STalign: Aligned Mouse Brain Spatial Transcriptomics Data for Performance Analysis

STalign alignment results for spatial transcriptomics data from mouse brain sections. The data includes cell positions and gene counts from aligning MERFISH and Visium datasets, as well as alignments to the 3D Allen Brain Atlas common coordinate framework. Kalen Clifton of Johns Hopkins University developed the STalign Python toolkit used to generate these alignments.

Use Cases

• Benchmarking spatial data alignment algorithms based on the provided cell position mappings.
• Analyzing gene expression correspondence across aligned tissue sections mentioned in the description.
• Mapping brain region annotations from the Allen Brain Atlas to MERFISH data using the described invertible mappings.
• Comparing alignment performance between diffeomorphic and affine methods as referenced in the performance analysis.

Strengths

• Includes alignment results from multiple technologies (MERFISH and Visium) as described.
• Data was used for the performance analysis of the STalign method, showing improved correspondence over landmark-based affine alignments.
• Alignments are provided to a 3D common coordinate framework from the Allen Brain Atlas, enabling anatomical structure interrogation.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Johns Hopkins University / JEFworks-Lab

Collection Method

Generated by applying the STalign algorithm to publicly available spatial transcriptomics datasets from Vizgen and 10X Genomics.