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.
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.
Allows to reconstruct the differentiation trajectory from the pluripotent state through mesendoderm to definitive endoderm (DE). WaveCrest permits to reorder single cells according to the expression of key gene markers. It can identify candidate genes that could function as pioneer regulators governing the transition from mesendoderm to the DE state. It takes a group of genes of interest and aims to recover a smooth expression profile along time for each of the genes in consideration in implementing a constrained extended nearest-insertion (ENI) algorithm to reorder cells.
Provides an analytical framework for the sensitive detection of population markers and differentially expressed genes. bigSCale aims to improve detection in large scRNAseq datasets. The software uses large sample sizes to estimate a highly accurate and comprehensive numerical model of noise and it determines the extent of the variation between cells without estimating actual gene expression value.
A software tool developed to better support in silico pseudo-time reconstruction in single-cell RNA-seq analysis. TSCAN uses a cluster-based minimum spanning tree (MST) approach to order cells. Cells are first grouped into clusters and an MST is then constructed to connect cluster centers. Pseudo-time is obtained by projecting each cell onto the tree, and the ordered sequence of cells can be used to study dynamic changes of gene expression along the pseudo-time. Clustering cells before MST construction reduces the complexity of the tree space. This often leads to improved cell ordering. It also allows users to conveniently adjust the ordering based on prior knowledge. TSCAN has a graphical user interface (GUI) to support data visualization and user interaction. Furthermore, quantitative measures are developed to objectively evaluate and compare different pseudo-time reconstruction methods.
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.
An R package providing functions for fitting a modified Michaelis-Menten (MM) equation to the pattern of dropouts observed in a single-cell sequencing experiment. Analyses based on the MM equation, such as identifying differentially expressed genes or biased quantification, are provided with automatically generated visualizations.
Recognizes the set of necessary and sufficient marker genes from an sc/snRNAseq experiment. NSforest is based on a random forest of decision trees machine learning approach. It creates standard cell type definitions. The result of this method can serve as a reference knowledgebase to support interoperability of information about the role of cellular phenotypes in human health and disease.
Identifies multiple and potentially correlated hidden sources of variation from scRNA-Seq data. IA-SVA is capable of high statistical power and low error rate and delivers marker genes associated with the hidden factor. It enables assessing the significance of each detected factor for explaining the unmodeled variation in the data. The tool uncovers hidden factors while adjusting for all potential confounders.
Identifies key regulators of differentiation events from scRNA-seq data. PIPER aims to infer progressive network changes across different cellular states and the regulator genes whose connection with other genes are significantly different between the network estimates. It can deal with various forms of data, such as time-series data containing intermediate states as well as data from one time-point representing mature differentiated cell types.
Detects subsets of genes informative to subpopulation clustering, without referencing to any known transcriptomic profiles. SCMarker enables the discovery of cell-types and cell-type specific biology from existing scRNA-seq data. It chooses markers by scrutinizing two subpopulation discriminative features: (1) bi/mul-modal distribution of subpopulation-informative gene expression in mixed cell population and (2) level of co-expression among subpopulation-specific gene pairs.
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