1 - 26 of 26 results

nondetects

Assists in modeling and imputing non-detects in the results of Quantitative real-time PCR (qPCR) experiments. nondetects allows users to manage a variety of study designs. It contains two methods to handle qPCR non-detects that provide consistent estimates of the first and second moments of gene expression: MI and DirEst. MI takes into account the uncertainty of the imputed data, and DirEst permits one to directly estimate within replicate means and variances for each gene and sample type.

HTqPCR

A package for the R statistical computing environment, to enable the processing and analysis of qPCR data across multiple conditions and replicates. HTqPCR performs quality assessment, normalization, data visualization and statistical significance testing for Ct values between features (genes and microRNAs) across multiple biological conditions, such as different cell culture treatments, comparative expression profiles or time-series experiments. As it is R based, HTqPCR runs on different operating systems and is easy to incorporate into an analysis pipeline, or used in conjunction with other tools available through the Bioconductor project.

ReadqPCR

Allows the user to read RT-qPCR data into R, deal with undetermined Cq values, find a suitable reference gene or genes for a given experiment using a method for optimal reference gene selection and normalise the data via the ΔCq and 2−ΔΔCqnormalisation methods. ReadqPCR provides tools for uploading RT-qPCR data into R, looks for the optimal reference genes, and normalises the data using the ΔΔCqmethod. This package, implementing popular optimal reference gene finding algorithms in the widely-used statistical software for genomic analysis, R, represents an important contribution to the RT-qPCR community, and increases the available options for the analysis of this type of data.

NormqPCR

Provides tools for uploading RT-qPCR data into R, looks for the optimal reference genes, and normalises the data using the ΔΔCq method. NormqPCR allows the user to read RT-qPCR data into R, to deal with undetermined Cq values, to find a suitable reference gene or genes for a given experiment using a method for optimal reference gene selection and to normalise the data via the ΔCq and 2−ΔΔCqnormalisation methods. The user can also use a number of existing bioconductor packages and functions to perform quality control on their data, and can check the adequacy of reference genes visually. Implementing popular optimal reference gene finding algorithms as NormqPCR in the widely-used statistical software for genomic analysis, R, represents an important contribution to the RT-qPCR community, and increases the available options for the analysis of this type of data.

LRE Analyzer

Automates real-time qPCR data analysis. LRE Analyzer uses a method called "Linear Regression of Efficiency" or LRE qPCR. The software enables large-scale absolute quantification without construction of target-specific standard curves. Absolute quantification allows to directly compare transcript quantities produced by any gene to any other gene, within and between any sample. LRE Analyzer allows to evaluate large amounts of data generated over multiple runs. It also provides a platform that facilitates data storage and exchange.

quantGenius

Allows to organize, make analysis of quantitative Polymerase Chain Reaction (qPCR) data and offers a decision support in various qPCR applications. quantGenius is based on a standard curve quantification approach which allows the calculation of comparable copy numbers on multi-plate experiments. It focuses on the quantification aspect of the qPCR data analysis pipeline. The tool permits to eliminate the need for additional interplate calibration if the same standard curve is used on all plates.

unifiedWMWqPCR

Analyses RT-qPCR data with the uWMW test. This test, referred to as the unified WMW (uWMW) test, incorporates a robust and intuitive normalization and quantifies the probability that the expression from one treatment group exceeds the expression from another treatment group. unifiedWMWqPCR provides an extension of the WMW test so that a separate normalization preprocessing step is no longer required before assessing differential expression. In addition to P-values, the package also provides informative plots to visualize treatment effects.

UBiT2 / User-friendly BioInformatics Tools

Provides installation-free, offline alignment, analysis, and visualization of RNA-sequencing as well as qPCR data. UBiT2 can perform gene set enrichment testing by using the non-parametric minimal hypergeometric test XL-mHG. The client-side alignment and quantification can be performed thank to the implementation of the BrowserGenome.org’s ability. The tool can perform full in-depth analyses and replace server-side high-performance computing.

chipPCR

An R package for the pre-processing and quality analysis of raw data of amplification curves. The package takes advantage of R’s S4 object model and offers an extensible environment. chipPCR contains tools for raw data exploration: normalization, baselining, imputation of missing values, a powerful wrapper for amplification curve smoothing and a function to detect the start and end of an amplification curve. The capabilities of the software are enhanced by the implementation of algorithms unavailable in R, such as a 5-point stencil for derivative interpolation. Simulation tools, statistical tests, plots for data quality management, amplification efficiency/quantification cycle calculation, and datasets from qPCR and qIA experiments are part of the package. Core functionalities are integrated in GUIs (web-based and standalone shiny applications), thus streamlining analysis and report generation.

REST / Relative Expression Software Tool

Compares two groups for reference and up to four target genes. REST allows for a relative quantification between groups, and a subsequent test for significance of the derived results with a suitable statistical model. It implements an efficiency corrected mathematical model for data analysis. The tool exhibits suitable reliability as well as reproducibility in individual runs. It is useful for any experiments requiring sensitive, specific and reproducible quantification of mRNA.

ddCt / Delta-Delta-Ct

The ddCt Algorithm is an approximation method to determine relative gene expression with quantitative real-time PCR (qRT-PCR) experiments. Compared to other approaches, it requires no standard curve for each primer-target pair, therefore reducing the working load and yet returning accurate enough results as long as the assumptions of the amplification efficiency hold. The ddCt package implements a pipeline to collect, analyse and visualize qRT-PCR results, for example those from TaqMan SDM software, mainly using the ddCt method. The pipeline can be either invoked by a script in command-line or through the API consisting of S4-Classes, methods and functions.

Monocle

Obsolete
Allows to analyze single-cell gene expression experiments. Monocle can realize differential expression analysis, clustering, visualization, and other useful tasks on single cell expression data. The software orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. It is designed to work with RNA-Seq and qPCR data, but could be used with other types as well. The tools Census and BEAM are implemented in Monocle.