TransST: Transfer Learning Embedded Spatial Factor Modeling of Spatial Transcriptomics Data
Background: Spatial transcriptomics have emerged as a powerful tool in biomedical research because of its ability to capture both the spatial contexts and abundance of the complete RNA transcript profile in organs of interest. However, limitations of the technology such as the relatively low resolution and comparatively insufficient sequencing depth make it difficult to reliably extract real biological signals from these data. To alleviate this challenge, we propose a novel transfer learning framework, referred to as TransST, to adaptively leverage the cell-labeled information from external sources in inferring cell-level heterogeneity of a target spatial transcriptomics data. Results: Applications in several real studies as well as a number of simulation settings show that our approach significantly improves existing techniques. For example, in the breast cancer study, TransST successfully identifies five biologically meaningful cell clusters, including the two subgroups of cancer in situ and invasive cancer; in addition, only TransST is able to separate the adipose tissues from the connective issues among all the studied methods. Conclusions: In summary, the proposed method TransST is both effective and robust in identifying cell subclusters and detecting corresponding driving biomarkers in spatial transcriptomics data.
Code (1)
Tasks
Transfer LearningSimilar Papers 제목 키워드 기반
Joint Spatial-Temporal and Appearance Modeling with Transformer for Multiple Object Tracking
The recent trend in multiple object tracking (MOT) is heading towards leveraging deep learning to boost the tracking performance. In this paper, we propose a novel solution named TransSTAM, which leverages Transformer to…
DecoderMultiple Object TrackingObject TrackingSSUMamba: Spatial-Spectral Selective State Space Model for Hyperspectral Image Denoising
Denoising is a crucial preprocessing step for hyperspectral images (HSIs) due to noise arising from intra-imaging mechanisms and environmental factors. Long-range spatial-spectral correlation modeling is beneficial for H…
Computational EfficiencyDenoisingHyperspectral Image DenoisingImage Denoising+24DStyleGaussian: Zero-shot 4D Style Transfer with Gaussian Splatting
3D neural style transfer has gained significant attention for its potential to provide user-friendly stylization with spatial consistency. However, existing 3D style transfer methods often fall short in terms of inferenc…
Style TransferA Transferable Intersection Reconstruction Network for Traffic Speed Prediction
Traffic speed prediction is the key to many valuable applications, and it is also a challenging task because of its various influencing factors. Recent work attempts to obtain more information through various hybrid mode…
PredictionParaphrasing Complex Network: Network Compression via Factor Transfer
Many researchers have sought ways of model compression to reduce the size of a deep neural network (DNN) with minimal performance degradation in order to use DNNs in embedded systems. Among the model compression methods,…
Model CompressionTransfer Learning