Batch correction for GeoMX data

geomxBatchCorrection(
  spe,
  k,
  factors,
  NCGs,
  n_assay = 2,
  batch = NULL,
  batch2 = NULL,
  covariates = NULL,
  design = matrix(1, ncol(spe), 1),
  method = c("RUV4", "Limma")
)

Arguments

spe: A Spatial Experiment object.
k: The number of unwanted factors to use. Can be 0. This is required for the RUV4 method.
factors: Column name(s) to indicate the factors of interest. This is required for the RUV4 method.
NCGs: Negative control genes. This is required for the RUV4 method.
n_assay: Integer to indicate the nth count table in the assay(spe) to be used.
batch: A vector indicating batches. This is required for the Limma method.
batch2: A vector indicating the second series of batches. This is specific for the Limma method.
covariates: A matrix or vector of numeric covariates to be adjusted for.
design: A design matrix relating to treatment conditions to be preserved, can be generated using stats::model.matrix function with all biological factors included.
method: Can be either RUV4 or Limma, by default is RUV4.

Value

A Spatial Experiment object, containing the ruv4-normalized count and normalization factor.

References

Gagnon-Bartsch, J. A., Jacob, L., & Speed, T. P. (2013). Removing unwanted variation from high dimensional data with negative controls. Berkeley: Tech Reports from Dep Stat Univ California, 1-112.

Ritchie, M. E., Phipson, B., Wu, D. I., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic acids research, 43(7), e47-e47.

Examples

data("dkd_spe_subset")
spe <- findNCGs(dkd_spe_subset, top_n = 100)
#> New names:
#> • `cv` -> `cv...1`
#> • `cv` -> `cv...2`
#> • `cv` -> `cv...3`
#> • `cv` -> `cv...4`
#> • `cv` -> `cv...5`
#> • `cv` -> `cv...6`
#> • `cv` -> `cv...7`
spe_ruv <- geomxBatchCorrection(spe,
  k = 3,
  factors = c("disease_status", "region"),
  NCGs = S4Vectors::metadata(spe)$NCGs
)