README.md
In yiluheihei/microbiomeMarker: microbiome biomarker analysis toolkit

microbiomeMarker

microbiomeMarker is still under development, your suggestion and contribution will be highly appreciated. If you think this project is helpful to you, you can give this project a :star:.

The aim of this package is to build a unified toolbox in R for microbiome biomarker discovery by integrating existing widely used differential analysis methods.

Install the package from Bioconductor directly:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("microbiomeMarker")

Or install the development version of the package from Github.

if (!requireNamespace("remotes", quietly=TRUE))
  install.packages("remotes")
remotes::install_github("yiluheihei/microbiomeMarker")

For more details on how to use microbiomeMarker, please see the help page or website of our package.

Kindly cite as follows:

Yang Cao, Qingyang Dong, Dan Wang, Pengcheng Zhang, Ying Liu, Chao Niu, microbiomeMarker: an R/Bioconductor package for microbiome marker identification and visualization. Bioinformatics, 2022, btac438. doi: 10.1093/bioinformatics/btac438

Please cite the corresponding methods paper too:

LEfSe: Segata, Nicola, Jacques Izard, et al. 2011. Metagenomic Biomarker Discovery and Explanation. Genome Biology 12 (6): 1–18. doi: 10.1186/gb-2011-12-6-r60
metagenomeSeq: Paulson, Joseph N, O Colin Stine, et al. 2013. Differential Abundance Analysis for Microbial Marker-Gene Surveys. Nature Methods 10 (12): 1200–1202. doi: 10.1038/nmeth.2658
ANCOM: Mandal, Siddhartha, Will Van Treuren, et al. 2015. Analysis of Composition of Microbiomes: A Novel Method for Studying Microbial Composition. Microbial Ecology in Health and Disease 26 (1): 27663. doi: 10.3402/mehd.v26.27663
ANCOMBC: Lin, Huang, and Shyamal Das Peddada. 2020. Analysis of Compositions of Microbiomes with Bias Correction. Nature Communications 11 (1): 1–11. doi: 10.1038/s41522-020-00160-w
ALDEx2: Fernandes, Andrew D, Jennifer Ns Reid, et al. 2014. Unifying the Analysis of High-Throughput Sequencing Datasets: Characterizing Rna-Seq, 16S rRNA Gene Sequencing and Selective Growth Experiments by Compositional Data Analysis. Genome Biology 15(2): 1–17. doi: 10.1186/2049-2618-2-15
edgeR: Robinson, Mark D, Davis J McCarthy, and Gordon K Smyth. 2010. EdgeR: A Bioconductor Package for Differential Expression Analysis of Digital Gene Expression Data. Bioinformatics 26 (1): 139–40. doi: 10.1093/bioinformatics/btp616
DESeq2: Love, Michael I, Wolfgang Huber, and Simon Anders. 2014. Moderated Estimation of Fold Change and Dispersion for Rna-Seq Data with Deseq2. Genome Biology 15 (12): 1–21. doi: 10.1186/s13059-014-0550-8
limma-voom: Law, Charity W, Yunshun Chen, et al. 2014. Voom: Precision Weights Unlock Linear Model Analysis Tools for Rna-Seq Read Counts. Genome biology, 15(2), 1-17. doi: 10.1186/gb-2014-15-2-r29

