Exploring single-cell data with deep multitasking neural networks

Amodio, Matthew; Dijk, David van; Srinivasan, K.; Chen, William S.; Mohsen, Hussein; Moon, Kevin R.; Campbell, Allison M.; Zhao, Yujiao; Wang, Xiaomei; Venkataswamy, Manjunatha M.; Desai, Anita; Ravi, Vasanthapuram; Kumar, Priti; Montgomery, Ruth R.; Wolf, Guy; Krishnaswamy, Smita

doi:10.1038/s41592-019-0576-7

Cited by 232 publications

(134 citation statements)

References 59 publications

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“…One concern in applying methods based on neural networks [21,57,58,59,60] in single-cell genomics and other domains is the robustness to hyperparameters choices [61]. This concern has been addressed to some extent by recent progress in the field, proposing search algorithms based on held-out log-likelihood maximization [59].…”

Section: Discussionmentioning

confidence: 99%

“…Further effort in the use of deep generative models for applications in computational biology should come with attempts to perform model interpretation. For instance, SAUCIE [21] experimentally proposes to add an entropy regularization to a hidden layer of its denoising auto-encoder in order to infer clustering. Future principled efforts may focus on putting a suitable prior such as sparsity on neural networks weights (e.g., as in [64]).…”

Section: Discussionmentioning

confidence: 99%

“…However, each of these correction approaches has underlying modeling assumptions. For example, SAUCIE [21] and MMD ResNet [22] both propose to correct the variational distribution by adding on their objective function a non-parametric measure of distance between distributions (maximum mean discrepancy [76]). This approach specifically assumes that each dataset has the same cell type composition and the same biological signal and which is not reasonable in other cases than biological replicates.…”

Section: Supplementary Note 1 Statistical Trade-offs In Scrna-seq Harmentioning

confidence: 99%

“…Another recent line of work makes use of neural networks to learn a joint representation of multiple datasets (SAUCIE [21]) or project one dataset into another (maximum mean discrepancy [MMD] ResNet [22]). These methods rely on an explicit non-parametric measure of discrepancy between probability distributions (MMD) to match either the latent spaces or directly the gene expression values from pairs of datasets.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic Harmonization and Annotation of Single-cell Transcriptomics Data with Deep Generative Models

Lopez

Mehlman

et al. 2019

Preprint

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As single-cell transcriptomics becomes a mainstream technology, the natural next step is to integrate the accumulating data in order to achieve a common ontology of cell types and states. However, owing to various nuisance factors of variation, it is not straightforward how to compare gene expression levels across data sets and how to automatically assign cell type labels in a new data set based on existing annotations. In this manuscript, we demonstrate that our previously developed method, scVI, provides an effective and fully probabilistic approach for joint representation and analysis of cohorts of single-cell RNA-seq data sets, while accounting for uncertainty caused by biological and measurement noise. We also introduce single-cell ANnotation using Variational Inference (scANVI), a semi-supervised variant of scVI designed to leverage any available cell state annotations -for instance when only one data set in a cohort is annotated, or when only a few cells in a single data set can be labeled using marker genes. We demonstrate that scVI and scANVI compare favorably to the existing methods for data integration and cell state annotation in terms of accuracy, scalability, and adaptability to challenging settings such as a hierarchical structure of cell state labels. We further show that different from existing methods, scVI and scANVI represent the integrated datasets with a single generative model that can be directly used for any probabilistic decision making task, using differential expression as our case study. scVI and scANVI are available as open source software and can be readily used to facilitate cell state annotation and help ensure consistency and reproducibility across studies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Supplementary Note 1 Statistical Trade-offs In Scrna-seq Harmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Probabilistic Harmonization and Annotation of Single-cell Transcriptomics Data with Deep Generative Models

Lopez

Mehlman

et al. 2019

Preprint

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“…Essentially, we are now able to measure different -omic methods under different conditions and integrate them to extract out a coarse-grained understanding of biology at the single cell level. To this end, we employ a sparse autoencoder for clustering, imputing, and embedding (SAUCIE) (Amodio et al, 2019). This deep neural network architecture allows for processing many measurements, as well as the capacity to integrate measurements that have a complex non-linear relationship, such as the expression of transcripts and proteins.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Heterogeneity of Cardiac Non-myocytes and the Effect of Cell Culture with Integrative Single Cell Analysis

Davis²,

et al. 2020

Preprint

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Cardiac non-myocytes comprise a diverse and crucial cell population in the heart that plays dynamic roles in cardiac wound healing and growth. Non-myocytes broadly fall into four cell types: endothelium, fibroblasts, leukocytes, and pericytes. Here we characterize the diversity of the non-myocytes in vivo and in vitro using mass cytometry. By leveraging single-cell RNA sequencing we inform the design of a mass cytometry panel. To aid in annotation of the mass cytometry datasets, we utilize data integration with a neural network. We introduce approximately 460,000~ single cell proteomes of non-myocytes as well as 5,000~ CD31 negative single cell transcriptomes. Using our data, as well as previously reported datasets, we characterize cardiac non-myocytes with high depth in six mice, characterizing novel surface markers (CD9, CD200, Notch3, and FolR2). Further, we find that extended cell culture promotes the proliferation of CD45+CD11b+FolR2+IAIE-myeloid cells in addition to fibroblasts.

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Distribution‐Agnostic Deep Learning Enables Accurate Single‐Cell Data Recovery and Transcriptional Regulation Interpretation

Su,

Yu,

Yang

et al. 2024

Advanced Science

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Single‐cell RNA sequencing (scRNA‐seq) is a robust method for studying gene expression at the single‐cell level, but accurately quantifying genetic material is often hindered by limited mRNA capture, resulting in many missing expression values. Existing imputation methods rely on strict data assumptions, limiting their broader application, and lack reliable supervision, leading to biased signal recovery. To address these challenges, authors developed Bis, a distribution‐agnostic deep learning model for accurately recovering missing sing‐cell gene expression from multiple platforms. Bis is an optimal transport‐based autoencoder model that can capture the intricate distribution of scRNA‐seq data while addressing the characteristic sparsity by regularizing the cellular embedding space. Additionally, they propose a module using bulk RNA‐seq data to guide reconstruction and ensure expression consistency. Experimental results show Bis outperforms other models across simulated and real datasets, showcasing superiority in various downstream analyses including batch effect removal, clustering, differential expression analysis, and trajectory inference. Moreover, Bis successfully restores gene expression levels in rare cell subsets in a tumor‐matched peripheral blood dataset, revealing developmental characteristics of cytokine‐induced natural killer cells within a head and neck squamous cell carcinoma microenvironment.

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Exploring single-cell data with deep multitasking neural networks

Cited by 232 publications

References 59 publications

Probabilistic Harmonization and Annotation of Single-cell Transcriptomics Data with Deep Generative Models

Probabilistic Harmonization and Annotation of Single-cell Transcriptomics Data with Deep Generative Models

Assessing the Heterogeneity of Cardiac Non-myocytes and the Effect of Cell Culture with Integrative Single Cell Analysis

Distribution‐Agnostic Deep Learning Enables Accurate Single‐Cell Data Recovery and Transcriptional Regulation Interpretation

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