Qihuang Zhang

CeLEry: Cell Location Recovery in Single-cell RNA Sequencing

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Q Zhang1, J Hu1, D Dai2, E Lee2, R Xiao1, M Li1

  1. Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
  2. Translational Neuropathology Research Laboratory, Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.


Single-cell RNA sequencing provides resourceful information to study the cells systematically. However, their locational information is usually unavailable. We present CeLEry, a supervised deep learning algorithm to recover the origin of tissues in assist of spatial transcriptomic data, integrating a data augmentation procedure via variational autoencoder to improve the robustness of methods in the overfitting and the data contamination. CeLEry provides a generic framework and can be implemented in multiple tasks depending on the research objectives, including the spatial coordinates discovery as well as the layer discovery. It can make use of the information of multiple tissues of spatial transcriptomics data. Thorough assessments exhibit that CeLEry achieves a leading performance compared to the state-of-art methods. We illustrated the usage of CeLEry in the discovery of neuron cell layers to study the development of Alzheimer's disease. The identified cell location information is valuable in many downstream analyses and can be indicative of the spatial organization of the tissues.


cell location recovery, single-cell RNA-seq, spatial transcriptomics, data augmentation, deep neural network, variational autoencoder, machine learning, deep learning, generation model, prediction


Really neat work and I love the acronym! Do you think this method would have applications to other brain diseases (e.g., beyond just AD) - perhaps also depression or other brain disorders?

Thanks for your comments! Yes. Our method can be applied to other brain diseases. For example, one possible extension is to look at which part of the brain will suffer the most from depression by comparing the spatial distribution of different cell types in the different disease groups (depressed vs non depressed brain).

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