UMAP: Dimension Reduction.

In bioinfoDZ/RISC: Robust Integration of Single-Cell RNA-Seq Datasets

Dimension Reduction.

Description

The UMAP is calculated based on the eigenvectors of single cell dataset, and the user can select the eigenvectors manually. Of note, the selected eigenvectors directly affect UMAP values. For the integrated data (the result of "scMultiIntegrate" funciton), RISC utilizes the PCR output "PLS" to calculate the UMAP, therefore, the user has to input "PLS" in "use = ", instead of the default parameter "PCA".

Usage

scUMAP(
  object,
  npc = 20,
  embedding = 2,
  use = "PCA",
  neighbors = 15,
  dist = 0.1,
  seed = 123,
  ...
)

Arguments

object	RISC object: a framework dataset.
npc	The number of the PCs (or the PLS) using for UMAP, the default is 20, but need to be modified by the users. The PCA for individual dataset, while PLS for the integrated data.
embedding	The number of components UMAP output.
use	What components used for UMAP: PCA or PLS.
neighbors	The n_neighbors parameter of UMAP.
dist	The min_dist parameter of UMAP.
seed	The random seed to keep tSNE result consistent.

Value

RISC single cell dataset, the DimReduction slot.

References

Becht et al., Nature Biotech. (2018)

Examples

# RISC object
obj0 = raw.mat[[3]]
obj0 = scPCA(obj0, npc = 10)
obj0 = scUMAP(obj0, npc = 3)
DimPlot(obj0, slot = "cell.umap", colFactor = 'Group', size = 2)

bioinfoDZ/RISC documentation built on March 30, 2024, 9:19 p.m.