APCoA: Covariate adjusted principal coordinates analysis

Yushu Shi; Liangliang Zhang; Kim Anh Do; Christine B. Peterson; Robert R. Jenq

doi:10.1093/bioinformatics/btaa276

APCoA: Covariate adjusted principal coordinates analysis

Yushu Shi, Liangliang Zhang, Kim Anh Do, Christine B. Peterson, Robert R. Jenq

Research output: Contribution to journal › Article › peer-review

15 Scopus citations

Abstract

In fields, such as ecology, microbiology and genomics, non-Euclidean distances are widely applied to describe pairwise dissimilarity between samples. Given these pairwise distances, principal coordinates analysis is commonly used to construct a visualization of the data. However, confounding covariates can make patterns related to the scientific question of interest difficult to observe. We provide adjusted principal coordinates analysis as an easy-to-use tool, available as both an R package and a Shiny app, to improve data visualization in this context, enabling enhanced presentation of the effects of interest.

Original language	English (US)
Pages (from-to)	4099-4101
Number of pages	3
Journal	Bioinformatics
Volume	36
Issue number	13
DOIs	https://doi.org/10.1093/bioinformatics/btaa276
State	Published - Jul 1 2020

ASJC Scopus subject areas

Statistics and Probability
Biochemistry
Molecular Biology
Computer Science Applications
Computational Theory and Mathematics
Computational Mathematics

MD Anderson CCSG core facilities

Biostatistics Resource Group
Microbiome Facility

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10.1093/bioinformatics/btaa276

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title = "APCoA: Covariate adjusted principal coordinates analysis",

abstract = "In fields, such as ecology, microbiology and genomics, non-Euclidean distances are widely applied to describe pairwise dissimilarity between samples. Given these pairwise distances, principal coordinates analysis is commonly used to construct a visualization of the data. However, confounding covariates can make patterns related to the scientific question of interest difficult to observe. We provide adjusted principal coordinates analysis as an easy-to-use tool, available as both an R package and a Shiny app, to improve data visualization in this context, enabling enhanced presentation of the effects of interest.",

author = "Yushu Shi and Liangliang Zhang and Do, {Kim Anh} and Peterson, {Christine B.} and Jenq, {Robert R.}",

note = "Funding Information: K.-A.D. was partially supported by the MD Anderson Moon Shot Programs, Prostate Cancer SPORE P50CA140388, NIH/NCI CCSG grant P30CA016672, CCTS 5UL1TR000371 and CPRIT RP160693 grants. C.B.P. was partially supported by the NIH/NCI CCSG grant P30CA016672 and MD Anderson Moon Shot Programs. R.R.J. was partially supported by NIH R01 HL124112 and CPRIT RR160089 grants. Funding Information: K.-A.D. was partially supported by the MD Anderson Moon Shot Programs, Prostate Cancer SPORE P50CA140388, NIH/NCI CCSG grant P30CA016672, CCTS 5UL1TR000371 and CPRIT RP160693 grants. C.B.P. Funding Information: was partially supported by the NIH/NCI CCSG grant P30CA016672 and MD Anderson Moon Shot Programs. R.R.J. was partially supported by NIH R01 HL124112 and CPRIT RR160089 grants. Publisher Copyright: {\textcopyright} 2020 The Author(s). Published by Oxford University Press. All rights reserved.",

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doi = "10.1093/bioinformatics/btaa276",

language = "English (US)",

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AU - Shi, Yushu

AU - Zhang, Liangliang

AU - Do, Kim Anh

AU - Peterson, Christine B.

AU - Jenq, Robert R.

N1 - Funding Information: K.-A.D. was partially supported by the MD Anderson Moon Shot Programs, Prostate Cancer SPORE P50CA140388, NIH/NCI CCSG grant P30CA016672, CCTS 5UL1TR000371 and CPRIT RP160693 grants. C.B.P. was partially supported by the NIH/NCI CCSG grant P30CA016672 and MD Anderson Moon Shot Programs. R.R.J. was partially supported by NIH R01 HL124112 and CPRIT RR160089 grants. Funding Information: K.-A.D. was partially supported by the MD Anderson Moon Shot Programs, Prostate Cancer SPORE P50CA140388, NIH/NCI CCSG grant P30CA016672, CCTS 5UL1TR000371 and CPRIT RP160693 grants. C.B.P. Funding Information: was partially supported by the NIH/NCI CCSG grant P30CA016672 and MD Anderson Moon Shot Programs. R.R.J. was partially supported by NIH R01 HL124112 and CPRIT RR160089 grants. Publisher Copyright: © 2020 The Author(s). Published by Oxford University Press. All rights reserved.

PY - 2020/7/1

Y1 - 2020/7/1

N2 - In fields, such as ecology, microbiology and genomics, non-Euclidean distances are widely applied to describe pairwise dissimilarity between samples. Given these pairwise distances, principal coordinates analysis is commonly used to construct a visualization of the data. However, confounding covariates can make patterns related to the scientific question of interest difficult to observe. We provide adjusted principal coordinates analysis as an easy-to-use tool, available as both an R package and a Shiny app, to improve data visualization in this context, enabling enhanced presentation of the effects of interest.

AB - In fields, such as ecology, microbiology and genomics, non-Euclidean distances are widely applied to describe pairwise dissimilarity between samples. Given these pairwise distances, principal coordinates analysis is commonly used to construct a visualization of the data. However, confounding covariates can make patterns related to the scientific question of interest difficult to observe. We provide adjusted principal coordinates analysis as an easy-to-use tool, available as both an R package and a Shiny app, to improve data visualization in this context, enabling enhanced presentation of the effects of interest.

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APCoA: Covariate adjusted principal coordinates analysis

Abstract

ASJC Scopus subject areas

MD Anderson CCSG core facilities

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