tools.norm.Nanostring: Normalize a Nanostring dataset

In jtcasemajor/GENEXPRESSO: Comparison of DEG and coexpression tools

Description

Normalization through different methods in several packages

Usage

tools.norm.Nanostring(raw.data, tool, nanoR = F, dir = NULL)

Arguments

raw.data	rcc type file. List of 4 elements: Samples.IDs is a dataframe with four columns: sample name, status (WildType, Mutated), filename. rcc.df contains expression levels with samples in column and genes in rows. annots.df is a dataframe with 3 columns: geneclass (endogenous, housekeeping, negative, positive), gene name, and the accession number. FOV is a dataframe that contains Field of views information.
tool	Normalization tool. "nappa.NS", "nappa.param1","nappa.param2","nappa.param3" are different parameters used with the NAPPA() function from the NAPPA package. "nanostringnorm.default","nanostringnorm.param1","nanostringnorm.param2" use the NanoStringNorm() normalization function from the package NanoStringNorm "nanostringR" uses the HKnorm() function from the package nanostringr. "nanoR.top100","nanoR.total" uses the nsNormalize() function from the nanoR package. For the nanoR package, it is needed to give the file path to rcc files
nanoR	Logical value. TRUE is necessary if the tool used is "nanoR.top100" or "nanoR.total". By default, the value is set on FALSE
dir	directory of rcc files. This parameter is only necessary if the nanoR normalizations are wanted.

Value

dataframe of normalized expression values

Examples

# Retrieve Nanostring Data
# Data = Simul.data(type = "Nanostring")
# Normalize data using one method :
# Norm.data = tools.norm.Nanostring(raw.data = Data, tool = "nappa.NS",nanoR=F)
#with nanoR : Give the rcc files location
#RCC.dir <- file.path("./DATA/NANOSTRING","GSE146204_RAW")
#Norm.data = tools.norm.Nanostring(raw.data = Data, tool = "nanoR.top100",dir = RCC.dir,nanoR=T)

jtcasemajor/GENEXPRESSO documentation built on Dec. 21, 2021, 4:11 a.m.