runMS: Run Modular Scoring

In NMikolajewicz/scMiko: scRNAseq analysis functions. Developed using Seurat framework.

Run Modular Scoring

Description

Wrapper for Seurat::AddModuleScore(). Calculates module scores for feature expression programs in single cells.

Usage

runMS(
  object,
  genelist,
  assay = DefaultAssay(object),
  scale = T,
  score.key = "MS",
  size = autoPointSize(ncol(object)),
  ncore = 10,
  raster = F,
  rescale = F,
  verbose = T,
  winsorize.quantiles = c(0, 1),
  return.plots = T,
  search = F,
  reduction = "umap",
  ...
)

Arguments

object	Seurat object
genelist	Named list of genesets.
assay	Assay used for expression matrix.
scale	scale module scores. Default is T.
score.key	Expression program prefix. default is "MS".
size	UMAP point size.
ncore	Number of workers for parallelized implementation. Default is 10.
raster	Convert points to raster format, default is FALSE.
rescale	rescale values from 0 to 1. Default is FALSE.
verbose	Print progress. Default is TRUE.
winsorize.quantiles	Rescale values to lie between lower and upper bound quanitle. Default = c(0,1).
return.plots	Logical to compute and return plots in results list. Default is TRUE.
search	Search for symbol synonyms for features in features that don't match features in object? Searches the HGNC's gene names database; see UpdateSymbolList for more details. Default is FALSE.
reduction	reduction slot used for visualized data. Default is "umap".
...	additional parameters passed to geom_point(...)

Value

list of results along with ggplot handles visualizing class predictions overlaid on UMAP.

Author(s)

Nicholas Mikolajewicz

See Also

AddModuleScore

Examples

# get genesets
verhaak.df <- geneSets[["Verhaak_CancerCell_2010"]]
verhaak.list <- wideDF2namedList(verhaak.df)

gsc.df <- geneSets[["Richards_NatureCancer_2021_sc"]]
gsc.list <- wideDF2namedList(gsc.df)

neftel.df <- geneSets[["GBM_Hs_Neftel2019"]]
neftel.list <- wideDF2namedList(neftel.df)

verhaak.list <- lapply(verhaak.list, toupper)
gsc.list <- lapply(gsc.list, toupper)
neftel.list <- lapply(neftel.list, toupper)

# classify cells based on provided genesets
v.auc <- runMS(object = so.query, genelist = verhaak.list)
v.auc$plot.max.score

g.auc <- runMS(object = so.query, genelist = gsc.list)
g.auc$plot.max.score

n.auc <- runMS(object = so.query, genelist = neftel.list)
n.auc$plot.max.score

NMikolajewicz/scMiko documentation built on June 28, 2023, 1:41 p.m.