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.
Facilitates the analysis of cellular heterogeneity, the identification of cell types, and comparison of functional markers in response to perturbations, based on a versatile method. SPADE helps to organize high-dimensional cytometry data in an unsupervised manner, and to investigate natural and pathogenic cellular heterogeneity for biological insight. The SPADE algorithm consists of four components: (i) density-dependent downsampling, (ii) clustering, (iii) linking clusters with a minimum spanning tree, and (iv) upsampling to restore all cells in the final result. This modularized process allows more efficient sub-algorithms to replace the current components. In this sense, SPADE can be viewed as a framework for cytometric data analysis and visualization that has the capacity to be evolved and adapted.
Allows users to analyze single-cell gene expression experiments. Monocle can realize differential expression analysis, clustering, visualization, and other useful tasks on single-cell expression data. The software enjoins individual cells according to a defined progress through a biological process, without knowing ahead of time which genes define progress through that process. It is designed to work with RNA-Seq and quantitative polymerase chain reaction (qPCR) data, and implements Census and BEAM tools.
An algorithm for the identification of rare and abundant cell types from single cell transcriptome data. RaceID is based on transcript counts obtained with unique molecular identifies. We demonstrate that this algorithm can resolve cell types represented by only a single cell in a population of randomly sampled organoid cells. We use this algorithm to identify Reg4 as a novel marker for enteroendocrine cells, a rare population of hormone-producing intestinal cells.
A divisive biclustering method based on sorting points into neighborhoods (SPIN). In contrast to the SPIN algorithm which does not identify clusters, here the aim was to identify groups of cells/genes in an unsupervised manner. SPIN is a powerful method to sort a distance/correlation matrix without reducing dimensionality, and it converges to a 1D order of the features.
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.
Projects single-cell transcriptomes into a space defined by variability in a reference data set. RCA is an R package for robust clustering analysis of single cell RNA sequencing data (scRNAseq). This method outperforms existing algorithms for clustering single-cell transcriptomes and generates tight cell clusters consisting almost entirely of cells of the same type. It also identifies multiple cell types in CRC tumors and normal mucosa, despite the strong batch effects in clinical samples.
Allows to cluster single cell RNA-seq data. SC3 integrates many different clustering solutions through a consensus approach, thereby increasing its accuracy and robustness against noise. To enhance the accessibility to users with limited bioinformatics expertise, SC3 features an interactive graphical implementation, which aids the biological interpretation by identifying marker genes, differentially expressed genes and outlier cells.
Allows to reconstruct gene regulatory networks (GRNs). SCENIC uses single-cell RNA-seq data to identify stable cell states. It analyzes all the co-expression modules using cis-regulatory motif analyses. The tool reduces data dimensionality by using transcription factor (TF) regulons rather than principal components. It accounts for noise and removes technical biases, and uncovers master regulators and gene regulatory networks for each cell type.
Quantifies fate bias, manifested by subtle lineage specific transcriptome modulations within a multipotent progenitor population. FateID is based on prior knowledge and a random forests-based classification method. It can differentiate committed stages of all lineages and tracks differentiation trajectories backward in time. This tool enables prediction of the likelihood of multipotent progenitors to give rise to each lineage.
Aims at the complete analysis of scRNA-seq data post genome alignment: from the parsing, filtering, and normalization of the input count data files, to the visual representation of the data, identification of cell clusters, differentially expressed genes (including cluster-specific marker genes), and functional gene set enrichment. ASAP combines a wide range of commonly used algorithms with sophisticated visualization tools. It allows researchers to interact with the data in a straightforward fashion and in real time.
An easy-to-use application for microarray, RNA-Seq and metabolomics analysis. For splicing sensitive platforms (RNA-Seq or Affymetrix Exon, Gene and Junction arrays), AltAnalyze will assess alternative exon (known and novel) expression along protein isoforms, domain composition and microRNA targeting. In addition to splicing-sensitive platforms, AltAnalyze provides comprehensive methods for the analysis of other data (RMA summarization, batch-effect removal, QC, statistics, annotation, clustering, network creation, lineage characterization, alternative exon visualization, gene-set enrichment and more).
Processes Chromium single cell 3’ RNA-seq output to align reads, generates gene-cell matrices and performs clustering and gene expression analysis. Cell Ranger combines Chromium-specific algorithms with the widely-used RNA-seq aligner STAR. It is delivered as a single, self-contained tar file that can be unpacked anywhere on the system. The tool includes four pipelines: cellranger mkfastq; cellranger count; cellranger aggr; cellranger reanalyze.
Serves for single-cell data analysis. Granatum is a program that provides biologists with access to single-cell bioinformatics methods, and software developers with the opportunity to promote and combine their tools with various others in customizable pipelines. Its architecture simplifies the incorporation of cutting-edge tools and enables handling of large datasets. Moreover, it can eliminate inter-module incompatibilities by isolating the dependencies of each module.
Permits analysis of single-cell RNA-seq data. Dpath divides the expression profiles with the awareness of the dropout events. It quantitatively evaluates the cellular state and prioritizes genes for both progenitor and committed cellular states. This tool simplifies and decodes the biological mechanisms that control stem cell and progenitor cell populations. It was tested on haematopoietic, endocardial and endothelial lineages.
Simulates experiment-specific technical replicates. BEARscc improves the unsupervised classification of cells and facilitates the biological interpretation of single-cell RNA-seq experiments. It provides additional insights for the interpretation of single-cell sequencing experiments. The tool models technical variance based on spike-ins, simulates technical replicates and clusters simulated replicates.
Reconstructs cell cycle time-series using single-cell transcriptome data. reCAT is a computational method consists of four steps: (i) the data processing, including quality control, normalization, and clustering of single cells, (ii) the order of the clusters is then recovered by finding a traveling salesman cycle, (iii) two scoring methods, Bayes-scores and mean-scores subsequently discriminate among cycle stages and (iv) a hidden Markov model (HMM) and a Kalman smoother finally estimate the underlying gene expression levels of the single-cell time-series.
Assigns a score to every individual expression profile under study. FiRE can assist users to focus on a fraction of expression profiles within ultra-large single-cell (scRNA-seq) data. It also embeds features for performing the estimation of the density around each subjected multidimensional data point. Furthermore, it can be used for detecting rare cell types.
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.
Assists in navigating through the expression profile. SAKE is an R package that uses non-negative matrix factorization (NMF) method for unsupervised clustering. It offers (i) quality controls modules to compare total sequenced reads to total gene transcripts detected, (ii) sample correlation heatmap plot, (iii) heatmap of sample assignment from NMF run, with dark red indicating high confidence in cluster assignments, and (iv) t-distributed stochastic neighbor embedding (t-SNE) plot to compare NMF assigned groups with t-SNE projections.