Martin Treppner, PhD
Martin Treppner, PhD
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The performance of deep generative models for learning joint embeddings of single-cell multi-omics data
Recent extensions of single-cell studies to multiple data modalities raise new questions regarding experimental design. For example, …
Eva Brombacher
,
Maren Hackenberg
,
Clemens Kreutz
,
Harald Binder
,
Martin Treppner
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Code
Designing Single Cell RNA-Sequencing Experiments for Learning Latent Representations
To investigate the complexity arising from single-cell RNA-sequencing (scRNA-seq) data, researchers increasingly resort to deep …
Martin Treppner
,
Stefan Haug
,
Anna Köttgen
,
Harald Binder
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Code
Interpretable generative deep learning: an illustration with single cell gene expression data
Deep generative models can learn the underlying structure, such as pathways or gene programs, from omics data. We provide an …
Martin Treppner
,
Harald Binder
,
Moritz Hess
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Code
Synthetic single cell RNA sequencing data from small pilot studies using deep generative models
Deep generative models, such as variational autoencoders (VAEs) or deep Boltzmann machines (DBMs), can generate an arbitrary number of …
Martin Treppner
,
Adrián Salas-Bastos
,
Moritz Hess
,
Stefan Lenz
,
Tanja Vogel
,
Harald Binder
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