scMC learns biological variation through the alignment of multiple single-cell genomics datasets

Genome biology, Journal Year: 2021, Volume and Issue: 22(1)

Published: Jan. 4, 2021

Distinguishing biological from technical variation is crucial when integrating and comparing single-cell genomics datasets across different experiments. Existing methods lack the capability in explicitly distinguishing these two variations, often leading to removal of both variations. Here, we present an integration method scMC remove while preserving intrinsic variation. learns via variance analysis subtract inferred unsupervised manner. Application simulated real RNA-seq ATAC-seq experiments demonstrates its detecting context-shared context-specific signals accurate alignment.

Language: Английский

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Methods and applications for single-cell and spatial multi-omics

Nature Reviews Genetics, Journal Year: 2023, Volume and Issue: 24(8), P. 494 - 515

Published: March 2, 2023

Language: Английский

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Best practices for single-cell analysis across modalities

Nature Reviews Genetics, Journal Year: 2023, Volume and Issue: 24(8), P. 550 - 572

Published: March 31, 2023

Language: Английский

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SARS-CoV-2 infection triggers profibrotic macrophage responses and lung fibrosis

Cell, Journal Year: 2021, Volume and Issue: 184(26), P. 6243 - 6261.e27

Published: Nov. 27, 2021

Language: Английский

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A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain

Nature, Journal Year: 2023, Volume and Issue: 624(7991), P. 317 - 332

Published: Dec. 13, 2023

The mammalian brain consists of millions to billions cells that are organized into many cell types with specific spatial distribution patterns and structural functional properties1-3. Here we report a comprehensive high-resolution transcriptomic cell-type atlas for the whole adult mouse brain. was created by combining single-cell RNA-sequencing (scRNA-seq) dataset around 7 million profiled (approximately 4.0 passing quality control), approximately 4.3 using multiplexed error-robust fluorescence in situ hybridization (MERFISH). is hierarchically 4 nested levels classification: 34 classes, 338 subclasses, 1,201 supertypes 5,322 clusters. We present an online platform, Allen Brain Cell Atlas, visualize whole-brain along MERFISH datasets. systematically analysed neuronal non-neuronal across identified high degree correspondence between identity specificity each type. results reveal unique features organization different regions-in particular, dichotomy dorsal ventral parts part contains relatively fewer yet highly divergent types, whereas more numerous closely related other. Our study also uncovered extraordinary diversity heterogeneity neurotransmitter neuropeptide expression co-expression types. Finally, found transcription factors major determinants classification combinatorial factor code defines all establishes benchmark reference foundational resource integrative investigations cellular circuit function, development evolution

Language: Английский

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Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models

Molecular Systems Biology, Journal Year: 2021, Volume and Issue: 17(1)

Published: Jan. 1, 2021

As the number of single-cell transcriptomics datasets grows, natural next step is to integrate accumulating data achieve a common ontology cell types and states. However, it not straightforward compare gene expression levels across automatically assign type labels in new dataset based on existing annotations. In this manuscript, we demonstrate that our previously developed method, scVI, provides an effective fully probabilistic approach for joint representation analysis scRNA-seq data, while accounting uncertainty caused by biological measurement noise. We also introduce ANnotation using Variational Inference (scANVI), semi-supervised variant scVI designed leverage state scANVI favorably state-of-the-art methods integration annotation terms accuracy, scalability, adaptability challenging settings. contrast methods, multiple with single generative model can be directly used downstream tasks, such as differential expression. Both are easily accessible through scvi-tools.

Language: Английский

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Mapping single-cell data to reference atlases by transfer learning

Nature Biotechnology, Journal Year: 2021, Volume and Issue: 40(1), P. 121 - 130

Published: Aug. 30, 2021

Abstract Large single-cell atlases are now routinely generated to serve as references for analysis of smaller-scale studies. Yet learning from reference data is complicated by batch effects between datasets, limited availability computational resources and sharing restrictions on raw data. Here we introduce a deep strategy mapping query datasets top called architectural surgery (scArches). scArches uses transfer parameter optimization enable efficient, decentralized, iterative building contextualization new with existing without Using examples mouse brain, pancreas, immune whole-organism atlases, show that preserves biological state information while removing effects, despite using four orders magnitude fewer parameters than de novo integration. generalizes multimodal mapping, allowing imputation missing modalities. Finally, retains coronavirus disease 2019 (COVID-19) variation when healthy reference, enabling the discovery disease-specific cell states. will facilitate collaborative projects construction, updating, efficient use atlases.

Language: Английский

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An integrated cell atlas of the lung in health and disease

Nature Medicine, Journal Year: 2023, Volume and Issue: 29(6), P. 1563 - 1577

Published: June 1, 2023

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations individual the variability present in population. Here we integrated Human Lung Cell Atlas (HLCA), combining 49 respiratory system into single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents consensus re-annotation with matching marker genes, including annotations rare previously undescribed types. Leveraging diversity individuals HLCA, identify gene modules that are associated demographic covariates such as age, sex body mass index, well changing expression along proximal-to-distal axis bronchial tree. Mapping new data to enables rapid annotation interpretation. Using reference for study disease, shared states across multiple lung diseases, SPP1

Language: Английский

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An atlas of healthy and injured cell states and niches in the human kidney

Nature, Journal Year: 2023, Volume and Issue: 619(7970), P. 585 - 594

Published: July 19, 2023

Abstract Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles interactions within tissue neighbourhoods 1 . Here we applied multiple single-cell single-nucleus assays (>400,000 nuclei or cells) spatial imaging technologies to a broad spectrum healthy reference kidneys (45 donors) diseased (48 patients). This has provided high-resolution cellular atlas 51 main types, which include rare previously undescribed populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors localizations spanning entire kidney. We also define 28 states across nephron segments interstitium that were altered in injury, encompassing cycling, adaptive (successful maladaptive repair), transitioning degenerative states. Molecular signatures permitted localization these injury using transcriptomics, while large-scale 3D analysis (around 1.2 million neighbourhoods) corresponding linkages active immune responses. These analyses defined biological pathways are relevant time-course niches, including underlying epithelial repair predicted with decline function. integrated multimodal human represents comprehensive benchmark neighbourhoods, outcome-associated publicly available interactive visualizations.

Language: Английский

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Multi-omics single-cell data integration and regulatory inference with graph-linked embedding

Nature Biotechnology, Journal Year: 2022, Volume and Issue: 40(10), P. 1458 - 1466

Published: May 2, 2022

Despite the emergence of experimental methods for simultaneous measurement multiple omics modalities in single cells, most single-cell datasets include only one modality. A major obstacle integrating data from is that different layers typically have distinct feature spaces. Here, we propose a computational framework called GLUE (graph-linked unified embedding), which bridges gap by modeling regulatory interactions across explicitly. Systematic benchmarking demonstrated more accurate, robust and scalable than state-of-the-art tools heterogeneous multi-omics data. We applied to various challenging tasks, including triple-omics integration, integrative inference human cell atlas construction over millions where was able correct previous annotations. features modular design can be flexibly extended enhanced new analysis tasks. The full package available online at https://github.com/gao-lab/GLUE .

Language: Английский

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