Simulating multiple faceted variability in single cell RNA sequencing

Zhang, Xiuwei; Xu, Chenling; Yosef, Nir

doi:10.1038/s41467-019-10500-w

Cited by 121 publications

(179 citation statements)

References 51 publications

Supporting

Mentioning

175

Contrasting

Order By: Relevance

“…Indeed, this is an open research topic. Consequently, we turned to simulations using Poisson log-normal distributions with added zero-inflation as well as Symsim [8], a realistic simulator for scRNA-seq data relying on Beta-Poisson distributions.…”

Section: Performance Benchmarks On Simulated Datasetsmentioning

confidence: 99%

“…Beta-Poisson datasets For a more biologically relevant simulation framework, we used known kinetic models of stochastic gene expression such as the Beta-Poisson model. SymSim [8] provides a natural way of sampling data from such models and adding technical noise. SymSim first randomly samples the promoter on rate (k on ), off rate (k off ) and synthesis rate (s) for each gene, and then generates simulated "true" counts using a Beta-Poisson distribution.…”

Section: Performance Benchmarks On Simulated Datasetsmentioning

confidence: 99%

“…This analysis was primarily based on datasets of "negative controls" (e.g., ERCC spike-ins) -namely exogenous transcripts that are constitutively expressed, thus reducing the contribution of biological factors as a cause for zero measurements. While the author found these negative controls to be adequately modeled by a NB with no zero-inflation, the question remains of whether this is also the case for cell-endogenous transcripts, and whether in such cases zero-inflation is a property specific only to a subset of the genes (e.g., due to their promoter kinetics [8]).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detecting Zero-Inflated Genes in Single-Cell Transcriptomics Data

Clivio

Lopez

Regier

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

In single-cell RNA sequencing data, biological processes or technical factors may induce an overabundance of zero measurements. Existing probabilistic approaches to interpreting these data either model all genes as zero-inflated, or none. But the overabundance of zeros might be gene-specific. Hence, we propose the AutoZI model, which, for each gene, places a spike-and-slab prior on a mixture assignment between a negative binomial (NB) component and a zero-inflated negative binomial (ZINB) component. We approximate the posterior distribution under this model using variational inference, and employ Bayesian decision theory to decide whether each gene is zero-inflated. On simulated data, AutoZI outperforms the alternatives. On negative control data, AutoZI retrieves predictions consistent to a previous study on ERCC spike-ins and recovers similar results on control RNAs. Applied to several datasets and instances of the 10x Chromium protocol, AutoZI allows both biological and technical interpretations of zero-inflation. Finally, AutoZI's decisions on mouse embyronic stem-cells suggest that zero-inflation might be due to transcriptional bursting.

show abstract

Section: Performance Benchmarks On Simulated Datasetsmentioning

confidence: 99%

Section: Performance Benchmarks On Simulated Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detecting Zero-Inflated Genes in Single-Cell Transcriptomics Data

Clivio

Lopez

Regier

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…We used SymSim (Zhang et al, 2019), an R software package, to generate simulated single-cell 418 RNA-seq data for five distinct cell populations with the tree structure represented in Fig. 2a.…”

Section: Dataset and Data Preparation 416mentioning

confidence: 99%

“…Case study 1: Simulated single cell RNA-seq data 158We used SymSim(Zhang, Xu, & Yosef, 2019), a software that explicitly models the processes that 159give rise to data observed in single-cell RNA-seq experiments, for simulating 200 cells with five 160 distinct single cell populations. The parameters used in the simulation process resemble the actual 161 single-cell RNA-seq data generation process.…”

mentioning

confidence: 99%

Feature extraction approach in single-cell gene expression profiling for cell-type marker identification

Adossa

Schauser

Gregersen

et al. 2019

Preprint

View full text Add to dashboard Cite

8Background: Recent advances in single-cell gene expression profiling technology have 9 revolutionized the understanding of molecular processes underlying developmental cell and tissue 10 differentiation, enabling the discovery of novel cell-types and molecular markers that characterize 11 developmental trajectories. Common approaches for identifying marker genes are based on pairwise 12 statistical testing for differential gene expression between cell-types in heterogeneous cell 13 populations, which is challenging due to unequal sample sizes and variance between groups resulting 14 in little statistical power and inflated type I errors. 15 Results:We developed an alternative feature extraction method, Marker gene Identification for Cell-16 type Identity (MICTI) that encodes the cell-type specific expression information to each gene in every 17 single-cell. This approach identifies features (genes) that are cell-type specific for a given cell-type 18 in heterogeneous cell population. To validate this approach, we used (i) simulated single cell RNA-19 seq data, (ii) human pancreatic islet single-cell RNA-seq data and (iii) a simulated mixture of human 20 single-cell RNA-seq data related to immune cells, particularly B cells, CD4+ memory cells, CD8+ 21 2 memory cells, dendritic cells, fibroblast cells, and lymphoblast cells. For all cases, we were able to 22 identify established cell-type-specific markers. 23 Conclusions:Our approach represents a highly efficient and fast method as an alternative to 24 differential expression analysis for molecular marker identification in heterogeneous single-cell 25 RNA-seq data. 26

show abstract

PathogenTrack and Yeskit: tools for identifying intracellular pathogens from single-cell RNA-sequencing datasets as illustrated by application to COVID-19

Zhang

et al. 2022

Front. Med.

View full text Add to dashboard Cite

Pathogenic microbes can induce cellular dysfunction, immune response, and cause infectious disease and other diseases including cancers. However, the cellular distributions of pathogens and their impact on host cells remain rarely explored due to the limited methods. Taking advantage of single-cell RNA-sequencing (scRNA-seq) analysis, we can assess the transcriptomic features at the single-cell level. Still, the tools used to interpret pathogens (such as viruses, bacteria, and fungi) at the single-cell level remain to be explored. Here, we introduced PathogenTrack, a python-based computational pipeline that uses unmapped scRNA-seq data to identify intracellular pathogens at the single-cell level. In addition, we established an R package named Yeskit to import, integrate, analyze, and interpret pathogen abundance and transcriptomic features in host cells. Robustness of these tools has been tested on various real and simulated scRNA-seq datasets. PathogenTrack is competitive to the state-of-the-art tools such as Viral-Track, and the first tools for identifying bacteria at the single-cell level. Using the raw data of bronchoalveolar lavage fluid samples (BALF) from COVID-19 patients in the SRA database, we found the SARS-CoV-2 virus exists in multiple cell types including epithelial cells and macrophages. SARS-CoV-2-positive neutrophils showed increased expression of genes related to type I interferon pathway and antigen presenting module. Additionally, we observed the Haemophilus parahaemolyticus in some macrophage and epithelial cells, indicating a co-infection of the bacterium in some severe cases of COVID-19. The PathogenTrack pipeline and the Yeskit package are publicly available at GitHub. Electronic Supplementary Material Supplementary material is available in the online version of this article at 10.1007/s11684-021-0915-9 and is accessible for authorized users.

show abstract

Simulating multiple faceted variability in single cell RNA sequencing

Cited by 121 publications

References 51 publications

Detecting Zero-Inflated Genes in Single-Cell Transcriptomics Data

Detecting Zero-Inflated Genes in Single-Cell Transcriptomics Data

Feature extraction approach in single-cell gene expression profiling for cell-type marker identification

PathogenTrack and Yeskit: tools for identifying intracellular pathogens from single-cell RNA-sequencing datasets as illustrated by application to COVID-19

Contact Info

Product

Resources

About