Offers a mapping strategy based on spatially distributed scores. DistMap is an algorithm which uses measured gene expression and allows researchers to map single cell RNA sequencing data without requiring transcript-level imputation. The software also includes functions that can be utilized to visualize the expression pattern corresponding to a gene’s gradient calculation.
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.
Allows to make unsupervised projection of single cells from an scRNA-seq experiment. scmap is easy to combine with other computational scRNA-seq methods. It is very fast, using 1,000 features taking only around twenty seconds to map 40,000 cells. Its run-time can be further improved since the centroids and features for each cluster can be pre-computed, and stored in memory, even for a very large atlas.
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).
Assists in cell type identification. ACTION provides a method to detect key marker genes for each cell type, as well as transcription factors that are responsible for mediating the observed expression of these markers. It is a program for: (1) identifying cell types, (2) characterizing their functional identity, and (3) uncovering underlying regulatory factors from single-cell expression datasets.
Enables researchers to assign a cell type to each cell in an experiment. CaSTLe is based on a workflow of univariate feature engineering steps. It includes functionnalities for classifying scRNA-seq datasets by using transfer learning. The feature engineering steps include: selecting genes with the top mean expression and mutual information gain, removing correlated genes, and binning the data according to pre-defined ranges.
Allows investigation of single-cell transcriptomics. FASTGenomics provides pre-defined data analysis and visualization workflows. It implements a rigorous multi-layer security concept of data encryption, controlled access and transfer to protect experiment data. This tool is able to compute analyses with different dataset sizes. It represents a useful solution for the clinical research and the pharmaceutical industry.
Finds similar cells based on their expression patterns. CellFishing.jl is a cell search method that employs locality-sensitive hashing (LSH) and an indexing technique of bit vectors to narrow down candidates of similar cells. The software creates a search database of reference cells from a matrix of transcriptome expression profiles of scRNA-seq experiments, and then searches the database for cells with an expression pattern similar to the query cells. It considers rare cell types and is robust in response to differences between batches, species, and protocols.
Simplifies analysis of mixed populations present in cell suspensions derived from whole lung. SingleR is a program that enables unbiased annotation of single cell RNA-seq (scRNA-seq). It is able to enhance cell type annotation of scRNA-seq to a good resolution. This tool can be used for unsupervised hierarchical subclustering of macrophages based on differential annotation using the full range of cell types.
Offers a platform for cell-alignment analyses. cellHarmony can align, visualize and confront scRNA-Seq data from either a perturbed or nonperturbed query sample against all referenced cell states. It performs its classification without considering batch effect. This program can be used with both cell-to-cell and cell-to-centroid assignments and includes a function to set a user-defined minimum correlation. This tool can also being run through the AltAnalyze software with additional features for downstream differential expression or multi-reference merge analyses.
Aims to improve the characterization of single-cell transcriptomic profiles. SuperCT permits researchers to study the molecular features from the scRNA-seq data generated and classified by other investigators. It can serve for identifying the similar cells in new samples. Furthermore, this tool is able to reveal the single-cell identities based on their RNA expression profiles.
Allows users to detect transcriptionally related groups of cells across datasets. scID uses the framework of Fisher's linear discriminant analysis (LDA) to distinguish each cluster from the remaining clusters within a reference scRNA-seq dataset and to project the cells onto a one-dimension space that leads to good separation between clusters. It aims to improve the efficiency of the analysis, especially for clusters that are transcriptionally close.
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.
Discovers robust partitioning of cell sub-populations within a reproducible framework. rCASC provides different functionalities such as jackknife resampling for cluster robustness evaluation. It can be customized by including pre/post processing methods. This tool supports the implementation of other clustering methods within the resampling framework.
Provides a weighted-allocation set of tools for counting reads. scBASE intends to improve estimation of allelic proportions and reduces sampling variability without dropping cell-to-cell heterogeneity. This method is composed of three different steps: read counting, classification and estimation. Its algorithm consists of a Monte Carlo Markov chain (MCMC) approach which randomly samples parameter values from their conditional posterior distributions. The classification and estimations steps include an expectation-maximization (EM) algorithm that assembles to the maximum a posteriori parameter estimates.
Assists users to build cell type classifiers from heterogeneous scRNA-Seq datasets. Moana can assists users in constructing cell type classifiers for complex tissues based on single-cell RNA (scRNA) Seq data. Furthermore, this tool enables the construction and deployment of cell type classifiers for high-throughput scRNA-Seq data.
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.
Allows exploration of single-cell next-gene sequencing data. SeqGeq permits the discovery of sub-populations, differentially expressed genes and allows the creation of publishable figures with an easy-to-use interface. It can serve to identify population’s relationships as well as examine and construct gene sets.
Assists users in training neural networks. gkm-DNN is a software designed for classification. It consists of calculating gapped k-mer frequency vector (gkm-fv) and training the neural networks. This method can also be easily extended to other problems such as regression and ranking. It provides five classes to implement the gapped k-mer deep neural network: MLPBuilder, PresaveData, Train, PredictDataset and PredictCSV.
Suggests labels/cell types for the clusters, on the basis of similarity to a reference dataset. Celaref is a cell-type identification tool that operates at the cluster-level. The software compares the reference sample rankings of the most distinctly enriched genes in each query group to match cell types.