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Introduction to pcapkg

pcapkg.Rmd

The pcapkg package contains the general functions necessary for the unsupervised exploratory analysis of high-dimensional proteomic data using Principal Component Analysis (PCA). Primary tools related to calculation of principal components and the visualization of rotation (loadings) and projection spaces of these components.

Useful functions in pcapkg

  • pca()
    • perform principal component analysis (PCA) on a matrix of high-dimensional proteomic data.
  • plot_scree()
    • generate a “scree”-plot of the top variances for a pca class object.
  • plot_rotation(), plot_projection()
    • plot specific rotation and projection space objects
  • get_*()
    • various “getter” functions to retreive features from the PCA.

The PCA

Perform principal component analysis (PCA) on a 2-dim data matrix, via the decomposition of the variance-covariance matrix (SVD). Some modification of the standard stats::prcomp() is performed. Data transformations (e.g. centering and/or scaling) can be performed via its arguments.

pr <- pca(pcapkg:::log10_ft(simdata))
pr
#> ── PCA Object ─────────────────────────────────────────────────────────
#> Rotation                 40 x 49 (PC1:PC40)
#> Projection               100 x 56 (PC1:PC40)
#> Projection variables     '.id', 'id', 'class_response', 'reg_response', 'time', 'status', 'SampleId', 'SlideId', 'Subarray', 'SiteId', 'age', 'gender', 'HybControlNormScale', 'NormScale_40', 'NormScale_0_005', 'NormScale_1'
#> ═══════════════════════════════════════════════════════════════════════

Plotting

There are convenient plotting methods for both the rotation and projection spaces.

Rotation space 

plot(pr, "r", identify = TRUE)

Projection space 

plot(pr, "p", color = SiteId)                   # unquoted string!

plot(pr, "p", color = SiteId, id.labels = id)   # with labels

Combine rotation and projection 

rot  <- plot(pr, "r", identify = TRUE)
proj <- plot(pr, "p", color = SiteId, id.labels = id) +
  theme(legend.position = "top")
rot + proj

Corresponding Scree plots 

a <- plot_scree(pr, n = 10L)
b <- plot_scree(pr, n = 10L, type = "l")
a + b