Showing 8 of total 8 results (show query)
r-lib
remotes:R Package Installation from Remote Repositories, Including 'GitHub'
Download and install R packages stored in 'GitHub', 'GitLab', 'Bitbucket', 'Bioconductor', or plain 'subversion' or 'git' repositories. This package provides the 'install_*' functions in 'devtools'. Indeed most of the code was copied over from 'devtools'.
Maintained by Gábor Csárdi. Last updated 1 months ago.
341 stars 17.11 score 5.8k scripts 245 dependents
mrcieu
TwoSampleMR:Two Sample MR Functions and Interface to MRC Integrative Epidemiology Unit OpenGWAS Database
A package for performing Mendelian randomization using GWAS summary data. It uses the IEU OpenGWAS database <https://gwas.mrcieu.ac.uk/> to automatically obtain data, and a wide range of methods to run the analysis.
Maintained by Gibran Hemani. Last updated 3 days ago.
476 stars 11.27 score 1.7k scripts 1 dependents
bioc
recount:Explore and download data from the recount project
Explore and download data from the recount project available at https://jhubiostatistics.shinyapps.io/recount/. Using the recount package you can download RangedSummarizedExperiment objects at the gene, exon or exon-exon junctions level, the raw counts, the phenotype metadata used, the urls to the sample coverage bigWig files or the mean coverage bigWig file for a particular study. The RangedSummarizedExperiment objects can be used by different packages for performing differential expression analysis. Using http://bioconductor.org/packages/derfinder you can perform annotation-agnostic differential expression analyses with the data from the recount project as described at http://www.nature.com/nbt/journal/v35/n4/full/nbt.3838.html.
Maintained by Leonardo Collado-Torres. Last updated 4 months ago.
coveragedifferentialexpressiongeneexpressionrnaseqsequencingsoftwaredataimportimmunooncologyannotation-agnosticbioconductorcountderfinderdeseq2exongenehumanilluminajunctionrecount
41 stars 9.57 score 498 scripts 3 dependents
canmod
iidda:Processing Infectious Disease Datasets in IIDDA.
Part of an open toolchain for processing infectious disease datasets available through the IIDDA data repository.
Maintained by Steve Walker. Last updated 4 months ago.
6.02 score 133 scripts 3 dependents
core-bioinformatics
ClustAssess:Tools for Assessing Clustering
A set of tools for evaluating clustering robustness using proportion of ambiguously clustered pairs (Senbabaoglu et al. (2014) <doi:10.1038/srep06207>), as well as similarity across methods and method stability using element-centric clustering comparison (Gates et al. (2019) <doi:10.1038/s41598-019-44892-y>). Additionally, this package enables stability-based parameter assessment for graph-based clustering pipelines typical in single-cell data analysis.
Maintained by Andi Munteanu. Last updated 2 months ago.
softwaresinglecellrnaseqatacseqnormalizationpreprocessingdimensionreductionvisualizationqualitycontrolclusteringclassificationannotationgeneexpressiondifferentialexpressionbioinformaticsgenomicsmachine-learningparameter-optimizationrobustnesssingle-cellunsupervised-learningcpp
23 stars 5.70 score 18 scripts
zachariahmclean
trace:Tandem Repeat Analysis by Capillary Electrophoresis
A pipeline for short tandem repeat instability analysis from fragment analysis data. Inputs of fsa files or peak tables, and a user supplied metadata data-frame. The package identifies ladders, calls peaks, identifies the modal peaks, calls repeats, then calculates repeat instability metrics (e.g. expansion index from Lee et al. (2010) <doi:10.1186/1752-0509-4-29>).
Maintained by Zachariah McLean. Last updated 18 days ago.
1 stars 5.61 score 74 scripts
greshamlab
vivaldi:Viral Variant Location and Diversity
Analysis of minor alleles in Illumina sequencing data of viral genomes. Functions in 'vivaldi' primarily operate on vcf files.
Maintained by David Gresham. Last updated 1 years ago.
3 stars 4.78 score 7 scriptsbioc
ASURAT:Functional annotation-driven unsupervised clustering for single-cell data
ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).
Maintained by Keita Iida. Last updated 5 months ago.
geneexpressionsinglecellsequencingclusteringgenesignalingcpp
4.32 score 21 scripts