YMS Series #24 - Spatial transcriptomics segmentation methods

​This session of YMS - Young Meets Senior - Seminar Series, we host @Elyas Heidari who will present #segger, a fast and accurate method for cell segmentation in imaging-based spatial transcriptomics data.

​Abstract: The accurate assignment of transcripts to their cells of origin remains the Achilles heel of imaging-based spatial transcriptomics, despite being critical for nearly all downstream analyses. Current cell segmentation methods are prone to over- and under-segmentation, misassign transcripts to cells, require manual intervention, and suffer from low sensitivity and scalability. We introduce segger, a versatile graph neural network based on a heterogeneous graph representation of individual transcripts and cells, that frames cell segmentation as a transcript-to-cell link prediction task and can leverage single-cell RNA-seq information to improve transcript assignments. On multiple Xenium dataset benchmarks, segger exhibits superior sensitivity and specificity, while requiring orders of magnitude less compute time than existing methods.

​Resources:

• ​Segger Github /dpeerlab/segger

• ​Segger pre-print https://www.biorxiv.org/content/10.1101/2025.03.14.643160v1

• ​Elyas Heidari https://elihei2.github.io/

​Event organized by the Romanian Society of Bioinformatics.

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​#Bionformatics #RSBI #Science