Dimension reduction software tools | Single-cell RNA sequencing data analysis

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 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 orders individual cells according to 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 qPCR data, but could be used with other types as well. The tools Census and BEAM are implemented in Monocle.

Finds common and rare cell clusters in normal tissues and disease samples. GiniClust aims to simplify the study of single-cell data. It can be applied to data set coming from different platform, such as multiplex qPCR data, tranditional single cell RNAseq or UMI-based single cell RNAseq. This tool can be useful to annotate the Human Cell Atlas. It integrates information from complementary clustering methods.

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).

Leads to low-dimensional representations of the data that account for zero inflation (dropouts), over-dispersion, and the count nature of the data. ZINB-WaVE is a general and flexible zero-inflated negative binomial model which is able to give a more stable and accurate low dimensional representation of the data than principal component analysis (PCA) and zero-inflated factor analysis (ZIFA), without the need for a preliminary normalization step.

Single cell RNA-seq data allows insight into normal cellular function and diseases including cancer through the molecular characterisation of cellular state at the single-cell level. Dimensionality reduction of such high-dimensional datasets is essential for visualization and analysis, but single-cell RNA-seq data is challenging for classical dimensionality reduction methods because of the prevalence of dropout events leading to zero-inflated data. ZIFA is a dimensionality reduction method which explicitly models the dropout characteristics.

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.

A toolkit designed for the analysis of short reads obtained from end-sequence RNA-seq. ESAT addresses mis-annotated or sample-specific transcript boundaries by providing a search step in which it identifies possible unannotated ends de novo. It provides a robust handling of multi mapped reads, which is critical in 3’ DGE analysis. ESAT provides a module specifically designed for alternative start or 3’ UTR (untranslated region) differential isoform expression. It also includes a set of features specifically designed for the analysis of single-cell RNA-seq data.

Offers a method for dimensionality reduction based on parametrization. t-SNE parametrizes the non-linear mapping between the data space and the latent space by means of a feed-forward neural network. This software is implemented into seven different languages, and, additionally, as Barnes-Hut and parametric implementation. This tool is fitted for the visualization of high-dimensional datasets.

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.

Learns representations for scRNA-seq data by considering the prior gene–gene association. SCRL is a data-driven and nonlinear dimension reduction method based on network-based embedding technique. It provides two advantages: (i) it can integrate both scRNA-seq data and prior biological knowledge for more insightful low-dimensional representations, and (ii) it can simultaneously learn a shared low-dimensional representation for both cells and 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.

An easy to use R package allowing for easy creation and plotting of diffusion maps. Diffusion maps are a spectral method for non-linear dimension reduction and have recently been adapted for the visualization of single cell expression data. This allows to visualize high-dimensional relations between data points in a low-dimensional plot. destiny includes a single-cell specific noise model allowing for missing and censored values.

Implements a methodological toolbox allowing flexible workflows under such a framework. Furthermore, Sincell contributes new algorithms to provide cell-state hierarchies with statistical support while accounting for stochastic factors in single-cell RNA seq. Graphical representations and functional association tests are provided to interpret hierarchies.

An integrated software tool for quality filtering, normalization, feature selection, iterative dimensionality reduction, clustering and the estimation of gene-expression gradients from large ensembles of single-cell RNA-seq datasets. SCell is open source, and implemented with an intuitive graphical interface.

Allows unsupervised and semi-supervised learning using Single Cell RNA-Seq data. To operate these learning, UNCURL provides a method for standardizing any prior biological information including bulk RNA-seq data, microarray data or even information about individual marker gene expression to a form compatible with scRNA-Seq data. Additionally, this package allows the integration of prior information which leads to large improvements in accuracy.

A tool for uncovering high-dimensional structure in single-cell gene expression data. From a table of gene expression measurement for single-cells, SPRING is able to build a k-nearest neighbor (knn) graph and display the graph using a force-directed layout algorithm that renders a real-time simulation in an interactive viewing window. SPRING offers an open-ended data exploration, including interactive discovery of markers genes, genes expression comparison between different subpopulations and selection tools for isolating subpopulations of interest.

Makes analysis more broadly accessible to researchers. Granatum is a web browser based scRNAseq analysis pipeline that conveniently walks the users through various steps of scRNA-seq analysis. It has a comprehensive list of modules, including plate merging and batch effect removal, outlier sample removal, gene filtering, gene expression normalization, cell clustering, differential gene expression analysis, pathway/ontology enrichment analysis, protein network interaction visualization, and pseudo-time cell series construction.

A software package for two-dimensional visualization of single cell data, which utilizes a plethora of projection methods and provides a way to systematically investigate the biological relevance of these low dimensional representations by incorporating domain knowledge. Annotated gene sets (referred to as gene 'signatures') are incorporated so that features in the projections can be understood in relation to the biological processes they might represent. FastProject provides a novel method of scoring each cell against a gene signature so as to minimize the effect of missed transcripts as well as a method to rank signature-projection pairings so that meaningful associations can be quickly identified. Additionally, FastProject is written with a modular architecture and designed to serve as a platform for incorporating and comparing new projection methods and gene selection algorithms.

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.

Provides factor model, takes advantage of the Poisson Gamma representation, with the use of Gamma priors on the distribution of principal components. pCMF is a dimension reduction method that is dedicated to over-dispersed counts with dropouts, in high dimension. It is based on matrix factorization method and is specifically designed to analyse single-cell RNA-seq data. This tool is suitable for data visualization and clustering.

Assists researchers in detecting lower dimensional embeddings of data points while minimizing distortions in distances between neighboring data points. g-SNE is an algorithm that offers functionalities to preserve the global structure of clusters in data. It is based on the t-distributed Stochastic Neighbor Embedding (t-SNE) method for the cluster separation and the multidimensional scaling (MDS) for the preservation of the inter-cluster structure.

