Allows users 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 enjoins individual cells according to a defined 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 quantitative polymerase chain reaction (qPCR) data, and implements Census and BEAM tools.
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.
User-friendly software tool for qPCR data analysis. It's based on the qBase and geNorm technologies and is fully MIQE-compliant. Whatever qPCR instrument or computer operating system, whatever type of data or experiment, qbase+ offers an open and flexible solution.
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.
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.
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.
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.
Provides systematic pipelines and steady criteria to process real-time PCR data. Chainy includes the calculation of efficiencies from raw data by kinetic methods, evaluation of the suitability of multiple references, standardized normalization using one or more references, and group-wise relative quantification statistical testing. It evaluates the suitability of references by the standard geNorm method providing a ranking based on their stability.
Enables the interpretation of changes in compositional data. SARP.compo is an R package allowing users to interpret the data respecting their compositional status. The software permits the construction of pairwise ratio analysis p-values matrix, and the conversion into a graph. It is suited for quantitative reverse transcription polymerase chain reaction (qRT-PCR) and RNA-Seq data analyzes.
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.
Normalizes the uploaded data using twelve different well known normalization methods and compares the resulting data based on quantitative and qualitative parameters. Normalyzer is completely automated online tool for data normalization. There are no parameters to configure and no scripts to install.
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.
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.
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.
An R package to assess and compare microRNA expression estimation methods using a benchmark data set. Based on a large miRNA dilution study, miRcomp provides tools to read in the raw amplification data and use these data to assess the performance of methods that estimate expression from the amplification curves.
Allows researchers to easily generate expression estimates using other algorithms. miRcompData is a R package that provides raw amplification data from a large microRNA mixture/dilution study. This set works with miRcomp, a software that assess and compares microRNA expression estimation methods. This package was created to facilitate the development of new methodology for microRNA expression estimation.