About: Query to reference single-cell integration with transfer learning

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Query to reference single-cell integration with transfer learning
Creator	Misharin, Alexander Theis, Fabian Luecken, Malte Avsec, Ziga Büttner, Maren Khajavi, Matin Lotfollahi, Mohammad Naghipourfar, Mohsen
Source	BioRxiv
abstract	Large single-cell atlases are now routinely generated with the aim of serving as reference to analyse future smaller-scale studies. Yet, learning from reference data is complicated by batch effects between datasets, limited availability of computational resources, and sharing restrictions on raw data. Leveraging advances in machine learning, we propose a deep learning strategy to map query datasets on top of a reference called single-cell architectural surgery (scArches, https://github.com/theislab/scarches). It uses transfer learning and parameter optimization to enable efficient, decentralized, iterative reference building, and the contextualization of new datasets with existing references without sharing raw data. Using examples from mouse brain, pancreas, and whole organism atlases, we showcase that scArches preserves nuanced biological state information while removing batch effects in the data, despite using four orders of magnitude fewer parameters compared to de novo integration. To demonstrate mapping disease variation, we show that scArches preserves detailed COVID-19 disease variation upon reference mapping, enabling discovery of new cell identities that are unseen during training. We envision our method to facilitate collaborative projects by enabling the iterative construction, updating, sharing, and efficient use of reference atlases.
has issue date	2020-07-16(xsd:dateTime)
bibo:doi	10.1101/2020.07.16.205997
has license	biorxiv
sha1sum (hex)	7f90939656b4291602db13f90dcb96f3401ed97a
schema:url	https://doi.org/10.1101/2020.07.16.205997
resource representing a document's title	Query to reference single-cell integration with transfer learning
schema:publication	bioRxiv
resource representing a document's body	covid:7f90939656b4291602db13f90dcb96f3401ed97a#body_text
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