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Seurat
Allows studying of spatial patterning of gene expression at the single-cell level. Seurat is an R package that enables quality control (QC), analysis, and exploration of single cell RNA-seq data. The software includes three computational methods: (1) unsupervised clustering and discovery of cell types and states, (2) spatial reconstruction of single cell data, and (3) integrated analysis of single cell RNA-seq across conditions, technologies, and species. It can also localize rare subpopulations, and map both spatially restricted and scattered groups.
f-scLVM / factorial single-cell Latent Variable Model
Uses prior pathway annotation to guide inference of the biological drivers underpinning the heterogeneity. f-scLVM is a factorial single-cell latent variable model that can capture three sources of variation: i) variation in expression attributable to pre-annotated gene sets; ii) variation attributable to sparse, putatively biologically meaningful, but unannotated gene sets; and iii) variation explained by confounding factors that are expected to affect the expression profile of the majority of genes.
Linnorm
Provides a linear model and normality based transformation method. Linnorm is an R package for the analysis of RNA-seq, scRNA-seq, ChIPseq count data or any large-scale count data. It transforms such datasets for parametric tests. Some pipelines are implemented: (i) library size/batch effect normalization, (ii) cell sub-population analysis and visualization, (iii) differential expression analysis or differential peak detection, (iv) highly variable gene discovery and visualization, (v) gene correlation network analysis and visualization, (vi) stable gene selection for scRNA-seq data and (vii) data imputation.
dropClust
Preserves distinct structural properties of the data. dropClust uses Locality Sensitive Hashing (LSH), a logarithmic-time algorithm to determine approximate neighborhood for individual transcriptomes. It employs an exponential decay function to select higher number of expression profiles from clusters of relatively smaller sizes. This tool is able to detect principal components (PCs) with multi-modal distribution of the projected transcriptomes by using mixtures of Gaussians.
Dr.seq
Allows quality control (QC) and analysis components of parallel single cell transcriptome and epigenome data. Dr.seq is a quality control (QC) and analysis pipeline that provides both multifaceted QC reports and cell clustering results. Parallel single cell transcriptome data generated by different technologies can be transformed to the standard input with contained functions. Using relevant commands, the software can also be used to report quality measurements based on four aspects and can generate detailed analysis results for scATAC-seq and Drop-ChIP datasets.
BASiCS / Bayesian Analysis of Single-Cell Sequencing data
Provides an integrated normalisation method where cell-specific normalising constants are estimated as model parameters. BASiCS is an integrated Bayesian hierarchical model where: (i) cell-specific normalisation constants are estimated as part of the model parameters, (ii) technical variability is quantified based on spike-in genes that are artificially introduced to each analysed cell’s lysate and (iii) the total variability of the expression counts is decomposed into technical and biological components.
CONICS / COpy-Number analysis In single-Cell RNA-Sequencing
Identifies large-scale copy-number variants (CNVs) in scRNA-seq. CONICS provides a method to separate neoplastic cells for downstream analysis. It includes algorithms to triage cells from a scRNA-seq assay, based on the presence of CNVs detected in an orthogonal DNA sequencing experiment. It integrates tumor-normal fold-changes with the minor-allele frequencies of point mutations to estimate false-discovery rates (FDRs) in CNV classification. Additionally, it includes routines to perform downstream phylogeny assessment and gene co-expression analysis.
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