reduce_dim: Reducing dimensionality in count data

View source: R/reduce_dim.R

reduce_dimR Documentation

Reducing dimensionality in count data

Description

A meta function for reducing dimensionality in count data.

Usage

reduce_dim(
  sce,
  prop = 0.1,
  min.dim = 13,
  max.dim = 50,
  model.var = list(assay.type = "logcounts"),
  top.hvg = list(),
  de.pca = list(assay.type = "logcounts"),
  pca = FALSE,
  tsne = list(dimred = "PCA", ncomponents = 2),
  umap = list(dimred = "PCA")
)

Arguments

sce

Normalized single cell data in SingleCellExperiment returned by norm_cell. Alternative forms include dgCMatrix, matrix, data.frame.

prop

Numeric scalar specifying the proportion of genes to report as highly variable genes (HVGs) in getTopHVGs. The default is 0.1.

min.dim, max.dim

Integer scalars specifying the minimum (min.dim) and maximum (max.dim) number of (principle components) PCs to retain respectively in denoisePCA. The default is min.dim=11, max. dim=50.

model.var

Additional arguments in a named list passed to modelGeneVar.

top.hvg

Additional arguments in a named list passed to getTopHVGs, such as top.hvg=list(n = 3000).

de.pca

Additional arguments in a named list passed to denoisePCA, such as de.pca=list(assay. type = "logcounts").

pca

Logical, if TRUE only the data with reduced dimentionality by PCA is returned and no clustering is performed. The default is FALSE and clustering is performed after dimensionality reduction.

tsne

Additional arguments in a named list passed to runTSNE, such as tsne=list(dimred="PCA", ncomponents=2).

umap

Additional arguments in a named list passed to runUMAP, such as umap=list(dimred="PCA").

Value

A SingleCellExperiment object.

Author(s)

Jianhai Zhang jzhan067@ucr.edu
Dr. Thomas Girke thomas.girke@ucr.edu

References

Amezquita R, Lun A, Becht E, Carey V, Carpp L, Geistlinger L, Marini F, Rue-Albrecht K, Risso D, Soneson C, Waldron L, Pages H, Smith M, Huber W, Morgan M, Gottardo R, Hicks S (2020). “Orchestrating single-cell analysis with Bioconductor.” Nature Methods, 17, 137–145. https://www.nature.com/articles/s41592-019-0654-x. Lun ATL, McCarthy DJ, Marioni JC (2016). “A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor.” F1000Res., 5, 2122. doi: 10.12688/f1000research.9501.2. McCarthy DJ, Campbell KR, Lun ATL, Willis QF (2017). “Scater: pre-processing, quality control, normalisation and visualisation of single-cell RNA-seq data in R.” Bioinformatics, 33, 1179-1186. doi: 10.1093/bioinformatics/btw777.

Examples

library(scran); library(scuttle) 
sce <- mockSCE()
sce.qc <- qc_cell(sce, qc.metric=list(subsets=list(Mt=rowData(sce)$featureType=='mito'), threshold=1))
sce.norm <- norm_cell(sce.qc)
sce.dimred <- reduce_dim(sce.norm)

jianhaizhang/spatialHeatmap documentation built on July 31, 2024, 2:59 a.m.