NormCap
NormCap is a normalization software that helps analyze genomic data. It standardizes genomic data to account for batch effects and other technical noise, enabling more accurate downstream analysis.
normalization
genomics
batch-effect-correction
NormCap: Genomic Data Normalization Software
Genomic data normalization software to standardize and remove batch effects, enabling accurate downstream analysis
What is NormCap?
NormCap is a genomic data normalization tool developed by the Broad Institute. It helps remove technical noise and batch effects that can obscure true biological signals in genomic data sets.
When working with genomic data from different experiments, platforms, or batches, systematic non-biological differences can creep in and distort analyses like differential expression testing, clustering, and more. NormCap uses empirical Bayes-based adjustments to standardize mean and variability across features in your data set.
After normalization with NormCap, technical artifacts are minimized so you can uncover real biology. It works for common genomics assays like RNA-seq, ATAC-seq, proteomics, and others. By harmonizing data sets, NormCap enables integrated analysis of data from public repositories alongside your own internal data.
NormCap is freely available as an R/Bioconductor package. It allows you to easily normalize data compendia for more accurate downstream analyes like differential expresion testing, biomarker discovery, cell type mapping, and more.
NormCap Features
Features
- Performs normalization of genomic data
- Removes technical noise and batch effects
- Works with gene expression data from microarrays and RNA-seq
- Has methods for paired and unpaired data
- Supports normalization of large datasets
- Has graphical user interface and command line interface
- Integrates with common genomic analysis pipelines
- Open source with active development community
Pricing
- Open Source
Pros
Improves accuracy of downstream genomic analyses
Easy to use graphical interface
Flexibility to handle different types of genomic data and experiments
Actively maintained and supported
Cons
Requires some bioinformatics expertise to optimize parameters
Only works with gene expression data, not other genomic data types
Limited documentation and support resources
Official Links
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