Identifies cells with active gene regulatory networks (GRNs) in single cell RNA-seq data. AUCell calculates the enrichment of the regulon as an area under the recovery curve (AUC) across the ranking of all genes in a particular cell. The tool was tested by using previously published neuronal and glial gene signatures. The results show that it is more robust than using the mean of the normalized expression values across the gene signature.
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.
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).
A flexible statistical framework for the analysis of single-cell RNA sequencing data. MAST is suitable for supervised analyses about differential expression of genes and gene modules, as well as unsupervised analyses of model residuals, to generate hypotheses regarding co-expression of genes. MAST accounts for the bimodality of single-cell data by jointly modeling rates of expression (discrete) and positive mean expression (continuous) values. Information from the discrete and continuous parts is combined to infer changes in expression levels using gene or gene set-based statistics. Because our approach uses a generalized linear framework, it can be used to jointly estimate nuisance variation from biological and technical sources, as well as biological effects of interest.
Provides a variation of Bayesian framework. AR1MA1-VBEM compiles scripts to perform Gene Regulatory Network (GRN) inference from time series or pseudo-time series data using a first-order autoregressive moving average (AR1MA1) model within a variational Bayesian Expectation-Maximization (VBEM) framework. This package was applied to single cell qPCR data to infer the GRN for mouse embryo development and single cell RNA-Seq data for zebra fish.
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.
Investigates and compares gene expression profiles in the retinal ganglion cell (RGC) subtypes. RGC Subtypes Gene Browser permits to analyze gene networks and pathways in RGC subtypes, including subtypes’ varying response to neuroprotective/regenerative treatments. This tool assists researchers in the examination of the molecular and physiological differences between RGC subtypes.
Identifies differentiation pathways in single-cell transcriptome data. GraphDDP provides a method based on a graph layout technique which can highlight differentiation trajectories in both intestinal epithelial and myeloid progenitor cells. This program exploits prior clustering information and is able to derive a layout that can simultaneously be used for visualizing compact groups and transitions between groups.
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.
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 users to deduce undirected networks. NetworkInference is a standalone software which implements four algorithms for providing a fully connected, weighted network with indication about edge’s confidence. It includes various functionalities such as options for discretize or estimate the probability distribution. Moreover, the generated network is coupled to a list which attributes an edge for each pair of genes.
Estimates multivariate information (MVI) measures. InformationMeasures is a package which supports information measures such as entropy, mutual information (MI), conditional mutual information (CMI) and partial information decomposition (PID); the maximum likelihood, Miller-Madow, Dirichlet and shrinkage estimators; and the Bayesian Blocks, uniform width and uniform count discretization methods.
Utilizes the estimated pseudotime of the cells to find gene co-expression that involves time delay. LEAP sorts cells according to the estimated pseudotime and then computes the maximum correlation of all possible time lags. In addition, LEAP can apply a time-series inspired lag-based correlation analysis to reveal linearly dependent genetic associations.
A computational tool for inferring gene regulatory network (GRN) from time-stamped cross-sectional single cell expression data. SINCERITIES is a network interference algorithm able to regularize the linear regressions based on temporal changes of the gene expression distributions. SINCERITIES development was based on single cell expression data and single transcriptional profiling of THP-1 monocytic human leukemia cell line.
Reconstructs and trains asynchronous Boolean models using single-cell expression data. BTR is a model learning algorithm that can infer both network structure and Boolean rules without needing information on trajectories through cell states. This package can be a useful addition to the current toolbox for processing and understanding single-cell expression data, as it provides significant new capabilities for regulatory network modelling in a user-friendly way.
Leverages morphing Gaussian mixtures to track biological progression and models the rewiring of these gene networks from static transcriptomes. SCIMITAR models account for heteroscedastic noise and increase the statistical power to detect progression associated genes when compared to traditional differential expression tests. It allows to detect modes in co-expression structure in the trajectory: defined coregulatory states that represent potential metastable and transitionary cell states.
Allows users to manipulate multivariate Hurdle models with a normal density. HurdleNormal provides a set of routines which mainly focuses on graphical model’s assessment by applying a group-lasso penalized neighborhood selection. This program also enables the selection and adjustment of undirected graphical models presenting conditional independences between genes as well as the deducing of network structure.
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.
Allows users to investigate and explore single-cell gene expression data. CellexalVR consists of a virtual reality (VR) platform built to work with the HTC Vive controller. It offers a way to select sub-populations directly by passing them through a selection tool from which heatmaps and transcription factor (TF) correlation networks can be constructed. This tool is able to integrate cell surface marker intensities captured during index sorting.
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 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.
Enables the construction of developmental landscapes. Topographer is a bioinformatic pipeline which permits the identification of de novo continuous developmental trajectories and the uncovering of stochastic cell-type dynamics. In addition, it can also identify various possible (bi- and tri-branching) cell trajectories with high resolution from single-cell data and infer dynamic connections of marker gene networks along the identified cell trajectories.
Identifies functionally relevant pairs of genes that are complementary to others measures. DECODE determines the extent of joint presence or absence of genes across different cells. This algorithm demonstrates that the network deducted captures biologically-meaningful pathways, connectivity patterns characteristic of complex networks and cell-type specific modules. It can recover the relationship between cells and gene-level relationships.
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.
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