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.
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 true biological variability from high levels of technical noise in single-cell experiments. This tool can quantify the relationship between technical noise and mean expression strength by normalizing the counts to represent sequencing depth and cellular RNA content. It can also check for each gene the null hypothesis that the biological coefficient of variation is less than a level chosen by the user.
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.
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.
Allows analysis of single-cell gene expression data. Scanpy integrates preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing and simulation of gene regulatory networks. It enables interfacing of advanced machine learning packages. This tool provides pseudotemporal-ordering and the reconstruction of branching trajectories. It allows simulating single cells governed by gene regulatory networks.
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.
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.
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.
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.
Allows users to fit Grade of membership models (GoM) for clustering of RNA-seq gene expression count data. CountClust also provides tools to identify which genes are most distinctively expressed in each cluster and to aid interpretation of results. The results can provide a richer summary of the structure in RNA-seq data than existing widely-used visualization methods such as Principal Components Analysis (PCA) and hierarchical clustering.
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.
Provides significant advantages for inferring the potential cell types. DTWscore is capable of managing unevenly and sparsely sampled time-series gene expression data without need for prior assumptions about the evenness or density of the time-series data. This method is capable of maintaining the sensitivity and specificity with scRNA-seq gene expression data that has been tested in various experimental designs.
0 - 0 of 0
1 - 2 of 2