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Benchmarking single-cell hashtag oligo demultiplexing methods.

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Seven hashtag oligo (HTO) demultiplexing tools were evaluated for single-cell RNA sequencing (scRNA-seq). Performance varied with HTO data quality, with some methods struggling on lower-quality datasets.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Sample multiplexing in single-cell RNA sequencing (scRNA-seq) reduces costs and batch effects.
  • Hashtag oligo (HTO) tagging allows cells from different samples to be pooled and sequenced together.
  • Accurate demultiplexing of HTO data is crucial for assigning cells to their original samples.

Purpose of the Study:

  • To critically assess and compare the performance of seven popular HTO demultiplexing tools.
  • To evaluate tool performance against a genetic 'ground truth' for HTO assignment.
  • To identify robust methods and suggest quality assessment strategies for HTO data.

Main Methods:

  • Evaluation of seven HTO demultiplexing tools: hashedDrops, HTODemux, GMM-Demux, demuxmix, deMULTIplex, BFF, and HashSolo.
  • Utilized scRNA-seq datasets with independently verified sample origin via genetic variants.
  • Assessed tool performance based on accuracy in assigning cells to samples using HTO data.

Main Results:

  • All evaluated HTO demultiplexing tools performed similarly on high-quality HTO labeling data.
  • Methods assuming a bimodal count distribution showed reduced performance on lower-quality HTO data.
  • Heuristic approaches for assessing HTO count quality were proposed.

Conclusions:

  • The choice of HTO demultiplexing tool can impact results, particularly with suboptimal HTO labeling.
  • Quality assessment of HTO counts is essential prior to or during demultiplexing.
  • Further development may be needed for robust demultiplexing in challenging HTO data scenarios.