Unifying single-cell annotations based on the Cell Ontology

Sheng, Wang; Pisco, Angela Oliveira; Karkanias, Jim; Altman, Russ B.

doi:10.1101/810234

Cited by 22 publications

(20 citation statements)

References 48 publications

(50 reference statements)

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“…In order to combine the two matrices into a single aggregated query, we "humanized" the mouse expression matrix by mapping mouse genes to their orthologous human genes. This mapping was computed using the biomaRt R package 47 , mapping mgi_symbol from the mmusculus_gene_ensembl database to hgnc_symbol from the hsapien_gene_ensembl database. We represented this map as a matrix, with mouse genes as rows, human genes as columns, and values in {0,1} assigned to denote whether a mouse gene maps to a human gene.…”

Section: Constructing the Pancreas Query With Mouse And Humanmentioning

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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Section: Constructing the Pancreas Query With Mouse And Humanmentioning

confidence: 99%

Efficient and precise single-cell reference atlas mapping with Symphony

Kang

Nathan

Zhang

et al. 2020

Preprint

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“…Such methods often tend to use class embeddings [46] and try to leverage prior knowledge on similarities between the classes. A similar approach has been applied in predicting novel cell types from gene expression data using Cell Ontology [22] embeddings [47]. Such approaches can additionally be useful for describing new terms that are occasionally added to the ontology, even before they accumulate many annotations.…”

Section: Prediction Methodsmentioning

confidence: 99%

Automatic Gene Function Prediction in the 2020’s

Makrodimitris

Ham

Reinders

2020

Genes

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The current rate at which new DNA and protein sequences are being generated is too fast to experimentally discover the functions of those sequences, emphasizing the need for accurate Automatic Function Prediction (AFP) methods. AFP has been an active and growing research field for decades and has made considerable progress in that time. However, it is certainly not solved. In this paper, we describe challenges that the AFP field still has to overcome in the future to increase its applicability. The challenges we consider are how to: (1) include condition-specific functional annotation, (2) predict functions for non-model species, (3) include new informative data sources, (4) deal with the biases of Gene Ontology (GO) annotations, and (5) maximally exploit the GO to obtain performance gains. We also provide recommendations for addressing those challenges, by adapting (1) the way we represent proteins and genes, (2) the way we represent gene functions, and (3) the algorithms that perform the prediction from gene to function. Together, we show that AFP is still a vibrant research area that can benefit from continuing advances in machine learning with which AFP in the 2020s can again take a large step forward reinforcing the power of computational biology.

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“…Generalized cell type prediction within an ontology adjusts for annotation coarseness A core difficulty for deploying predictive models for cell type labels based on single-cell RNA-seq is that the changing universe of cell types is redefined and new types discovered as part of cellular atlas efforts 12 . We address this issue by defining versioned cell type universes, which are sets of leave nodes of a cell ontology that correspond to the most fine grained cell types that are characterized within a given tissue.…”

Section: Sfaira Versions Decentralized Parametric Models To Allow Repmentioning

confidence: 99%

Sfaira accelerates data and model reuse in single cell genomics

Fischer

Dony

Koenig

et al. 2020

Preprint

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Exploratory analysis of single-cell RNA-seq data sets is currently based on statistical and machine learning models that are adapted to each new data set from scratch. A typical analysis workflow includes a choice of dimensionality reduction, selection of clustering parameters, and mapping of prior annotation. These steps typically require several iterations and can take up significant time in many single-cell RNA-seq projects. Here, we introduce sfaira, which is a single-cell data and model zoo which houses data sets as well as pre-trained models. The data zoo is designed to facilitate the fast and easy contribution of data sets, interfacing to a large community of data providers. Sfaira currently includes 233 data sets across 45 organs and 3.1 million cells in both human and mouse. Using these data sets we have trained eight different example model classes, such as autoencoders and logistic cell type predictors: The infrastructure of sfaira is model agnostic and allows training und usage of many previously published models. Sfaira directly aids in exploratory data analysis by replacing embedding and cell type annotation workflows with end-to-end pre-trained parametric models. As further example use cases for sfaira, we demonstrate the extraction of gene-centric data statistics across many tissues, improved usage of cell type labels at different levels of coarseness, and an application for learning interpretable models through data regularization on extremely diverse data sets.

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Unifying single-cell annotations based on the Cell Ontology

Cited by 22 publications

References 48 publications

Efficient and precise single-cell reference atlas mapping with Symphony

Efficient and precise single-cell reference atlas mapping with Symphony

Automatic Gene Function Prediction in the 2020’s

Sfaira accelerates data and model reuse in single cell genomics

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