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dr_pcoa

Source: R/dimension_reduction.R
dr_pcoa.Rd

Principal Coordinates Analysis (PCoA) is a dimensionality reduction method that transforms a distance matrix from a high-dimensional space into a low-dimensional space while retaining the original data's essential properties.

Usage

dr_pcoa(
  processed_se,
  dist_method = "euclidean",
  correction = "cailliez",
  clustering = "kmeans",
  cluster_num = 2,
  kmedoids_metric = NULL,
  distfun = NULL,
  hclustfun = NULL,
  eps = NULL,
  minPts = NULL
)

Arguments

processed_se

A SummarizedExperiment object constructed by as_summarized_experiment and processed by data_process.

dist_method

Character. The distance measure to be used for constructing the distance matrix. One of "bray", "euclidean", "manhattan", "jaccard", "canberra", etc. Default is "euclidean".

correction

Character. Correction methods for negative eigenvalues. One of "none", "lingoes", "cailliez". Default is "cailliez".

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 2 and (number of samples - 1).

  • "hclustering": A positive integer between 2 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

Numeric. The number of minimum points in the eps region (for core points) when choosing "dbscan" as clustering method. If "dbscan" is not selected as the clustering method, set the value to NULL.

Value

Return a list with 2 tables, 1 interactive plot, and 1 static plot.

  1. pcoa_rotated_data: a data frame of PCoA coordinates

  2. table_pcoa_contribution: a data frame, variance explained by each axis

  3. interactive_pcoa & static_pcoa: PCoA plot

Examples

data("profiling_data")
processed_se <- data_process(profiling_data, exclude_missing=TRUE, exclude_missing_pct=70,
replace_na_method='min', replace_na_method_ref=0.5, normalization='Percentage', transform='log10')
result_pca <- dr_pcoa(processed_se, dist_method='euclidean', correction='cailliez',
clustering='kmeans', cluster_num=2, kmedoids_metric=NULL, distfun=NULL,
hclustfun=NULL, eps=NULL, minPts=NULL)
#> Registered S3 methods overwritten by 'vegan':
#>   method         from      
#>   reorder.hclust seriation 
#>   rev.hclust     dendextend