miko_integrate: scRNAseq integration wrapper

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

scRNAseq integration wrapper

Description

scRNAseq normalization and integration wrapper. Given seurat object input, data are split, normalized and integrated.

Usage

miko_integrate(
  object,
  split.by = "Barcode",
  min.cell = 50,
  k.anchor = 20,
  k.weight = 35,
  nfeatures = 3000,
  split.prenorm = F,
  assay = "RNA",
  variable.features.n = 3000,
  verbose = T,
  use.existing.sct = F,
  conserve.memory = F,
  vars.to.regress = "percent.mt"
)

Arguments

object	Seurat object
split.by	Meta data feature to split and integrate data.
min.cell	Minimum number of cells permitted per object prior to integration.
k.anchor	How many neighbors (k) to use when picking anchors.
k.weight	Number of neighbors to consider when weighting anchors.
nfeatures	Number of features to return (passed to SelectIntegrationFeatures)
split.prenorm	Split data before (TRUE) or after (FALSE) normalization.
assay	Assay to use for normalization.
variable.features.n	Use this many features as variable features after ranking by residual variance; default is 3000.
verbose	Print progress. Default is TRUE.
use.existing.sct	If TRUE, existing SCT model is used. Default is FALSE (new SCT model is fit)
conserve.memory	If set to TRUE the residual matrix for all genes is never created in full when running SCTransform; useful for large data sets, but will take longer to run; this will also set return.only.var.genes to TRUE; default is FALSE
vars.to.regress	meta features to regress out. Default is "percent.mt". Set to NULL if unspecified.

Value

Integrated seurat object

Author(s)

Nicholas Mikolajewicz

See Also

IntegrateData

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