> For the complete documentation index, see [llms.txt](https://help.connected.illumina.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.connected.illumina.com/dragen-stratamap/dragen-spatial-transcriptome/outputs/results-folder/global-and-local-coordinates.md).

# Global and Local Coordinates

### Overview

When working with spatial transcriptomics data, it is crucial to understand the difference between **local** and **global** coordinate systems:

* **Local Coordinates**: Coordinates relative to an individual sample/region (0,0 starts at the sample's top-left corner).
* **Global Coordinates**: Coordinates relative to the entire substrate (flow cell) that contains all samples.

### Coordinate System Summary

<table><thead><tr><th width="418">Data Type</th><th>Coordinate System</th></tr></thead><tbody><tr><td>Registered images (OME-TIFF)</td><td><strong>Local</strong> (sample-specific)</td></tr><tr><td>Cell/nuclei masks (TIFF)</td><td><strong>Local</strong> (sample-specific)</td></tr><tr><td>Transcript data (barcodes.tsv.gz)</td><td><strong>Global</strong> (substrate-wide)</td></tr><tr><td>Cell contour coordinates (CSV)</td><td><strong>Global</strong> (substrate-wide)</td></tr></tbody></table>

<figure><img src="/files/YGhn7iY6IuBhIzIC7JLb" alt=""><figcaption></figcaption></figure>

### Why Two Coordinate Systems?

The substrate (flow cell) can contain multiple samples, each positioned at different locations. Global coordinates allow all transcripts from all samples to be referenced in a single coordinate space, while local coordinates make it easier to work with individual sample images and masks.

***

### 1. Transcript Coordinates (Global Space)

Transcript coordinates are always in **global substrate coordinate space**.

#### Raw Transcripts/Grid-Binned Data/Cell-Binned Data (`barcodes.tsv.gz`)

<pre><code>Format: 
sbc:Y:X (in micrometers)
bin10:Y:X (in micrometers)
cell&#x3C;ID>:Y:X (in micrometers)

<strong>Example: cell100003:6441:3653
</strong>  - cell100003 = cell ID (matches cell mask files)
  - 6441 = Y-coordinate of cell centroid (µm)
  - 3653 = X-coordinate of cell centroid (µm)
</code></pre>

### 2. Image Coordinates (Local Space)

Registered OME-TIFF files contain multiple image planes in **local sample coordinate space**:

1. **Original-resolution H\&E image** (1 pixel = variable µm, depends on microscope)
2. **1 µm/pixel H\&E image** (1 pixel = 1 µm)
3. **Tissue mask** (1 pixel = 1 µm)

#### Global Positioning Information

Each sample has a global position on the substrate stored as:

* **Global\_top**: Y-coordinate offset (µm)
* **Global\_left**: X-coordinate offset (µm)

These values define where the sample's local (0,0) is positioned in global substrate space.

***

### 3. Cell/Nuclei Mask Coordinates

Cell and nuclei mask files come in two formats:

#### TIFF Masks (Local Space)

* Format: TIFF with pixel intensities = cell IDs
* Resolution: 1 µm/pixel
* Coordinate system: **Local** to the sample

#### Contour CSV Files (Global Space)

* `*nuclei_contour_coords.csv`
* `*Expanded_5um_cell_contour_coords.csv`
* Format: 3 columns (cell-ID, X-coordinate µm, Y-coordinate µm)
* Coordinate system: **Global** substrate space

***

### How to Extract Global Coordinates

#### Method 1: From CSV Stats File (Recommended)

The easiest way to get global coordinates is from the stats CSV file.

**File location:**

```
intermediate_results/summary/[RunName]_all_stats.csv
or
intermediate_results/stats/sample_prep_stats_sample.csv
```

The columns of interest are Global\_top and Global\_left.

#### Method 2: From OME-TIFF Metadata

Global coordinates are also embedded in the OME-TIFF metadata:

**Python code using tifffile:**

```python
import tifffile
from ome_types import from_xml

# Read OME-TIFF
ome_tiff_path = "results/15WF_Rep2/15WF_Rep2_registered.ome.tiff"

with tifffile.TiffFile(ome_tiff_path) as tif:
    ome_xml = tif.ome_metadata
    ome = from_xml(ome_xml)
    
    # Get first image metadata
    if ome.images:
        image = ome.images[0]
        if image.pixels and image.pixels.planes:
            # Get position from first plane
            plane = image.pixels.planes[0]
            global_left = plane.position_x  # X-offset (µm)
            global_top = plane.position_y   # Y-offset (µm)
            
            print(f"Sample: {image.name}")
            print(f"Global Top (Y-offset): {global_top} µm")
            print(f"Global Left (X-offset): {global_left} µm")
```

**Installation requirements:**

```bash
pip install tifffile ome-types
```


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