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Selecting cell-type deconvolution methods is challenging due to inconsistent results. This study assesses 31 methods using single-cell RNA sequencing data, proposing the CATD pipeline for efficient evaluation and streamlined deconvolution.

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Area of Science:

  • Computational biology
  • Genomics
  • Bioinformatics

Background:

  • Cell-type deconvolution infers cellular composition from bulk transcriptomic data.
  • A wide array of deconvolution methods exist, but their performance is often inconsistent, necessitating clear guidance for method selection.
  • Single-cell RNA sequencing (scRNA-seq) datasets, frequently paired with bulk expression data, offer opportunities for benchmarking deconvolution techniques.

Purpose of the Study:

  • To comprehensively assess the performance of 31 cell-type deconvolution methods.
  • To evaluate the impact of reference data and bulk-reference differences on deconvolution accuracy.
  • To introduce and validate the Critical Assessment of Transcriptomic Deconvolution (CATD) pipeline for streamlined method evaluation.

Main Methods:

  • Utilized scRNA-seq data from diverse human and mouse tissues for benchmarking.
  • Conducted simulations under various scenarios to assess method efficacy.
  • Investigated the influence of sample, study, and technology on bulk-reference discrepancies.
  • Validated results using a gold-standard dataset and proposed a consensus prediction approach.

Main Results:

  • Regression-based deconvolution methods show efficacy but are sensitive to reference selection.
  • Differences between bulk and reference samples (study, technology) impact deconvolution performance.
  • A consensus prediction method was developed and validated, offering robust proportion estimation.
  • The CATD pipeline effectively streamlines the generation of references, pseudo-bulks, and the execution of deconvolution methods.

Conclusions:

  • The study provides a comprehensive evaluation of cell-type deconvolution methods, highlighting the strengths of regression-based approaches and the importance of reference quality.
  • The proposed CATD pipeline offers a practical and efficient solution for evaluating deconvolution methods and processing numerous bulk samples.
  • This work aids researchers in selecting appropriate deconvolution tools and facilitates the rapid assessment of novel methods.