Single cell analyses of development in the modern era

Klein, Allon M.; Treutlein, Barbara

doi:10.1242/dev.181396

Cited by 12 publications

(15 citation statements)

References 28 publications

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“…The availability of large numbers of comprehensive single-cell transcriptomics studies (Svensson, Vento-Tormo, and Teichmann 2018) is making possible the study of biological variation in unprecedented detail (Klein and Treutlein 2019). One interesting aspect of this "big data" biology consisting of a large set of measurements from many cells is that it can yield insights even after initial published analysis of individual datasets.…”

Section: Introductionmentioning

confidence: 99%

A curated database reveals trends in single-cell transcriptomics

Svensson

Beltrame

2019

Preprint

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The more than 500 single-cell transcriptomics studies that have been published to date constitute a valuable and vast resource for biological discovery. While various "atlas" projects have collated some of the associated datasets, most questions related to specific tissue types, species, or other attributes of studies require identifying papers through manual and challenging literature search. To facilitate discovery with published single-cell transcriptomics data, we have assembled a near exhaustive, manually curated database of single-cell transcriptomics studies with key information: descriptions of the type of data and technologies used, along with descriptors of the biological systems studied. Additionally, the database contains summarized information about analysis in the papers, allowing for analysis of trends in the field. As an example, we show that the number of cell types identified in scRNA-seq studies is proportional to the number of cells analysed. The database is available at www.nxn.se/single-cell-studies/gui. Tissue StudiesBrain

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Section: Introductionmentioning

confidence: 99%

A curated database reveals trends in single-cell transcriptomics

Svensson

Beltrame

2019

Preprint

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“…Then, cells are corrected by cell-specific linear factors weighted by cluster membership to remove batch-dependent effects. The full algorithm and implementation are detailed in Korsunsky et al (2019) 47 .In the scenario of mapping m query cells against n reference cells, the de novo integration strategy would model all cells as in(1), where the H subscript denotes the Harmony solution, in contrast to the Symphony model which is presented in(2). Let % 3 ∈ {0,1} ()/')×(2/%) represent the one-hot encoded design matrix assigning all cells across batches.…”

mentioning

confidence: 99%

Efficient and precise single-cell reference atlas mapping with Symphony

Kang

Nathan

Zhang

et al. 2020

Preprint

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Recent advances in single-cell technologies and integration algorithms make it possible to construct large, comprehensive reference atlases from multiple datasets encompassing many donors, studies, disease states, and sequencing platforms. Much like mapping sequencing reads to a reference genome, it is essential to be able to map new query cells onto complex, multimillion-cell reference atlases to rapidly identify relevant cell states and phenotypes. We present Symphony, a novel algorithm for building compressed, integrated reference atlases of ≥106 cells and enabling efficient query mapping within seconds. Based on a linear mixture model framework, Symphony precisely localizes query cells within a low-dimensional reference embedding without the need to reintegrate the reference cells, facilitating the downstream transfer of many types of reference-defined annotations to the query cells. We demonstrate the power of Symphony by (1) mapping a query containing multiple levels of experimental design to predict pancreatic cell types in human and mouse, (2) localizing query cells along a smooth developmental trajectory of human fetal liver hematopoiesis, and (3) harnessing a multimodal CITE-seq reference atlas to infer query surface protein expression in memory T cells. Symphony will enable the sharing of comprehensive integrated reference atlases in a convenient, portable format that powers fast, reproducible querying and downstream analyses.

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“…ADTs act as pseudo-transcripts and could be sequenced together with mRNA. CITE-seq has gained popularity in a wide range of biological and clinical studies, such as resolving tumor heterogeneity (Ortega, et al, 2017;Wagner, et al, 2019) and dissecting cell populations in complex tissues or organs (Cuevas-Diaz Duran, et al, 2017;Cui, et al, 2019;Klein and Treutlein, 2019;Landhuis, 2018;Schiller, et al, 2019). Figure 1A and 1B shows an example of cell type classification results of a peripheral blood mononuclear cells (PBMCs) CITE-seq dataset, using its RNA and surface marker (ADT) profiles separately.…”

Section: Introductionmentioning

confidence: 99%

Artificial-Cell-Type Aware Cell Type Classification in CITE-seq

Lian

Xin

et al. 2020

Preprint

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Motivation: Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq), couples the measurement of surface marker proteins with simultaneous sequencing of mRNA at single cell level, which brings accurate cell surface phenotyping to single cell transcriptomics. Unfortunately, multiplets in CITE-seq datasets create artificial cell types and complicates the automation of cell surface phenotyping. Results: We propose CITE-sort, an artificial-cell-type aware surface marker clustering method for CITE-seq. CITEsort is aware of and is robust to multiplet-induced artificial cell types. We benchmarked CITE-sort with real and simulated CITE-seq datasets and compared CITE-sort against canonical clustering methods. We show that CITEsort produces the best clustering performance across the board. CITE-sort not only accurately identifies real biological cell types but also consistently and reliably separates multiplet-induced artificial-cell-type droplet clusters from real biological-cell-type droplet clusters. In addition, CITE-sort organizes its clustering process with a binary tree, which facilitates easy interpretation and verification of its clustering result and simplifies cell type annotation with domain knowledge in CITE-seq.

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Single cell analyses of development in the modern era

Cited by 12 publications

References 28 publications

A curated database reveals trends in single-cell transcriptomics

A curated database reveals trends in single-cell transcriptomics

Efficient and precise single-cell reference atlas mapping with Symphony

Artificial-Cell-Type Aware Cell Type Classification in CITE-seq

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