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POMA:Tools for Omics Data Analysis
The POMA package offers a comprehensive toolkit designed for omics data analysis, streamlining the process from initial visualization to final statistical analysis. Its primary goal is to simplify and unify the various steps involved in omics data processing, making it more accessible and manageable within a single, intuitive R package. Emphasizing on reproducibility and user-friendliness, POMA leverages the standardized SummarizedExperiment class from Bioconductor, ensuring seamless integration and compatibility with a wide array of Bioconductor tools. This approach guarantees maximum flexibility and replicability, making POMA an essential asset for researchers handling omics datasets. See https://github.com/pcastellanoescuder/POMAShiny. Paper: Castellano-Escuder et al. (2021) <doi:10.1371/journal.pcbi.1009148> for more details.
Maintained by Pol Castellano-Escuder. Last updated 4 months ago.
batcheffectclassificationclusteringdecisiontreedimensionreductionmultidimensionalscalingnormalizationpreprocessingprincipalcomponentregressionrnaseqsoftwarestatisticalmethodvisualizationbioconductorbioinformaticsdata-visualizationdimension-reductionexploratory-data-analysismachine-learningomics-data-integrationpipelinepre-processingstatistical-analysisuser-friendlyworkflow
11 stars 8.16 score 20 scripts 1 dependentsbioc
CytoMDS:Low Dimensions projection of cytometry samples
This package implements a low dimensional visualization of a set of cytometry samples, in order to visually assess the 'distances' between them. This, in turn, can greatly help the user to identify quality issues like batch effects or outlier samples, and/or check the presence of potential sample clusters that might align with the exeprimental design. The CytoMDS algorithm combines, on the one hand, the concept of Earth Mover's Distance (EMD), a.k.a. Wasserstein metric and, on the other hand, the Multi Dimensional Scaling (MDS) algorithm for the low dimensional projection. Also, the package provides some diagnostic tools for both checking the quality of the MDS projection, as well as tools to help with the interpretation of the axes of the projection.
Maintained by Philippe Hauchamps. Last updated 2 months ago.
flowcytometryqualitycontroldimensionreductionmultidimensionalscalingsoftwarevisualization
1 stars 5.23 score 2 scriptsbioc
systemPipeTools:Tools for data visualization
systemPipeTools package extends the widely used systemPipeR (SPR) workflow environment with an enhanced toolkit for data visualization, including utilities to automate the data visualizaton for analysis of differentially expressed genes (DEGs). systemPipeTools provides data transformation and data exploration functions via scatterplots, hierarchical clustering heatMaps, principal component analysis, multidimensional scaling, generalized principal components, t-Distributed Stochastic Neighbor embedding (t-SNE), and MA and volcano plots. All these utilities can be integrated with the modular design of the systemPipeR environment that allows users to easily substitute any of these features and/or custom with alternatives.
Maintained by Daniela Cassol. Last updated 5 months ago.
infrastructuredataimportsequencingqualitycontrolreportwritingexperimentaldesignclusteringdifferentialexpressionmultidimensionalscalingprincipalcomponent
4.00 score 4 scripts