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BatchQC:Batch Effects Quality Control Software
Sequencing and microarray samples often are collected or processed in multiple batches or at different times. This often produces technical biases that can lead to incorrect results in the downstream analysis. BatchQC is a software tool that streamlines batch preprocessing and evaluation by providing interactive diagnostics, visualizations, and statistical analyses to explore the extent to which batch variation impacts the data. BatchQC diagnostics help determine whether batch adjustment needs to be done, and how correction should be applied before proceeding with a downstream analysis. Moreover, BatchQC interactively applies multiple common batch effect approaches to the data and the user can quickly see the benefits of each method. BatchQC is developed as a Shiny App. The output is organized into multiple tabs and each tab features an important part of the batch effect analysis and visualization of the data. The BatchQC interface has the following analysis groups: Summary, Differential Expression, Median Correlations, Heatmaps, Circular Dendrogram, PCA Analysis, Shape, ComBat and SVA.
Maintained by Jessica Anderson. Last updated 14 days ago.
batcheffectgraphandnetworkmicroarraynormalizationprincipalcomponentsequencingsoftwarevisualizationqualitycontrolrnaseqpreprocessingdifferentialexpressionimmunooncology
7 stars 9.06 score 54 scriptsbioc
scDD:Mixture modeling of single-cell RNA-seq data to identify genes with differential distributions
This package implements a method to analyze single-cell RNA- seq Data utilizing flexible Dirichlet Process mixture models. Genes with differential distributions of expression are classified into several interesting patterns of differences between two conditions. The package also includes functions for simulating data with these patterns from negative binomial distributions.
Maintained by Keegan Korthauer. Last updated 5 months ago.
immunooncologybayesianclusteringrnaseqsinglecellmultiplecomparisonvisualizationdifferentialexpression
33 stars 7.92 score 50 scriptsbioc
broadSeq:broadSeq : for streamlined exploration of RNA-seq data
This package helps user to do easily RNA-seq data analysis with multiple methods (usually which needs many different input formats). Here the user will provid the expression data as a SummarizedExperiment object and will get results from different methods. It will help user to quickly evaluate different methods.
Maintained by Rishi Das Roy. Last updated 5 months ago.
geneexpressiondifferentialexpressionrnaseqtranscriptomicssequencingcoveragegenesetenrichmentgo
4 stars 5.00 score 7 scriptsbioc
Oscope:Oscope - A statistical pipeline for identifying oscillatory genes in unsynchronized single cell RNA-seq
Oscope is a statistical pipeline developed to identifying and recovering the base cycle profiles of oscillating genes in an unsynchronized single cell RNA-seq experiment. The Oscope pipeline includes three modules: a sine model module to search for candidate oscillator pairs; a K-medoids clustering module to cluster candidate oscillators into groups; and an extended nearest insertion module to recover the base cycle order for each oscillator group.
Maintained by Ning Leng. Last updated 5 months ago.
immunooncologystatisticalmethodrnaseqsequencinggeneexpression
4.92 score 14 scripts 1 dependentsbioc
scDDboost:A compositional model to assess expression changes from single-cell rna-seq data
scDDboost is an R package to analyze changes in the distribution of single-cell expression data between two experimental conditions. Compared to other methods that assess differential expression, scDDboost benefits uniquely from information conveyed by the clustering of cells into cellular subtypes. Through a novel empirical Bayesian formulation it calculates gene-specific posterior probabilities that the marginal expression distribution is the same (or different) between the two conditions. The implementation in scDDboost treats gene-level expression data within each condition as a mixture of negative binomial distributions.
Maintained by Xiuyu Ma. Last updated 17 days ago.
singlecellsoftwareclusteringsequencinggeneexpressiondifferentialexpressionbayesiancpp
4.68 score 19 scriptsbioc
DEsubs:DEsubs: an R package for flexible identification of differentially expressed subpathways using RNA-seq expression experiments
DEsubs is a network-based systems biology package that extracts disease-perturbed subpathways within a pathway network as recorded by RNA-seq experiments. It contains an extensive and customizable framework covering a broad range of operation modes at all stages of the subpathway analysis, enabling a case-specific approach. The operation modes refer to the pathway network construction and processing, the subpathway extraction, visualization and enrichment analysis with regard to various biological and pharmacological features. Its capabilities render it a tool-guide for both the modeler and experimentalist for the identification of more robust systems-level biomarkers for complex diseases.
Maintained by Aristidis G. Vrahatis. Last updated 5 months ago.
systemsbiologygraphandnetworkpathwayskegggeneexpressionnetworkenrichmentnetworkrnaseqdifferentialexpressionnormalizationimmunooncology
3.78 score 1 scripts