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Performs differential gene expression analysis. DEseq is a method that integrates methodological advances with features to facilitate quantitative analysis of comparative RNA-seq data using shrinkage estimators for dispersion and fold change. The software is suitable for small studies with few replicates as well as for large observational studies. Its heuristics for outlier detection assist in recognizing genes for which the modeling assumptions are unsuitable and so avoids type-I errors caused by these.
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.
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.
edgeR / empirical analysis of DGE in R
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Allows differential expression analysis of digital gene expression data. edgeR implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi likelihood tests. The package and methods are general, and can work on other sources of count data, such as barcoding experiments and peptide counts.
A package for differential abundance analysis in sparse high-throughput marker gene survey data. metagnomeSeq relies on a normalization technique and a statistical model that accounts for under-sampling: a common feature of large-scale marker gene studies. It provides a way to determine features (Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations.
limma / Linear Models for Microarray Data
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Provides an integrated solution for analysing data from gene expression experiments. limma contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. It also contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions: (i) it can perform both differential expression and differential splicing analyses of RNA-seq data; (ii) the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences.
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).
MAST / Model-based Analysis of Single-cell Transcriptomics
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.
Estimates, from RNA-seq experiments, the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. voom opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. The voom methodology is implemented in the voom function of the limma package available from the Bioconductor project repository.
scDD / single-cell Differential Distributions
A method to characterize differences in expression in the presence of distinct expression states within and among biological conditions. Using simulated and case study data, we demonstrate that the modeling framework is able to detect differential expression patterns of interest under a wide range of settings. Compared to existing approaches, scDD has higher power to detect subtle differences in gene expression distributions that are more complex than a mean shift, and is able to characterize those differences.
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.
Preserves distinct structural properties of the data. dropClust uses Locality Sensitive Hashing (LSH), a logarithmic-time algorithm to determine approximate neighborhood for individual transcriptomes. It employs an exponential decay function to select higher number of expression profiles from clusters of relatively smaller sizes. This tool is able to detect principal components (PCs) with multi-modal distribution of the projected transcriptomes by using mixtures of Gaussians.
BASiCS / Bayesian Analysis of Single-Cell Sequencing data
Provides an integrated normalisation method where cell-specific normalising constants are estimated as model parameters. BASiCS is an integrated Bayesian hierarchical model where: (i) cell-specific normalisation constants are estimated as part of the model parameters, (ii) technical variability is quantified based on spike-in genes that are artificially introduced to each analysed cell’s lysate and (iii) the total variability of the expression counts is decomposed into technical and biological components.
Simulates and evaluates differential expression from bulk and especially single-cell RNA-seq data. powsimR can not only estimate sample sizes necessary to achieve a certain power, but also informs about the power to detect differential expression (DE) in a data set at hand. This module integrates estimated and simulated expression differences to calculate marginal and conditional error matrices. To calculate these matrices, the user can specify nominal significance levels, methods for multiple testing correction and gene filtering schemes.
BPSC / Beta-Poisson model for Single-Cell RNA-seq data analyses
A model for gene expression of single-cell RNA-seq data based on the beta-Poisson mixture model. BPSC addresses practical and realistic issues such as non-integer expression values or low expression values. Theoretically it is suitable for both transcript-level and gene-level expression, which is usually higher than the transcript-level expression. BPSC includes a generalized linear model (GLM) based on the beta-Poisson model to perform differential expression analyses of single-cell RNA-seq data. The results from several real single-cell RNA-seq datasets indicate that ~90% of the transcripts are well characterized by the beta-Poisson model; the model-fit from BPSC is better than the fit of the standard gamma-Poisson model in >80% of the transcripts. Moreover, in differential expression analyses of simulated and real datasets, BPSC performs well against edgeR, a conventional method widely used in bulk-cell RNA-sequencing data, and against scde and MAST, two recent methods specifically designed for single-cell RNA-seq data.
Makes analysis more broadly accessible to researchers. Granatum is a web browser based scRNAseq analysis pipeline that conveniently walks the users through various steps of scRNA-seq analysis. It has a comprehensive list of modules, including plate merging and batch effect removal, outlier sample removal, gene filtering, gene expression normalization, cell clustering, differential gene expression analysis, pathway/ontology enrichment analysis, protein network interaction visualization, and pseudo-time cell series construction.
