project {theftdlc}

Project a feature matrix into a two-dimensional representation using PCA, MDS, t-SNE, or UMAP ready for plotting

Description

Project a feature matrix into a two-dimensional representation using PCA, MDS, t-SNE, or UMAP ready for plotting

Usage

project(
  data,
  norm_method = c("zScore", "Sigmoid", "RobustSigmoid", "MinMax"),
  unit_int = FALSE,
  low_dim_method = c("PCA", "tSNE", "ClassicalMDS", "KruskalMDS", "SammonMDS", "UMAP"),
  na_removal = c("feature", "sample"),
  seed = 123,
  ...
)

reduce_dims(
  data,
  norm_method = c("zScore", "Sigmoid", "RobustSigmoid", "MinMax"),
  unit_int = FALSE,
  low_dim_method = c("PCA", "tSNE", "ClassicalMDS", "KruskalMDS", "SammonMDS", "UMAP"),
  na_removal = c("feature", "sample"),
  seed = 123,
  ...
)

Arguments

data

feature_calculations object containing the raw feature matrix produced by theft::calculate_features

norm_method

character denoting the rescaling/normalising method to apply. Can be one of "zScore", "Sigmoid", "RobustSigmoid", "MinMax", or "MaxAbs". Defaults to "zScore"

unit_int

Boolean whether to rescale into unit interval [0,1] after applying normalisation method. Defaults to FALSE

low_dim_method

character specifying the low dimensional embedding method to use. Can be one of "PCA", "tSNE", "ClassicalMDS", "KruskalMDS", "SammonMDS", or "UMAP". Defaults to "PCA"

na_removal

character defining the way to deal with NAs produced during feature calculation. Can be one of "feature" or "sample". "feature" removes all features that produced any NAs in any sample, keeping the number of samples the same. "sample" omits all samples that produced at least one NA. Defaults to "feature"

seed

integer to fix R's random number generator to ensure reproducibility. Defaults to 123

...

arguments to be passed to stats::prcomp or Rtsne::Rtsne, stats::cmdscale, MASS::isoMDS, MASS::sammon, or umap::umap depending on selection in low_dim_method

Value

object of class feature_project which is a named list containing the feature_calculations data supplied to the function, the wide matrix of filtered data, a tidy data.frame of the projected 2-D data, and the model fit object

Author(s)

Trent Henderson

Examples

library(theft)

features <- theft::calculate_features(theft::simData,
  group_var = "process",
  feature_set = "catch22")

pca <- project(features,
  norm_method = "zScore",
  low_dim_method = "PCA")