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http://bioconductor.org/packages/GenomicRanges/
Software R package for computing and annotating genomic ranges. Used for storing and manipulating genomic intervals and variables defined along genome.
Proper citation: Genomic Ranges (RRID:SCR_017051) Copy
Software R package as search tool for single cell RNA-seq data by gene lists. Builds index from scRNA-seq datasets which organizes information in suitable and compact manner so that datasets can be very efficiently searched for either cells or cell types in which given list of genes is expressed.
Proper citation: Scfind (RRID:SCR_017339) Copy
http://www.bioconductor.org/packages/2.12/bioc/html/PICS.html
R package with tools that use probabilistic inference of ChIP-Seq. It follows an empirical Bayes mixture model approach.
Proper citation: PICS (RRID:SCR_001093) Copy
https://www.bioconductor.org/packages/release/bioc/html/GSVA.html
Open source software R package for assaying variation of gene set enrichment over sample population.Used for microarray and RNA-seq data analysis. Gene set enrichment method that estimates variation of pathway activity over sample population in unsupervised manner.
Proper citation: GSVA (RRID:SCR_021058) Copy
https://bioconductor.org/packages/release/bioc/html/PhenStat.html
Software R package for statistical analysis of phenotypic data.Tool kit for standardized analysis of high throughput phenotypic data.
Proper citation: PhenStat (RRID:SCR_021317) Copy
https://bioconductor.org/packages/SimFFPE/
Software R package to simulate artifact chimeric reads specifically generated in next generation sequencing process of formalin fixed paraffin embedded tissue. Simulates normal reads as well as artifact chimeric reads that are enriched in FFPE samples. These artifact chimeric reads can lead to large amounts of false positive structural variant calls.
Proper citation: SimFFPE (RRID:SCR_021085) Copy
https://bioconductor.org/packages/KEGGgraph/
Software R package interface between KEGG pathway and graph object as well as collection of tools to analyze, dissect and visualize these graphs.
Proper citation: KEGGgraph (RRID:SCR_023788) Copy
https://bioconductor.org/packages/release/bioc/html/DESeq2.html
Software package for differential gene expression analysis based on the negative binomial distribution. Used for analyzing RNA-seq data for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates.
Proper citation: DESeq2 (RRID:SCR_015687) Copy
https://bioconductor.org/packages/AUCell/
Software R package to identify cells with active gene sets in single cell RNA-seq data. Used for analysis of gene set activity in single cell RNA-seq data.Used to calculate whether critical subset of input gene set is enriched within expressed genes for each cell.
Proper citation: AUCell (RRID:SCR_021327) Copy
http://www.bioconductor.org/packages/release/bioc/html/ReadqPCR.html
A software package that provides functions to read raw RT-qPCR data of different platforms.
Proper citation: ReadqPCR (RRID:SCR_000030) Copy
http://bioconductor.org/packages/release/bioc/html/topGO.html
Software package which provides tools for testing GO terms while accounting for the topology of the GO graph. Different test statistics and different methods for eliminating local similarities and dependencies between GO terms can be implemented and applied.
Proper citation: topGO (RRID:SCR_014798) Copy
Web-based microarray data analysis and visualization system powered by CRC, or Chinese Restaurant cluster, a Dirichlet process model-based clustering algorithm recently developed by Dr. Steve Qin. It also incorporates several gene expression analysis programs from Bioconductor, including GOStats, genefilter, and Heatplus. CRCView also installs from the Bioconductor system 78 annotation libraries of microarray chips for human (31), mouse (24), rat (14), zebrafish (1), chicken (1), Drosophila (3), Arabidopsis (2), Caenorhabditis elegans (1), and Xenopus Laevis (1). CRCView allows flexible input data format, automated model-based CRC clustering analysis, rich graphical illustration, and integrated Gene Ontology (GO)-based gene enrichment for efficient annotation and interpretation of clustering results. CRC has the following features comparing to other clustering tools: 1) able to infer number of clusters, 2) able to cluster genes displaying time-shifted and/or inverted correlations, 3) able to tolerate missing genotype data and 4) provide confidence measure for clusters generated. You need to register for an account in the system to store your data and analyses. The data and results can be visited again anytime you log in.
Proper citation: CRCView (RRID:SCR_007092) 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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