dr_umap

Source: R/dimension_reduction.R

Uniform Manifold Approximation and Projection (UMAP) 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_umap(
  processed_se,
  n_neighbors = 15,
  scaling = c("none", FALSE, NULL, "Z", "scale", TRUE, "maxabs", "range", "colrange"),
  umap_metric = c("euclidean", "cosine", "manhattan", "hamming", "categorical"),
  clustering = c("kmeans", "kmedoids", "hclustering", "dbscan", "group_info"),
  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. (NOTE: A SummarizedExperiment object generated by deSp_twoGroup, deChar_twoGroup, deSp_multiGroup, or deChar_multiGroup is also allowed.)

n_neighbors

Numeric. The size of local neighborhood (in terms of number of neighboring sample points) used for manifold approximation.

scaling

Logical/Character. Scaling to apply to X if it is a data frame or matrix:

  • "none" or FALSE or NULL: No scaling.

  • "Z" or "scale" or TRUE: Scale each column to zero mean and variance 1.

  • "maxabs": Center each column to mean 0, then divide each element by the maximum absolute value over the entire matrix.

  • "range": Range scale the entire matrix, so the smallest element is 0 and the largest is 1.

  • "colrange": Scale each column in the range (0,1).

Default is TRUE.

umap_metric

Character. Type of distance metric to use to find nearest neighbors. One of "euclidean", "cosine", "manhattan", "hamming", "categorical". Default is 'euclidean'.

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

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 1 data frame, 1 interactive plot, and 1 static plot.

  1. umap_result: a data frame of UMAP data.

  2. interactive_umap & static_umap: UMAP 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')
result_umap <- dr_umap(processed_se, n_neighbors=15, scaling=TRUE, umap_metric='euclidean',
    clustering='kmeans', 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?