STAligner: Integrating spatial transcriptomics data across d

technologies and developmental stages. Nat Comput Sci 3， please see the installation steps in The use of the mclust algorithm requires the rpy2 package (Python) and the mclust package (R). See https://pypi.org/project/rpy2/ and https://cran.r-project.org/web/packages/mclust/index.html for detail. Install STAligner. python setup . py build python setup . py install Citation Zhou， X.， S. Integrating spatial transcriptomics data across different conditions， and constructs the spot triplets based on current embeddings to guide the alignment process by attracting similar spots and discriminating dissimilar spots across slices. STAligner introduces the triplet loss to update the spot embedding to reduce the distance from the anchor to positive spot。

Dong， K. Zhang。

disease conditions (III) and consecutive slices of a tissue for 3D slice alignment (IV). Installation First clone the repository. git clone https : // github . com / zhoux85 / STAligner . git cd STAligner - main It’s recommended to create a separate conda environment for running STAligner: #create an environment called env_STAligner conda create - n env_STAligner python = 3.8 #activate your environment conda activate env_STAligner Install all the required packages. pip install - r requiements . txt The torch-geometric library is also required。

developmental (embryonic) stages (II)， 894–906 (2023). https://doi.org/10.1038/s43588-023-00528-w ， and developmental stages Overview of STAligner a . STAligner first normalizes the expression profiles for all spots and constructs a spatial neighbor network using the spatial coordinates. STAligner further employs a graph attention auto-encoder neural network to extract spatially aware embedding， and increase the distance from the anchor to negative spot. The triplet construction and auto-encoder training are optimized iteratively until batch-corrected embeddings are generated. b . STAligner can be applied to integrate ST datasets to achieve alignment and simultaneous identification of spatial domains from different biological samples in (a)， STAligner: Integrating spatial transcriptomics data across different conditions，。

technological platforms (I)， technologies。