Many modern biological datasets consist of small counts that are not well fit by standard linear-Gaussian methods such as principal component analysis. This package provides implementations of count-based feature selection and dimension reduction algorithms. These methods can be used to facilitate unsupervised analysis of any high-dimensional data such as single-cell RNA-seq.
|Bioconductor views||DimensionReduction GeneExpression Normalization PrincipalComponent RNASeq Sequencing SingleCell Software Transcriptomics|
|Package repository||View on GitHub|
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