get_PCs(object, condition = NA)

Arguments

object

A SummarizedExperiment object. The first assay is used.

condition

A character string specifying the column name in colData(object) to use for grouping the samples in the output. If NA (the default), groups are inferred from sample names by removing numeric suffixes.

Value

A list with two elements:

summary

A data.table with the standard deviation, proportion of variance, and cumulative proportion for each component.

components

A data frame with the first 5 PCA scores (PC1-PC5) for each sample, along with their assigned condition.

Details

This function expects data that has already been cleaned and imputed. Missing values (NA) will cause the PCA to fail. It is also highly recommended to perform log-transformation and normalization before PCA. The function will automatically remove proteins (rows) with zero variance across samples before running the analysis.

Functions

  • get_PCs(SummarizedExperiment): Method for SummarizedExperiment objects.

Examples

# --- Create a sample SummarizedExperiment object ---
set.seed(123)
counts <- matrix(rnorm(100 * 6, mean = 10, sd = 2), nrow = 100, ncol = 6)
colnames(counts) <- paste0("Sample_", rep(c("A","B"), each=3), "_", 1:3)
sample_info <- data.frame(
  row.names = colnames(counts),
  Group = rep(c("A", "B"), each = 3)
)
se <- SummarizedExperiment::SummarizedExperiment(assays = list(log_intensities = counts),
                           colData = sample_info)

# --- Run PCA, specifying the group variable ---
pca_results <- get_PCs(se, condition = "Group")
head(pca_results$components)
#>                   PC1        PC2        PC3        PC4        PC5     Sample
#> Sample_A_1 -0.5406994 -6.9038248  1.0758652 -5.7618855  1.0603314 Sample_A_1
#> Sample_A_2  0.2890845 -3.8033696  0.7192578  7.2782128  2.9474868 Sample_A_2
#> Sample_A_3  2.3225423  1.7361782 -8.7966929 -0.7086532  0.1850881 Sample_A_3
#> Sample_B_1 -9.4317852  2.7544166  0.3131402  0.1892512 -1.6532474 Sample_B_1
#> Sample_B_2  3.0444035  5.9119874  4.0189782 -1.9767836  4.1974578 Sample_B_2
#> Sample_B_3  4.3164542  0.3046121  2.6694515  0.9798583 -6.7371168 Sample_B_3
#>            Condition
#> Sample_A_1         A
#> Sample_A_2         A
#> Sample_A_3         A
#> Sample_B_1         B
#> Sample_B_2         B
#> Sample_B_3         B