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Updated: Jun 23, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

JADE: Joint Alignment and Deep Embedding for Multi-Slice Spatial Transcriptomics.

Yuanchuan Guo1, Jun S Liu2, Huimin Cheng3

  • 1Department of Statistics, Harvard University.

Advances in Neural Information Processing Systems
|June 22, 2026
PubMed
Summary
This summary is machine-generated.

Joint Alignment and Deep Embedding (JADE) integrates multiple spatial transcriptomics slices by simultaneously aligning them and learning shared gene expression patterns. This novel framework improves tissue reconstruction and identifies consistent biological signals across datasets.

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Last Updated: Jun 23, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
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Methods to Enable Spatial Transcriptomics of Bone Tissues
07:43

Methods to Enable Spatial Transcriptomics of Bone Tissues

Published on: May 3, 2024

Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Spatially resolved transcriptomics (SRT) generates gene expression data within tissue context.
  • Analyzing multiple SRT slices is crucial for reconstructing tissue architecture and identifying consistent biological patterns.
  • Existing methods struggle with physical distortions, technical variability, and batch effects in multi-slice SRT data.

Purpose of the Study:

  • To develop a unified computational framework for joint analysis of multi-slice SRT data.
  • To simultaneously address spatial alignment and feature integration across tissue slices.
  • To overcome challenges posed by distortions, variability, and batch effects in multi-slice SRT.

Main Methods:

  • Introduced Joint Alignment and Deep Embedding for multi-slice SRT (JADE), a novel computational framework.
  • Employed a roundtrip framework alternating between alignment and embedding refinement.
  • Utilized attention mechanisms to dynamically weight embedding dimensions for robust alignment.

Main Results:

  • JADE jointly optimizes spatial alignment and representation learning in a shared latent space.
  • Demonstrated superior performance of JADE over existing methods on human DLPFC and axolotl brain datasets.
  • Achieved robust multi-slice integration by bridging spatial alignment and feature integration.

Conclusions:

  • JADE offers a scalable and accurate solution for cross-slice analysis of SRT data.
  • The framework enables improved tissue structure reconstruction and identification of consistent spatial gene expression patterns.
  • JADE represents a significant advancement in integrated analysis of multi-slice spatial transcriptomics.