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alignment-stalign

Align cell-level spatial transcriptomics slices with STalign using landmark pairs and LDDMM.

3|Updated Apr 3, 2026
Search Tags:#alignment#spatial-transcriptomics#landmarks#STalign#LDDMM#cell-level
One-click install
npx skills add https://github.com/chenyhvvvv/STAT-agent --skill alignment-stalign
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: alignment-stalign
Source: https://github.com/chenyhvvvv/STAT-agent/tree/main/stat_agent/skills/alignment-STalign
Command: npx skills add https://github.com/chenyhvvvv/STAT-agent --skill alignment-stalign

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Align two cell-level spatial transcriptomics slices using landmark points to transform one slice into the coordinate space of the other, enabling direct comparison and integration of spatial data.

Core Features & Use Cases

  • Align two slices with user-provided landmark pairs to initialize a transform and refine with LDDMM.
  • Produce a new aligned slice with transformed coordinates while preserving the original slices.
  • Use case: compare spatial distributions across samples by aligning slices before downstream analyses.

Quick Start

Provide the source slice, target slice, and at least three landmark point pairs to generate an aligned slice.

Frequently Asked Questions about alignment-stalign

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I align two spatial transcriptomics slices using landmark points?▼

To align spatial transcriptomics slices, provide a source slice ID, target slice ID, and at least three landmark point pairs to compute an initial affine transformation and refine it with LDDMM.

Does landmark-guided alignment preserve the original coordinates of spatial transcriptomics slices?▼

Landmark-guided alignment preserves original slice coordinates. It computes and outputs a new aligned slice containing the transformed cell-level coordinates while keeping the source and target slices intact.

Can I align cell-level spatial transcriptomics data with fewer than three landmark pairs?▼

Aligning cell-level spatial transcriptomics data requires at least three landmark point pairs. This minimum is necessary to compute the initial affine transformation before LDDMM refinement.

What is the best way to compare spatial distributions across different cell-level samples?▼

The best way to compare spatial distributions is to align slices first. Transforming one slice into the coordinate space of another via STalign enables direct comparison and integration of spatial data.

How does LDDMM refine the initial affine transformation in spatial slice alignment?▼

LDDMM refines spatial slice alignment by using user-provided landmark pairs to calculate an initial affine transform, then applying a diffeomorphic mapping to accurately warp cell-level coordinates.

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