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DESeq

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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.

Seurat

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.

AltAnalyze

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).

Dr.seq

Allows quality control (QC) and analysis components of parallel single cell transcriptome and epigenome data. Dr.seq is a quality control (QC) and analysis pipeline that provides both multifaceted QC reports and cell clustering results. Parallel single cell transcriptome data generated by different technologies can be transformed to the standard input with contained functions. Using relevant commands, the software can also be used to report quality measurements based on four aspects and can generate detailed analysis results for scATAC-seq and Drop-ChIP datasets.

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.

Linnorm

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.

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.

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.

dropClust

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.

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.

SCnorm

Allows robust normalization of single cell RNA-seq data. SCnorm uses quantile regression to estimate the dependence of transcript expression on sequencing depth for every gene. The software groups genes with similar dependence, and uses a second quantile regression to estimate scale factors within each group. It then performed within-group adjustment for sequencing depth, using the estimated scale factors, to provide normalized estimates of expression. The approach allows to accurately normalize data for sequencing depth and improves downstream inference.

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.

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.

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.

Granatum

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.

SCONE / Single-Cell Overview of Normalized Expression

Assists in implementing and assessing the performance of a range of normalization workflows. SCONE evaluates the performance of each workflow and ranks them by aggregating over a set of performance metrics. It is applicable to different single-cell RNASeq (scRNAseq) protocols including microfluidic, plate, and droplet, methods. It allows researchers to compare a set of default normalizations as well as to include user-defined normalization methods.

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.

scPLS

A statistical method that solves a key challenge in data normalization for single cell RNA sequencing: controlling for the hidden confounding factors (e.g. batch effects, cell cycle effects etc.) and removing unwanted variation. Compare to some recent methods using a small set of control genes to infer and control for confounding effects, we propose instead modeling both control and non-control genes jointly. Through extensive simulations and case studies, we demonstrate that joint modeling enables much more accurate data normalization than previous approaches.

scater

Contains useful tools for the analysis of single-cell gene expression data using the statistical software R. scater places an emphasis on tools for quality control, visualisation and pre-processing of data before further downstream analysis. scater enables the following: (i) automated computation of QC metrics; (ii) transcript quantification from read data with pseudo-alignment; (iii) data format standardisation; (iv) rich visualisations for exploratory analysis; (v) seamless integration into the Bioconductor universe; (vi) simple normalisation methods.