sc-OTGM: Single-Cell Perturbation Modeling by Solving Optimal Mass Transport on the Manifold of Gaussian Mixtures

• URL: http://arxiv.org/abs/2405.03726v1
• Date: Mon, 6 May 2024 06:46:11 GMT
• Title: sc-OTGM: Single-Cell Perturbation Modeling by Solving Optimal Mass Transport on the Manifold of Gaussian Mixtures
• Authors: Andac Demir, Elizaveta Solovyeva, James Boylan, Mei Xiao, Fabrizio Serluca, Sebastian Hoersch, Jeremy Jenkins, Murthy Devarakonda, Bulent Kiziltan,
• Abstract summary: sc-OTGM is an unsupervised model grounded in the inductive bias that the scRNAseq data can be generated. sc-OTGM is effective in cell state classification, aids in the analysis of differential gene expression, and ranks genes for target identification. It also predicts the effects of single-gene perturbations on downstream gene regulation and generates synthetic scRNA-seq data conditioned on specific cell states.
• Score: 0.9674145073701153
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Influenced by breakthroughs in LLMs, single-cell foundation models are emerging. While these models show successful performance in cell type clustering, phenotype classification, and gene perturbation response prediction, it remains to be seen if a simpler model could achieve comparable or better results, especially with limited data. This is important, as the quantity and quality of single-cell data typically fall short of the standards in textual data used for training LLMs. Single-cell sequencing often suffers from technical artifacts, dropout events, and batch effects. These challenges are compounded in a weakly supervised setting, where the labels of cell states can be noisy, further complicating the analysis. To tackle these challenges, we present sc-OTGM, streamlined with less than 500K parameters, making it approximately 100x more compact than the foundation models, offering an efficient alternative. sc-OTGM is an unsupervised model grounded in the inductive bias that the scRNAseq 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 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. 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. It also predicts the effects of single-gene perturbations on downstream gene regulation and generates synthetic scRNA-seq data conditioned on specific cell states.

Related papers

• Generating Multi-Modal and Multi-Attribute Single-Cell Counts with CFGen [76.02070962797794]
  We present Cell Flow for Generation, a flow-based conditional generative model for multi-modal single-cell counts. Our results suggest improved recovery of crucial biological data characteristics while accounting for novel generative tasks.
  arXiv  Detail & Related papers  (2024-07-16T14:05:03Z)
• Scalable Amortized GPLVMs for Single Cell Transcriptomics Data [9.010523724015398]
  Dimensionality reduction is crucial for analyzing large-scale single-cell RNA-seq data. We introduce an improved model, the amortized variational model (BGPLVM) BGPLVM is tailored for single-cell RNA-seq with specialized encoder, kernel, and likelihood designs.
  arXiv  Detail & Related papers  (2024-05-06T21:54:38Z)
• Mixed Models with Multiple Instance Learning [51.440557223100164]
  We introduce MixMIL, a framework integrating Generalized Linear Mixed Models (GLMM) and Multiple Instance Learning (MIL) Our empirical results reveal that MixMIL outperforms existing MIL models in single-cell datasets.
  arXiv  Detail & Related papers  (2023-11-04T16:42:42Z)
• Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling [3.2435888122704037]
  We propose a deep generative model of single-cell gene expression data for which each perturbation is treated as an intervention targeting an unknown, but sparse, subset of latent variables. We benchmark these methods on simulated single-cell data to evaluate their performance at latent units recovery, causal target identification and out-of-domain generalization.
  arXiv  Detail & Related papers  (2022-11-07T15:47:40Z)
• Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs [6.708052194104378]
  We extend a popular approach for probabilistic non-linear dimensionality reduction, the Gaussian process latent variable model, to scale to massive single-cell datasets. The key idea is to use an augmented kernel which preserves the factorisability of the lower bound allowing for fast variational inference.
  arXiv  Detail & Related papers  (2022-09-14T15:25:15Z)
• Deep Generative Modeling on Limited Data with Regularization by Nontransferable Pre-trained Models [32.52492468276371]
  We propose regularized deep generative model (Reg-DGM) to reduce the variance of generative modeling with limited data. Reg-DGM uses a pre-trained model to optimize a weighted sum of a certain divergence and the expectation of an energy function. Empirically, with various pre-trained feature extractors and a data-dependent energy function, Reg-DGM consistently improves the generation performance of strong DGMs with limited data.
  arXiv  Detail & Related papers  (2022-08-30T10:28:50Z)
• Benchmarking Machine Learning Robustness in Covid-19 Genome Sequence Classification [109.81283748940696]
  We introduce several ways to perturb SARS-CoV-2 genome sequences to mimic the error profiles of common sequencing platforms such as Illumina and PacBio. We show that some simulation-based approaches are more robust (and accurate) than others for specific embedding methods to certain adversarial attacks to the input sequences.
  arXiv  Detail & Related papers  (2022-07-18T19:16:56Z)
• Multi-modal Self-supervised Pre-training for Regulatory Genome Across Cell Types [75.65676405302105]
  We propose a simple yet effective approach for pre-training genome data in a multi-modal and self-supervised manner, which we call GeneBERT. We pre-train our model on the ATAC-seq dataset with 17 million genome sequences.
  arXiv  Detail & Related papers  (2021-10-11T12:48:44Z)
• Score-based Generative Modeling in Latent Space [93.8985523558869]
  Score-based generative models (SGMs) have recently demonstrated impressive results in terms of both sample quality and distribution coverage. Here, we propose the Latent Score-based Generative Model (LSGM), a novel approach that trains SGMs in a latent space. Moving from data to latent space allows us to train more expressive generative models, apply SGMs to non-continuous data, and learn smoother SGMs in a smaller space.
  arXiv  Detail & Related papers  (2021-06-10T17:26:35Z)
• Continual Learning with Fully Probabilistic Models [70.3497683558609]
  We present an approach for continual learning based on fully probabilistic (or generative) models of machine learning. We propose a pseudo-rehearsal approach using a Gaussian Mixture Model (GMM) instance for both generator and classifier functionalities. We show that GMR achieves state-of-the-art performance on common class-incremental learning problems at very competitive time and memory complexity.
  arXiv  Detail & Related papers  (2021-04-19T12:26:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.