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scGIST: gene panel design for spatial transcriptomics with prioritized gene sets.

Mashrur Ahmed YafiMd Hasibul Husain HishamFrancisco GrisantiJames F MartinAtif RahmanMd Abul Hassan Samee
Published in: Genome biology (2024)
A critical challenge of single-cell spatial transcriptomics (sc-ST) technologies is their panel size. Being based on fluorescence in situ hybridization, they are typically limited to panels of about a thousand genes. This constrains researchers to build panels from only the marker genes of different cell types and forgo other genes of interest, e.g., genes encoding ligand-receptor complexes or those in specific pathways. We propose scGIST, a constrained feature selection tool that designs sc-ST panels prioritizing user-specified genes without compromising cell type detection accuracy. We demonstrate scGIST's efficacy in diverse use cases, highlighting it as a valuable addition to sc-ST's algorithmic toolbox.
Keyphrases
  • single cell
  • genome wide
  • genome wide identification
  • bioinformatics analysis
  • genome wide analysis
  • rna seq
  • transcription factor
  • machine learning
  • bone marrow
  • label free
  • loop mediated isothermal amplification