Year: 2020
Type: article
Abstract: SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: The R package 'aPCoA' and Shiny app can be accessed at https://cran.r-project.org/web/packages/aPCoA/index.html and https://biostatistics.mdanderson.org/shinyapps/aPCoA/.
Source: Bioinformatics
Institution: The University of Texas MD Anderson Cancer Center
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: bronze
Awards: Center for Clinical and Translational Sciences (CCTS), MD Anderson Cancer Center Prostate Cancer SPORE, G293715275, Recruitment of Rising Stars, G3929693838, G4276892241, Acute Myeloid Leukemia in the Immunosuppressed Microenvironment, Protecting colonic mucus to mitigate acute intestinal graft-versus-host disease, G5059082296, G5520316217, G6368863098, G6906829653, G9000348896, Cancer Center Support Grant
OpenAlex ID: W3019918846