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Batch effects removal for microbiome data via conditional quantile regression.

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|September 15, 2022
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This summary is machine-generated.

Batch effects in microbiome data can obscure true signals. We developed Conditional Quantile Regression (ConQuR) to remove these effects, generating usable data for further analysis.

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Area of Science:

  • Microbiome research
  • Bioinformatics
  • Statistical modeling

Background:

  • Microbiome data often contain batch effects due to specimen processing.
  • Existing methods for genomic data are inadequate for zero-inflated, over-dispersed microbiome data.
  • Current microbiome-specific methods have limited applications in association testing or specialized designs.

Purpose of the Study:

  • To develop a novel method for mitigating batch effects in microbiome data.
  • To create a flexible approach applicable to general study designs and analytical goals.
  • To generate batch-corrected microbiome data suitable for downstream analyses.

Main Methods:

  • Conditional Quantile Regression (ConQuR) approach.
  • Utilizes a two-part quantile regression model.
  • Employs non-parametric modeling to handle complex microbial read count distributions.

Main Results:

  • ConQuR effectively removes batch effects from microbiome datasets.
  • The method preserves biologically relevant signals of interest.
  • Generated batch-removed data are suitable for subsequent analyses.

Conclusions:

  • ConQuR offers a comprehensive solution for microbiome batch effect correction.
  • The approach accommodates the unique characteristics of microbiome data.
  • Enables broader applications of microbiome data analysis beyond association testing.