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dr_plsda

Source: R/dimension_reduction.R
dr_plsda.Rd

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.

Usage

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". The option "group_info" is currently available only when the input is a SummarizedExperiment object resulting from differential expression analysis (e.g., deSp_twoGroup(), deSp_multiGroup()); in this case, dimensionality reduction is performed based on the significant features and group information derived from the DE results.

cluster_num

Numeric. The interpretation of cluster_num depends on the value of clustering:

  • "group_info": A positive integer equal to the number of groups.

  • "kmeans" or "kmedoids": A positive integer between 1 and (number of samples - 1).

  • "hclustering": A positive integer between 1 and the number of samples.

  • "dbscan": Should be NULL.

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.

  1. plsda_result: A data frame of PLS-DA data.

  2. table_plsda_loading: table for plotting PLS-DA loading plot.

  3. interacitve_plsda & static_plsda: PLS-DA plot.

  4. 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', transform='log10')
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)