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.
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.
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).
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.
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.
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.
Models time series accounting for noise in the temporal dimension. This R package implements the DeLorean model to estimate pseudotimes for cell expression data. The DeLorean model uses a Gaussian process latent variable model to model uncertainty in the capture time of cross-sectional data. This method was specifically designed for single cell transcriptome experiments. It was fit to three separate datasets each using a different biological assay (microarrays, single cell nCounter and single cell RNA-seq) in three organisms (human, mouse and Arabidopsis).
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.
Provides a comprehensive analysis of single-cell RNA-sequencing (scRNA-seq) data. iS-CellR integrates Seurat package and employs a fully-integrated web browser interface to process, analyze and visually interpret scRNA-seq data. The software offers a strategy for the analysis and visualization of scRNA-seq data without the need for specific programming skills. Users can explore heterogeneous populations of cells. The program can be modified and extended according to user needs to perform more intricate and targeted analysis.
Permits the detection of specifically expressed genes (SEGs) from numeric gene expression data. SEGtool is based on modified fuzzy c-means (FCM), Jaccard index and greedy annealing methods. It is able to detect SEGs with either high- or low-specific expression profiling in numeric expression context. The tool needs more than four tissues/conditions/samples to identify SEGs because the FCM step requires three central points to proceed.
A generally applicable analytic pipeline for processing single-cell RNA-seq data from a whole organ or sorted cells. SINCERA provides a panel of analytic tools for users to conduct data filtering, normalization, clustering, cell type identification, and gene signature prediction, transcriptional regulatory network construction and important regulatory node identification. The pipeline enables RNA-seq analysis from heterogeneous single cell preparations after the nucleotide sequence reads are aligned to the genome of interest.
Allows learning of the underlying biology of the cancer samples. Dhaka is a variational autoencoder based single cell analysis tool that combines Bayesian inference with such unsupervised deep learning. The software extracts useful features with biological significance from single cell data. It can also perform gene selection, if needed, with three options for selecting informative genes for analysis: Coefficient of variation (CV) score, Entropy and Average expression value.
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 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.
Assists users in threshold decision for single cell RNA-seq analyzing. scFeatureFilter is a package which intends to facilitate features selection, with a special focus on case where no spike-in controls can be found in the investigated data. This application determines and generates correlation information and uses it for filtering high-noise features. It had been developed to be easily integrated into various workflows.
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.
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