NanoStringQCPro: Quality metrics and data processing methods for NanoString mRNA gene expression dat

addCodesetAnnotation	Add NanoString codeset annotation to an RccSet
addQCFlags	Add sample QC flags to an rccSet
allSumPlot	allSumPlot
assessHousekeeping	assessHousekeeping
bdPlot	Binding density plot
buildCodesetAnnotation	Build NanoString codeset annotation
checkRccSet	Check an RccSet
colByCovar	colByCovar
colByFun	colByFun
contentNorm	Content normalization
copyRccSet	Deep-copy a NanoString RccSet
countsInBlankSamples_verticalPlot	Plot counts in blank samples (vertical orientation)
ctrlsOverviewPlot	ctrlsOverviewPlot
ctrlsZprimePlot	ctrlsZprimePlot
cutoffByMMAD	cutoffByMMAD
cutoffByVar	cutoffByVar
dCoVar	dCoVar
densityPlot	densityPlot
example_rccSet	NanoStringQCPro example dataset
flagSamplesCount	flagSamplesCount
flagSamplesCtrl	flagSamplesCtrl
flagSamplesTech	flagSamplesTech
fovPlot	Fields of view (FOV) plot
geneClustering	Gene clustering heatmap
getBackground	Get background estimates for a NanoString RccSet
getBlankLabel	Get the SampleType value that indicates blank samples
getSpikeInInput	getSpikeInInput
iqrPlot	iqrPlot
lodAssess	lodAssess
lodPlot	lodPlot
makeQCReport	Make NanoString QC report
myCols	myCols
NanoStringQCPro	NanoStringQCPro
negCtrlsByLane	negCtrlsByLane
negCtrlsByLane_verticalPlot	Plot of negative controls by lane (vertical orientation)
negCtrlsPairs	negCtrlsPairs
negCtrlsPlot	negCtrlsPlot
newRccSet	Create a new RccSet object
nSolverBackground	nSolver Analysis Software background estimation
nSolverCsv.to.pdata_fdata_adata	nSolverCsv.to.pdata_fdata_fdata
panelCor	panelCor
pcaPlot	pcaPlot
pdata_fdata_adata.to.rccSet	pdata_fdata_adata.to.rccSet
posCtrlNorm	Positive control normalization
posNormFactPlot	posNormFactPlot
posR2Plot	posR2Plot
posRatioPlot	posRatioPlot
posSlopePlot	posSlopePlot
posSumVsAllSumPlot	posSumVsAllSumPlot
preprocRccSet	Preprocess an RccSet
presAbsCall	Presence/absence call
previewPNG	Create a preview of a PNG
rccFiles.to.pdata_fdata_adata	rccFiles.to.pdata_fdata_adata
RccSet	RccSet constructor methods
RccSet-class	RccSet class, derived from ExpressionSet
readCdrDesignData	Read .CSV containing CDR 'Design Data' extract
readRcc	Read an .RCC file
readRccBatch	Read RCC files
readRccCollectorToolExport	Read RCC Collector Tool Export
readRlf	Read RLF file
sampleClustering	Clustering by sample correlation
scatterPair	scatterPair
subtractBackground	Subtract background estimates for a NanoString RccSet
zfacFun	zfacFun

RccSet: RccSet constructor methods

details

Arguments accepted by constructors are identical to those for the ExpressionSet constructors.

See RccSet class documentation for examples of constructor use.

Constructor calls for which mandatory phenoData or featureData columns are missing will successfully create objects that include mandatory columns, but with NA values. See RccSet documentation for a list of mandatory columns.

Value

A new RccSet object.

Description

This function is a wrapper to perform any combination of positive control normalization, background correction, and content normalization on the input RccSet. For each completed preprocessing step, a matrix is added to the assayData of the resulting RccSet object:

• posCtrlData: expression data after positive control normalization

• bgEstimates: background estimates

• bgCorrData: expression data after positive control normalization and background correction

• normData: expression data after positive control normalization, background correction, and content normalization

(NOTE: normData is on a log2 scale while all the other matrices are on a linear scale.)

