changeCtLayout: Changing the dimensions (rows x columns) of qPCRset objects
In HTqPCR: Automated analysis of high-throughput qPCR data

Description Usage Arguments Details Value Note Author(s) Examples

A function for splitting up the individual qPCR cards, in case there are multiple samples present on each card. I.e. for cases where the layout isn't 1 sample x 384 features, but for example 4 samples x 96 features on each 384 well card.

1	changeCtLayout(q, sample.order)

`q`	a qPCRset object.
`sample.order`	vector, same length as number of features on each card (e.g. 384). See details.

The result from each qPCR run of a given card typically gets presented together, such as in a file with 384 lines, one per feature, for 384 well plates. However, some cards may contain multiple samples, such as commercial cards that are designed to be loaded with two separate samples and then include 192 individual features.

Per default, each card is read into the qPCRset object as consisting of a single sample, and hence one column in the Ct data matrix. When this is not the case, the data can subsequently be split into the correct features x samples (rows x columns) dimensions using this function. The parameter sample.order is a vector, that for each feature in the qPCRset indicates what sample it actually belongs to.

In the new qPCRset the samples (Ct columns) are ordered first by sample.order then by the original sampleNames, as shown in the examples below.

A qPCRset object like the input, but with the dimensions changed according to the new layout.

Since the actual biological samples are likely to differ on each card, after applying changeCtLayout renaming of the samples in qPCRset using sampleNames is advisable.

The features are assumed to be identical for all samples on a given card! I.e. if for example sample.order=rep(c("A", "B"), each=192), then feature number 1 (the first for sample A) should be the same as feature number 193 (the first for sample B). The new featureNames are taken for those features listed as belonging to the first sample in sample.order.

Heidi Dvinge

# Example data
data(qPCRraw)
# With e.g. 2 or 4 samples per 384 well card.
sample2.order <- rep(c("subSampleA", "subSampleB"), each=192)
sample4.order <- rep(c("subA", "subB", "subC", "subD"), each=96)
# Splitting the data into all individual samples
qPCRnew2 <- changeCtLayout(qPCRraw, sample.order=sample2.order)
show(qPCRnew2)
qPCRnew4 <- changeCtLayout(qPCRraw, sample.order=sample4.order)
show(qPCRnew4)
sampleNames(qPCRnew4)

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An object of class "qPCRset"
Size:  192 features, 12 samples
Feature types:		  
Feature names:		 Gene1 Gene2 Gene3 ...
Feature classes:		  
Feature categories:	 OK, Undetermined 
Sample names:		 subSampleA:sample1 subSampleA:sample2 subSampleA:sample3 ...
An object of class "qPCRset"
Size:  96 features, 24 samples
Feature types:		  
Feature names:		 Gene1 Gene2 Gene3 ...
Feature classes:		  
Feature categories:	 OK, Undetermined 
Sample names:		 subA:sample1 subA:sample2 subA:sample3 ...
 [1] "subA:sample1" "subA:sample2" "subA:sample3" "subA:sample4" "subA:sample5"
 [6] "subA:sample6" "subB:sample1" "subB:sample2" "subB:sample3" "subB:sample4"
[11] "subB:sample5" "subB:sample6" "subC:sample1" "subC:sample2" "subC:sample3"
[16] "subC:sample4" "subC:sample5" "subC:sample6" "subD:sample1" "subD:sample2"
[21] "subD:sample3" "subD:sample4" "subD:sample5" "subD:sample6"