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 4 months ago
batcheffectgeneexpressionnormalizationrnaseqsequencingsinglecellsoftwaretranscriptomics
65 stars 4.00 score 178 dependencies 1 dependentsCiteFuse - 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 4 months ago
singlecellgeneexpression
26 stars 2.53 score 153 dependenciesscClassify - 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 4 months ago
singlecellgeneexpressionclassification
22 stars 2.42 score 147 dependencies