qPCRset-class: Class "qPCRset"
In HTqPCR: Automated analysis of high-throughput qPCR data

Description Objects from the Class Slots Extends Methods Author(s) Examples

This is a class for containing the raw or normalized cycle threshold (Ct) values and some related quality information. It is suitable for TaqMan Low Density Arrays or any other type of (high-throughput) qPCR data, where gene expression is measured for any number of genes, across several samples/conditions. It inherits from eSet for microarray data.

Objects can be created by calls of the form new("qPCRset", assayData, phenoData, featureData, experimentData, annotation, protocolData, ...) or using readCtData.

CtHistory:: Object of class "data.frame" indicating how the data has been read in, normalized, filtered etc. Gives the exact commands used during these operations.
assayData:: Object of class "AssayData", containing the Ct values.
phenoData:: Object of class "AnnotatedDataFrame", where information about samples can be added.
featureData:: Object of class "AnnotatedDataFrame", where information about features can be added. If the object is from readCtData, the featureData will contain the columns 'featureName', 'featurePos' and 'featureType'.
experimentData:: Object of class "MIAxE", where details about the experiment can be stored.
annotation:: Object of class "character", where the name of the qPCR assay can be stored.
protocolData:: Object of class "AnnotatedDataFrame", where details of the protocol can be stored.
.__classVersion__:: Object of class "Versions".

Furthermore, the following information is contained within the object.

flag:: Object of class "data.frame" containing the flag for each Ct value, as supplied by the input files.
featureCategory:: Object of class "data.frame" representing the quality of the measurement for each Ct value, such as "OK", "Undetermined" or "Unreliable" if the Ct value is considered too high.

Class "eSet", directly. Class "VersionedBiobase", by class "eSet", distance 2. Class "Versioned", by class "eSet", distance 3.

[: signature(x = "qPCRset"): Subsets by genes or samples.
exprs: signature(object = "qPCRset"): Extracts the Ct matrix. Is identical to getCt
exprs<-: signature(object = "qPCRset", value = "matrix"): Replaces the Ct matrix. Is identical to setCt<-
getCt: signature(object = "qPCRset"): Extracts the Ct matrix. Is identical to exprs.
setCt<-: signature(object = "qPCRset", value = "matrix"): Replaces the Ct matrix. Is identical to exprs<-.
featureNames: signature(object = "qPCRset"): Extracts the features (gene names) on the card.
featureNames<-: signature(object = "qPCRset", value = "character"): Replaces the features (gene names) on the card.
sampleNames: signature(object = "qPCRset"): Extracts the sample names.
sampleNames<-: signature(object = "qPCRset", value = "character"): Replaces the sample names.
featureType: signature(object = "qPCRset"): Extracts the different types of features on the card, such as controls and target genes.
featureType<-: signature(object = "qPCRset", value = "factor"): Replaces the feature type for each gene.
featurePos: signature(object = "qPCRset"): Extracts the position of each feature (gene) on the assay, representing the location "well" (such as well A1, A2, ...). If data does not come from a card format, the positions will be given consecutive names.
featurePos<-: signature(object = "qPCRset", value = "character"): Replaces the position of each feature (gene) on the card.
featureClass: signature(object = "qPCRset"): Extracts the feature class for each gene.
featureClass<-: signature(object = "qPCRset", value = "factor"): Replaces the feature class for each gene, for example if it is a marker, transcription factor or similar.
featureCategory: signature(object = "qPCRset"): Extracts the category of each Ct value.
featureCategory<-: signature(object = "qPCRset", value = "data.frame"): Replaces the category of each Ct value.
flag: signature(object = "qPCRset"): Extracts the flag of each Ct value.
flag<-: signature(object = "qPCRset"): Replaces the flag of each Ct value.
n.wells: signature(object = "qPCRset"): Extracts information about the number of wells on the card.
n.samples: signature(object = "qPCRset"): Extracts information about the number of samples in the set.
getCtHistory: signature(object = "qPCRset"): Extracts data frame containing information about the history of the object (which operations have been performed on it).
setCtHistory<-: signature(object = "qPCRset"): Add information about the history of the object.
show: signature(object = "qPCRset"): Displays some abbreviated information about the data object.
summary: signature(object = "qPCRset"): Displays a summary of the Ct values from each sample.

Heidi Dvinge

# The data format
data(qPCRraw)
show(qPCRraw)
getCtHistory(qPCRraw)
showClass("qPCRset")
str(qPCRraw)
# Information about samples
phenoData(qPCRraw)
pData(qPCRraw)
pData(qPCRraw)[,"Rep"] <- c(1,1,2,2,3,3)
# Information about features
featureData(qPCRraw)
head(fData(qPCRraw))

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Warning message:
In read.dcf(con) :
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An object of class "qPCRset"
Size:  384 features, 6 samples
Feature types:		  
Feature names:		 Gene1 Gene2 Gene3 ...
Feature classes:		  
Feature categories:	 OK, Undetermined 
Sample names:		 sample1 sample2 sample3 ...
                                          history
1 readCtData(files = exFiles$File, path = exPath)
Class "qPCRset" [package "HTqPCR"]

Slots:
                                                               
