normalize | R Documentation |
This package performs normalization on NanoStringRccSet data using one of three methods.
normalize(object, ...)
object |
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... |
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Normalization is performed in one of three ways with data pulled from one slot of assayData and inserted into another. It is possible to overwrite the original slot of assayData if the fromElt and toElt are set to the same slot. nSolver
normalization uses positive controls to scale and housekeepers to standardize the data and mimics the normalization performed by default in the nSolver software. The Housekeeping-Log2
normalization calculates the log2 sizeFactor of the housekeeping genes and then takes 2^ log2 expression data centered by the log transformed sizeFactor. PositiveControl-Log2Log2
regresses the log2 positive control probes greater than 0.5 concentration on their geometric mean and then uses the intercept and slope to predict normalized values from the log2 transformed expression values. The predictions are then rescaled by 2^.
Additional parameters with NanoStringRccSet method include:
type
normalization method to use. Options are nSolver
, Housekeeping-Log2
, and PositiveControl-Log2Log2
fromElt
assayData slot from which to pull raw data
toElt
assayData slot to which normalized data will be inserted
The function returns a new NanoStringRccSet with either an additional assayData slot of normalized data, or overwrites the original assayData depending on whether fromElt and toElt are identical.
Patrick Aboyoun
NanoString nSolver User Manual https://www.nanostring.com/download_file/view/1168
datadir <- system.file("extdata", "3D_Bio_Example_Data",
package = "NanoStringNCTools")
rccs <- dir(datadir, pattern = "SKMEL.*\\.RCC$", full.names = TRUE)
rlf <- file.path(datadir, "3D_SolidTumor_Sig.rlf")
pheno <- file.path(datadir, "3D_SolidTumor_PhenoData.csv")
solidTumor <-
readNanoStringRccSet(rccs, rlfFile = rlf, phenoDataFile = pheno)
solidTumor <- normalize(solidTumor, "nSolver" , fromElt = "exprs", toElt = "exprs_norm")
head( assayDataElement( solidTumor , elt = "exprs_norm" ) )
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