In:
PLOS ONE, Public Library of Science (PLoS), Vol. 18, No. 2 ( 2023-2-3), p. e0281315-
Abstract:
Recent progress in Single-Cell Genomics has produced different library protocols and techniques for molecular profiling. We formulate a unifying, data-driven, integrative, and predictive methodology for different libraries, samples, and paired-unpaired data modalities. Our design of scAEGAN includes an autoencoder (AE) network integrated with adversarial learning by a cycleGAN (cGAN) network. The AE learns a low-dimensional embedding of each condition, whereas the cGAN learns a non-linear mapping between the AE representations. We evaluate scAEGAN using simulated data and real scRNA-seq datasets, different library preparations (Fluidigm C1, CelSeq, CelSeq2, SmartSeq), and several data modalities as paired scRNA-seq and scATAC-seq. The scAEGAN outperforms Seurat3 in library integration, is more robust against data sparsity, and beats Seurat 4 in integrating paired data from the same cell. Furthermore, in predicting one data modality from another, scAEGAN outperforms Babel. We conclude that scAEGAN surpasses current state-of-the-art methods and unifies integration and prediction challenges.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0281315
DOI:
10.1371/journal.pone.0281315.g001
DOI:
10.1371/journal.pone.0281315.g002
DOI:
10.1371/journal.pone.0281315.g003
DOI:
10.1371/journal.pone.0281315.g004
DOI:
10.1371/journal.pone.0281315.t001
DOI:
10.1371/journal.pone.0281315.s001
DOI:
10.1371/journal.pone.0281315.s002
DOI:
10.1371/journal.pone.0281315.s003
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2023
detail.hit.zdb_id:
2267670-3
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