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.
Assists in detecting of otherwise undetectable subpopulations of cells. scLVM is a program that can be used for estimating the proportion of variance in expression across cells that is explained by technical noise, biological variability and cell cycle. It also can be applied for creating a ‘corrected’ gene expression data set, in which the effect of the identified factor is removed.
Allows to perform several low-level analyses on of single-cell RNA-seq data. Scran is a package that provides functions to normalize cell-specific biases, assign cell cycle phase, and detect highly variable and significantly correlated genes.
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.
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.
Permits to compare, validate and substantiate cell type transcriptional profiles across scRNA-seq datasets. MetaNeighbor can readily identify cells of the same type across datasets, without relying on specific knowledge of marker genes. The tool returns a performance score for each gene set and task that is the mean area under the receiver operator characteristic curve (AUROC) across all folds of cross-dataset validation.
Determines significant variably expressed genes (VEGs) using a gene expression variation model (GEVM). scVEGs utilizes the relation between coefficient of variation (CV) and average expression level to address the over-dispersion of single-cell data, and its corresponding statistical significance to quantify the variably expressed genes.