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JIND: Joint Integration and Discrimination for Automated Single-Cell Annotation

Mohit Goyal, Guillermo Serrano, Ilan Shomorony, Mikel Hernaez, Idoia Ochoa
doi: https://doi.org/10.1101/2020.10.06.327601
Mohit Goyal
1Department of Electrical and Computer Engineering, University of Illinois, Urbana, IL 61801
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Guillermo Serrano
2Center for Applied Medical Research (CIMA), University of Navarra, Spain
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Ilan Shomorony
1Department of Electrical and Computer Engineering, University of Illinois, Urbana, IL 61801
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Mikel Hernaez
2Center for Applied Medical Research (CIMA), University of Navarra, Spain
3Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana, IL 61801
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  • For correspondence: mhernaez@unav.es
Idoia Ochoa
1Department of Electrical and Computer Engineering, University of Illinois, Urbana, IL 61801
4Department of Electrical Engineering, University of Navarra, Spain
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Abstract

Single-cell RNA-seq is a powerful tool in the study of the cellular composition of different tissues and organisms. A key step in the analysis pipeline is the annotation of cell-types based on the expression of specific marker genes. Since manual annotation is labor-intensive and does not scale to large datasets, several methods for automated cell-type annotation have been proposed based on supervised learning. However, these methods generally require feature extraction and batch alignment prior to classification, and their performance may become unreliable in the presence of cell-types with very similar transcriptomic profiles, such as differentiating cells. We propose JIND, a framework for automated cell-type identification based on neural networks that directly learns a low-dimensional representation (latent code) in which cell-types can be reliably determined. To account for batch effects, JIND performs a novel asymmetric alignment in which the transcriptomic profile of unseen cells is mapped onto the previously learned latent space, hence avoiding the need of retraining the model whenever a new dataset becomes available. JIND also learns cell-type-specific confidence thresholds to identify and reject cells that cannot be reliably classified. We show on datasets with and without batch effects that JIND classifies cells more accurately than previously proposed methods while rejecting only a small proportion of cells. Moreover, JIND batch alignment is parallelizable, being more than five or six times faster than Seurat integration. Availability: https://github.com/mohit1997/JIND.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted October 07, 2020.
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JIND: Joint Integration and Discrimination for Automated Single-Cell Annotation
Mohit Goyal, Guillermo Serrano, Ilan Shomorony, Mikel Hernaez, Idoia Ochoa
bioRxiv 2020.10.06.327601; doi: https://doi.org/10.1101/2020.10.06.327601
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JIND: Joint Integration and Discrimination for Automated Single-Cell Annotation
Mohit Goyal, Guillermo Serrano, Ilan Shomorony, Mikel Hernaez, Idoia Ochoa
bioRxiv 2020.10.06.327601; doi: https://doi.org/10.1101/2020.10.06.327601

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