geomxNorm: Perform normalization to GeoMX data

In DavisLaboratory/standR: Spatial transcriptome analyses of Nanostring's DSP data in R

Perform normalization to GeoMX data

Description

Perform normalization to GeoMX data

Usage

geomxNorm(
  spe_object,
  method = c("TMM", "RPKM", "TPM", "CPM", "upperquartile", "sizefactor"),
  log = TRUE
)

Arguments

spe_object	A SpatialExperiment object.
method	Normalization method to use. Options: TMM, RPKM, TPM, CPM, upperquartile, sizefactor. RPKM and TPM require gene length information, which should be added into rowData(spe). Note that TMM here is TMM + CPM.
log	Log-transformed or not.

Value

A SpatialExperiment object, with the second assay being the normalized count matrix. The normalised count is stored in the assay slot called "logcounts" by default. With method TMM and sizefactor, the norm.factor will be saved in the metadata of the SpatialExperiment object.

Note

The normalised count is not intended to be used directly for linear modelling. For linear modelling, it is better to include the normalized factors in the "norm.factors" column of the DGEList object.

References

Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139-140.

Love, M., Anders, S., & Huber, W. (2014). Differential analysis of count data–the DESeq2 package. Genome Biol, 15(550), 10-1186.

Examples

data("dkd_spe_subset")

spe_tmm <- geomxNorm(dkd_spe_subset, method = "TMM")
spe_upq <- geomxNorm(dkd_spe_subset, method = "upperquartile")
spe_deseqnorm <- geomxNorm(dkd_spe_subset, method = "sizefactor")

DavisLaboratory/standR documentation built on Dec. 6, 2024, 9:55 p.m.