scMerge - scMerge: Merging multiple batches of scRNA-seq data
Like all gene expression data, single-cell data suffers from batch effects and other unwanted variations that makes accurate biological interpretations difficult. The scMerge method leverages factor analysis, stably expressed genes (SEGs) and (pseudo-) replicates to remove unwanted variations and merge multiple single-cell data. This package contains all the necessary functions in the scMerge pipeline, including the identification of SEGs, replication-identification methods, and merging of single-cell data.
Last updated 27 days ago
batcheffectgeneexpressionnormalizationrnaseqsequencingsinglecellsoftwaretranscriptomicsbioinformaticssingle-cell
8.21 score 67 stars 1 packages 135 scripts 908 downloadsscClassify - scClassify: single-cell Hierarchical Classification
scClassify is a multiscale classification framework for single-cell RNA-seq data based on ensemble learning and cell type hierarchies, enabling sample size estimation required for accurate cell type classification and joint classification of cells using multiple references.
Last updated 27 days ago
singlecellgeneexpressionclassification
6.85 score 22 stars 27 scripts 172 downloadsCiteFuse - CiteFuse: multi-modal analysis of CITE-seq data
CiteFuse pacakage implements a suite of methods and tools for CITE-seq data from pre-processing to integrative analytics, including doublet detection, network-based modality integration, cell type clustering, differential RNA and protein expression analysis, ADT evaluation, ligand-receptor interaction analysis, and interactive web-based visualisation of the analyses.
Last updated 27 days ago
singlecellgeneexpressionbioinformaticssingle-cell
5.57 score 26 stars 18 scripts 186 downloads