SSA: a novel method for Single-cell and Spatial transcriptomics Alignment

14 pages•Published: July 12, 2024

Abstract

Single-cell RNA sequencing (scRNA-seq) provides expression profiles of individual cells but fails to preserve crucial spatial information. On the other hand, Spatial Transcrip- tomics technologies are able to analyze specific regions within tissue sections, but lack of the capability to examine in single-cell resolution. To overcome these issues, we present Single-cell and Spatial transcriptomics Alignment (SSA), a novel technique that employs an optimal transport algorithm to assign individual cells from a scRNA-seq atlas to their spa- tial locations in actual tissue based on their expression profiles. SSA has demonstrated su- perior performance compared to existing methods SpaOTsc, Tangram, Seurat and DistMap using 10 semi-simulated datasets generated from a high-resolution spatial transcriptomics human breast cancer dataset with 100,064 cells. This advancement provides a refined tool for researchers to delve deeper in understanding of the relationship between cellular spatial organization and gene expression.

Keyphrases: alignment, scrna seq, sequencing, spatial transcriptomics

In: Hisham Al-Mubaid, Tamer Aldwairi and Oliver Eulenstein (editors). Proceedings of the 16th International Conference on Bioinformatics and Computational Biology (BICOB-2024), vol 101, pages 25-38.

Links:	https://easychair.org/publications/paper/dhf2
	https://doi.org/10.29007/9cr1

BibTeX entry

@inproceedings{BICOB-2024:SSA_novel_method_Single,
  author    = {Bang Tran and Dao Tran and Tin Nguyen},
  title     = {SSA: a novel method for Single-cell and Spatial transcriptomics Alignment},
  booktitle = {Proceedings of the 16th International Conference on Bioinformatics and Computational Biology (BICOB-2024)},
  editor    = {Hisham Al-Mubaid and Tamer Aldwairi and Oliver Eulenstein},
  series    = {EPiC Series in Computing},
  volume    = {101},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/dhf2},
  doi       = {10.29007/9cr1},
  pages     = {25-38},
  year      = {2024}}

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