If any step is omitted, the corresponding matrix will not be present in the output's assayData. The parameters for all steps are recorded in the output's experimentData@preprocessing list (accessible through preproc(rccSet) where rccSet is an RccSet output by this function). In addition:

• If blanks are not present in the data, use bgReference="negatives" to prevent the function from throwing an error.

• If positive control normalization is performed, a column named 'PosCtrl' is added to the output's phenoData to record the positive control scaling factors.

• If the presence/absence call is performed, a matrix named ‘paData’ is added to the output's assayData to indicate the presence/absence of each feature in each sample. See the ‘pa’ argument for details.

• If housekeeping normalization is performed, a column labeled ‘Housekeeping’ is added to the featureData to indicate which features were used for it.

preprocRccSet: Preprocess an RccSet

Usage

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## S4 method for signature 'RccSet' preprocRccSet(rccSet, doPosCtrlNorm = TRUE, doBackground = TRUE, doPresAbs = TRUE, doContentNorm = TRUE, pcnSummaryFunction = "sum", bgReference = c("both", "blanks", "negatives"), bgSummaryFunction = "median", bgStringency = 1, nSolverBackground.w1 = 2.18, nSolverBackground.shrink = TRUE, paStringency = 2, normMethod = c("global", "housekeeping"), normSummaryFunction = "median", hkgenes = NULL, hkfeatures = NULL, quietly = FALSE)

Arguments

rccSet	An RccSet.
doPosCtrlNorm	Boolean specifying whether or not to perform positive control normalization. (‘pcd’ is short for ‘posCtrlData’, the matrix which gets added to assayData when this step is performed.)
doBackground	Boolean specifying whether or not to perform background correction.
doPresAbs	Boolean specifying whether or not the presence/absence call should be performed. For details, see presAbsCall().
doContentNorm	Boolean specifying whether or not content normalization should be performed.
pcnSummaryFunction	Function to be used for the positive control normalization (e.g. "mean", "median", or "sum"). User-defined functions similar to these can be specified here as well.
bgReference	Measurements to use for background estimates: either "blank" (for blank samples), "negatives" (for negative control probes), or "both". For details on exactly how the background estimates are computed in each case, see getBackground().
bgSummaryFunction	Summary function for background measurements (e.g. "mean" or "median"). User-defined functions similar to these can be specified here as well.
bgStringency	Factor by which deviation (SD or MAD) of the summarization output will be multiplied to obtain final background estimates.
nSolverBackground.w1	Value to use for the 'w1' argument to nSolverBackground(). (Only takes effect if bgReference == "both"; see getBackground().)
nSolverBackground.shrink	Value to use for the 'shrink' argument to nSolverBackground(). (Only takes effect if bgReference == "both"; see getBackground().)
paStringency	Multiplier to use in establishing the presence/absence call. For details, see presAbsCall().
normMethod	Specifies the features to be used for content normalization. "global" indicates that all features should be used and "housekeeping" indicates that only housekeeping features should be used. If "housekeeping" is specified and the ‘hk’ argument (below) is also specified, then the features indicated by ‘hk’ will be used. If "housekeeping" is specified and ‘hk’ is left NULL, then the default housekeeping features (i.e. those with CodeClass == "Housekeeping") will be used.
normSummaryFunction	Character specifying the summary function to apply to the selected features (e.g. "mean" or "median") during the content normalization step. User-defined functions similar to these can be specified here as well.
hkgenes	Character vector with gene symbols to be used for content normalization if housekeeping is specified as the normalization method. If specified, all features that match any of the specified symbols will be used. (To specify specific features, use the ‘hkfeatures’ argument instead; see below.)
hkfeatures	Character vector with full feature names ("<CodeClass>_<GeneName>_<Accession>", e.g. "Endogenous_ACTG1_NM_001614.1") to be used for content normalization if housekeeping is specified as the normalization method. (Note: if this argument is specified at the same time as ‘hkgenes’, an error will be thrown.)
quietly	Boolean specifying whether or not messages and warnings should be omitted.

Details

For more information on the rationale behind the recommended preprocessing and normalization steps, please see the vignette.

Value

A copy of the input RccSet with additional matrices in the assayData for each successive preprocessing step along with parameters for each step recorded in the experimentData@preprocessing list.