Analysis of compositions of microbiomes with bias correction

Analysis of compositions of microbiomes with bias correction

Differential abundance analysis of microbiome data continues to be challenging due to data complexity. The authors propose a method which estimates the unknown sampling fractions and corrects the bias induced by their differences among samples.

Bibliographic Details
Main Authors: Huang Lin, Shyamal Das Peddada
Format: Article
Language:English
Published: Nature Publishing Group 2020-07-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-020-17041-7
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spelling doaj-3652022ed6a7403aa4db2996430cdd4c2021-07-18T11:45:08ZengNature Publishing GroupNature Communications2041-17232020-07-0111111110.1038/s41467-020-17041-7Analysis of compositions of microbiomes with bias correctionHuang Lin0Shyamal Das Peddada1Department of Biostatistics, University of PittsburghDepartment of Biostatistics, University of PittsburghDifferential abundance analysis of microbiome data continues to be challenging due to data complexity. The authors propose a method which estimates the unknown sampling fractions and corrects the bias induced by their differences among samples.https://doi.org/10.1038/s41467-020-17041-7
collection DOAJ
language English
format Article
sources DOAJ
author Huang Lin
Shyamal Das Peddada
spellingShingle Huang Lin
Shyamal Das Peddada
Analysis of compositions of microbiomes with bias correction
Nature Communications
author_facet Huang Lin
Shyamal Das Peddada
author_sort Huang Lin
title Analysis of compositions of microbiomes with bias correction
title_short Analysis of compositions of microbiomes with bias correction
title_full Analysis of compositions of microbiomes with bias correction
title_fullStr Analysis of compositions of microbiomes with bias correction
title_full_unstemmed Analysis of compositions of microbiomes with bias correction
title_sort analysis of compositions of microbiomes with bias correction
publisher Nature Publishing Group
series Nature Communications
issn 2041-1723
publishDate 2020-07-01
description Differential abundance analysis of microbiome data continues to be challenging due to data complexity. The authors propose a method which estimates the unknown sampling fractions and corrects the bias induced by their differences among samples.
url https://doi.org/10.1038/s41467-020-17041-7
work_keys_str_mv AT huanglin analysisofcompositionsofmicrobiomeswithbiascorrection
AT shyamaldaspeddada analysisofcompositionsofmicrobiomeswithbiascorrection
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