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BGI-Qingdao / GEM3D_toolkit

A tool to use stereo-seq GEM and ssDNA to generate single-cell and 3D spatial resolved transtriptomics data
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GEM3D_toolkit

Specifically designed for Stereo-seq platform, GEM_toolkit is a collection of scrips that cooperates with ImageJ/TrakEM2 and CellProfiler to semi-automatically preprocess single-cell or spatially resolved transtriptomics data in 2D/3D using the gene expression matrix (GEM) and ssDNA image.

GEM_toolkit also provides several handly tools for file format conversion, image or data subset, color-code gene heatmap or ssDNA image masking, ROI extraction, affine coordinate calculation, and GEM or other image visualization. Enjoy with GEM_toolkit.

Overview of this workflow :

image

Dependencies

Quick start

./GEM_toolkit.py -h

Usage : GEM_toolkit.py action [options]

Actions:

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Workflow for single slice to generate single-cell resolved data:
    prepare_registration_heatmap  generate 8bit spot-level heatmap with highlighted tracklines.
    prepare_registration_ssdna    generate 8bit close-spot-level ssDNA iamge with highlighted tracklines.
    second_registration           second round registration.
    gem_to_gemc                   convert GEM into GEMC based on cellbin result and registration results.

Workflow for multiply slices (3D mode) to generate 3D resolved coordinates:
     prepare_alignment_image       generate 8bit spot-level binary/annatation image for 3D alignment.
     apply_alignment               set 3D coordinate for GEM(C)/h5ad/ssDNA/cell.mask.

Format coverting tools:
    gem_to_h5ad                   convert GEM to h5ad by a certain binsize.
    gemc_to_h5ad                  convert GEMC to h5ad.

Affine tools:
    affine_gem                    modify the 2D coordinate in GEM(C) by user-defined affine matrix.
    affine_h5ad                   modify the 2D coordinate in GEM(C) by user-defined affine matrix.
    affine_ssdna                  affine the ssdna image based on user-defined affine matrix.
    affine_txt                    affine txt like cell.mask based on user-defined affine matrix.

Region of interest(ROI) tools:
    chop_image                    chop region of interests from whole image.
    chop_gem                      chop region of interests from GEM(C).

Mask tools:
    mask_gem                      mask GEM(C) by mask image.
    mask_h5ad                     mask h5ad data by mask image.

Visualization tools:
    draw_heatmap                  draw heatmap of expression counts in bin1 resolution with/without cellbin and with/without ssDNA.
    image_blend                   merge heatmap/annotation image with ssDNA and border image

Other tools:
    chop_paste                    chop or paste ssDNA image. This tools is useful for ultra-large ssDNA image.
    trakEM2_to_affine             covert trakEM2_matrix to standard affine matrix.
    get_xml_matrix                get matrix from trakEM2 xml file.
    split_gem                     split gem by x or y coordinate.
    merge_h5ad                    merge files of h5ad.
    gem_xy                        get xmin ymin of gem

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-h/--help               show this short usage

Frequent Q & A

Why cannot some of the valid actions be found in usage?

We renamed some action names in this new version and keep the old action names valid for backward compatibility:

prepareregistrationheatmap ==  prepare_registration_heatmap
prepareregistrationssdna == prepare_registration_ssdna
secondregistration == second_registration
gem_to_cfm == gem_to_gemc

Cite us

Please cite our GitHub url: http://github.com/BGI-Qingdao/GEM3D_toolkit directly.

The registration scripts have no update between dev branch and the old main branch. However, if you need a software version, then please cite the zendo doi DOI that we have specially released for the previous stable codes.

References

The Stereo-seq platform: Chen, et al., Cell, 2022 Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays

ImageJ/TrakEM2: Cardona1, et al., PLOS ONE, 2012 TrakEM2 Software for Neural Circuit Reconstruction

CellProfiler: DR, et al., BMC Bioinformatics, 2021 CellProfiler 4: improvements in speed, utility and usability