reduce_dim: Reducing dimensionality in count data

In jianhaizhang/spatialHeatmap: spatialHeatmap: Visualizing Spatial Assays in Anatomical Images and Large-Scale Data Extensions

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 [e-mail]
Dr. Thomas Girke [e-mail]

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 Nov. 28, 2024, 4:44 p.m.