Showing 12 of total 12 results (show query)
bioc
metagene2:A package to produce metagene plots
This package produces metagene plots to compare coverages of sequencing experiments at selected groups of genomic regions. It can be used for such analyses as assessing the binding of DNA-interacting proteins at promoter regions or surveying antisense transcription over the length of a gene. The metagene2 package can manage all aspects of the analysis, from normalization of coverages to plot facetting according to experimental metadata. Bootstraping analysis is used to provide confidence intervals of per-sample mean coverages.
Maintained by Eric Fournier. Last updated 5 months ago.
chipseqgeneticsmultiplecomparisoncoveragealignmentsequencing
16.5 match 4 stars 5.45 score 8 scripts
famuvie
breedR:Statistical Methods for Forest Genetic Resources Analysts
Statistical tools to build predictive models for the breeders community. It aims to assess the genetic value of individuals under a number of situations, including spatial autocorrelation, genetic/environment interaction and competition. It is under active development as part of the Trees4Future project, particularly developed having forest genetic trials in mind. But can be used for animals or other situations as well.
Maintained by Facundo Muñoz. Last updated 8 months ago.
14.9 match 33 stars 5.44 score 24 scripts
guido-s
meta:General Package for Meta-Analysis
User-friendly general package providing standard methods for meta-analysis and supporting Schwarzer, Carpenter, and Rücker <DOI:10.1007/978-3-319-21416-0>, "Meta-Analysis with R" (2015): - common effect and random effects meta-analysis; - several plots (forest, funnel, Galbraith / radial, L'Abbe, Baujat, bubble); - three-level meta-analysis model; - generalised linear mixed model; - logistic regression with penalised likelihood for rare events; - Hartung-Knapp method for random effects model; - Kenward-Roger method for random effects model; - prediction interval; - statistical tests for funnel plot asymmetry; - trim-and-fill method to evaluate bias in meta-analysis; - meta-regression; - cumulative meta-analysis and leave-one-out meta-analysis; - import data from 'RevMan 5'; - produce forest plot summarising several (subgroup) meta-analyses.
Maintained by Guido Schwarzer. Last updated 27 days ago.
3.3 match 84 stars 14.84 score 2.3k scripts 29 dependents
blasseigne
ProliferativeIndex:Calculates and Analyzes the Proliferative Index
Provides functions for calculating and analyzing the proliferative index (PI) from an RNA-seq dataset. As described in Ramaker & Lasseigne, et al. bioRxiv, 2016 <doi:10.1101/063057>.
Maintained by Brittany Lasseigne. Last updated 7 years ago.
cancercancer-genomicsgene-expressiongenomicsindexmetagene
10.0 match 3.70 score 10 scripts
katlande
PCBS:Principal Component BiSulfite
A system for fast, accurate, and flexible whole genome bisulfite sequencing (WGBS) data analysis of two-condition comparisons. Principal Component BiSulfite, 'PCBS', assigns methylated loci eigenvector values from the treatment-delineating principal component in lieu of running millions of pairwise statistical tests, which dramatically increases analysis flexibility and reduces computational requirements. Methods: <https://katlande.github.io/PCBS/articles/Differential_Methylation.html>.
Maintained by Kathryn Lande. Last updated 7 months ago.
8.3 match 1 stars 3.85 scorebioc
ribor:An R Interface for Ribo Files
The ribor package provides an R Interface for .ribo files. It provides functionality to read the .ribo file, which is of HDF5 format, and performs common analyses on its contents.
Maintained by Michael Geng. Last updated 5 months ago.
7.0 match 4.51 score 32 scriptsbioc
groHMM:GRO-seq Analysis Pipeline
A pipeline for the analysis of GRO-seq data.
Maintained by Tulip Nandu. Last updated 4 days ago.
4.8 match 1 stars 5.48 score 25 scriptsbioc
ribosomeProfilingQC:Ribosome Profiling Quality Control
Ribo-Seq (also named ribosome profiling or footprinting) measures translatome (unlike RNA-Seq, which sequences the transcriptome) by direct quantification of the ribosome-protected fragments (RPFs). This package provides the tools for quality assessment of ribosome profiling. In addition, it can preprocess Ribo-Seq data for subsequent differential analysis.
Maintained by Jianhong Ou. Last updated 1 months ago.
riboseqsequencinggeneregulationqualitycontrolvisualizationcoverage
4.5 match 4.88 score 17 scripts
welch-lab
rliger:Linked Inference of Genomic Experimental Relationships
Uses an extension of nonnegative matrix factorization to identify shared and dataset-specific factors. See Welch J, Kozareva V, et al (2019) <doi:10.1016/j.cell.2019.05.006>, and Liu J, Gao C, Sodicoff J, et al (2020) <doi:10.1038/s41596-020-0391-8> for more details.
Maintained by Yichen Wang. Last updated 2 months ago.
nonnegative-matrix-factorizationsingle-cellopenblascpp
1.9 match 408 stars 10.77 score 334 scripts 1 dependentsbioc
wavClusteR:Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data
The package provides an integrated pipeline for the analysis of PAR-CLIP data. PAR-CLIP-induced transitions are first discriminated from sequencing errors, SNPs and additional non-experimental sources by a non- parametric mixture model. The protein binding sites (clusters) are then resolved at high resolution and cluster statistics are estimated using a rigorous Bayesian framework. Post-processing of the results, data export for UCSC genome browser visualization and motif search analysis are provided. In addition, the package allows to integrate RNA-Seq data to estimate the False Discovery Rate of cluster detection. Key functions support parallel multicore computing. Note: while wavClusteR was designed for PAR-CLIP data analysis, it can be applied to the analysis of other NGS data obtained from experimental procedures that induce nucleotide substitutions (e.g. BisSeq).
Maintained by Federico Comoglio. Last updated 5 months ago.
immunooncologysequencingtechnologyripseqrnaseqbayesian
3.3 match 4.60 score 3 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
3.8 match 4.00 score 9 scriptsbioc
GenomicPlot:Plot profiles of next generation sequencing data in genomic features
Visualization of next generation sequencing (NGS) data is essential for interpreting high-throughput genomics experiment results. 'GenomicPlot' facilitates plotting of NGS data in various formats (bam, bed, wig and bigwig); both coverage and enrichment over input can be computed and displayed with respect to genomic features (such as UTR, CDS, enhancer), and user defined genomic loci or regions. Statistical tests on signal intensity within user defined regions of interest can be performed and represented as boxplots or bar graphs. Parallel processing is used to speed up computation on multicore platforms. In addition to genomic plots which is suitable for displaying of coverage of genomic DNA (such as ChIPseq data), metagenomic (without introns) plots can also be made for RNAseq or CLIPseq data as well.
Maintained by Shuye Pu. Last updated 2 months ago.
alternativesplicingchipseqcoveragegeneexpressionrnaseqsequencingsoftwaretranscriptionvisualizationannotation
1.2 match 3 stars 5.62 score 4 scripts