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Splatter
Offers a simple interface for creating complex simulations that are reproducible and well-documented. Splatter is an R package for reproducible and accurate simulation of single-cell RNA sequencing data. It enables researchers to quickly simulate scRNA-seq count data in a reproducible fashion and make comparisons between simulations and real data. This framework can empower researchers to rapidly and rigorously develop new scRNA-seq analysis methods, ultimately leading to new discoveries in cell biology.
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.
powsimR
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.
PROSSTT / PRObabilistic Simulation of Single-cell RNA-seq Tree-like Topologies
Simulates realistic single-cell RNA sequencing (scRNA-seq) datasets of differentiating cells. PROSSTT consist of four steps: (1) generation of a tree, (2) simulation of average gene expression along tree, (3) sampling cells from tree, and (4) simulation of unique molecular identifier (UMI) counts. The software can serve to test the influence of noise models and give biological insights into how to model and interpret scRNA-seq data.
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