dataScaling: dataScaling

In WilsonImmunologyLab/LinQView: Integrate information from both transcriptome and cell surface protein signals to better identify cell heterogeneity

Description

data scaling for both RNA and ADT (for later Heatmap viosualization)

Usage

dataScaling(
  object = NULL,
  features = NULL,
  assay = NULL,
  vars.to.regress = NULL,
  model.use = "linear",
  use.umi = FALSE,
  do.scale = TRUE,
  do.center = TRUE,
  scale.max = 10,
  block.size = 1000,
  min.cells.to.block = 3000,
  verbose = TRUE
)

Arguments

object	= NULL, A Seurat object
features	= NULL, features on data scaling
assay	= NULL, assay name, RNA, ADT, HTO
vars.to.regress	= NULL, vars.to.regress, same as Seurat, Variables to regress out (previously latent.vars in RegressOut). For example, nUMI, or percent.mito.
model.use	= "linear", same as Seurat, Use a linear model or generalized linear model (poisson, negative binomial) for the regression. Options are 'linear' (default), 'poisson', and 'negbinom'
use.umi	= FALSE, same as Seurat, Regress on UMI count data. Default is FALSE for linear modeling, but automatically set to TRUE if model.use is 'negbinom' or 'poisson'
do.scale	= TRUE, same as Seurat, Whether to scale the data.
do.center	= TRUE, same as Seurat, Whether to center the data.
scale.max	= 10, same as Seurat, Max value to return for scaled data. The default is 10. Setting this can help reduce the effects of feautres that are only expressed in a very small number of cells.
block.size	= 1000, same as Seurat, Default size for number of feautres to scale at in a single computation. Increasing block.size may speed up calculations but at an additional memory cost.
min.cells.to.block	= 3000, same as Seurat, If object contains fewer than this number of cells, don't block for scaling calculations.
verbose	= TRUE, same as Seurat, Displays a progress bar for scaling procedure

WilsonImmunologyLab/LinQView documentation built on Jan. 3, 2022, 10 p.m.