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Exploring public cancer gene expression signatures across bulk, single-cell and spatial transcriptomics data with signifinder Bioconductor package

  • 0Department of Biology, University of Padua, Padua 35121, Italy.
Nar Genomics and Bioinformatics +

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October 4, 2024

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October 4, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

Signifinder is a new R package that helps researchers analyze cancer gene expression data from bulk, single-cell, and spatial samples. It aids in understanding tumor complexity and improving cancer research.

Area Of Science

  • Oncology
  • Bioinformatics
  • Genomics

Background

  • Gene-expression signatures are vital for cancer research, aiding in mechanism understanding, subtype definition, prognosis prediction, and therapy efficacy assessment.
  • Recent transcriptomic technologies like single-cell RNA sequencing and spatial transcriptomics reveal tumor cellular heterogeneity, requiring advanced computational tools.

Purpose Of The Study

  • To introduce signifinder, a novel R Bioconductor package for analyzing cancer transcriptional signatures across diverse transcriptomic data types.
  • To provide a streamlined framework for collecting and utilizing cancer signatures in bulk, single-cell, and spatial transcriptomics.

Main Methods

  • Implementation of signifinder as an R Bioconductor package.
  • Leveraging publicly available, curated cancer transcriptional signatures.
  • Demonstration of utility through three distinct case studies using bulk, single-cell, and spatial transcriptomic data.

Main Results

  • Signifinder facilitates the assessment of tumor characteristics, including hallmark processes, therapy responses, and tumor microenvironment features.
  • Case studies illustrate the application and insights gained from transcriptional signatures in various high-resolution transcriptomic analyses.
  • The package enables cell-resolution transcriptional signature analysis in oncology.

Conclusions

  • Signifinder offers a comprehensive framework for interpreting high-resolution cancer transcriptomic data.
  • The package addresses the complexity of tumor heterogeneity and advances cancer data analysis.
  • It enhances the utility of transcriptional signatures in understanding cancer biology and improving patient outcomes.