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bioc
netZooR:Unified methods for the inference and analysis of gene regulatory networks
netZooR unifies the implementations of several Network Zoo methods (netzoo, netzoo.github.io) into a single package by creating interfaces between network inference and network analysis methods. Currently, the package has 3 methods for network inference including PANDA and its optimized implementation OTTER (network reconstruction using mutliple lines of biological evidence), LIONESS (single-sample network inference), and EGRET (genotype-specific networks). Network analysis methods include CONDOR (community detection), ALPACA (differential community detection), CRANE (significance estimation of differential modules), MONSTER (estimation of network transition states). In addition, YARN allows to process gene expresssion data for tissue-specific analyses and SAMBAR infers missing mutation data based on pathway information.
Maintained by Tara Eicher. Last updated 15 days ago.
networkinferencenetworkgeneregulationgeneexpressiontranscriptionmicroarraygraphandnetworkgene-regulatory-networktranscription-factors
105 stars 7.98 scorebioc
SpaNorm:Spatially-aware normalisation for spatial transcriptomics data
This package implements the spatially aware library size normalisation algorithm, SpaNorm. SpaNorm normalises out library size effects while retaining biology through the modelling of smooth functions for each effect. Normalisation is performed in a gene- and cell-/spot- specific manner, yielding library size adjusted data.
Maintained by Dharmesh D. Bhuva. Last updated 5 months ago.
softwaregeneexpressiontranscriptomicsspatialcellbiology
9 stars 5.86 score 3 scriptsbioc
yarn:YARN: Robust Multi-Condition RNA-Seq Preprocessing and Normalization
Expedite large RNA-Seq analyses using a combination of previously developed tools. YARN is meant to make it easier for the user in performing basic mis-annotation quality control, filtering, and condition-aware normalization. YARN leverages many Bioconductor tools and statistical techniques to account for the large heterogeneity and sparsity found in very large RNA-seq experiments.
Maintained by Joseph N Paulson. Last updated 5 months ago.
softwarequalitycontrolgeneexpressionsequencingpreprocessingnormalizationannotationvisualizationclustering
4.49 score 31 scripts