ffpe (version 1.16.0)

sampleQC:

Sample quality control for FFPE expression data

Description

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.

Usage

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, ...) "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, ...) "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, ...) "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, ...)

Arguments

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 (

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().

Value

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.

Details

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

References

Under review.

Examples

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"))