Schema: metric learning enables interpretable synthesis of heterogeneous single-cell modalities

Singh, Rohit; Hie, Brian; Narayan, Ashwin; Berger, Bonnie

doi:10.1186/s13059-021-02313-2

Cited by 32 publications

(43 citation statements)

References 55 publications

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“…Recent algorithms for the analysis of multi-modal data were developed to process paired datasets, in which both modalities have been profiled at the same cell [8, 7]. These algorithms handle multi-modal data, but lack the ability to integrate single modality datasets into the same analysis.…”

Section: Discussionmentioning

confidence: 99%

“…Given such a dataset (which are much more common than multi-modal datasets), use of multi-modal data can enable an inference of the “missing” modality and thus reach new conclusions about the diversity and regulation of cell states in a wide array of tissues, cell types and experimental or clinical settings. While some computational methods have emerged that can analyze multi-modal data in isolation [7, 8], the joint analysis of multi-modal and single-modality data necessitates the development of novel computational methods. These methods must be capable of leveraging the power of multi-modal data while accounting for the general caveats of single cell genomics data, most prominently - batch effects, limited sensitivity and noise, and taking into consideration the unique statistical properties of each modality (i.e., quantitative signal for scRNA-seq and a largely binary signal for scATAC-seq).…”

Section: Introductionmentioning

confidence: 99%

“…The emerging area of multi-modal profiling has benefited greatly from new statistical methods that jointly account for both modalities in a range of analysis tasks [7,8]. Another promising application of multi-modal assays, however, is to improve the way by which the more common and less costly single-modality datasets (profiled with scRNA-or scATAC-seq) are analyzed and interpreted.…”

Section: Introductionmentioning

confidence: 99%

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MultiVI: deep generative model for the integration of multi-modal data

Ashuach

Gabitto

Jordan³

et al. 2021

Preprint

121

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The ability to jointly profile the transcriptional and chromatin land-scape of single-cells has emerged as a powerful technique to identify cellular populations and shed light on their regulation of gene expression. Current computational methods analyze jointly profiled (paired) or individual data modalities (unpaired), but do not offer a principled method to analyze both paired and unpaired samples jointly. Here we present MultiVI, a probabilistic framework that leverages deep neural networks to jointly analyze scRNA, scATAC and multiomic (scRNA + scATAC) data. MultiVI creates an informative low-dimensional latent space that accurately reflects both chromatin and transcriptional properties of the cells even when one of the modalities is missing. MultiVI accounts for technical effects in both scRNA and scATAC-seq while correcting for batch effects in both data modalities. We use public datasets to demonstrate that MultiVI is stable, easy to use, and outperforms current approaches for the joint analysis of paired and unpaired data. MultiVI is available as an open source package, implemented in the scvi-tools frame-work: https://docs.scvi-tools.org/.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MultiVI: deep generative model for the integration of multi-modal data

Ashuach

Gabitto

Jordan³

et al. 2021

Preprint

121

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“…The advent of single-cell multimodal omics has posed additional computational challenges in extrapolating useful information from the different layers measured for each cell. This has motivated the introduction of harmonization methods for performing a joint analysis, such as clustering, to exploit the power given by all available cell modalities ( Argelaguet et al, 2020 ; Wang et al, 2020 ; Gayoso et al, 2021 ; Hao et al, 2021 ; Singh et al, 2021 ; Zuo and Chen, 2021 ). It is worth to remark that DL approaches (e.g., deep generative models) represent a significant part of harmonization methods for single-cell datasets.…”

Section: Computational Approaches To Analyze Single-cell Data Of the Tmementioning

confidence: 99%

Breaking the Immune Complexity of the Tumor Microenvironment Using Single-Cell Technologies

et al. 2022

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Tumors are not a simple aggregate of transformed cells but rather a complicated ecosystem containing various components, including infiltrating immune cells, tumor-related stromal cells, endothelial cells, soluble factors, and extracellular matrix proteins. Profiling the immune contexture of this intricate framework is now mandatory to develop more effective cancer therapies and precise immunotherapeutic approaches by identifying exact targets or predictive biomarkers, respectively. Conventional technologies are limited in reaching this goal because they lack high resolution. Recent developments in single-cell technologies, such as single-cell RNA transcriptomics, mass cytometry, and multiparameter immunofluorescence, have revolutionized the cancer immunology field, capturing the heterogeneity of tumor-infiltrating immune cells and the dynamic complexity of tenets that regulate cell networks in the tumor microenvironment. In this review, we describe some of the current single-cell technologies and computational techniques applied for immune-profiling the cancer landscape and discuss future directions of how integrating multi-omics data can guide a new “precision oncology” advancement.

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“…These follow two main frameworks: metric learning and latent variable learning. Weighted nearest neighbors (WNN) ( Hao et al , 2021 )) and Schema ( Singh et al , 2021 ) explore, respectively, nearest neighbors and quadratic programming to estimate a single distance matrix representing the integrated multimodal data. Both approaches explore efficient algorithms, but do not explicitly provide models associating molecular features to the ‘latent space’.…”

Section: Introductionmentioning

confidence: 99%

MOJITOO: a fast and universal method for integration of multimodal single-cell data

Cheng

Costa

2022

Bioinformatics

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Motivation The advent of multi-modal single-cell sequencing techniques have shed new light on molecular mechanisms by simultaneously inspecting transcriptomes, epigenomes and proteomes of the same cell. However, to date, the existing computational approaches for integration of multimodal single-cell data are either computationally expensive, require the delineation of parameters or can only be applied to particular modalities. Results Here we present a single-cell multi-modal integration method, named Multi-mOdal Joint IntegraTion of cOmpOnents (MOJITOO). MOJITOO uses canonical correlation analysis for a fast and parameter free detection of a shared representation of cells from multimodal single-cell data. Moreover, estimated canonical components can be used for interpretation, i.e. association of modality-specific molecular features with the latent space. We evaluate MOJITOO using bi- and tri-modal single-cell datasets and show that MOJITOO outperforms existing methods regarding computational requirements, preservation of original latent spaces and clustering. Availability and implementation The software, code and data for benchmarking are available at https://github.com/CostaLab/MOJITOO and https://doi.org/10.5281/zenodo.6348128. Supplementary information Supplementary data are available at Bioinformatics online.

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Schema: metric learning enables interpretable synthesis of heterogeneous single-cell modalities

Cited by 32 publications

References 55 publications

MultiVI: deep generative model for the integration of multi-modal data

MultiVI: deep generative model for the integration of multi-modal data

Breaking the Immune Complexity of the Tumor Microenvironment Using Single-Cell Technologies

MOJITOO: a fast and universal method for integration of multimodal single-cell data

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