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.
Assists in analyzing single-cell RNA-seq data. SCDE is a set of statistical methods that fits individual error models for single-cell RNA-seq measurements. It also compares groups of single cells and tests for differential expression. It contains pagoda routines that characterize aspects of transcriptional heterogeneity in populations of single cells using pre-defined gene sets as well as 'de novo' gene sets derived from the data.
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.
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.
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.
An analytic framework that models transcriptome dynamics through the analysis of aggregated cell-cell statistical distances within biomolecular pathways. Within an elaborate case study of circulating tumor cells derived from prostate cancer patients, we develop analytic methods of aggregated distances to identify five differentially expressed pathways associated to therapeutic resistance. Our aggregation analyses perform comparably to Gene Set Enrichment Analysis (GSEA) and better than differentially expressed genes followed by gene set enrichment. However, these methods were not designed to inform on differential pathway expression for a single cell. As such, our framework culminates with the novel aggregation method, cell-centric statistics (CCS).
Provides a package of multiple kernel learning (MKL) algorithms for gene sets to assess early- and late-stage cancers. GSBC features different functions to replicate various experiments (random forest, vector machine, unweighted MKL and group Lasso MKL) on the Cancer Genome Atlas (TCGA) cohorts. Several methods included in the package facilitate the support vector machine classification solver and some training and test procedures are also available.
Approximates the expression data of a gene set by its first principal component. ROMA is based on the simplest uni-factor linear model of gene regulation. It addresses the issue of quantifying the activity of gene sets characterized by coordinated gene expression. The tool allows to determine genes within a group of genes and to estimate the statistical significance of the amount of variance.
Produces tree structures outlining the hierarchical relationship between single-cell samples, while identifying latent groups of genes that can provide biological insights. CellTree can infer complex underlying hierarchical structures in cell populations from expression data alone, and also provide biological backing for the model it creates. The package can provide reasonable default values for most of the parameters used by the model inference, visualisation and analysis algorithms, making it possible for an unfamiliar user of the software to quickly evaluate a new dataset in a few simple lines of R code.
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.
Discovers key biological properties that dominate the variability between cells in a sample. VISION highlights numerous gradients or sub-clusters which reflect different cellular functions or states, and which may not be captured by a rigid precomputed labeling. It can recognize biological properties that differ between precomputed stratifications or that change smoothly along a given cellular trajectory.
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.
Provides convenient functions for users to assign genes into different gene expression patterns such as constant, monotone increasing and increasing then decreasing. SEPA then performs GO enrichment analysis to analysis the functional roles of genes with same or similar patterns.
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