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debrowser:Interactive Differential Expresion Analysis Browser
Bioinformatics platform containing interactive plots and tables for differential gene and region expression studies. Allows visualizing expression data much more deeply in an interactive and faster way. By changing the parameters, users can easily discover different parts of the data that like never have been done before. Manually creating and looking these plots takes time. With DEBrowser users can prepare plots without writing any code. Differential expression, PCA and clustering analysis are made on site and the results are shown in various plots such as scatter, bar, box, volcano, ma plots and Heatmaps.
Maintained by Alper Kucukural. Last updated 5 months ago.
sequencingchipseqrnaseqdifferentialexpressiongeneexpressionclusteringimmunooncology
61 stars 7.80 score 65 scriptsbioc
GSEABenchmarkeR:Reproducible GSEA Benchmarking
The GSEABenchmarkeR package implements an extendable framework for reproducible evaluation of set- and network-based methods for enrichment analysis of gene expression data. This includes support for the efficient execution of these methods on comprehensive real data compendia (microarray and RNA-seq) using parallel computation on standard workstations and institutional computer grids. Methods can then be assessed with respect to runtime, statistical significance, and relevance of the results for the phenotypes investigated.
Maintained by Ludwig Geistlinger. Last updated 5 months ago.
immunooncologymicroarrayrnaseqgeneexpressiondifferentialexpressionpathwaysgraphandnetworknetworkgenesetenrichmentnetworkenrichmentvisualizationreportwritingbioconductor-packageu24ca289073
13 stars 6.55 score 23 scriptsbioc
POWSC:Simulation, power evaluation, and sample size recommendation for single cell RNA-seq
Determining the sample size for adequate power to detect statistical significance is a crucial step at the design stage for high-throughput experiments. Even though a number of methods and tools are available for sample size calculation for microarray and RNA-seq in the context of differential expression (DE), this topic in the field of single-cell RNA sequencing is understudied. Moreover, the unique data characteristics present in scRNA-seq such as sparsity and heterogeneity increase the challenge. We propose POWSC, a simulation-based method, to provide power evaluation and sample size recommendation for single-cell RNA sequencing DE analysis. POWSC consists of a data simulator that creates realistic expression data, and a power assessor that provides a comprehensive evaluation and visualization of the power and sample size relationship.
Maintained by Kenong Su. Last updated 5 months ago.
differentialexpressionimmunooncologysinglecellsoftware
4.00 score 7 scripts