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Adjusting a Traverse

Adjusting a Traverse

In the site survey of a four-sided traverse, internal angles are essential to ensure geometric accuracy. The survey revealed that the sum of the measured internal angles was 359 degrees and 48 minutes, which is 12 minutes less than the expected 360 degrees. This discrepancy signals an error likely arising from measurement inaccuracies during the fieldwork.To rectify this error, the adjustment process involved distributing the 12-minute shortfall equally across the four internal angles. By...

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Related Experiment Video

Updated: Jun 15, 2026

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Published on: May 16, 2025

STalign: Alignment of spatial transcriptomics data using diffeomorphic metric mapping.

Kalen Clifton^1,2, Manjari Anant^1,3, Gohta Aihara^1,2

¹Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA.

Nature Communications

|December 8, 2023

Summary

This summary is machine-generated.

STalign aligns spatial transcriptomics (ST) datasets, overcoming challenges in comparing gene expression across tissues and technologies. This method improves spatial correspondence and enables detailed analysis of anatomical structures.

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Related Experiment Videos

Last Updated: Jun 15, 2026

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

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Published on: May 16, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

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Published on: July 6, 2022

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

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Published on: June 27, 2020

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Spatial transcriptomics (ST) technologies offer high-throughput gene expression analysis in tissue sections.
Comparing ST data across different sections, samples, and technologies presents significant challenges due to variations and distortions.

Purpose of the Study:

To develop a novel computational tool, STalign, for accurate alignment of ST datasets.
To enable robust comparisons of gene expression data across diverse ST experiments and platforms.

Main Methods:

Developed STalign using diffeomorphic metric mapping to handle non-linear distortions and partially matched tissue sections.
Applied STalign for intra- and inter-technology dataset alignment.
Integrated STalign with a 3D common coordinate framework for anatomical referencing.

Main Results:

STalign demonstrated superior gene expression and cell-type correspondence compared to traditional affine alignment methods.
Successful alignment of mouse brain ST datasets to the Allen Brain Atlas 3D common coordinate framework.
Facilitated lifting of brain region annotations and analysis of compositional heterogeneity.

Conclusions:

STalign provides a robust solution for aligning and integrating diverse spatial transcriptomics datasets.
The tool enhances the ability to perform cross-platform comparisons and anatomical analyses.
STalign is an open-source toolkit facilitating broader adoption in spatial biology research.