scPerturb: Harmonized Single-Cell Perturbation Data

Peidli, Stefan; Green, Tessa Durakis; Shen, Ciyue; Groß, Torsten; Min, Joseph K.; Taylor-King, Jake P.; Marks, Daniel; Luna, Augustin; Blüthgen, Nils; Sander, Chris

doi:10.1101/2022.08.20.504663

Cited by 23 publications

(30 citation statements)

References 91 publications

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“…We selected distance metrics based on common usage in genomics analysis and machine learning, including those used previously for model evaluation, as loss functions in model training, or for data analysis. We tested classically used distances (Euclidean, cosine distance, mean absolute error (MAE), mean squared error (MSE), linear maximum mean discrepancy (linear MMD) 16 , Wasserstein 17,18 ), in addition to some statistics commonly used in biological analysis (t-statistic, Pearson correlation, Spearman correlation, Kendall tau distance, coefficient of determination (R 2 ), energy distance (E-distance) 19 ), and previously unexplored ones (Kolmogorov-Smirnov test, symmetric Kullback-Leibler divergence, linear classification probability). After scaling of the metric, linear MMD, MSE, and Euclidean are mathematically identical, but we included all three implementations for completeness.…”

Section: Methodsmentioning

confidence: 99%

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Optimal distance metrics for single-cell RNA-seq populations

Ji,

Green,

Peidli

et al. 2023

Preprint

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In single-cell data workflows and modeling, distance metrics are commonly used in loss functions, model evaluation, and subpopulation analysis. However, these metrics behave differently depending on the source of variation, conditions and subpopulations in single-cell expression profiles due to data sparsity and high dimensionality. Thus, the metrics used for downstream tasks in this domain should be carefully selected. We establish a set of benchmarks with three evaluation measures, capturing desirable facets of absolute and relative distance behavior. Based on seven datasets using perturbation as ground truth, we evaluated 16 distance metrics applied to scRNA-seq data and demonstrated their application to three use cases. We find that linear metrics such as mean squared error (MSE) performed best across our three evaluation criteria. Therefore, we recommend the use of MSE for comparing single-cell RNA-seq populations and evaluating gene expression prediction models.

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Section: Methodsmentioning

confidence: 99%

“…Reproducibility code and notebooks can be found at https://github.com/theislab/perturbation-metrics. All datasets used in this study are available from scPerturb 19 .…”

Section: Data and Code Availabilitymentioning

confidence: 99%

Optimal distance metrics for single-cell RNA-seq populations

Ji,

Green,

Peidli

et al. 2023

Preprint

Self Cite

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“…To enhance the interpretability of high-content perturbation resources, the dataset analyzing module provides visualization for five major analytic results that cover the primary requirements, including the quality control, denoise, identification of DEGs, perturbation function analysis, and the correlation between perturbations. Compared to scPerturb, PerturBase offers modules for querying, analyzing and visualizing a more comprehensive scPerturbation data through an interactive interface (28).…”

Section: Conclusion and Future Developmentmentioning

confidence: 99%

PerturBase: a comprehensive database for single-cell perturbation data analysis and visualization

Wei,

Si,

Duan

et al. 2024

Preprint

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Single cell perturbation sequencing techniques (scPerturbation), represented by single cell genetic perturbation sequencing (e.g., Perturb seq) and single cell chemical perturbation sequencing (e.g., sci Plex), result from the integration of single cell toolkits with conventional bulk screening methods. These innovative sequencing techniques empower researchers to dissect perturbation functions and mechanisms in complex biological systems at an unprecedented resolution. Despite these advancements, a notable gap exists in the availability of a dedicated database for exploring and querying scPerturbation data. To address this gap and facilitate seamless data sharing for researchers, we present PerturBase the first and most comprehensive database designed for the analysis and visualization of scPerturbation data (http://www.perturbase.cn/). PerturBase consolidates 122 datasets from 46 publicly accessible research studies, covering 115 single modal and 7 multi modal datasets that include 24254 genetic and 230 chemical perturbations from about 6 million cells. The database provides insights through various software analyzed results, encompassing quality control, denoising, differential expression gene analysis, perturbation function analysis, and correlation characterization between perturbations. All datasets and in depth analyses are presented in user friendly, easy to browse pages and can be visualized through intuitive tables and various image formats. In summary, PerturBase stands as a pioneering high content database, intended for searching, visualizing, and analyzing scPerturbation datasets, contributing to an enhanced understanding of perturbation functions and mechanisms.

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“…A processed version of the Norman et al . data can also be found at http://projects.sanderlab.org/scperturb[31].…”

Section: Data Availabilitymentioning

confidence: 99%

Modeling interpretable correspondence between cell state and perturbation response with CellCap

Xu,

Fleming,

Tegtmeyer

et al. 2024

Preprint

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Single-cell transcriptomics, in conjunction with genetic and compound perturbations, offers a robust approach for exploring cellular behaviors in diverse contexts. Such experiments allow uncovering cell-state-specific responses to perturbations, a crucial aspect in unraveling the intricate molecular mechanisms governing cellular behavior and potentially discovering novel regulatory pathways and therapeutic targets. However, prevailing computational methods predominantly focus on predicting average cellular responses, disregarding the inherent response heterogeneity associated with cell state diversity. In this study, we present CellCap, a deep generative model designed for the end-to-end analysis of single-cell perturbation experiments. CellCap employs sparse dictionary learning in a latent space to deconstruct cell-state-specific perturbation responses into a set of transcriptional response programs. These programs are then utilized by each perturbation condition and each cell at varying degrees. The incorporation of specific model design choices, such as dot-product cross-attention between cell states and response programs, along with a linearly-decoded latent space, underlay the interpretation power of CellCap. We evaluate CellCap's model interpretability through multiple simulated scenarios and apply it to two real single-cell perturbation datasets. These datasets feature either heterogeneous cellular populations or a complex experimental setup. Our results demonstrate that CellCap successfully uncovers the relationship between cell state and perturbation response, unveiling novel insights overlooked in previous analyses. The model's interpretability, coupled with its effectiveness in capturing heterogeneous responses, positions CellCap as a valuable tool for advancing our understanding of cellular behaviors in the context of perturbation experiments.

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scPerturb: Harmonized Single-Cell Perturbation Data

Cited by 23 publications

References 91 publications

Optimal distance metrics for single-cell RNA-seq populations

Optimal distance metrics for single-cell RNA-seq populations

PerturBase: a comprehensive database for single-cell perturbation data analysis and visualization

Modeling interpretable correspondence between cell state and perturbation response with CellCap

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