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bioc
hermes:Preprocessing, analyzing, and reporting of RNA-seq data
Provides classes and functions for quality control, filtering, normalization and differential expression analysis of pre-processed `RNA-seq` data. Data can be imported from `SummarizedExperiment` as well as `matrix` objects and can be annotated from `BioMart`. Filtering for genes without too low expression or containing required annotations, as well as filtering for samples with sufficient correlation to other samples or total number of reads is supported. The standard normalization methods including cpm, rpkm and tpm can be used, and 'DESeq2` as well as voom differential expression analyses are available.
Maintained by Daniel Sabanés Bové. Last updated 5 months ago.
rnaseqdifferentialexpressionnormalizationpreprocessingqualitycontrolrna-seqstatistical-engineering
11 stars 7.77 score 48 scripts 1 dependents
xjsun1221
tinyarray:Expression Data Analysis and Visualization
The Gene Expression Omnibus (<https://www.ncbi.nlm.nih.gov/geo/>) and The Cancer Genome Atlas (<https://portal.gdc.cancer.gov/>) are widely used medical public databases. Our platform integrates routine analysis and visualization tools for expression data to provide concise and intuitive data analysis and presentation.
Maintained by Xiaojie Sun. Last updated 10 months ago.
91 stars 6.67 score 138 scriptsbioc
scQTLtools:An R package for single-cell eQTL analysis and visualization
This package specializes in analyzing and visualizing eQTL at the single-cell level. It can read gene expression matrices or Seurat data, or SingleCellExperiment object along with genotype data. It offers a function for cis-eQTL analysis to detect eQTL within a given range, and another function to fit models with three methods. Using this package, users can also generate single-cell level visualization result.
Maintained by Xiaofeng Wu. Last updated 6 days ago.
softwaregeneexpressiongeneticvariabilitysnpdifferentialexpressiongenomicvariationvariantdetectiongeneticsfunctionalgenomicssystemsbiologyregressionsinglecellnormalizationvisualizationrna-seqsc-eqtl
3 stars 5.02 score