An ultrafast algorithm which uses a novel yet very simple "implicit imputation" approach to alleviate the impact of dropouts in single cell RNA-seq (scRNA-seq) data in a principled manner. Using a range of simulated and real data, we have shown that CIDR outperforms the state-of-the-art methods, namely t-SNE, ZIFA and RaceID, by at least 50% in terms of clustering accuracy, and typically completes within seconds for processing a dataset of hundreds of cells. We believe that single-cell mRNA sequencing in combination with the RaceID algorithm is a powerful tool to unravel heterogeneity of rare cell types in both healthy and diseased organs.

Infers computational models of linear dynamic processes in an accurate and data-driven approach. Scorpius is an R package that enables de novo investigation and characterization of dynamic processes and identified well-known properties of dendritic cells (DCs) in a purely data-driven way. It accurately reconstructs trajectories for a wide variety of dynamic cellular processes, automatically identifies marker genes, speeding up knowledge discovery and is fully unsupervised.

Offers a general workflow for scRNA-Seq processing. SCTK is a toolkit encompassing independent modules for interactive browsing and analysis. The package offers a wide range of functionalities enabling data summary, batch correction, differential expression, or a method to estimate tradeoff between sample size or sequencing depths. It also includes a pipeline starting from filtering step to pathway activity analysis.

Allows users to capture and visualize the low-dimensional structures in single-cell gene expression data. scvis is a robust latent variable model that allows to spot underlying low-dimensional structures in scRNA-seq data. It learns a parametric mapping from the high-dimensional space to a low-dimensional embedding. This tool estimates the uncertainty of mapping a high-dimensional point to a low-dimensional space which adds rich capacity to interpret results.

Allows creation of workflow for the analysis of Single cell RNA sequencing (scRNA-seq) experiments. ascend can handle data generated from any single cell library preparation platform. It includes functions to leverage multiple CPUs, allowing most analyses to be performed on a standard desktop or laptop. In summary, this tool implements a state-of-the-art unsupervised clustering method and integrates established analysis techniques for normalization and differential gene expression.

Provides a method for imputing missing values, and restoring the structure of the data. After the use of MAGIC, two- and three-dimensional gene interactions are restored. MAGIC is able to impute complex and non-linear shapes of interactions. MAGIC also retains cluster structure, enhances cluster-specific gene interactions and restores trajectories, as demonstrated in mouse retinal bipolar cells, hematopoiesis, and a generated epithelial-to-mesenchymal transition dataset.

Permits downstream analysis. This tool contains four steps: (1) dimensionality reduction accounting for zero inflation and over-dispersion, and adjusting for gene and cell-level covariates; (2) robust and stable cell clustering using resampling-based sequential ensemble clustering; (3) inference of cell lineages and ordering of the cells by developmental progression along lineages; and (4) differentially expressed (DE) analysis along lineages.

Analyzes and visualizes single cell RNA sequencing (scRNA-seq data). VASC is a deep variational autoencoder can capture non-linear variations and automatically learn a hierarchical representation of the input data. One of its purpose is to simplify visualization of scRNA-seq datasets. VASC has three major parts called: (1) the encoder network, (2) the decoder network and (3) the zero-inflated layer.

Allows users to analyze and visualize RNA-Seq data. PIVOT furnishes four mains functionalities (i) a graphical interface that is able to wrap existing open source packages in a single user-interface (ii) multiple tools to manipulate datasets to perform derivation or normalization (iii) a way for allowing the compatibility between inputs and outputs from different analysis modules and, (iv) functions for automatically generate reports, publication-quality figures, and reproducible computations.

Offers a platform for data exploration either in biomedical and nonbiomedical context. PHATE is a standalone software, available in three different programming languages, that provides unsupervised data-driven visualization of both local and global structures in high dimensional data. It can be used with several datatypes such as single-cell RNA sequencing, CyTOF, image data, and connectivity data like social networks or HI-C DNA contact maps.

Serves as a visualization tool by using a dimension reduction technique for high-dimensional cytometry and single-cell RNA sequencing. UMAP allows visualization of multibranched cellular trajectories via mass-cytometry and scRNAse datasets. It can generate and organize informative clusters from T and NK cells. This ordering permits the identification of major cell lineages and provides a common axis that recapitulates their differentiation stages.

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.

Provides a computational method for single cell data analysis. SoptSC is a MATLAB package.

Contains useful tools for the analysis of single-cell gene expression data using the statistical software R. scater places an emphasis on tools for quality control, visualisation and pre-processing of data before further downstream analysis. scater enables the following: (i) automated computation of QC metrics; (ii) transcript quantification from read data with pseudo-alignment; (iii) data format standardisation; (iv) rich visualisations for exploratory analysis; (v) seamless integration into the Bioconductor universe; (vi) simple normalisation methods.

Visualizes transcriptome (RNA expression) data from hundreds of samples. Flotilla is a Python package. Flotilla is an open source, community-driven software written in Python that enables biologists with rudimentary knowledge of statistical methods and programming to analyze and visualize hundreds of RNA-seq datasets. This package includes interactive functions for common and important tasks in computational analyses of biological datasets such as dimensionality reduction, covariance analysis, classification, regression and outlier detection.

Analyses RNA-seq data using an approach based on Multiple Correspondence Analysis (MCA). MCA is a dimensionality reduction technique that allows the representation of both individuals (cells) and variables (genes) within the same Euclidean space, thus allowing the simultaneous identification of subpopulations of cells and their gene signatures. An extensive visualization tools integrated in a shiny interface, allowing user to intuitively inspect results and easily obtain a functional interpretation to complement the package. MCXpress enhances greatly the interpretation of single cell but also bulk RNA-sequencing data analysis.