Machine learning for single-cell genomics data analysis

Raimundo, Félix; Meng-Papaxanthos, Laetitia; Vallot, Céline; Vert, Jean-Philippe

doi:10.1016/j.coisb.2021.04.006

Cited by 21 publications

(6 citation statements)

References 84 publications

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“…The application of ML in exploring plant single-cell genomic data offers opportunities to unravel cellular heterogeneity, decode regulatory networks, and identify novel cell types. Recent studies ( Silva et al., 2019 ; Raimundo et al., 2021 ) highlight ML’s potential in tasks such as generating low-dimensional representations, classifying cell types, inferring trajectories, deducing gene regulatory networks, and integrating multimodal data. Challenges related to low sequencing coverage and amplified artifacts in single-cell RNA (scRNA) sequencing are addressed by ML approaches, such as the SIMLR algorithm ( Wang et al., 2017 ) and neural network models ( Lin et al., 2017 ), providing more reliable insights into the intricate landscape of single-cell genomics.…”

Section: Application Of Ai In Plant Omics Against Stressmentioning

confidence: 99%

A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions

Murmu,

Sinha,

Chaurasia

et al. 2024

Front. Plant Sci.

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Plants intricately deploy defense systems to counter diverse biotic and abiotic stresses. Omics technologies, spanning genomics, transcriptomics, proteomics, and metabolomics, have revolutionized the exploration of plant defense mechanisms, unraveling molecular intricacies in response to various stressors. However, the complexity and scale of omics data necessitate sophisticated analytical tools for meaningful insights. This review delves into the application of artificial intelligence algorithms, particularly machine learning and deep learning, as promising approaches for deciphering complex omics data in plant defense research. The overview encompasses key omics techniques and addresses the challenges and limitations inherent in current AI-assisted omics approaches. Moreover, it contemplates potential future directions in this dynamic field. In summary, AI-assisted omics techniques present a robust toolkit, enabling a profound understanding of the molecular foundations of plant defense and paving the way for more effective crop protection strategies amidst climate change and emerging diseases.

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Section: Application Of Ai In Plant Omics Against Stressmentioning

confidence: 99%

A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions

Murmu,

Sinha,

Chaurasia

et al. 2024

Front. Plant Sci.

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“…Deep learning methods have also emerged as an outstanding category in the field of biology, leveraging the power of neural networks to tackle complex challenges. These approaches have attracted significant attention due to their ability to discern the underlying structure of scRNA-seq data and mitigate batch effects through intricate nonlinear transformations [ 13 ]. For instance, MMD-ResNet [ 14 ] employs a residual neural network to minimize distribution discrepancies among datasets.…”

Section: Introductionmentioning

confidence: 99%

Integration of scRNA-seq data by disentangled representation learning with condition domain adaptation

Liu,

Qian,

et al. 2024

BMC Bioinformatics

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Background The integration of single-cell RNA sequencing data from multiple experimental batches and diverse biological conditions holds significant importance in the study of cellular heterogeneity. Results To expedite the exploration of systematic disparities under various biological contexts, we propose a scRNA-seq integration method called scDisco, which involves a domain-adaptive decoupling representation learning strategy for the integration of dissimilar single-cell RNA data. It constructs a condition-specific domain-adaptive network founded on variational autoencoders. scDisco not only effectively reduces batch effects but also successfully disentangles biological effects and condition-specific effects, and further augmenting condition-specific representations through the utilization of condition-specific Domain-Specific Batch Normalization layers. This enhancement enables the identification of genes specific to particular conditions. The effectiveness and robustness of scDisco as an integration method were analyzed using both simulated and real datasets, and the results demonstrate that scDisco can yield high-quality visualizations and quantitative outcomes. Furthermore, scDisco has been validated using real datasets, affirming its proficiency in cell clustering quality, retaining batch-specific cell types and identifying condition-specific genes. Conclusion scDisco is an effective integration method based on variational autoencoders, which improves analytical tasks of reducing batch effects, cell clustering, retaining batch-specific cell types and identifying condition-specific genes.

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“…An important problem in scRNAseq research is the identification of cell types that can assist researchers to conduct further analyses such as distinguishing diseased cells from healthy cells. There are two main ways to annotate cells: (i) unsupervised learning, that is using cell clustering techniques and then finding marker genes specific to a cluster and annotate cells belonging to that cluster as per the ontological functions of their genes [2]; and (ii) supervised or semi-supervised learning techniques where we are given cell-samples with true cell-types (or labels), perform modelfitting, machine-learning or deep-learning techniques and perform label-transfer on unseen cell-samples [3]. Clustering techniques are unsupervised-they annotate cell types based on the underlying structure of the dataset and no knowledge of ground truth cell types is required in creating a model.…”

Section: Introductionmentioning

confidence: 99%

Attention-Based Graph Neural Network for Label Propagation in Single-Cell Omics

Bhadani

Chen

2023

Genes

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Single-cell data analysis has been at forefront of development in biology and medicine since sequencing data have been made available. An important challenge in single-cell data analysis is the identification of cell types. Several methods have been proposed for cell-type identification. However, these methods do not capture the higher-order topological relationship between different samples. In this work, we propose an attention-based graph neural network that captures the higher-order topological relationship between different samples and performs transductive learning for predicting cell types. The evaluation of our method on both simulation and publicly available datasets demonstrates the superiority of our method, scAGN, in terms of prediction accuracy. In addition, our method works best for highly sparse datasets in terms of F1 score, precision score, recall score, and Matthew’s correlation coefficients as well. Further, our method’s runtime complexity is consistently faster compared to other methods.

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Machine learning for single-cell genomics data analysis

Cited by 21 publications

References 84 publications

A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions

A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions

Integration of scRNA-seq data by disentangled representation learning with condition domain adaptation

Attention-Based Graph Neural Network for Label Propagation in Single-Cell Omics

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