dr_plsda — dr_plsda • LipidSigR

Partial least squares Discriminant Analysis (PLS-DA) is a dimensionality reduction method that transforms data from a high-dimensional space into a low-dimensional space while retaining the original data's essential properties.

dr_plsda(
  de_se,
  ncomp = 2,
  scaling = TRUE,
  clustering = c("kmeans", "kmedoids", "hclustering", "dbscan", "group_info"),
  cluster_num = 2,
  kmedoids_metric = NULL,
  distfun = NULL,
  hclustfun = NULL,
  eps = NULL,
  minPts = NULL
)

Arguments

de_se: The resulting SummarizedExperiment object from the differential expression analysis function, such as deSp_twoGroup, deSp_multiGroup, deChar_twoGroup, and deChar_multiGroup.
ncomp: Numeric. The number of components to include in the model. Default is 2.
scaling: Logical. If scaling = TRUE, each block is standardized to zero means and unit variances. Default is TRUE.
clustering: Character. The method to be used for clustering. Allowed method include "kmeans", "kmedoids", "hclustering", "dbscan", "group_info". Default is "kmeans".
cluster_num: Numeric. A positive integer specifying the number of clusters. The number must be between 1 and 10. Default is 2.
kmedoids_metric: Character. The metric to be used for calculating dissimilarities between observations when choosing "kmedoids" as clustering method. Must be one of "euclidean" and "manhattan". If "kmedoids" is not selected as the clustering method, set the value to NULL.
distfun: Character. The distance measure to be used when choosing "hclustering" as clustering method. Allow method include "pearson", "kendall", "spearman", "euclidean", "manhattan", "maximum", "canberra", "binary", and "minkowski". If "hclustering" is not selected as the clustering method, set the value to NULL.
hclustfun: Character. The agglomeration method to be used when choosing "hclustering" as clustering method. This should be (an unambiguous abbreviation of) one of "ward.D", "ward.D2", "single", "complete", "average" (=UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC), or "centroid" (= UPGMC). If "hclustering" is not selected as the clustering method, set the value to NULL.
eps: Numeric. The size of the epsilon neighborhood when choosing "dbscan" as clustering method. If "dbscan" is not selected as the clustering method, set the value to NULL.
minPts: number of minimum points in the eps region (for core points) when choosing dbscan as clustering method.

Value

Return a list with 1 data frame, 1 interactive plot, and 1 static plot.

plsda_result: A data frame of PLS-DA data.
table_plsda_loading: table for plotting PLS-DA loading plot.
interacitve_plsda & static_plsda: PLS-DA plot.
interactive_loadingPlot & static_loadingPlot: PLS-DA loading plot, display the variables that contribute to the definition of each component.

Examples

data("de_data_twoGroup")
processed_se <- data_process(
    de_data_twoGroup, exclude_missing=TRUE, exclude_missing_pct=70,
    replace_na_method='min', replace_na_method_ref=0.5,
    normalization='Percentage')
deSp_se <- deSp_twoGroup(processed_se, ref_group='ctrl', test='t-test',
    significant='pval', p_cutoff=0.05, FC_cutoff=1, transform='log10')
result_plsda <- dr_plsda(deSp_se, ncomp=2, scaling=TRUE, clustering='group_info',
    cluster_num=2, kmedoids_metric=NULL, distfun=NULL, hclustfun=NULL, eps=NULL, minPts=NULL)
#> Warning: The following aesthetics were dropped during statistical transformation:
#> x_plotlyDomain, y_plotlyDomain
#> ℹ This can happen when ggplot fails to infer the correct grouping structure in
#>   the data.
#> ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
#>   variable into a factor?