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X Demographics
Mendeley readers
Attention Score in Context
| Title |
coda4microbiome: compositional data analysis for microbiome cross-sectional and longitudinal studies
|
|---|---|
| Published in |
BMC Bioinformatics, March 2023
|
| DOI | 10.1186/s12859-023-05205-3 |
| Pubmed ID | |
| Authors |
M. Luz Calle, Meritxell Pujolassos, Antoni Susin |
| Abstract |
One of the main challenges of microbiome analysis is its compositional nature that if ignored can lead to spurious results. Addressing the compositional structure of microbiome data is particularly critical in longitudinal studies where abundances measured at different times can correspond to different sub-compositions. We developed coda4microbiome, a new R package for analyzing microbiome data within the Compositional Data Analysis (CoDA) framework in both, cross-sectional and longitudinal studies. The aim of coda4microbiome is prediction, more specifically, the method is designed to identify a model (microbial signature) containing the minimum number of features with the maximum predictive power. The algorithm relies on the analysis of log-ratios between pairs of components and variable selection is addressed through penalized regression on the "all-pairs log-ratio model", the model containing all possible pairwise log-ratios. For longitudinal data, the algorithm infers dynamic microbial signatures by performing penalized regression over the summary of the log-ratio trajectories (the area under these trajectories). In both, cross-sectional and longitudinal studies, the inferred microbial signature is expressed as the (weighted) balance between two groups of taxa, those that contribute positively to the microbial signature and those that contribute negatively. The package provides several graphical representations that facilitate the interpretation of the analysis and the identified microbial signatures. We illustrate the new method with data from a Crohn's disease study (cross-sectional data) and on the developing microbiome of infants (longitudinal data). coda4microbiome is a new algorithm for identification of microbial signatures in both, cross-sectional and longitudinal studies. The algorithm is implemented as an R package that is available at CRAN ( https://cran.r-project.org/web/packages/coda4microbiome/ ) and is accompanied with a vignette with a detailed description of the functions. The website of the project contains several tutorials: https://malucalle.github.io/coda4microbiome/. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 25% |
| Sweden | 1 | 25% |
| United Kingdom | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 50% |
| Scientists | 2 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 50 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 14 | 28% |
| Student > Ph. D. Student | 7 | 14% |
| Student > Master | 4 | 8% |
| Student > Bachelor | 3 | 6% |
| Unspecified | 3 | 6% |
| Other | 7 | 14% |
| Unknown | 12 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 11 | 22% |
| Biochemistry, Genetics and Molecular Biology | 8 | 16% |
| Environmental Science | 4 | 8% |
| Unspecified | 3 | 6% |
| Engineering | 3 | 6% |
| Other | 6 | 12% |
| Unknown | 15 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 March 2023.
All research outputs
#16,145,006
of 23,947,581 outputs
Outputs from BMC Bioinformatics
#5,504
of 7,467 outputs
Outputs of similar age
#233,057
of 422,841 outputs
Outputs of similar age from BMC Bioinformatics
#105
of 137 outputs
Altmetric has tracked 23,947,581 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,467 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 422,841 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 137 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.