Plot.NanoStringNorm: Plot.NanoStringNorm

Description

Do some diagnostic and descriptive plots

Usage

Plot.NanoStringNorm(x, plot.type = 'norm.factors', samples = NA, trait = NA, label.best.guess = TRUE, label.ids = NA, title = TRUE)

Arguments

An object of class NanoStringNorm i.e. the list output of NanoStringNorm

plot.type

A vector of the different plots that you would like. Currently the following are implemented: normalization factors 'norm.factors', coefficient of variance 'cv' and mean vs sd 'mean.sd', 'volcano', 'missing', 'RNA.estimates', 'batch.effects' and 'positiv

samples

List of samples to use. Not yet implemented.

trait

A trait for to use for pretty plotting. Not yet implemented.

label.best.guess

Labels will automatically be added for samples and genes that are either outliers (~3sd from mean) or the most significant. The default is TRUE.

label.ids

Explicitly label points. To use this feature supply a list with with two elements named genes and samples. By default this is set to NA.

title

Should the plot titles be inclued. By default they are included but this may want to be turned off if generating plots for publication or presentation.

Value

The output is single or series of plots. Plots focus on either genes or samples. Due to variation in sample quality it is a good idea to review data in order to determine outliers. Outliers could effect normalization of other samples and bias results of downstream statistical analysis. Systematic batch effects in terms of cartridge, sample type or covariates should also be evaluated to minimize confounding results. \ The mean.sd plot has labels corresponding to highly expressed genes with the most and least variability. \ The volcano plot shows the relationship between p-value and foldchange for traits supplied in the normalization step. Orange dots have a fold-change greater than 2 and are sized proportional to the mean expression. \ The rna.content plot compares estimates of the amount of RNA. Any inconsistency between houskeeping and global RNA content estimates should be evaluated for assay issues. In the case of miRNA's this could be due to the housekeeping genes undergoing a different processing (no ligation). \ The batch.effect plot looks for differences in sample summary statistics among groups defined by traits supplied during normalization. Green dots are sized proportionally to their p-value which is less that 0.05. A cartridge variable is automatically added to the checked traits. Cartridge is assumed to be determined by column position in the input dataset. That is sample 1-12 from cartridge 1, 2-24 from cartridge 2 etc. Note that the Mean, SD and Missing are post normalization and therefore may show an opposite trend than expected due to overcompensating normalization factors. \ The norm.factors plot looks at the calculated normalization parameters for each sample. By default samples are labeled if one of the normalization factors greater than 100% from the mean. \ The positive.control plots compare the expected concentration with observed counts. Orange dots are the negative controls. The intercept reflects the sensitivity of the assay for that sample
} examples{
# load data data(NanoString);
# specifiy housekeeping genes in annotation NanoString.mRNA[NanoString.mRNA$Name %in% c('Eef1a1','Gapdh','Hprt1','Ppia','Sdha'),'Code.Class'] <- 'Housekeeping';
# setup the traits sample.names <- names(NanoString.mRNA)[-c(1:3)]; strain1 <- rep(1, times = (ncol(NanoString.mRNA)-3)); strain1[grepl('HW',sample.names)] <- 2; strain2 <- rep(1, times = (ncol(NanoString.mRNA)-3)); strain2[grepl('WW',sample.names)] <- 2; strain3 <- rep(1, times = (ncol(NanoString.mRNA)-3)); strain3[grepl('LE',sample.names)] <- 2; trait.strain <- data.frame( row.names = sample.names, strain1 = strain1, strain2 = strain2, strain3 = strain3 );
# normalize NanoString.mRNA.norm <- NanoStringNorm( x = NanoString.mRNA, anno = NA, CodeCount = 'geo.mean', Background = 'mean.2sd', SampleContent = 'housekeeping.geo.mean', round.values = TRUE, log = TRUE, traits = trait.strain, return.matrix.of.endogenous.probes = FALSE );
# plot all the plots as PDF report pdf('NanoStringNorm_Example_Plots_All.pdf') Plot.NanoStringNorm( x = NanoString.mRNA.norm, label.best.guess = TRUE, plot.type = 'all'); dev.off()
# publication quality tiff volcano plot tiff('NanoStringNorm_Example_Plots_Volcano.tiff', units = 'in', height = 6, width = 6, compression = 'lzw', res = 1200, pointsize = 10); Plot.NanoStringNorm( x = NanoString.mRNA.norm, label.best.guess = TRUE, plot.type = c('volcano'), title = FALSE ); dev.off()
# all plots as seperate files output for a presentation png('NanoStringNorm_Example_Plots_%03d.png', units = 'in', height = 6, width = 6, res = 250, pointsize = 10); Plot.NanoStringNorm( x = NanoString.mRNA.norm, label.best.guess = TRUE, plot.type = c('cv','mean.sd','RNA.estimates','volcano','missing','norm.factors','positive.controls','batch.effects') ); dev.off()
# user specified labelling with optimal resolution for most digital displays png('NanoStringNorm_Example_Plots_Normalization_Factors.png', units = 'in', height = 6, width = 6, res = 250, pointsize = 10); Plot.NanoStringNorm( x = NanoString.mRNA.norm, label.best.guess = FALSE, label.ids = list(genes = rownames(NanoString.mRNA.norm$gene.summary.stats.norm), samples = rownames(NanoString.mRNA.norm$sample.summary.stats)), plot.type = c('norm.factors') ); dev.off()
# testing png('test.png', units = 'in', height = 6, width = 6, res = 250, pointsize = 10); Plot.NanoStringNorm( x = NanoString.mRNA.norm, label.best.guess = FALSE, label.ids = list(genes = rownames(NanoString.mRNA.norm$gene.summary.stats.norm), samples = rownames(NanoString.mRNA.norm$sample.summary.stats)), plot.type = c('RNA.estimates') ); dev.off()
} author{Daryl M. Waggott}
keyword{NanoString} keyword{Normalization} keyword{mRNA} keyword{miRNA}