Davidson Natalie R, Zhang Fan, Greene Casey S
Department of Biomedical Informatics, University of Colorado Anschutz School of Medicine, Aurora, Colorado, United States of America · Funded by the Gordon and Betty Moore Foundation (GBMF 4552), NHGRI of the National Institutes of Health (K99HG012945), NCI of the National Institutes of Health (R01CA237170, R01CA243188, R01CA200854).
Department of Medicine Rheumatology, University of Colorado Anschutz School of Medicine, Aurora, Colorado, United States of America; Department of Biomedical Informatics, University of Colorado Anschutz School of Medicine, Aurora, Colorado, United States of America · Funded by the Arthritis National Research Foundation Award, the PhRMA foundation, and the University of Colorado Translational Research Scholars Program Award.
bioRxiv. 2024 Apr 4:2023.07.20.549951. doi: 10.1101/2023.07.20.549951.
While single-cell experiments provide deep cellular resolution within a single sample, some single-cell experiments are inherently more challenging than bulk experiments due to dissociation difficulties, cost, or limited tissue availability. This creates a situation where we have deep cellular profiles of one sample or condition, and bulk profiles across multiple samples and conditions. To bridge this gap, we propose BuDDI (BUlk Deconvolution with Domain Invariance). BuDDI utilizes domain adaptation techniques to effectively integrate available corpora of case-control bulk and reference scRNA-seq observations to infer cell-type-specific perturbation effects. BuDDI achieves this by learning independent latent spaces within a single variational autoencoder (VAE) encompassing at least four sources of variability: 1) cell type proportion, 2) perturbation effect, 3) structured experimental variability, and 4) remaining variability. Since each latent space is encouraged to be independent, we simulate perturbation responses by independently composing each latent space to simulate cell-type-specific perturbation responses. We evaluated BuDDI's performance on simulated and real data with experimental designs of increasing complexity. We first validated that BuDDI could learn domain invariant latent spaces on data with matched samples across each source of variability. Then we validated that BuDDI could accurately predict cell-type-specific perturbation response when no single-cell perturbed profiles were used during training; instead, only bulk samples had both perturbed and non-perturbed observations. Finally, we validated BuDDI on predicting sex-specific differences, an experimental design where it is not possible to have matched samples. In each experiment, BuDDI outperformed all other comparative methods and baselines. As more reference atlases are completed, BuDDI provides a path to combine these resources with bulk-profiled treatment or disease signatures to study perturbations, sex differences, or other factors at single-cell resolution.
虽然单细胞实验可在单个样本中提供深度细胞分辨率，但由于解离困难、成本或组织可用性有限，一些单细胞实验本质上比批量实验更具挑战性。这就造成了一种情况，即我们有一个样本或条件的深度细胞图谱，以及多个样本和条件的批量图谱。为了弥合这一差距，我们提出了BuDDI（具有域不变性的批量反卷积）。BuDDI利用域适应技术有效地整合病例对照批量和参考单细胞RNA测序观察的可用数据集，以推断细胞类型特异性扰动效应。BuDDI通过在单个变分自编码器（VAE）中学习独立的潜在空间来实现这一点，该潜在空间包含至少四个变异来源：1）细胞类型比例，2）扰动效应，3）结构化实验变异，以及4）剩余变异。由于鼓励每个潜在空间相互独立，我们通过独立组合每个潜在空间来模拟细胞类型特异性扰动反应，从而模拟扰动反应。我们在具有日益复杂实验设计的模拟和真实数据上评估了BuDDI的性能。我们首先验证了BuDDI可以在每个变异来源具有匹配样本的数据上学习域不变潜在空间。然后我们验证了在训练期间不使用单细胞扰动图谱时，BuDDI可以准确预测细胞类型特异性扰动反应；相反，只有批量样本同时具有扰动和未扰动的观察结果。最后，我们在预测性别特异性差异方面验证了BuDDI，在这种实验设计中不可能有匹配样本。在每个实验中，BuDDI的表现均优于所有其他比较方法和基线。随着更多参考图谱的完成，BuDDI提供了一条将这些资源与批量分析的治疗或疾病特征相结合的途径，以在单细胞分辨率下研究扰动、性别差异或其他因素。
