NanoStringNorm (version 1.1.6)

norm.comp: norm.comp

Description

Compare normalizaton methods using signal to noise CV and replicates ICC.

Usage

norm.comp(x, anno, replicates,  CodeCount.methods = c('none', 'sum', 'geo.mean'), Background.methods = c('none','mean', 'mean.2sd','max'), SampleContent.methods = c('none','housekeeping.sum', 'housekeeping.geo.mean', 'total.sum','top.mean', 'top.geo.mean'), OtherNorm.methods = c('none','quantile','zscore', 'rank.normal', 'vsn'),  histogram = FALSE, verbose = TRUE)

Arguments

x

The data used for Normalization. This is typically the raw expression data as exported from an Excel spreadsheet. If anno is NA then the first three columns must be c('Code.Class', 'Name', 'Accession') and the remaining columns refer to the sam

anno

Alternatively, anno can be used to specify the first three annotation columns of the expression data. If anno used then it assumed that 'x' does not contain these data. Anno allows flexible inclusion of alternative annotation data. The only requirement

replicates

A vector of IDs indicating what samples are replicates. Replicate samples need to have the same ID.

CodeCount.methods

vector of methods to compare

Background.methods

vector of methods to compare

SampleContent.methods

vector of methods to compare

OtherNorm.methods

vector of methods to compare

histogram

logical if histogram is to be output

verbose

logical if logging should be output

Details

The methods used for OtherNorm are designed to be extensible to alternate and evolving NanoString pre-processing analysis. These can be combined with standard CodeCount, Background, SampleContent methods (i.e. positive, negative and housekeeping controls).

Examples

# load the NanoString.mRNA dataset
data(NanoString);


# specifiy housekeeping genes in annotation
NanoString.mRNA[NanoString.mRNA$Name %in% c('Eef1a1','Gapdh','Hprt1','Ppia','Sdha'),'Code.Class'] <- 'Housekeeping';

# strain x experimental condition i.e. replicate.
# this is only a small subset of the original data used for the plot
biological.replicates <- c("HW_1.5_0","HW_1.5_0","HW_1.5_0","HW_1.5_100","HW_1.5_100","HW_1.5_100","HW_6_100","HW_6_100","HW_3_100","HW_3_100","HW_3_100","HW_3_100","LE_19_0","LE_19_0","LE_19_0","LE_96_0","LE_96_0","LE_96_0","HW_10_100","HW_10_100","HW_10_100","HW_10_100","HW_6_100","HW_6_100","HW_96_0");

norm.comp.results <- norm.comp(
x = NanoString.mRNA,
replicates = biological.replicates,
CodeCount.methods = 'none',
Background.methods = 'none',
SampleContent.methods = c('none','housekeeping.sum', 'housekeeping.geo.mean','top.mean', 'top.geo.mean'),
OtherNorm.methods = 'none',
verbose = FALSE
);