sampleQC: Sample quality control for FFPE expression data
In ffpe: Quality assessment and control for FFPE microarray expression data

Description Usage Arguments Details Value Author(s) References Examples

Expression data from FFPE tissues may contain a much larger range of quality than data from fresh-frozen tissues. This function sorts samples by some specified measure of array quality, Interquartile Range (IQR) by default, and plots the correlation of each sample's expression values against a “typical” sample for the study, as a function of the quality measure. A “typical” sample can either be the median pseudochip (default), or a specified number of samples with quality measure most similar to that sample. An attempt is made to automatically select a threshold for rejection of low-quality samples, at the point of largest negative inflection of a Loess smoothing curve. for this plot.

sampleQC(data.obj,logtransform = TRUE, goby = 3, xaxis = "notindex", QCmeasure = "IQR", cor.to = "pseudochip", pseudochip.samples = 1:ncol(data.obj), detectionTh = 0.01, manualcutoff = NULL, mincor = 0, maxcor = 0.8, below.smoothed.threshold = 1.5, lowess.f = 1/3, labelnote = NULL, pch = 1, lw = 4, linecol = "red", make.legend = TRUE, main.title = NA, ...)
       ## S4 method for signature 'LumiBatch'
sampleQC(data.obj,logtransform = TRUE, goby = 3, xaxis = "notindex", QCmeasure = "IQR", cor.to = "pseudochip", pseudochip.samples = 1:ncol(data.obj), detectionTh = 0.01, manualcutoff = NULL, mincor = 0, maxcor = 0.8, below.smoothed.threshold = 1.5, lowess.f = 1/3, labelnote = NULL, pch = 1, lw = 4, linecol = "red", make.legend = TRUE, main.title = NA, ...)
       ## S4 method for signature 'AffyBatch'
sampleQC(data.obj,logtransform = TRUE, goby = 3, xaxis = "notindex", QCmeasure = "IQR", cor.to = "pseudochip", pseudochip.samples = 1:ncol(data.obj), detectionTh = 0.01, manualcutoff = NULL, mincor = 0, maxcor = 0.8, below.smoothed.threshold = 1.5, lowess.f = 1/3, labelnote = NULL, pch = 1, lw = 4, linecol = "red", make.legend = TRUE, main.title = NA, ...)
       ## S4 method for signature 'matrix'
sampleQC(data.obj,logtransform = TRUE, goby = 3, xaxis = "notindex", QCmeasure = "IQR", cor.to = "pseudochip", pseudochip.samples = 1:ncol(data.obj), detectionTh = 0.01, manualcutoff = NULL, mincor = 0, maxcor = 0.8, below.smoothed.threshold = 1.5, lowess.f = 1/3, labelnote = NULL, pch = 1, lw = 4, linecol = "red", make.legend = TRUE, main.title = NA, ...)

`data.obj`	A data object of class LumiBatch, AffyBatch, or matrix. If matrix, the columns should contain samples and the rows probes. If using QCmeasure="IQR", it is critical that the data not be normalized. QCmeasure="ndetectedprobes" currently works only for LumiBatch objects.
`logtransform`	If TRUE, data will be log2-transformed before calculating IQR and correlation.
`goby`	This number of samples above and below each sample will be used to for calculating correlation. Used only if cor.to="similar".
`xaxis`	If "index", the QC measure will be converted to ranks. This can be useful for very discontinuous values of the QC measure, which interfere with generation of a smoothing line. If "notindex", the QC measure is used as-is.
`QCmeasure`	Automated options include "IQR" and "ndetectedprobes". These are the Interquartile Range and number of probes called present, respectively. QCmeasure can also be a numeric vector of length equal to the number of samples, to manually specify some other quality metric.
`cor.to`	"similar" to calculate correlation of each chip to neighbors within a sliding window of size 2*goby+1, or "pseudochip" to calculate correlation to a study-wide pseudochip. The former can be more sensitive, or more appropriate with a large number of failed chips, but does not work well with small sample size (<20). The default is "pseudochip".
`pseudochip.samples`	An integer vector, specifying the column numbers of samples to use in calculation of the median pseudochip. Default is to use all samples.
`detectionTh`	Nnominal detection p-value to consider a probe as detected or not (0.01 by default). Used only if QCmeasure="ndetectedprobes" and class(data.obj)=="LumiBatch"
`manualcutoff`	Optional manual specification of a cutoff for good and bad samples. If xaxis="index", manualcutoff specifies the number of samples that will be rejected. If xaxis="notindex", it is the value of the QC measure plotted on the x-axis, below which samples will be rejected.
`mincor`	Optional specification of a minimum correlation to the sliding window samples or median pseudochip, below which all samples will be rejected for QC. This is drawn as a horizontal line on the output plot.
`maxcor`	Optional specification of an upper limit of correlation to sliding window samples or median pseudochip, above which samples will not be considered in determining the QC cutoff. This can be useful if some structure in the high-quality end of the plot has the maximum downward inflection, causing most samples to be incorrectly rejected. In such case, specifying maxcor can force the otherwise automatically-determined threshold into a more reasonable region. Only values above at least 0.25, and probably below 0.8, make sense.
`below.smoothed.threshold`	Samples falling more than below.smoothed.threshold times the IQR of the residuals will be rejected for low QC. Large negative residuals from the Loess best-fit line may indicate outlier samples even if that sample has a high IQR or other quality measure.
`lowess.f`	Degree of smoothing of the Loess best-fit line (see ?loess)
`labelnote`	An optional label for the plot, used only if QCmeasure is a numeric vector.
`pch`	Plotting character to be used for points (see ?par).
`lw`	Line width for Loess curve.
`linecol`	Line color for Loess curve.
`make.legend`	If TRUE, an automatic legend will be added to the plot.
`main.title`	If specified, this over-rides the automatically-generated title.
`...`	Other arguments passed on to plot().

These methods aid in the identification of low-quality samples from FFPE expression data, when technical replication is not available.

If only one method is specified (one value each for xaxis, QCmeasure, and cor.to, the output is a dataframe with the following columns:

`i`	index or QC measure of each sample
`spearman`	spearman correlation to sliding window samples or to median pseudochip
`movingaverage`	moving average smoothing of spearman correlation
`interpolate.i`	evenly spaced QC measure (index or actual QC measure) used for plotting Loess curve
`smoothed`	values of the Loess curve
`ddy`	second derivative of the Loess curve
`rejectQC`	was this sample rejected in the QC process? logical TRUE or FALSE

If more than one method is specified, the output is a list, where each element contains a dataframe of the above description.

Levi Waldron <lwaldron@hsph.harvard.edu>

Under review.

library(ffpeExampleData)
data(lumibatch.GSE17565)

QC <- sampleQC(lumibatch.GSE17565,xaxis="index",cor.to="pseudochip",QCmeasure="IQR")

##sort samples
QCvsRNA <- data.frame(inputRNA.ng=lumibatch.GSE17565$inputRNA.ng,rejectQC=QC$rejectQC)
QCvsRNA <- QCvsRNA[order(QCvsRNA$rejectQC,-QCvsRNA$inputRNA.ng),]

##QC rejects samples with lowest input RNA concentration\n
par(mgp=c(4,2,0))
dotchart(log10(QCvsRNA$inputRNA.ng),
         QCvsRNA$rejectQC,
         xlab="log10(RNA conc. in ng)",
         ylab="rejected?",
         col=ifelse(QCvsRNA$rejectQC,"red","black"))