Read simulation software tools | Single-cell RNA sequencing data analysis
A common way to test the performance of an analysis method is through a simulation. Simulated data provides a known truth to test against, making it possible to see whether a method has been implemented correctly, whether the assumptions of the method are appropriate, and demonstrating its limitations.
Allows users to simulate and analyze single-cell RNA-sequencing data. scRutiNy is a python program that performs simulation of single sceel RNA-seq (scRNA-seq) data thanks to a dynamical system from an underlying gene regulatory network. It also can deduce pseudotime and a gene regulatory network from scRNA-seq data. Moreover, for inferring a genetic regulatory network from scRNA-seq data, a cross-validated Lasso regularized regression is used.
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.
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.
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.
Provides assistance for the analysis for single-cell RNASeq (scRNAseq) data. WGAN is based on a deep-learning approach in integrating multiple gene expression datasets. This software combines data from cells in different environments with technical and biological noise and allows inference of gene associations underlying to epidermal cells. It can be extended to incorporate cells from multiple tissues.
Assists users with simulation of single cell RNA-Seq data. SymSim models three of the main sources of variation that govern single cell expression patterns: allele intrinsic variation, extrinsic variation, and technical factors. It also recapitulates properties of the data such as high abundance of zeros or increased noise in non-unique molecular identifiers (UMI) protocols, without the need to explicitly force them as factors in a distributional model.