Package: HTqPCR 1.67.0

Matthew N. McCall

HTqPCR: Automated analysis of high-throughput qPCR data

Analysis of Ct values from high throughput quantitative real-time PCR (qPCR) assays across multiple conditions or replicates. The input data can be from spatially-defined formats such ABI TaqMan Low Density Arrays or OpenArray; LightCycler from Roche Applied Science; the CFX plates from Bio-Rad Laboratories; conventional 96- or 384-well plates; or microfluidic devices such as the Dynamic Arrays from Fluidigm Corporation. HTqPCR handles data loading, quality assessment, normalization, visualization and parametric or non-parametric testing for statistical significance in Ct values between features (e.g. genes, microRNAs).

Authors:Heidi Dvinge, Paul Bertone

HTqPCR_1.67.0.tar.gz
HTqPCR_1.67.0.zip(r-4.7)HTqPCR_1.67.0.zip(r-4.6)HTqPCR_1.67.0.zip(r-4.5)
HTqPCR_1.67.0.tgz(r-4.6-any)HTqPCR_1.67.0.tgz(r-4.5-any)
HTqPCR_1.67.0.tar.gz(r-4.7-any)HTqPCR_1.67.0.tar.gz(r-4.6-any)
HTqPCR_1.67.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
HTqPCR/json (API)

# Install 'HTqPCR' in R:

install.packages('HTqPCR', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

qPCRpros - Example processed qPCR data
qPCRraw - Example raw qPCR data.

On BioConductor:HTqPCR-1.67.0(bioc 3.24)HTqPCR-1.66.0(bioc 3.23)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

microtitreplateassay differentialexpression geneexpression dataimport qualitycontrol preprocessing visualization multiplecomparison qpcr

4.89 score 1 packages 13 scripts 50 mentions 53 exports 15 dependencies

Last updated from:a861773133. Checks:1 ERROR, 7 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
bioc-checks	ERROR	142
linux-devel-x86_64	NOTE	192
source / vignettes	OK	159
linux-release-x86_64	NOTE	144
macos-release-arm64	NOTE	80
macos-oldrel-arm64	NOTE	135
windows-devel	NOTE	103
windows-release	NOTE	89
windows-oldrel	NOTE	85
wasm-release	OK	103

Exports:changeCtLayout clusterCt exprs exprs<-featureCategory featureCategory<-featureClass featureClass<-featureNames featureNames<-featurePos featurePos<-featureType featureType<-filterCategory filterCtData flag flag<-getCt getCtHistory heatmapSig limmaCtData mannwhitneyCtData n.samples n.wells normalizeCtData plotCtArray plotCtBoxes plotCtCard plotCtCategory plotCtCor plotCtDensity plotCtHeatmap plotCtHistogram plotCtLines plotCtOverview plotCtPairs plotCtPCA plotCtReps plotCtRQ plotCtScatter plotCtSignificance plotCtVariation plotCVBoxes readCtData sampleNames sampleNames<-setCategory setCt<-setCtHistory<-show summary ttestCtData

Dependencies:affy affyio Biobase BiocGenerics BiocManager bitops caTools generics gplots gtools KernSmooth limma preprocessCore RColorBrewer statmod

qPCR analysis in R

Rendered fromHTqPCR.Rnwusingutils::Sweave

Last update: 2025-03-31
Started: 2013-11-01

Help page	Topics
Analysis of High-Throughput qPCR data (HTqPCR)	HTqPCR-package HTqPCR
Combine qPCRset objects	cbind cbind.qPCRset rbind rbind.qPCRset
Changing the dimensions (rows x columns) of qPCRset objects	changeCtLayout
Clustering of qPCR Ct values	clusterCt
Filter Ct values based on their feature categories.	filterCategory
Filter out features (genes) from qPCR data.	filterCtData
Heatmap of deltadeltaCt values from qPCR data.	heatmapSig
Differentially expressed features with qPCR: limma	limmaCtData
Differentially expressed features with qPCR: Mann-Whitney	mannwhitneyCtData
Normalization of Ct values from qPCR data.	normalizeCtData
Image plot of qPCR Ct values from an array format	plotCtArray
Boxplots for qPCR Ct values.	plotCtBoxes
Image plot of qPCR Ct values from a card format	plotCtCard
Summarising the feature categories for Ct values.	plotCtCategory
Correlation between Ct values from qPCR data	plotCtCor
Distribution plot for qPCR Ct values.	plotCtDensity
Heatmap of qPCR Ct values.	plotCtHeatmap
Histrogram of Ct values from qPCR experiments.	plotCtHistogram
Plotting Ct values from qPCR across multiple samples.	plotCtLines
Overview plot of qPCR Ct values across multiple conditions.	plotCtOverview
Pairwise scatterplot of multiple sets of Ct values from qPCR data.	plotCtPairs
PCA for qPCR Ct values.	plotCtPCA
Scatter plot of features analysed twice during each qPCR experiment.	plotCtReps
Plot the relative quantification of Ct values from qPCR experiments.	plotCtRQ
Scatterplot of two sets of Ct values from qPCR data.	plotCtScatter
Barplot with Ct values between genes from qPCR.	plotCtSignificance
Plot variation in Ct values across replicates	plotCtVariation
Boxplots of CV for qPCR Ct values.	plotCVBoxes
Example processed qPCR data	qPCRpros
Example raw qPCR data.	qPCRraw
Class "qPCRset"	exprs,qPCRset-method exprs<-,qPCRset,ANY-method featureCategory featureCategory,qPCRset-method featureCategory<- featureCategory<-,qPCRset-method featureClass featureClass,qPCRset-method featureClass<- featureClass<-,qPCRset-method featureNames,qPCRset-method featureNames<-,qPCRset,character-method featurePos featurePos,qPCRset-method featurePos<- featurePos<-,qPCRset-method featureType featureType,qPCRset-method featureType<- featureType<-,qPCRset-method flag flag,qPCRset-method flag<- flag<-,qPCRset-method getCt getCtHistory n.samples n.wells qPCRset-class sampleNames,qPCRset-method sampleNames<-,qPCRset,character-method setCt<- setCtHistory<- show,qPCRset-method summary,qPCRset-method [,qPCRset-method
Reading Ct values from qPCR experiments data into a qPCRset	.readCtBioMark .readCtCFX .readCtLightCycler .readCtOpenArray .readCtPlain .readCtSDS readCtData
Assign categories to Ct values from qPCR data.	setCategory
Differentially expressed features with qPCR: t-test	ttestCtData

Package: HTqPCR 1.67.0

HTqPCR: Automated analysis of high-throughput qPCR data

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Help Manual

Usage by other packages (reverse dependencies)