Showing 7 of total 7 results (show query)
bioc
Biobase:Biobase: Base functions for Bioconductor
Functions that are needed by many other packages or which replace R functions.
Maintained by Bioconductor Package Maintainer. Last updated 5 months ago.
infrastructurebioconductor-packagecore-package
63.4 match 9 stars 16.45 score 6.6k scripts 1.8k dependents
tinnlab
RCPA:Consensus Pathway Analysis
Provides a set of functions to perform pathway analysis and meta-analysis from multiple gene expression datasets, as well as visualization of the results. This package wraps functionality from the following packages: Ritchie et al. (2015) <doi:10.1093/nar/gkv007>, Love et al. (2014) <doi:10.1186/s13059-014-0550-8>, Robinson et al. (2010) <doi:10.1093/bioinformatics/btp616>, Korotkevich et al. (2016) <arxiv:10.1101/060012>, Efron et al. (2015) <https://CRAN.R-project.org/package=GSA>, and Gu et al. (2012) <https://CRAN.R-project.org/package=CePa>.
Maintained by Ha Nguyen. Last updated 4 months ago.
biobasedeseq2geoqueryedgerlimmarcyjsfgseabrowservizsummarizedexperimentannotationdbirontotools
10.0 match 1 stars 5.50 score 70 scriptsbioc
biobroom:Turn Bioconductor objects into tidy data frames
This package contains methods for converting standard objects constructed by bioinformatics packages, especially those in Bioconductor, and converting them to tidy data. It thus serves as a complement to the broom package, and follows the same the tidy, augment, glance division of tidying methods. Tidying data makes it easy to recombine, reshape and visualize bioinformatics analyses.
Maintained by John D. Storey. Last updated 5 months ago.
multiplecomparisondifferentialexpressionregressiongeneexpressionproteomicsdataimport
5.4 match 49 stars 8.22 score 280 scripts 1 dependentsbioc
DAPAR:Tools for the Differential Analysis of Proteins Abundance with R
The package DAPAR is a Bioconductor distributed R package which provides all the necessary functions to analyze quantitative data from label-free proteomics experiments. Contrarily to most other similar R packages, it is endowed with rich and user-friendly graphical interfaces, so that no programming skill is required (see `Prostar` package).
Maintained by Samuel Wieczorek. Last updated 5 months ago.
proteomicsnormalizationpreprocessingmassspectrometryqualitycontrolgodataimportprostar1
6.5 match 2 stars 5.42 score 22 scripts 1 dependentsbioc
BioMVCClass:Model-View-Controller (MVC) Classes That Use Biobase
Creates classes used in model-view-controller (MVC) design
Maintained by Elizabeth Whalen. Last updated 5 months ago.
visualizationinfrastructuregraphandnetwork
2.9 match 3.30 scorebioc
quantiseqr:Quantification of the Tumor Immune contexture from RNA-seq data
This package provides a streamlined workflow for the quanTIseq method, developed to perform the quantification of the Tumor Immune contexture from RNA-seq data. The quantification is performed against the TIL10 signature (dissecting the contributions of ten immune cell types), carefully crafted from a collection of human RNA-seq samples. The TIL10 signature has been extensively validated using simulated, flow cytometry, and immunohistochemistry data.
Maintained by Federico Marini. Last updated 3 months ago.
geneexpressionsoftwaretranscriptiontranscriptomicssequencingmicroarrayvisualizationannotationimmunooncologyfeatureextractionclassificationstatisticalmethodexperimenthubsoftwareflowcytometry
1.8 match 4.65 score 3 scripts 1 dependentsbioc
TMixClust:Time Series Clustering of Gene Expression with Gaussian Mixed-Effects Models and Smoothing Splines
Implementation of a clustering method for time series gene expression data based on mixed-effects models with Gaussian variables and non-parametric cubic splines estimation. The method can robustly account for the high levels of noise present in typical gene expression time series datasets.
Maintained by Monica Golumbeanu. Last updated 5 months ago.
softwarestatisticalmethodclusteringtimecoursegeneexpression
1.7 match 3.60 score 5 scripts