Multiplexed images to AnnData¶
For image-based spatial single-cell technologies, SpatialTis could help you transform your images data into AnnData. You need to prepare:
Stacked .tiff images
Cell mask image
Warning
SpatialTis CAN’T do segmentation
The file structure should look like:
Data
├── Patient1
│ ├── Sample1
│ │ ├── ROI1
│ │ ├── ROI2
│ ├──Sample2
│ │ ├── ROI1
│ │ ├── ROI2
│ └──Sample3
│ ├── ROI1
│ └── ROI2
├── Patient2
├── Patient3
└── ...
The minimum folder is the ROI folder, each contains two data files:
ROI1
├── stacked.tiff
└── Patient1_Body_ROI1_mask.tiff
One stacked image
One mask image
>>> import spatialtis as st
First, we need to specify the entry points
>>> entry = "./Data"
And then, we need to describe how the experiment is designed. The names should be corresponded to each level of the folder, if you look back to the file tree that we show before, it’s easy to understand
>>> obs_name = ['Patient', 'Sample', 'ROI']
Another information is the markers data, it needs to be stored into a dataframe. This allow you to add as many columns as you want, for example you can add an extra “channels” columns. But remember the order of your markers must align with the layers’ order in your image file. Either following the order of channels or pages, depends on the structure of your .tiff:
>>> channels = ['Dy161', 'Dy162', 'Dy164', 'Er166', ...]
>>> markers = ['CD20', 'CD8', 'CD99', 'NFkB', ...]
>>> var = pd.DataFrame({"channels":channels, "markers":markers})
Now, let’s read it out
>>> reader = read_ROIs(entry, obs_name, var, mask_pattern="mask", img_pattern="stacked")
mask_pattern allows you to tell SpatialTis which file is the mask image, in our example, file name contains “mask” will be used as mask image. The same for img_pattern, so if you have other files in the same directory, this will help SpatialTis identify which is mask image and which is data image.
Finally, we can start processing your images into anndata, the speed is related to the size of your image, in my own test an 1000*1000 ROI from IMC data takes ~15s.
>>> data = reader.to_anndata()
>>> data.write("sample.h5ad")
>>> print(data)
AnnData object with n_obs × n_vars = 152037 × 36
obs: 'Patient', 'Sample', 'ROI', 'area', 'cell_shape', 'centroid', 'eccentricity'
var: 'channels', 'markers'