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.
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Nature Publishing Group
2020-07-01
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Series: | Nature Communications |
Online Access: | https://doi.org/10.1038/s41467-020-17041-7 |
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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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1721295764691353600 |