R-universe search: needs:widgetTools

Showing 8 of total 8 results (show query)

bioc

Mfuzz:Soft clustering of omics time series data

The Mfuzz package implements noise-robust soft clustering of omics time-series data, including transcriptomic, proteomic or metabolomic data. It is based on the use of c-means clustering. For convenience, it includes a graphical user interface.

Maintained by Matthias Futschik. Last updated 5 months ago.

microarray clustering timecourse preprocessing visualization

7.64 score 338 scripts 4 dependents

junjunlab

ClusterGVis:One-Step to Cluster and Visualize Gene Expression Data

Streamlining the clustering and visualization of time-series gene expression data from RNA-Seq experiments, this tool supports fuzzy c-means and k-means clustering algorithms. It is compatible with outputs from widely-used packages such as 'Seurat', 'Monocle', and 'WGCNA', enabling seamless downstream visualization and analysis. See Lokesh Kumar and Matthias E Futschik (2007) <doi:10.6026/97320630002005> for more details.

Maintained by Jun Zhang. Last updated 23 days ago.

sequencing clusterprofiler summarizedexperiment mfuzz complexheatmap gene-clustering gene-expression visualization

281 stars 6.80 score 30 scripts

fbertran

Patterns:Deciphering Biological Networks with Patterned Heterogeneous Measurements

A modeling tool dedicated to biological network modeling (Bertrand and others 2020, <doi:10.1093/bioinformatics/btaa855>). It allows for single or joint modeling of, for instance, genes and proteins. It starts with the selection of the actors that will be the used in the reverse engineering upcoming step. An actor can be included in that selection based on its differential measurement (for instance gene expression or protein abundance) or on its time course profile. Wrappers for actors clustering functions and cluster analysis are provided. It also allows reverse engineering of biological networks taking into account the observed time course patterns of the actors. Many inference functions are provided and dedicated to get specific features for the inferred network such as sparsity, robust links, high confidence links or stable through resampling links. Some simulation and prediction tools are also available for cascade networks (Jung and others 2014, <doi:10.1093/bioinformatics/btt705>). Example of use with microarray or RNA-Seq data are provided.

Maintained by Frederic Bertrand. Last updated 11 months ago.

4 stars 6.16 score 18 scripts

bioc

tkWidgets:R based tk widgets

Widgets to provide user interfaces. tcltk should have been installed for the widgets to run.

Maintained by J. Zhang. Last updated 5 months ago.

infrastructure

6.04 score 72 scripts 6 dependents

bioc

MultiRNAflow:An R package for integrated analysis of temporal RNA-seq data with multiple biological conditions

Our R package MultiRNAflow provides an easy to use unified framework allowing to automatically make both unsupervised and supervised (DE) analysis for datasets with an arbitrary number of biological conditions and time points. In particular, our code makes a deep downstream analysis of DE information, e.g. identifying temporal patterns across biological conditions and DE genes which are specific to a biological condition for each time.

Maintained by Rodolphe Loubaton. Last updated 5 months ago.

sequencing rnaseq geneexpression transcription timecourse preprocessing visualization normalization principalcomponent clustering differentialexpression genesetenrichment pathways

6 stars 5.26 score 4 scripts

bioc

cycle:Significance of periodic expression pattern in time-series data

Package for assessing the statistical significance of periodic expression based on Fourier analysis and comparison with data generated by different background models

Maintained by Matthias Futschik. Last updated 5 months ago.

microarray timecourse

3.72 score 13 scripts

bioc

OLINgui:Graphical user interface for OLIN

Graphical user interface for the OLIN package

Maintained by Matthias Futschik. Last updated 5 months ago.

microarray twochannel qualitycontrol preprocessing visualization

3.60 score 1 scripts

cran

SeqFeatR:A Tool to Associate FASTA Sequences and Features

Provides user friendly methods for the identification of sequence patterns that are statistically significantly associated with a property of the sequence. For instance, SeqFeatR allows to identify viral immune escape mutations for hosts of given HLA types. The underlying statistical method is Fisher's exact test, with appropriate corrections for multiple testing, or Bayes. Patterns may be point mutations or n-tuple of mutations. SeqFeatR offers several ways to visualize the results of the statistical analyses, see Budeus (2016) <doi:10.1371/journal.pone.0146409>.

Maintained by Bettina Budeus. Last updated 6 years ago.

jags cpp

2.30 score