Based on the above framework, we further explore the influence of both spectral and spatial domains on the robustness of the model against adversarial attacks. Specifically, a weighted fusion of spectral transformer and spatial self-attention (WFSS) is designed to achieve the multi-scale fusion of...
Spatial transcriptomics using combinatorial fluorescence spectral and lifetime encoding, imaging and analysis. Nat Commun. 2022;13(1):169. CAS PubMed PubMed Central Google Scholar Vickovic S, Lötstedt B, Klughammer J, Mages S, Segerstolpe Å, Rozenblatt-Rosen O, et al. SM-omics is ...
dropout events and spectral overlap may occur more frequently, which may affect the accuracy of target identification. To address this issue, some methods have integrated smFISH with expansion microscopy (ExM), which physically expands the tissue sample and increases the RNA ...
Splane employs a graph convolutional network (GCN) approach44,45 and an adversarial learning algorithm46 to identify spatial domains by jointly analyzing multiple ST slices (Fig. 1c, Methods). First, for each ST slice, Splane calculates an adjacency matrix of cells/spots based on their distance...
SpatialDE uses Gaussian process regression in which it can detect genes across regions or multiple conditions but is computationally intensive and cannot identify domains. SpaGCN uses a graph convolutional network (GCN) and provides both gene and domain level spatial analyses, but tissue structures iden...
. Finally, the compact form of GCN is defined as: (7)H=D˜−12A˜D˜−12XW, where X∈RN×F is the input matrix, W∈RF×F′ is the parameter and H∈RN×F′ is the convolved matrix. F and F′ are the dimensions of the input and the output, respectively. Note that ...
spectral features, which are more predominant in the sub band levels of the image in addition to the spatial, semantic as well as channel details. This helps to include more finer details of each object, for example, the spatial orientation of the objects, colour details etc. In addition to...
In summary, our ablation studies highlight the intricate interplay among TCN, Temporal Attention, Self Attention, and GCN within the DSAM architecture. The findings underscore the necessity for a holistic integration of these components to effectively capture dynamic connectivity patterns, showcasing the...
SpatialDE uses Gaussian process regression in which it can detect genes across regions or multiple conditions but is computationally intensive and cannot identify domains. SpaGCN uses a graph convolutional network (GCN) and provides both gene and domain level spatial analyses, but tissue structures iden...
2018) alternated between convolutional neural networks (CNN) and spectral graph convolution networks (GCN) (Defferrard et al. 2016). DCRNN (Li et al. 2018) used GRU and diffusion convolution. Notice that similar the spatiotemporal analysis has also been applied to problems such as prediction of...