scRNAseq integration wrapper

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

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

See also

IntegrateData

Author

Nicholas Mikolajewicz