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Quantification and visualization of cis -regulatory dynamics in single-cell multi-omics data with TREASMO.

Chaozhong LiuLinhua WangZhandong Liu
Published in: NAR genomics and bioinformatics (2024)
Recent advances in single-cell multi-omics technologies have provided unprecedented insights into regulatory processes. We introduce TREASMO, a versatile Python package designed to quantify and visualize transcriptional regulatory dynamics in single-cell multi-omics datasets. TREASMO has four modules, spanning data preparation, correlation quantification, downstream analysis and visualization, enabling comprehensive dataset exploration. By introducing a novel single-cell gene-peak correlation strength index, TREASMO facilitates accurate identification of regulatory changes at single-cell resolution. Validation on a hematopoietic stem and progenitor cell dataset showcases TREASMO's capacity in quantifying the gene-peak correlation strength at the single-cell level, identifying regulatory markers and discovering temporal regulatory patterns along the trajectory.
Keyphrases
  • single cell
  • rna seq
  • transcription factor
  • high throughput
  • electronic health record
  • genome wide
  • copy number
  • gene expression
  • dna methylation
  • high resolution
  • oxidative stress
  • data analysis