Interpretable autoencoders trained on single cell sequencing data can transfer directly to data from unseen tissues
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Interpretable autoencoders trained on single cell sequencing data can transfer directly to data from unseen tissues. / Walbech, Julie Sparholt; Kinalis, Savvas; Winther, Ole; Nielsen, Finn Cilius; Bagger, Frederik Otzen.
I: Cells, Bind 11, 85, 2022.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Interpretable autoencoders trained on single cell sequencing data can transfer directly to data from unseen tissues
AU - Walbech, Julie Sparholt
AU - Kinalis, Savvas
AU - Winther, Ole
AU - Nielsen, Finn Cilius
AU - Bagger, Frederik Otzen
N1 - Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022
Y1 - 2022
N2 - Autoencoders have been used to model single-cell mRNA-sequencing data with the purpose of denoising, visualization, data simulation, and dimensionality reduction. We, and others, have shown that autoencoders can be explainable models and interpreted in terms of biology. Here, we show that such autoencoders can generalize to the extent that they can transfer directly without additional training. In practice, we can extract biological modules, denoise, and classify data correctly from an autoencoder that was trained on a different dataset and with different cells (a foreign model). We deconvoluted the biological signal encoded in the bottleneck layer of scRNA-models using saliency maps and mapped salient features to biological pathways. Biological concepts could be associated with specific nodes and interpreted in relation to biological pathways. Even in this unsupervised framework, with no prior information about cell types or labels, the specific biological pathways deduced from the model were in line with findings in previous research. It was hypothesized that autoencoders could learn and represent meaningful biology; here, we show with a systematic experiment that this is true and even transcends the training data. This means that carefully trained autoencoders can be used to assist the interpretation of new unseen data.
AB - Autoencoders have been used to model single-cell mRNA-sequencing data with the purpose of denoising, visualization, data simulation, and dimensionality reduction. We, and others, have shown that autoencoders can be explainable models and interpreted in terms of biology. Here, we show that such autoencoders can generalize to the extent that they can transfer directly without additional training. In practice, we can extract biological modules, denoise, and classify data correctly from an autoencoder that was trained on a different dataset and with different cells (a foreign model). We deconvoluted the biological signal encoded in the bottleneck layer of scRNA-models using saliency maps and mapped salient features to biological pathways. Biological concepts could be associated with specific nodes and interpreted in relation to biological pathways. Even in this unsupervised framework, with no prior information about cell types or labels, the specific biological pathways deduced from the model were in line with findings in previous research. It was hypothesized that autoencoders could learn and represent meaningful biology; here, we show with a systematic experiment that this is true and even transcends the training data. This means that carefully trained autoencoders can be used to assist the interpretation of new unseen data.
KW - Artificial neural networks
KW - Autoencoders (AE)
KW - Deep learning
KW - Single-cell mRNA-sequencing data
KW - Transfer learning
U2 - 10.3390/cells11010085
DO - 10.3390/cells11010085
M3 - Journal article
C2 - 35011647
AN - SCOPUS:85121702045
VL - 11
JO - Cells
JF - Cells
SN - 2073-4409
M1 - 85
ER -
ID: 303674211