2024年5月6日 · The core function of sc-OTGM is to create a probabilistic latent space utilizing a GMM as its prior distribution and distinguish between distinct cell populations by learning their respective marginal PDFs. It uses a Hit-and-Run Markov chain sampler to determine the OT plan across these PDFs within the GMM framework.

本文的研究基于生物学的数学基础，利用 最优输运理论 建立了一个新的框架，称为 CellOT，用于学习单细胞在扰动下的演化轨迹。 通过学习最优输运映射，CellOT能够预测细胞在扰动下的分布变化，并揭示细胞群体结构在外部因素影响下的演化。 这项研究为理解细胞命运决策和细胞逃逸机制提供了新的工具和方法。 研究思路： 本研究的研究问题是如何学习单细胞扰动响应。 研究者提出了一种基于神经最优传输的方法，称为CellOT，用于预测细胞在扰动下的状态变化。 该方法 …

This is the official codebase for "sc-OTGM: Single-Cell Perturbation Modeling by Solving Optimal Mass Transport on the Manifold of Gaussian Mixtures" - Novartis/scOTGM

we present sc-OTGM, streamlined with less than 500K parameters, making it ap-proximately 100x more compact than the foundation models, offering an efficient alternative. sc-OTGM is an unsupervised model grounded in the inductive bias that the scRNA-seq data can be generated from a combination of the finite multivariate Gaussian distributions.

The core function of sc-OTGM is to create a probabilistic latent space utilizing a Gaussian mixture model (GMM) as its prior distribution and distinguish between distinct cell populations by learning their respective marginal probability density functions (PDFs).

Contribute to STU-BioMLGroup/OTGM development by creating an account on GitHub.

2024年5月6日 · We evaluated our model against a CRISPR-mediated perturbation dataset, called CROP-seq, consisting of 57 one-gene perturbations. Our results demonstrate that sc-OTGM is effective in cell state classification, aids in the analysis of differential gene expression, and ranks genes for target identification through a recommender system.

通过使用单细胞基因组学数据，我们提出了一种名为sc-OTGM的简化模型，它通过无监督学习实现了细胞状态分类和基因干扰响应预测，并且在分析差异基因表达，基因推荐系统以及生成特定细胞状态的合成单细胞RNA测序数据方面表现出良好的效果。

2024年9月17日 · Graph matching is a significant task for handling the matching problem of finding correspondences between keypoints in different graphs. Prior research primarily concentrates on performing one-to-one matching in topologic perspective for keypoints across various graphs, assuming that the paired keypoints are accurately linked.

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