Name:           CtHistory          assayData          phenoData
Class:         data.frame          AssayData AnnotatedDataFrame
                                                               
Name:         featureData     experimentData         annotation
Class: AnnotatedDataFrame              MIAxE          character
                                            
Name:        protocolData  .__classVersion__
Class: AnnotatedDataFrame           Versions

Extends: 
Class "eSet", directly
Class "VersionedBiobase", by class "eSet", distance 2
Class "Versioned", by class "eSet", distance 3
Formal class 'qPCRset' [package ".GlobalEnv"] with 8 slots
  ..@ CtHistory        :'data.frame':	1 obs. of  1 variable:
  .. ..$ history: chr "readCtData(files = exFiles$File, path = exPath)"
  ..@ assayData        :<environment: 0x3baf3a8> 
  ..@ phenoData        :Formal class 'AnnotatedDataFrame' [package "Biobase"] with 4 slots
  .. .. ..@ varMetadata      :'data.frame':	1 obs. of  1 variable:
  .. .. .. ..$ labelDescription: chr "Sample numbering"
  .. .. ..@ data             :'data.frame':	6 obs. of  1 variable:
  .. .. .. ..$ sample: int [1:6] 1 2 3 4 5 6
  .. .. ..@ dimLabels        : chr [1:2] "sampleNames" "sampleColumns"
  .. .. ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slot
  .. .. .. .. ..@ .Data:List of 1
  .. .. .. .. .. ..$ : int [1:3] 1 1 0
  ..@ featureData      :Formal class 'AnnotatedDataFrame' [package "Biobase"] with 4 slots
  .. .. ..@ varMetadata      :'data.frame':	4 obs. of  1 variable:
  .. .. .. ..$ labelDescription: chr [1:4] NA NA NA NA
  .. .. ..@ data             :'data.frame':	384 obs. of  4 variables:
  .. .. .. ..$ featureNames: Factor w/ 191 levels "Gene1","Gene10",..: 1 104 115 126 137 148 159 170 181 2 ...
  .. .. .. ..$ featureType : Factor w/ 2 levels "Endogenous Control",..: 1 2 2 2 2 2 2 2 2 2 ...
  .. .. .. ..$ featurePos  : Factor w/ 384 levels "A1","A10","A11",..: 1 12 18 19 20 21 22 23 24 2 ...
  .. .. .. ..$ featureClass: Factor w/ 3 levels "Kinase","Marker",..: 1 2 1 3 2 2 2 3 1 2 ...
  .. .. ..@ dimLabels        : chr [1:2] "featureNames" "featureColumns"
  .. .. ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slot
  .. .. .. .. ..@ .Data:List of 1
  .. .. .. .. .. ..$ : int [1:3] 1 1 0
  ..@ experimentData   :Formal class 'MIAME' [package "Biobase"] with 13 slots
  .. .. ..@ name             : chr ""
  .. .. ..@ lab              : chr ""
  .. .. ..@ contact          : chr ""
  .. .. ..@ title            : chr ""
  .. .. ..@ abstract         : chr ""
  .. .. ..@ url              : chr ""
  .. .. ..@ pubMedIds        : chr ""
  .. .. ..@ samples          : list()
  .. .. ..@ hybridizations   : list()
  .. .. ..@ normControls     : list()
  .. .. ..@ preprocessing    : list()
  .. .. ..@ other            : list()
  .. .. ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slot
  .. .. .. .. ..@ .Data:List of 2
  .. .. .. .. .. ..$ : int [1:3] 1 0 0
  .. .. .. .. .. ..$ : int [1:3] 1 1 0
  ..@ annotation       : chr(0) 
  ..@ protocolData     :Formal class 'AnnotatedDataFrame' [package "Biobase"] with 4 slots
  .. .. ..@ varMetadata      :'data.frame':	0 obs. of  1 variable:
  .. .. .. ..$ labelDescription: chr(0) 
  .. .. ..@ data             :'data.frame':	6 obs. of  0 variables
  .. .. ..@ dimLabels        : chr [1:2] "sampleNames" "sampleColumns"
  .. .. ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slot
  .. .. .. .. ..@ .Data:List of 1
  .. .. .. .. .. ..$ : int [1:3] 1 1 0
  ..@ .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slot
  .. .. ..@ .Data:List of 3
  .. .. .. ..$ : int [1:3] 2 14 0
  .. .. .. ..$ : int [1:3] 2 14 0
  .. .. .. ..$ : int [1:3] 1 3 0
An object of class 'AnnotatedDataFrame'
  sampleNames: sample1 sample2 ... sample6 (6 total)
  varLabels: sample
  varMetadata: labelDescription
        sample
sample1      1
sample2      2
sample3      3
sample4      4
sample5      5
sample6      6
An object of class 'AnnotatedDataFrame'
  featureNames: 1 2 ... 384 (384 total)
  varLabels: featureNames featureType featurePos featureClass
  varMetadata: labelDescription
  featureNames        featureType featurePos featureClass
1        Gene1 Endogenous Control         A1       Kinase
2        Gene2             Target         A2       Marker
3        Gene3             Target         A3       Kinase
4        Gene4             Target         A4           TF
5        Gene5             Target         A5       Marker
6        Gene6             Target         A6       Marker