Kernel-Based Testing for Single-Cell Differential Analysis

• URL: http://arxiv.org/abs/2307.08509v3
• Date: Fri, 12 Apr 2024 11:48:03 GMT
• Title: Kernel-Based Testing for Single-Cell Differential Analysis
• Authors: Anthony Ozier-Lafontaine, Camille Fourneaux, Ghislain Durif, Polina Arsenteva, Céline Vallot, Olivier Gandrillon, Sandrine Giraud, Bertrand Michel, Franck Picard,
• Abstract summary: We propose a kernel-testing framework for non-linear cell-wise distribution comparison. Our method allows feature-wise and global transcriptome/epigenome comparisons, revealing cell population heterogeneities.
• Score: 23.769396655341204
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Single-cell technologies offer insights into molecular feature distributions, but comparing them poses challenges. We propose a kernel-testing framework for non-linear cell-wise distribution comparison, analyzing gene expression and epigenomic modifications. Our method allows feature-wise and global transcriptome/epigenome comparisons, revealing cell population heterogeneities. Using a classifier based on embedding variability, we identify transitions in cell states, overcoming limitations of traditional single-cell analysis. Applied to single-cell ChIP-Seq data, our approach identifies untreated breast cancer cells with an epigenomic profile resembling persister cells. This demonstrates the effectiveness of kernel testing in uncovering subtle population variations that might be missed by other methods.

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)
• Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View [49.03501451546763]
  We identify the importance of implicit correspondences across biological contexts for exploiting domain-invariant pathological composition. We propose self-adaptive dynamic distillation to secure instance-aware trade-offs across different model constituents.
  arXiv  Detail & Related papers  (2024-07-14T04:41:16Z)
• Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types [50.55583697209676]
  We develop an attention-enhanced graph autoencoder, which is designed to efficiently capture the topological features between cells. During the clustering process, we integrated both sets of information and reconstructed the features of both cells and genes to generate a discriminative representation. This research offers enhanced insights into the characteristics and distribution of cells, thereby laying the groundwork for early diagnosis and treatment of diseases.
  arXiv  Detail & Related papers  (2023-11-28T09:14:55Z)
• Single-cell Multi-view Clustering via Community Detection with Unknown Number of Clusters [64.31109141089598]
  We introduce scUNC, an innovative multi-view clustering approach tailored for single-cell data. scUNC seamlessly integrates information from different views without the need for a predefined number of clusters. We conducted a comprehensive evaluation of scUNC using three distinct single-cell datasets.
  arXiv  Detail & Related papers  (2023-11-28T08:34:58Z)
• Regression-Based Analysis of Multimodal Single-Cell Data Integration Strategies [0.0]
  Multimodal single-cell technologies enable the simultaneous collection of diverse data types from individual cells. This study highlights the exceptional performance of Echo State Networks, boasting a remarkable correlation score of 0.94. These findings hold promise for advancing comprehension of cellular differentiation and function, leveraging the potential of Machine Learning.
  arXiv  Detail & Related papers  (2023-11-21T16:31:27Z)
• 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)
• Granger causal inference on DAGs identifies genomic loci regulating transcription [77.58911272503771]
  GrID-Net is a framework based on graph neural networks with lagged message passing for Granger causal inference on DAG-structured systems. Our application is the analysis of single-cell multimodal data to identify genomic loci that mediate the regulation of specific genes.
  arXiv  Detail & Related papers  (2022-10-18T21:15:10Z)
• Topological Data Analysis in Time Series: Temporal Filtration and Application to Single-Cell Genomics [13.173307471333619]
  We propose the single-cell topological simplicial analysis (scTSA) Applying this approach to the single-cell gene expression profiles from local networks of cells reveals a previously unseen topology of cellular ecology. Benchmarked on the single-cell RNA-seq data of zebrafish embryogenesis spanning 38,731 cells, 25 cell types and 12 time steps, our approach highlights the gastrulation as the most critical stage.
  arXiv  Detail & Related papers  (2022-04-29T12:46:14Z)
• Interpretable Single-Cell Set Classification with Kernel Mean Embeddings [14.686560033030101]
  Kernel Mean Embedding encodes the cellular landscape of each profiled biological sample. We train a simple linear classifier and achieve state-of-the-art classification accuracy on 3 flow and mass datasets.
  arXiv  Detail & Related papers  (2022-01-18T21:40:36Z)
• Distinguishing Cell Phenotype Using Cell Epigenotype [0.0]
  Relationship between microscopic observations and macroscopic behavior is a fundamental open question in biophysical systems. We develop a unified approach that---in contrast with existing methods---predicts cell type from macromolecular data even when accounting for the scale of human tissue diversity and limitations in the available data.
  arXiv  Detail & Related papers  (2020-03-20T18:00:07Z)

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.