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limma / Linear Models for Microarray Data

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Provides an integrated solution for analysing data from gene expression experiments. limma contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. It also contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions: (i) it can perform both differential expression and differential splicing analyses of RNA-seq data; (ii) the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences.

ChAMP

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).

minfi

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.

CpGassoc

An R package that can efficiently perform the statistical analysis needed for increasingly large methylation datasets. CpGassoc can perform standard analyses of large datasets very quickly with no need to impute the data. It can handle mixed effects models with chip or batch entering the model as a random intercept. CpGassoc also includes tools to apply quality control filters, perform permutation tests, and create QQ plots, manhattan plots, and scatterplots for individual CpG sites.

WFMM / Wavelet-Based Functional Mixed Models

A Windows command-line application that implements a Bayesian wavelet-based functional mixed model methodology for functional data analysis. WFMM can be generally applied to any complex functional data sampled on a fine grid, not just methylation data, and so can be readily applied to other genome-wide data including copy number and tiling transcriptome arrays. The method is computationally intensive, but the software is optimized so that it can handle very large data sets.

D3M

A method for detection of differential distribution of methylation, based on distribution-valued data. D3M can detect the differences in high-order moments, such as shapes of underlying distributions in methylation profiles, based on the Wasserstein metric. We test the significance of the difference between case and control groups and provide an interpretable summary of the results. The simulation results show that the proposed method achieves promising accuracy and shows favorable results compared with previous methods.

ADMIRE / Analysis of DNA methylation in genomic regions

Allows to analyze and visualize differential methylation in genomic regions. ADMIRE is a semi-automatic pipeline that features five different normalization methods and performs two one-sided two-sample rank tests (Mann–Whitney U tests). The software features arbitrary experimental settings, quality control, automatic filtering, normalization, multiple testing, differential analyses on arbitrary genomic regions. It additionally implements a gene set enrichment procedure.