Interactive Dendrograms: The R Packages idendro and idendr0

Sieger, Tomáš; Hurley, Catherine B.; Fišer, Karel; Beleites, Claudia

doi:10.18637/jss.v076.i10

“…To assess whether inferred concentrations provide similar or disparate classification of samples, we clustered samples using complete linkage hierarchical clustering based on Euclidean distances (21) by inferred and absolute concentrations (Figure S3) . We compared the resulting dendrograms using the entanglement coefficient from the dendextend package in R (24), where a value of 1 corresponds to complete discordance and a value of 0 indicates perfect alignment. The two dendrograms were found to be in agreement, with a low entanglement coefficient 0.11.…”

Section: Resultsmentioning

confidence: 99%

“…We tested concordance between pairs of dendrograms using the entanglement coefficient found in the dendextend package in R (24). To calculate the coefficient, first all the samples are numbered in the order they appear for each tree.…”

Section: Matherials and Methodsmentioning

confidence: 99%

Complementing 16S rRNA gene amplicon sequencing with estimates of total bacterial load to infer absolute species concentrations in the vaginal microbiome

Boshier

¹

,

Srinivasan

²

,

Lopez

³

et al. 2019

Preprint

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Whereas 16S rRNA gene amplicon sequencing quantifies relative abundances of bacterial taxa, variation in total bacterial load between samples restricts its ability to reflect absolute concentration of individual species. Quantitative PCR (qPCR) can quantify individual species, but it is not practical to develop a suite of qPCR assays for every bacterium present in a diverse sample. We analyzed 1320 samples from 20 women with a history of frequent bacterial vaginosis, who self-collected vaginal swabs daily over 60 days. We inferred bacterial concentrations by taking the product of species relative abundance (assessed by 16S rRNA gene amplicon sequencing) and total bacterial load (measured by broad-range 16S rRNA gene qPCR). Log10-converted inferred concentrations correlated with targeted qPCR (r = 0. 935, p<2.2e-16) for seven key bacterial species. The mean inferred concentration error varied across bacteria, with rarer bacterial vaginosis-associated bacteria associated with larger errors. 92% of errors >0.5 log10 occurred when relative abundance was <10%. Many errors occurred during early bacterial expansion or late contraction. When relative abundance of a species is >10%, inferred concentrations are reliable proxies for targeted qPCR. However, targeted qPCR is required to capture bacteria at low relative abundance, particularly with BV-associated bacteria during the early onset of bacterial vaginosis.

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“…We constructed the dendrograms for clustering analysis by complete linkage hierarchical clustering of species abundance and/or concentration based on Euclidean distance between all sample pairs. We tested concordance between pairs of dendrograms using the entanglement coefficient found in the dendextend package in R (20). To calculate the coefficient, all of the samples are first numbered in the order they appear for each tree.…”

Section: Methodsmentioning

confidence: 99%

“…S5). We compared the resulting dendrograms using the entanglement coefficient from the dendextend package in R (20), where a value of 1 corresponds to complete discordance and a value of 0 indicates perfect alignment. The two dendrograms were found to be in agreement, with a low entanglement coefficient 0.11.…”

Section: Figmentioning

confidence: 99%

Complementing 16S rRNA Gene Amplicon Sequencing with Total Bacterial Load To Infer Absolute Species Concentrations in the Vaginal Microbiome

Boshier

¹

,

Srinivasan

²

,

Lopez

³

et al. 2020

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Whereas 16S rRNA gene amplicon sequencing quantifies relative abundances of bacterial taxa, variation in total bacterial load between samples restricts its ability to reflect absolute concentrations of individual bacterial species. Quantitative PCR (qPCR) can quantify individual species, but it is not practical to develop a suite of qPCR assays for every bacterium present in a diverse sample. We sought to determine the accuracy of an inferred measure of bacterial concentration using total bacterial load and relative abundance. We analyzed 1,320 samples from 20 women with a history of frequent bacterial vaginosis who self-collected vaginal swabs daily over 60 days. We inferred bacterial concentrations by taking the product of species relative abundance (assessed by 16S rRNA gene amplicon sequencing) and bacterial load (measured by broad-range 16S rRNA gene qPCR). Log10-converted inferred concentrations correlated with targeted qPCR (r = 0. 935, P < 2.2e–16) for seven key bacterial species. The mean inferred concentration error varied across bacteria, with rarer bacteria associated with larger errors. A total of 92% of the >0.5-log10 errors occurred when the relative abundance was <10%. Many errors occurred during early bacterial expansion from or late contraction to low abundance. When the relative abundance of a species is >10%, inferred concentrations are reliable proxies for targeted qPCR in the vaginal microbiome. However, targeted qPCR is required to capture bacteria at low relative abundance and is preferable for characterizing growth and decay kinetics of single species. IMPORTANCE Microbiome studies primarily use 16S rRNA gene amplicon sequencing to assess the relative abundance of bacterial taxa in a community. However, these measurements do not accurately reflect absolute taxon concentrations. We sought to determine whether the product of species’ relative abundance and total bacterial load measured by broad-range qPCR is an accurate proxy for individual species’ concentrations, as measured by taxon-specific qPCR assays. Overall, the inferred bacterial concentrations were a reasonable proxy of species-specific qPCR values, particularly when bacteria are present at a higher relative abundance. This approach offers an opportunity to assess the concentrations of bacterial species and how they change in a community over time without developing individual qPCR assays for each taxon.

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“…The next evolutionary step has been to create interactive cluster heatmaps, and several solutions are already available. However, these solutions, such as the idendro R package (Sieger et al, 2017), are often focused on providing an interactive output that can be explored only on the researcher's personal computer. Some solutions do exist for creating shareable interactive heatmaps.…”

Section: Introductionmentioning

confidence: 99%

heatmaply: an R package for creating interactive cluster heatmaps for online publishing

Galili

¹

,

O’Callaghan

²

,

Savage

³

et al. 2017

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Summary heatmaply is an R package for easily creating interactive cluster heatmaps that can be shared online as a stand-alone HTML file. Interactivity includes a tooltip display of values when hovering over cells, as well as the ability to zoom in to specific sections of the figure from the data matrix, the side dendrograms, or annotated labels. Thanks to the synergistic relationship between heatmaply and other R packages, the user is empowered by a refined control over the statistical and visual aspects of the heatmap layout.Availability and implementationThe heatmaply package is available under the GPL-2 Open Source license. It comes with a detailed vignette, and is freely available from: http://cran.r-project.org/package=heatmaply.Supplementary information Supplementary data are available at Bioinformatics online.

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Interactive Dendrograms: The R Packages idendro and idendr0

Cited by 8 publications

References 3 publications

Complementing 16S rRNA gene amplicon sequencing with estimates of total bacterial load to infer absolute species concentrations in the vaginal microbiome

Complementing 16S rRNA gene amplicon sequencing with estimates of total bacterial load to infer absolute species concentrations in the vaginal microbiome

Complementing 16S rRNA Gene Amplicon Sequencing with Total Bacterial Load To Infer Absolute Species Concentrations in the Vaginal Microbiome

heatmaply: an R package for creating interactive cluster heatmaps for online publishing

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