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SciCrunch Registry is a curated repository of scientific resources, with a focus on biomedical resources, including tools, databases, and core facilities - visit SciCrunch to register your resource.
https://github.com/powellgenomicslab/ascend
Software R package for analysis of single cell RNA-seq expression, normalization and differential expression data. Provides framework to perform cell and gene filtering, quality control, normalization, dimension reduction, clustering, differential expression, and visualization functions.
Proper citation: ascend (RRID:SCR_017257) Copy
http://www.bioconductor.org/packages/release/bioc/html/flowCyBar.html
A software package to analyze flow cytometric data using gate information to follow population / community dynamics.
Proper citation: flowCyBar (RRID:SCR_002319) Copy
http://bioconductor.org/packages/devel/bioc/html/massiR.html
Software that predicts the sex of samples in gene expression microarray datasets.
Proper citation: massiR (RRID:SCR_001157) Copy
http://www.bioconductor.org/packages/release/bioc/html/CGEN.html
Software R package for analysis of case-control studies in genetic epidemiology.
Proper citation: CGEN (RRID:SCR_001251) Copy
https://www.bioconductor.org/packages//2.12/bioc/html/maigesPack.html
Software package that uses functions to handle and analyze cDNA microarray data.
Proper citation: maigesPack (RRID:SCR_001351) Copy
http://www.bioconductor.org/packages/release/bioc/html/SamSPECTRAL.html
Software that identifies cell population in flow cytometry data. It demonstrates significant advantages in proper identification of populations with non-elliptical shapes, low density populations close to dense ones, minor subpopulations of a major population and rare populations. It samples large data such that spectral clustering is possible while preserving density information in edge weights. More specifically, given a matrix of coordinates as input, SamSPECTRAL first builds the communities to sample the data points. Then, it builds a graph and after weighting the edges by conductance computation, the graph is passed to a classic spectral clustering algorithm to find the spectral clusters. The last stage of SamSPECTRAL is to combine the spectral clusters. The resulting connected components estimate biological cell populations in the data sample.
Proper citation: SamSPECTRAL (RRID:SCR_001858) Copy
https://www.bioconductor.org/packages//2.10/bioc/html/spade.html
An analysis and visualization software tool for high dimensional flow cytometry data that organizes cells into hierarchies of related phenotypes.
Proper citation: SPADE (RRID:SCR_001810) Copy
https://github.com/yongchao/flowPeaks
Software for fast and automatic clustering to classify the cells into subpopulations based on finding the peaks from the overall density function generated by K-means.
Proper citation: flowPeaks (RRID:SCR_000407) Copy
Software package for noise-robust soft clustering of gene expression time-series data (including a graphical user interface)., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.
Proper citation: Mfuzz (RRID:SCR_000523) Copy
https://sydneybiox.github.io/CiteFuse/
Software R package consisting of suite of tools for doublet detection, modality integration, clustering, differential RNA and protein expression analysis, antibody-derived tag evaluation, ligand-receptor interaction analysis and interactive web-based visualization of CITE-seq data.
Proper citation: CiteFuse (RRID:SCR_019321) Copy
http://www.bioconductor.org/packages/2.12/bioc/html/PING.html
Software program for probabilistic inference of ChIP-Seq using an empirical Bayes mixture model approach.
Proper citation: PING (RRID:SCR_005394) Copy
http://www.bioconductor.org/packages/release/bioc/html/flowClust.html
A Bioconductor software package for automated gating of flow cytometry data that implements a robust model-based clustering approach based on multivariate t mixture models with the Box-Cox transformation.
Proper citation: flowClust (RRID:SCR_001807) Copy
http://www.bioconductor.org/packages/release/bioc/html/flowMatch.html
Software for matching cell populations and building meta-clusters and templates from a collection of flow cytometry (FC) samples.
Proper citation: flowMatch (RRID:SCR_002283) Copy
http://www.bioconductor.org/packages/release/bioc/html/flowMeans.html
Software that identifies cell populations in Flow Cytometry data using non-parametric clustering and segmented-regression-based change point detection.
Proper citation: flowMeans (RRID:SCR_002275) Copy
http://www.bioconductor.org/packages/release/bioc/html/flowMerge.html
Software for merging of mixture components for model-based automated gating of flow cytometry data using the flowClust framework.
Proper citation: flowMerge (RRID:SCR_002224) Copy
https://bioconductor.org/packages/2.11/bioc/html/flowPhyto.html
An R package that performs aggregate statistics on virtually unlimited collections of raw flow cytometry files and provides a memory efficient, parallelized solution for analyzing high-throughput flow cytometric data.
Proper citation: flowPhyto (RRID:SCR_002183) Copy
http://www.bioconductor.org/packages/release/bioc/html/flowFP.html
A Bioconductor software package for fingerprint generation of flow cytometry data, used to facilitate the application of machine learning and datamining tools for flow cytometry.
Proper citation: flowFP (RRID:SCR_001537) Copy
http://www.bioconductor.org/packages/release/bioc/html/TransView.html
Software package to generate, access and display read densities of sequencing based data sets such as from RNA-Seq and ChIP-Seq.
Proper citation: TransView (RRID:SCR_000358) Copy
http://www.bioconductor.org/packages/release/bioc/html/SigFuge.html
Algorithm for testing significance of clustering in RNA-seq data.
Proper citation: SigFuge (RRID:SCR_000444) Copy
http://master.bioconductor.org/packages/2.13/bioc/html/BHC.html
Software package that performs bottom-up hierarchical clustering, using a Dirichlet Process (infinite mixture) to model uncertainty in the data and Bayesian model selection to decide at each step which clusters to merge. This avoids several limitations of traditional methods, for example how many clusters there should be and how to choose a principled distance metric. This implementation accepts multinomial (i.e. discrete, with 2+ categories) or time-series data and also includes a randomised algorithm which is more efficient for larger data sets.
Proper citation: BHC (RRID:SCR_006399) Copy
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