Evidence map›Paper›PMID 38866720›Full record

ArticleFEMS microbiology ecology2024

The microgeo: an R package rapidly displays the biogeography of soil microbial community traits on maps.

Chaonan Li, Chi Liu, Hankang Li, Haijun Liao, Lin Xu, Minjie Yao, Xiangzhen Li

Abstract read
In one paragraph

Article in FEMS microbiology ecology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Chaonan LiEcological Security and Protection Key Laboratory of Sichuan Province, Mianyang Normal University, Mianyang 621000, China.
Chi LiuEngineering Research Center of Soil Remediation of Fujian Province University, College of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Hankang LiDepartment of Electrical Engineering and Computer Science, University of California, Irvine, CA 92697, United States.
Haijun LiaoEcological Security and Protection Key Laboratory of Sichuan Province, Mianyang Normal University, Mianyang 621000, China.
Lin XuNational Forestry and Grassland Administration Key Laboratory of Forest Resources Conservation and Ecological Safety on the Upper Reaches of the Yangtze River & Forestry Ecological Engineering in the Upper Reaches of the Yangtze River Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China.
Minjie YaoEngineering Research Center of Soil Remediation of Fujian Province University, College of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou 350002, China.ORCID 0000-0001-7501-0519
Xiangzhen LiEngineering Research Center of Soil Remediation of Fujian Province University, College of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou 350002, China.ORCID 0000-0001-6499-0179

Funding

Mianyang Normal University QD2021A37National Natural Science Foundation of China 32071548
6 · The paper itself

Abstract

Many R packages provide statistical approaches for elucidating the diversity of soil microbes, yet they still struggle to visualize microbial traits on a geographical map. This creates challenges in interpreting microbial biogeography on a regional scale, especially when the spatial scale is large or the distribution of sampling sites is uneven. Here, we developed a lightweight, flexible, and user-friendly R package called microgeo. This package integrates many functions involved in reading, manipulating, and visualizing geographical boundary data; downloading spatial datasets; and calculating microbial traits and rendering them onto a geographical map using grid-based visualization, spatial interpolation, or machine learning. Using this R package, users can visualize any trait calculated by microgeo or other tools on a map and can analyze microbiome data in conjunction with metadata derived from a geographical map. In contrast to other R packages that statistically analyze microbiome data, microgeo provides more-intuitive approaches in illustrating the biogeography of soil microbes on a large geographical scale, serving as an important supplement to statistically driven comparisons and facilitating the biogeographic analysis of publicly accessible microbiome data at a large spatial scale in a more convenient and efficient manner. The microgeo R package can be installed from the Gitee (https://gitee.com/bioape/microgeo) and GitHub (https://github.com/ChaonanLi/microgeo) repositories. Detailed tutorials for the microgeo R package are available at https://chaonanli.github.io/microgeo.

Indexed as

MicrobiotaSoftwareSoil MicrobiologyBacteriaPhylogeographybiogeographic analysisgeographical mapgrid-based visualizationmachine learningsoil microbesspatial interpolation

Identifiers

PMID38866720
PMCPMC11212663

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.