plot_dim: Embedding plots of single cells/bulk tissues after...

In jianhaizhang/spatialHeatmap: spatialHeatmap

Embedding plots of single cells/bulk tissues after co-clustering

Description

Embedding plots of single cells/bulk tissues after co-clustering

Usage

plot_dim(
  sce,
  dim = NULL,
  color.by,
  group.sel = NULL,
  row.sel = NULL,
  cocluster.only = TRUE,
  x.break = NULL,
  y.break = NULL,
  panel.grid = FALSE,
  lgd.title.size = 13,
  lgd.key.size = 0.03,
  lgd.text.size = 12,
  point.size = 3,
  bulk.size = 5,
  alpha = 0.7,
  stroke = 0.2,
  bulk.stroke = 1,
  axis.text.size = 10,
  axis.title.size = 11,
  lgd.pos = "right",
  lgd.ncol = 1,
  lgd.l = 0,
  lgd.r = 0.01
)

Arguments

sce	A SingleCellExperiment object with reduced dimensions seen by reducedDimNames(sce).
dim	One of PCA, UMAP, TSNE, the method for reducing dimensionality.
color.by	One of the column names in the colData slot of sce.
group.sel	An entry in the color.by column. All cells under this entry are selected as a group to show.
row.sel	A numeric vector of row numbers in the colData slot of sce. The cells corresponding to these rows are highlighted and plotted on top of other cells.
cocluster.only	Logical, only applicable when color.by='cluster'. If TRUE (default), only coclusters (including bulk and cells) are colored and the rest are in gray.
x.break, y.break	Two numeric vectors for x, y axis breaks respectively. E.g. seq(-10, 10, 2). The default is NULL.
panel.grid	Logical. If TRUE, the panel grid will be shown.
lgd.title.size, lgd.key.size, lgd.text.size	The size of legend plot title, legend key, legend text respectively.
point.size, bulk.size	The size of cells and bulk tissues respectively.
alpha	The transparency of cells and bulk tissues. The default is 0.6.
stroke, bulk.stroke	The line width of cells and bulk tissues respectively.
axis.text.size, axis.title.size	The size of axis text and title respectively.
lgd.pos	The legend position, one of top, right, bottom, left.
lgd.ncol	The number of legend columns.
lgd.l, lgd.r	The left and right margins of legends.

Value

An object of ggplot.

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 H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016. Morgan M, Obenchain V, Hester J, Pagès H (2021). SummarizedExperiment: SummarizedExperiment container. R package version 1.24.0, https://bioconductor.org/packages/SummarizedExperiment. 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 <- logNormCounts(sce) 
# Modelling the variance.
var.stats <- modelGeneVar(sce) 
sce <- denoisePCA(sce, technical=var.stats, subset.row=rownames(var.stats)) 
plot_dim(sce, dim='PCA', color.by='Cell_Cycle')
# See function "coclus_meta" by running "?coclus_meta".

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