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GSR: Simulator - PROSSTT
| Attribute | Value |
|---|---|
| Title | PROSSTT |
| Short Description | PROSSTT: probabilistic simulation of single-cell RNA-seq data for complex differentiation processes |
| Long Description | PROSSTT (PRObabilistic Simulations of ScRNA-seq Tree-like Topologies) is a package with code for the simulation of scRNAseq data for dynamic processes such as cell differentiation. PROSSTT is open source GPL-licensed software implemented in Python. Single-cell RNAseq is revolutionizing cellular biology, and many algorithms are developed for the analysis of scRNAseq data. PROSSTT provides an easy way to test the performance of trajectory inference methods on realistic data with a known "gold standard". The algorithm can produce datasets with user-defined topologies while simulating any number of sampled cells and genes. |
| Keywords | Single-cell RNA sequencing, lineage trees, topology complexity |
| Version | 1.4 |
| Project Started | 2018 |
| Last Release | 6 years, 3 months ago |
| Homepage | https://github.com/soedinglab/prosstt |
| Citations | Papadopoulos N, Gonzalo PR, Söding J, PROSSTT: probabilistic simulation of single-cell RNA-seq data for complex differentiation processes., Bioinformatics, Sept. 15, 2019 [ Abstract, cited in PMC ] |
| GSR Certification |  ✔ Accessibility |
| Last evaluated | Dec. 9, 2022 (986 days ago) |
| Author verification | The basic description provided was derived from a website or publications by the GSR team and has not yet been verified by the simulation author. To modify this entry or add more information, propose changes to this simulator. |
| Attribute Category | Attribute |
|---|---|
| Target | |
| Type of Simulated Data | RNA, Single-Cell Sequencing, |
| Variations | Single Nucleotide Variation, Other, |
| Simulation Method | Phylogenetic, |
| Input | |
| Data Type | |
| File format | |
| Output | |
| Data Type | Gene Expression, |
| Sequencing Reads | |
| File Format | |
| Sample Type | |
| Phenotype | |
| Trait Type | |
| Determinants | |
| Evolutionary Features | |
| Demographic | |
| Population Size Changes | |
| Gene Flow | |
| Spatiality | |
| Life Cycle | |
| Mating System | |
| Fecundity | |
| Natural Selection | |
| Determinant | |
| Models | |
| Recombination | |
| Mutation Models | |
| Events Allowed | |
| Other | |
| Interface | Command-line, Script-based, |
| Development | |
| Tested Platforms | |
| Language | Python, |
| License | GNU Public License, |
| GSR Certification | Accessibility, Documentation, Application, Support, |
Number of Primary Citations: 1
Number of Non-Primary Citations: 4
The following 4 publications are selected examples of applications that used PROSSTT.
2022
Watson ER, Mora A, Taherian Fard A, Mar JC, How does the structure of data impact cell-cell similarity? Evaluating how structural properties influence the performance of proximity metrics in single cell RNA-seq data., Brief Bioinform, Nov. 19, 2022 [Abstract]
2021
Chen Y, Zhang Y, Li JYH, Ouyang Z, LISA2: Learning Complex Single-Cell Trajectory and Expression Trends., Front Genet, Aug. 23, 2021 [Abstract]
Luo Z, Xu C, Zhang Z, Jin W, A topology-preserving dimensionality reduction method for single-cell RNA-seq data using graph autoencoder., Sci Rep, Oct. 8, 2021 [Abstract]
Smolander J, Junttila S, Venäläinen MS, Elo LL, scShaper: an ensemble method for fast and accurate linear trajectory inference from single-cell RNA-seq data., Bioinformatics, Dec. 9, 2021 [Abstract]