Normalizes data in a grandR object and puts the normalized data into a new slot
Usage
Normalize(
data,
genes = Genes(data),
name = "norm",
slot = "count",
set.to.default = TRUE,
size.factors = NULL,
return.sf = FALSE
)
NormalizeFPKM(
data,
genes = Genes(data),
name = "fpkm",
slot = "count",
set.to.default = TRUE,
tlen = GeneInfo(data, "Length")
)
NormalizeRPM(
data,
genes = Genes(data),
name = "rpm",
slot = "count",
set.to.default = TRUE,
factor = 1e+06
)
NormalizeTPM(
data,
genes = Genes(data),
name = "tpm",
slot = "count",
set.to.default = TRUE,
tlen = GeneInfo(data, "Length")
)Arguments
- data
the grandR object
- genes
compute the normalization w.r.t. these genes (see details)
- name
the name of the new slot for the normalized data
- slot
the name of the slot for the data to normalize
- set.to.default
set the new slot as the default slot
- size.factors
numeric vector; if not NULL, use these size factors instead of computing size factors
- return.sf
return the size factors and not a grandR object
- tlen
the transcript lengths (for FPKM and TPM)
- factor
the rpm factor (default: 1 (m)illion)
Details
Normalize will perform DESeq2 normalization, i.e. it will use estimateSizeFactorsForMatrix to estimate size factors, and divide each value by this. If genes are given, size factors will be computed only w.r.t. these genes (but then all genes are normalized).
NormalizeFPKM will compute fragments per kilobase and million mapped reads. If genes are given, the scaling factor will only be computed w.r.t. these genes (but then all genes are normalized).
NormalizeRPM will compute reads per million mapped reads. If genes are given, the scaling factor will only be computed w.r.t. these genes (but then all genes are normalized).
NormalizeTPM will compute transcripts per million mapped reads. If genes are given, the scaling factor will only be computed w.r.t. these genes (but then all genes are normalized).
Genes can be referred to by their names, symbols, row numbers in the gene table, or a logical vector referring to the gene table rows.
Examples
sars <- ReadGRAND(system.file("extdata", "sars.tsv.gz", package = "grandR"),
design=c("Cell",Design$dur.4sU,Design$Replicate))
#> Warning: Duplicate gene symbols (n=1, e.g. MATR3) present, making unique!
sars <- Normalize(sars)
DefaultSlot(sars)
#> [1] "norm"