normalize.per.chip: Wrapper function to normalize raw data, per cartridge

In uclahs-cds/public-R-NanoStringNormCNV: Helper Functions to Normalize NanoString CNV Data

Wrapper function to normalize raw data, per cartridge

Description

Wrapper function to apply all normalization functions requested to samples by cartridge

Usage

  normalize.per.chip(
  	phenodata, raw.data, cc, bc, sc, oth, do.rcc.inv, covs,
    transform.data = TRUE, plot.types = 'all'
  	)

Arguments

phenodata	A data-frame of sample annotation which includes the fields 'SampleID' and 'Cartridge'. Recommended to use output of load.phenodata
raw.data	A gene by sample data-frame of raw NanoString counts for all samples in experiment. First three column names must be 'CodeClass', 'Name', and 'Accession', followed by sample IDs
cc	Requested CodeCount NSN normalization. Can be 'none', 'sum', and 'geo.mean'.
bc	Requested Background NSN normalization. Can be 'none', 'mean', 'mean.2sd' and 'max'.
sc	Requested SampleContent NSN normalization. Can be 'none', 'housekeeping.geo.mean', 'total.sum', 'top.geo.mean' and 'low.cv.geo.mean'. To run 'housekeeping.geo.mean', user must ensure the NanoString dataset includes probes belonging to the 'Housekeeping' code class. These can be probes that were found to have consistently low variance across data.
oth	Requested OtherNorm NSN normalization. Can be 'none', 'vsn' and 'quant'.
do.rcc.inv	Whether or not to run invariant probe normalization. This method corrects for sample content and can be used in place of 'sc'. It is run separately from NSN and is the technique recommended in NanoString guidelines.
covs	A data-frame of covariates with which to assess batch effects in NanoStringNorm. Passed directly to NanoStringNorm::NanoStringNorm 'traits' argument (see function for specifics). Set to NA if none.
transform.data	Whether to transform data so output does not contain negative values. Defaults to TRUE
plot.types	Which NSN plots to generate, if any, after normalization. Passed directly to NanoStringNorm::Plot.NanoStringNorm. Defaults to 'all'

Details

Normalizes samples in experiment one cartridge at a time using NanoStringNorm and/or invariant probe normalization. Normalization methods requested are implemented in the following order: code count, background correction, sample content, invariant probe, and other. See NanoStringNorm and invariant.probe.norm for more details.

Value

A gene by sample data-frame of normalized counts, where first three columns contain 'CodeClass', 'Name', and 'Accession' information

Author(s)

Cindy Yao and Emilie Lalonde

References

See NanoString website for PDFs on analysis guidelines: https://www.nanostring.com/support/product-support/support-documentation

The NanoString assay is described in the paper: Fortina, P. & Surrey, S. Digital mRNA profiling. Nature Biotechnology 26, 293-294 (2008).

The NanoStringNorm package is described in the paper: Waggott, D., Chu, K., Yin, S., Wouters, B.G., Liu, F.F. & Boutros, P.C. NanoStringNorm: an extensible R package for the pre-processing of NanoString mRNA and miRNA data. Bioinformatics 28(11), 1546-1548 (2012).

See Also

NanoStringNorm::NanoStringNorm, normalize.global

Examples

## Not run: 
  # load data
  data(NanoString.DNA.raw);
  data(PhenoData);

  ## perform only invariant probe normalization
  NanoString.DNA.norm <- normalize.per.chip(
    raw.data = NanoString.DNA.raw,
    cc = 'none',
    bc = 'none',
    sc = 'none',
    oth = 'none',
    do.rcc.inv = TRUE,
    covs = NA,
    phenodata = PhenoData
    );

  ## accounting for batch effects
  # include covariates for sample cartridge and sample type 
  # must be binary as these are passed directly to NanoStringNorm 'traits'
  covs <- as.data.frame(matrix(
    1,
    nrow = nrow(PhenoData),
    ncol = length(unique(PhenoData$Cartridge)),
    dimnames = list(
      PhenoData$SampleID,
      paste0("Cartridge", unique(PhenoData$Cartridge))
      )
    ));

  for (n in 1:nrow(PhenoData)) {
    covs[n, which(unique(PhenoData$Cartridge) == PhenoData$Cartridge[n])] <- 2;
    }

  covs$Type <- ifelse(PhenoData$Type == 'Reference', 1, 2);

  # normalize
  NanoString.DNA.norm <- normalize.per.chip(
    raw.data = NanoString.DNA.raw,
    cc = 'none',
    bc = 'none',
    sc = 'none',
    oth = 'none',
    do.rcc.inv = TRUE,
    covs = covs,
    phenodata = PhenoData
    );

## End(Not run)

uclahs-cds/public-R-NanoStringNormCNV documentation built on May 31, 2024, 9:09 p.m.