Showing 5 of total 5 results (show query)
bioc
nnSVG:Scalable identification of spatially variable genes in spatially-resolved transcriptomics data
Method for scalable identification of spatially variable genes (SVGs) in spatially-resolved transcriptomics data. The method is based on nearest-neighbor Gaussian processes and uses the BRISC algorithm for model fitting and parameter estimation. Allows identification and ranking of SVGs with flexible length scales across a tissue slide or within spatial domains defined by covariates. Scales linearly with the number of spatial locations and can be applied to datasets containing thousands or more spatial locations.
Maintained by Lukas M. Weber. Last updated 1 months ago.
spatialsinglecelltranscriptomicsgeneexpressionpreprocessing
17 stars 7.57 score 183 scripts 1 dependentsbioc
BANDITS:BANDITS: Bayesian ANalysis of DIfferenTial Splicing
BANDITS is a Bayesian hierarchical model for detecting differential splicing of genes and transcripts, via differential transcript usage (DTU), between two or more conditions. The method uses a Bayesian hierarchical framework, which allows for sample specific proportions in a Dirichlet-Multinomial model, and samples the allocation of fragments to the transcripts. Parameters are inferred via Markov chain Monte Carlo (MCMC) techniques and a DTU test is performed via a multivariate Wald test on the posterior densities for the average relative abundance of transcripts.
Maintained by Simone Tiberi. Last updated 5 months ago.
differentialsplicingalternativesplicingbayesiangeneticsrnaseqsequencingdifferentialexpressiongeneexpressionmultiplecomparisonsoftwaretranscriptionstatisticalmethodvisualizationopenblascpp
17 stars 5.75 score 11 scripts 1 dependentsbioc
CEMiTool:Co-expression Modules identification Tool
The CEMiTool package unifies the discovery and the analysis of coexpression gene modules in a fully automatic manner, while providing a user-friendly html report with high quality graphs. Our tool evaluates if modules contain genes that are over-represented by specific pathways or that are altered in a specific sample group. Additionally, CEMiTool is able to integrate transcriptomic data with interactome information, identifying the potential hubs on each network.
Maintained by Helder Nakaya. Last updated 5 months ago.
geneexpressiontranscriptomicsgraphandnetworkmrnamicroarrayrnaseqnetworknetworkenrichmentpathwaysimmunooncology
5.58 score 38 scripts
frederikziebell
RNAseqQC:Quality Control for RNA-Seq Data
Functions for semi-automated quality control of bulk RNA-seq data.
Maintained by Frederik Ziebell. Last updated 9 months ago.
2 stars 5.21 score 27 scriptsbioc
ccfindR:Cancer Clone Finder
A collection of tools for cancer genomic data clustering analyses, including those for single cell RNA-seq. Cell clustering and feature gene selection analysis employ Bayesian (and maximum likelihood) non-negative matrix factorization (NMF) algorithm. Input data set consists of RNA count matrix, gene, and cell bar code annotations. Analysis outputs are factor matrices for multiple ranks and marginal likelihood values for each rank. The package includes utilities for downstream analyses, including meta-gene identification, visualization, and construction of rank-based trees for clusters.
Maintained by Jun Woo. Last updated 5 months ago.
transcriptomicssinglecellimmunooncologybayesianclusteringgslcpp
4.00 score 9 scripts