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Benchmark of cellular deconvolution methods using a multi-assay reference dataset from postmortem human prefrontal

Louise A Huuki-Myers1, Kelsey D Montgomery1, Sang Ho Kwon1,2

  • 1Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA.

Biorxiv : the Preprint Server for Biology
|February 26, 2024
PubMed
Summary
This summary is machine-generated.

Accurate cell type deconvolution from bulk RNA sequencing data requires robust computational methods. This study evaluated six algorithms using a multi-assay dataset, finding Bisque and hspe to be the most accurate and reliable across different RNA extraction and library preparation methods.

Keywords:
DeconvolutionRNA-seqRNAScopebenchmarkhuman brainimmunofluorescencemulti-assaysmFISHsnRNA-seqtranscriptomics

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Area of Science:

  • Neuroscience
  • Genomics
  • Bioinformatics

Background:

  • Cellular deconvolution of bulk RNA sequencing (RNA-seq) data is crucial for estimating cell type composition in complex tissues like the human brain.
  • Existing computational deconvolution methods have limitations due to a lack of integrated datasets with orthogonal measurements and evaluations across varied RNA extraction and library preparation techniques.
  • Performance benchmarks have primarily used simulated or pseudobulked data, not fully reflecting real-world biological variability.

Purpose of the Study:

  • To develop and validate computational deconvolution algorithms using a comprehensive multi-assay dataset from human brain tissue.
  • To evaluate the impact of different RNA extraction methods and library preparation types on deconvolution accuracy.
  • To compare the performance of six deconvolution algorithms against orthogonal cell type proportion measurements.

Main Methods:

  • Generation of a multi-assay dataset from postmortem human dorsolateral prefrontal cortex (DLPFC), including spatially-resolved transcriptomics, single-nuclei RNA sequencing (snRNA-seq), and bulk RNA-seq across six library/extraction combinations.
  • Application of the Mean Ratio method (DeconvoBuddies R package) for selecting cell type marker genes.
  • Evaluation of six computational deconvolution algorithms and comparison of their predicted cell type proportions with RNAScope/Immunofluorescence (RNAScope/IF) measurements.

Main Results:

  • The Bisque and hspe deconvolution algorithms demonstrated the highest accuracy and robustness across different RNA library types and extraction methods.
  • The study identified cell size differences, differential marker gene quantification across RNA libraries, and reference snRNA-seq composition variability as key factors impacting deconvolution accuracy.
  • Orthogonal validation using RNAScope/IF provided a reliable benchmark for assessing computational deconvolution performance.

Conclusions:

  • Bisque and hspe are recommended as the most accurate and robust computational deconvolution methods for bulk RNA-seq data, particularly in heterogeneous tissues like the brain.
  • The findings highlight the critical need to consider RNA extraction, library preparation, and reference dataset quality when performing and interpreting cell type deconvolution.
  • This multi-assay dataset provides a valuable resource for future development and benchmarking of improved deconvolution algorithms.