Dataset Information

Mitigating the adverse impact of batch effects in sample pattern detection.

ABSTRACT:

Motivation

It is well known that batch effects exist in RNA-seq data and other profiling data. Although some methods do a good job adjusting for batch effects by modifying the data matrices, it is still difficult to remove the batch effects entirely. The remaining batch effect can cause artifacts in the detection of patterns in the data.

Results

In this study, we consider the batch effect issue in the pattern detection among the samples, such as clustering, dimension reduction and construction of networks between subjects. Instead of adjusting the original data matrices, we design an adaptive method to directly adjust the dissimilarity matrix between samples. In simulation studies, the method achieved better results recovering true underlying clusters, compared to the leading batch effect adjustment method ComBat. In real data analysis, the method effectively corrected distance matrices and improved the performance of clustering algorithms.

Availability and implementation

The R package is available at: https://github.com/tengfei-emory/QuantNorm.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Fei T

PROVIDER: S-EPMC6061843 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Mitigating the adverse impact of batch effects in sample pattern detection.

Fei Teng T Zhang Tengjiao T Shi Weiyang W Yu Tianwei T

Bioinformatics (Oxford, England) 20180801 15

<h4>Motivation</h4>It is well known that batch effects exist in RNA-seq data and other profiling data. Although some methods do a good job adjusting for batch effects by modifying the data matrices, it is still difficult to remove the batch effects entirely. The remaining batch effect can cause artifacts in the detection of patterns in the data.<h4>Results</h4>In this study, we consider the batch effect issue in the pattern detection among the samples, such as clustering, dimension reduction and ...[more]

PMID: 29506177

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Dataset Information

Mitigating the adverse impact of batch effects in sample pattern detection.

Motivation

Results

Availability and implementation

Supplementary information

Publications

Mitigating the adverse impact of batch effects in sample pattern detection.

Similar Datasets

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