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Allows Illumina HumanMethylation BeadChip analysis. ChAMP is an integrated analysis pipeline including functions for (i) filtering low quality probes, adjustment for Infinium I and Infinium II probe design, (ii) batch effect correction, detecting differentially methylated positions (DMPs), (iii) finding differentially methylated regions (DMRs) and (iv) detection of copy number aberrations. The software also allows detection of differentially methylated genomic blocks (DMB) and Gene Set Enrichment Analysis (GSEA).
A package for the treatment and analysis of batch effects based in high-dimensional molecular data via batch effect adjustment and addon quantile normalization. bapred implements a plot for the visualization of batch effects using principal component analysis. The main functions of the package for batch effect adjustment are ba and baaddon which enable batch effect removal and addon batch effect removal respectively. Another important function is bametric which is a wrapper function for all implemented methods for evaluating the success of batch effect removal.
A suite of computational tools that incorporate state-of-the-art statistical techniques for the analysis of DNAm data. minfi provides methods for preprocessing, quality assessment and detection of differentially methylated regions from the kilobase to the megabase scale. Several preprocessing algorithms are available and the infrastructure provides a convenient way for developers to easily implement their techniques as Bioconductor tools. By making SNP annotation available, users can choose to be cautious about probes that may behave unexpectedly due to the inclusion of a SNP in the probe sequence. minfi is unique in that it provides both bump hunting and block finding capabilities, and the assessment of statistical significance for the identified regions. Finally, because the package is implemented in Bioconductor, it gives users access to the countless analysis and visualization tools available in R.
A package based on a single-array preprocessing algorithm that retains the advantages of multiarray algorithms and removes certain batch effects by downweighting probes that have high between-batch residual variance. By using a large biologically diverse database of microarrays from a large number of different laboratories spanning several years, the fRMA algorithm is able to differentiate between outliers and probes that show a consistent susceptibility to batch effects. These batchy probes are downweighted during summarization to minimize their effect on expression estimates. The frmaTools package which allows users to create their own frozen parameter vectors, has also been updated to work with oligo GeneFeatureSet and ExonFeatureSet objects. This allows users to create custom vectors for the HuEx and HuGene platforms and to implement fRMA on other Affymetrix Exon and Gene ST platforms.
Identifies portions of the data that deviate statistically significant from the remaining data and replaces these portions by typical values reconstructed from neighboring data entries based on latent factor models. BEclear does not change the apparently unaffected parts of the data. It can be useful process data from diagnostic chips in order to show some inhomogeneity or ambiguity in certain areas/entries. The tool controls batch effects in the data remaining after application of standard normalization techniques.
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