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✨
card.svg |card.png
HTqPCR/json (API)
NEWS

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

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.

microtitreplateassaydifferentialexpressiongeneexpressiondataimportqualitycontrolpreprocessingvisualizationmultiplecomparisonqpcr

5.10 score 1 packages 21 scripts 578 downloads 50 mentions 53 exports 15 dependencies

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

TargetResultTimeFilesSyslog
bioc-checksERROR144
linux-devel-x86_64NOTE192
source / vignettesOK172
linux-release-x86_64NOTE175
macos-release-arm64NOTE101
macos-oldrel-arm64NOTE102
windows-develNOTE135
windows-releaseNOTE99
windows-oldrelNOTE88
wasm-releaseOK100

Exports:changeCtLayoutclusterCtexprsexprs<-featureCategoryfeatureCategory<-featureClassfeatureClass<-featureNamesfeatureNames<-featurePosfeaturePos<-featureTypefeatureType<-filterCategoryfilterCtDataflagflag<-getCtgetCtHistoryheatmapSiglimmaCtDatamannwhitneyCtDatan.samplesn.wellsnormalizeCtDataplotCtArrayplotCtBoxesplotCtCardplotCtCategoryplotCtCorplotCtDensityplotCtHeatmapplotCtHistogramplotCtLinesplotCtOverviewplotCtPairsplotCtPCAplotCtRepsplotCtRQplotCtScatterplotCtSignificanceplotCtVariationplotCVBoxesreadCtDatasampleNamessampleNames<-setCategorysetCt<-setCtHistory<-showsummaryttestCtData

Dependencies:affyaffyioBiobaseBiocGenericsBiocManagerbitopscaToolsgenericsgplotsgtoolsKernSmoothlimmapreprocessCoreRColorBrewerstatmod

qPCR analysis in R

Rendered fromHTqPCR.Rnwusingutils::Sweaveon May 30 2026.

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

Readme and manuals

Help Manual

Help pageTopics
Analysis of High-Throughput qPCR data (HTqPCR)HTqPCR-package HTqPCR
Combine qPCRset objectscbind cbind.qPCRset rbind rbind.qPCRset
Changing the dimensions (rows x columns) of qPCRset objectschangeCtLayout
Clustering of qPCR Ct valuesclusterCt
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: limmalimmaCtData
Differentially expressed features with qPCR: Mann-WhitneymannwhitneyCtData
Normalization of Ct values from qPCR data.normalizeCtData
Image plot of qPCR Ct values from an array formatplotCtArray
Boxplots for qPCR Ct values.plotCtBoxes
Image plot of qPCR Ct values from a card formatplotCtCard
Summarising the feature categories for Ct values.plotCtCategory
Correlation between Ct values from qPCR dataplotCtCor
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 replicatesplotCtVariation
Boxplots of CV for qPCR Ct values.plotCVBoxes
Example processed qPCR dataqPCRpros
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-testttestCtData