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A test metric for assessing single-cell RNA-seq batch correction.

Maren Büttner1, Zhichao Miao2,3, F Alexander Wolf1

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We developed k-nearest-neighbor batch-effect test (kBET) to quantify batch effects in single-cell transcriptomics data. kBET reliably assesses batch correction, preserving biological variability for accurate cell population analysis.

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Single-cell transcriptomics enables exploration of cellular heterogeneity.
  • Batch effects are a common challenge in genomics experiments, impacting data integration and interpretation.
  • Current methods for evaluating batch-effect correction, like visual inspection of low-dimensional embeddings, are imprecise.

Purpose of the Study:

  • To introduce k-nearest-neighbor batch-effect test (kBET), a user-friendly and robust method for quantifying batch effects.
  • To evaluate the effectiveness of common batch-correction and normalization approaches using kBET.
  • To assess the ability of kBET to distinguish biological variability from batch effects in complex datasets.

Main Methods:

  • Development and application of the k-nearest-neighbor batch-effect test (kBET).
  • Assessment of standard batch-regression and normalization techniques.
  • Analysis of peripheral blood mononuclear cells (PBMCs) from healthy donors.

Main Results:

  • kBET provides a sensitive and quantitative measure of batch effects.
  • The study demonstrates kBET's utility in evaluating batch-correction methods.
  • kBET successfully differentiated cell-type-specific variability from population proportion changes in PBMC data.

Conclusions:

  • kBET offers a reliable approach for quantifying batch effects in single-cell transcriptomics.
  • Accurate batch-effect assessment is crucial for robust data integration in large-scale projects like the Human Cell Atlas.
  • kBET facilitates the reliable analysis of biological variability within heterogeneous cell populations.