{"title":"基于注意力机制,scPRAM 可准确预测单细胞基因表达扰动反应。","authors":"Qun Jiang, Shengquan Chen, Xiaoyang Chen, Rui Jiang","doi":"10.1093/bioinformatics/btae265","DOIUrl":null,"url":null,"abstract":"MOTIVATION\nWith the rapid advancement of single-cell sequencing technology, it becomes gradually possible to delve into the cellular responses to various external perturbations at the gene expression level. However, obtaining perturbed samples in certain scenarios may be considerably challenging, and the substantial costs associated with sequencing also curtail the feasibility of large-scale experimentation. A repertoire of methodologies has been employed for forecasting perturbative responses in single-cell gene expression. However, existing methods primarily focus on the average response of a specific cell type to perturbation, overlooking the single-cell specificity of perturbation responses and a more comprehensive prediction of the entire perturbation response distribution.\n\n\nRESULTS\nHere we present scPRAM, a method for predicting Perturbation Responses in single-cell gene expression based on Attention Mechanisms. Leveraging variational autoencoders and optimal transport, scPRAM aligns cell states before and after perturbation, followed by accurate prediction of gene expression responses to perturbations for unseen cell types through attention mechanisms. Experiments on multiple real perturbation datasets involving drug treatments and bacterial infections demonstrate that scPRAM attains heightened accuracy in perturbation prediction across cell types, species, and individuals, surpassing existing methodologies. Furthermore, scPRAM demonstrates outstanding capability in identifying differentially expressed genes under perturbation, capturing heterogeneity in perturbation responses across species, and maintaining stability in the presence of data noise and sample size variations.\n\n\nAVAILABILITY AND IMPLEMENTATION\nhttps://github.com/jiang-q19/scPRAM and https://doi.org/10.5281/zenodo.10935038.\n\n\nSUPPLEMENTARY INFORMATION\nSupplementary data are available at Bioinformatics online.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.4000,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism.\",\"authors\":\"Qun Jiang, Shengquan Chen, Xiaoyang Chen, Rui Jiang\",\"doi\":\"10.1093/bioinformatics/btae265\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"MOTIVATION\\nWith the rapid advancement of single-cell sequencing technology, it becomes gradually possible to delve into the cellular responses to various external perturbations at the gene expression level. However, obtaining perturbed samples in certain scenarios may be considerably challenging, and the substantial costs associated with sequencing also curtail the feasibility of large-scale experimentation. A repertoire of methodologies has been employed for forecasting perturbative responses in single-cell gene expression. However, existing methods primarily focus on the average response of a specific cell type to perturbation, overlooking the single-cell specificity of perturbation responses and a more comprehensive prediction of the entire perturbation response distribution.\\n\\n\\nRESULTS\\nHere we present scPRAM, a method for predicting Perturbation Responses in single-cell gene expression based on Attention Mechanisms. Leveraging variational autoencoders and optimal transport, scPRAM aligns cell states before and after perturbation, followed by accurate prediction of gene expression responses to perturbations for unseen cell types through attention mechanisms. Experiments on multiple real perturbation datasets involving drug treatments and bacterial infections demonstrate that scPRAM attains heightened accuracy in perturbation prediction across cell types, species, and individuals, surpassing existing methodologies. 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引用次数: 0
摘要
动机随着单细胞测序技术的飞速发展,从基因表达水平深入研究细胞对各种外部扰动的反应逐渐成为可能。然而,在某些情况下获取扰动样本可能具有相当大的挑战性,而且测序相关的高昂成本也限制了大规模实验的可行性。目前已有一系列方法用于预测单细胞基因表达的扰动反应。然而,现有的方法主要关注特定细胞类型对扰动的平均响应,忽略了扰动响应的单细胞特异性以及对整个扰动响应分布的更全面预测。结果在此,我们提出了基于注意机制的单细胞基因表达扰动响应预测方法 scPRAM。利用变异自编码器和最优传输,scPRAM 对扰动前后的细胞状态进行了调整,然后通过注意机制准确预测了未见细胞类型的基因表达对扰动的反应。在涉及药物治疗和细菌感染的多个真实扰动数据集上进行的实验表明,scPRAM 在跨细胞类型、物种和个体的扰动预测方面达到了更高的准确性,超越了现有方法。此外,scPRAM 在识别扰动下的差异表达基因、捕捉不同物种扰动反应的异质性以及在数据噪声和样本量变化的情况下保持稳定性方面表现出了卓越的能力。AVAILABILITY AND IMPLEMENTATIONhttps://github.com/jiang-q19/scPRAM and https://doi.org/10.5281/zenodo.10935038.SUPPLEMENTARY INFORMATIONSupplementary data are available at Bioinformatics online.
scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism.
MOTIVATION
With the rapid advancement of single-cell sequencing technology, it becomes gradually possible to delve into the cellular responses to various external perturbations at the gene expression level. However, obtaining perturbed samples in certain scenarios may be considerably challenging, and the substantial costs associated with sequencing also curtail the feasibility of large-scale experimentation. A repertoire of methodologies has been employed for forecasting perturbative responses in single-cell gene expression. However, existing methods primarily focus on the average response of a specific cell type to perturbation, overlooking the single-cell specificity of perturbation responses and a more comprehensive prediction of the entire perturbation response distribution.
RESULTS
Here we present scPRAM, a method for predicting Perturbation Responses in single-cell gene expression based on Attention Mechanisms. Leveraging variational autoencoders and optimal transport, scPRAM aligns cell states before and after perturbation, followed by accurate prediction of gene expression responses to perturbations for unseen cell types through attention mechanisms. Experiments on multiple real perturbation datasets involving drug treatments and bacterial infections demonstrate that scPRAM attains heightened accuracy in perturbation prediction across cell types, species, and individuals, surpassing existing methodologies. Furthermore, scPRAM demonstrates outstanding capability in identifying differentially expressed genes under perturbation, capturing heterogeneity in perturbation responses across species, and maintaining stability in the presence of data noise and sample size variations.
AVAILABILITY AND IMPLEMENTATION
https://github.com/jiang-q19/scPRAM and https://doi.org/10.5281/zenodo.10935038.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
期刊介绍:
The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.