SINCERA / SINgle CEll RNA-seq profiling Analysis
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.
Sake / Single-cell RNA-Seq Analysis and Klustering Evaluation
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.
Pseudocounted Quantile/NODES
A normalization technique that substantially reduces technical variability and improves the quality of downstream analyses. pQ homogenizes the expression of all genes below a fixed rank in each cell. We also introduce a nonparametric method for detecting differentially expressed genes that scales to > 1,000 cells and is both more accurate and ~ 10 times faster than existing parametric approaches. NODES provides a transformative reduction in computational complexity and execution time, which will be crucial for analyzing the massive single-cell datasets generated by inDrop/Drop-seq and other high-throughput single cell technologies.
An empirical Bayes model to characterize genes with expression changes in ordered single cell RNA-seq experiments. SCPattern utilizes the non-parametrical Kolmogorov-Smirnov statistic, thus it has the flexibility to identify genes with a wide variety of types of changes. Additionally, the Bayes framework allows SCPattern to classify genes into expression patterns with probability estimates. Simulation results show that SCPattern is well powered for identifying genes with expression changes while the false discovery rate is well controlled. SCPattern is also able to accurately classify these dynamic genes into directional expression patterns. Applied to a scRNA-seq time course dataset studying human embryonic cell differentiation, SCPattern detected a group of important genes that are involved in mesendoderm and definitive endoderm cell fate decisions, positional patterning, and cell cycle.
zingeR / Zero Inflated Negative binomial Gene Expression in R
Permits to proceed scRNA-seq differentially expressed (DE) analysis. zingeR identifies excess zeros and provides observation weights to unlock bulk RNA-seq pipelines for zero-inflation. It is based on a zero-inflated NB (ZINB) distribution method. The tool allows user to supply custom normalization factors, which opens the zingeR data analysis workflow towards any normalization method that produces normalization factors or offsets.
TASC / Toolkit for Analysis of Single Cell RNA-seq
Incorporates the technical parameters, which reflect cell-to-cell batch effects, into a hierarchical mixture model to estimate the biological variance of a gene and detect differentially expressed genes. TASC is a statistical framework, to reliably model the cell-specific dropout rates and amplification bias by use of external RNA spike-ins. It is programmed to be computationally efficient, taking advantage of multi-threaded parallelization.
Evaluates differential expression analysis methods in single-cell RNA-seq data. conquer uses five steps: it (i) builds a quasi-mapping transcriptome index from the combined set of annotated cDNA and ncRNA sequences, (ii) finds the corresponding SRA run ID(s), performs quality control and estimates transcript abundances for each scRNA-seq sample (cell), (iii) summarizes diagnostics for samples in the data set, (iv) creates a MultiAssayExperiment object, and (v) performs quality control, exploratory analysis and visualization of the gene-level abundances.
ascend / Analysis of Single Cell Expression, Normalisation and Differential expression
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.
SOMSC / Self-Organization-Map for high-dimensional Single-Cell data
Identifies cellular states, reconstructs cellular state transition paths and builds the pseudotime ordering of cells. SOMSC proceeds by following six main steps: (1) measuring a topographic chart of single cell data, (2) identifying basins of the topographic chart, (3) organizing the cellular states and building their transition paths, (4) constructing the cellular state map for all cells, (5) detecting the state-driven genes and ultimately (6) estimating the cellular state replication and transition probabilities.
PIVOT / Platform for Interactive analysis and Visualization Of Transcriptomics data
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.
DESCEND / DEconvolution of Single Cell ExpressioN Distribution
Deconvolves the true cross-cell gene expression distribution from observed scRNA-seq counts. DESCEND is a statistical method that quantifies the dependence between features of distribution and cell level covariates such as cell size and cell type. It adopts the “G-modeling” empirical Bayes distribution deconvolution framework which avoids constraining parametric assumptions. It can accurately deconvolve the true gene expression distribution, leading to improved characterization of dispersion and expression burstiness.
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