Jorge F, Froissard C, Dheilly N M, et al. Bacterial community dynamics following antibiotic exposure in a trematode parasite. International journal for parasitology, 2022, 52(5): 265-274. https://doi.org/10.1016/j.ijpara.2021.11.006
Ghosh A, Thakur M, Sharma L K, et al. Linking gut microbiome with the feeding behavior of the Arunachal macaque (Macaca munzala). Scientific reports, 2021, 11(1): 1-10. https://doi.org/10.1038/s41598-021-01316-0
Doi R, Wu Y, Kawai Y, et al. Transition and regulation mechanism of bacteria biota in Kishu saba-narezushi (mackerel narezushi) during its fermentation step. Journal of bioscience and bioengineering, 2021, 132(6): 606-612. https://doi.org/10.1016/j.jbiosc.2021.09.002
Nielsen K R, Ingham A C, Midjord J, et al. Similar Gut Bacterial Composition Between Patients With Ulcerative Colitis and Healthy Controls in a High Incidence Population: A Cross-sectional Study of the Faroe Islands IBD Cohort. Inflammatory Bowel Diseases. https://doi.org/10.1093/ibd/izab355
Prevel R, Enaud R, Orieux A, et al. Gut bacteriobiota and mycobiota are both associated with Day-28 mortality among critically ill patients. Critical Care, 2022, 26(1): 1-9. https://doi.org/10.1186/s13054-022-03980-8
Tandon K, Chiou Y J, Yu S P, et al. Microbiome restructuring: dominant coral bacterium Endozoicomonas species display differential adaptive capabilities to environmental changes[J]. bioRxiv, 2021. https://doi.org/10.1101/2021.10.31.466697
Dai D, Zhu J, Sun C, et al. GMrepo v2: a curated human gut microbiome database with special focus on disease markers and cross-dataset comparison. Nucleic acids research, 2022, 50(D1): D777-D784. https://doi.org/10.1093/nar/gkab1019
Molinero N, Taladrid D, Zorraquín-Peña I, et al. Ulcerative Colitis Seems to Imply Oral Microbiome Dysbiosis. Current Issues in Molecular Biology, 2022, 44(4): 1513-1527. https://doi.org/10.3390/cimb44040103
Ricci F, Tandon K, Black J R, et al. Host Traits and Phylogeny Contribute to Shaping Coral-Bacterial Symbioses[J]. Msystems, 2022, 7(2): e00044-22. https://doi.org/10.1128/msystems.00044-22
Chavarria K A, Saltonstall K, Vinda J, et al. Land use influences stream bacterial communities in lowland tropical watersheds. Scientific reports, 2021, 11(1): 1-12. https://doi.org/10.1038/s41598-021-01193-7
Lu H, Gao N L, Tong F, et al. Alterations of the Human Lung and Gut Microbiomes in Non-Small Cell Lung Carcinomas and Distant Metastasis. Microbiology spectrum, 2021, 9(3): e00802-21. https://doi.org/10.1128/Spectrum.00802-21
Ingham A C, Kielsen K, Mordhorst H, et al. Microbiota long-term dynamics and prediction of acute graft-versus-host disease in pediatric allogeneic stem cell transplantation[J]. Microbiome, 2021, 9(1): 1-28. https://doi.org/10.1186/s40168-021-01100-2
Wang R, Cao S, Bashir M E H, et al. Microbial metabolite butyrate-prodrug polymeric micelles promote gut health and treat food allergies. bioRxiv,
1. https://doi.org/10.1101/2022.05.01.490224
Shanmugam G, Lee SH, Jeon J. EzMAP: Easy Microbiome Analysis Platform. BMC bioinformatics. 2021 Dec;22(1):1-0. https://doi.org/10.1186/s12859-021-04106-7
Altaib H, Nakamura K, Abe M, et al. Differences in the concentration of the fecal neurotransmitters GABA and glutamate are associated with microbial composition among healthy human subjects. Microorganisms. 2021. Feb;9(2):378. https://doi.org/10.3390/microorganisms9020378
Künstner A, Aherrahrou R, Hirose M, et al. Effect of Differences in the Microbiome of Cyp17a1-Deficient Mice on Atherosclerotic Background. Cells. 2021 Jun;10(6):1292. https://doi.org/10.3390/cells10061292
Ingham AC, Urth TR, Sieber RN, et al. Dynamics of the human nasal microbiota and Staphylococcus aureus CC398 carriage in pig truck drivers across one workweek. Applied and Environmental Microbiology. 2021 Jun 30:AEM-01225. https://doi.org/10.1128/AEM.01225-21
Shibata T, Nakagawa M, Coleman HN, et al. Evaluation of DNA extraction protocols from liquid-based cytology specimens for studying cervical microbiota. Plos one 16, no. 8 2021. https://doi.org/10.1371/journal.pone.0237556

If you have any question, please file an issue on the issue tracker following the instructions in the issue template:

Please briefly describe your problem, what output actually happened, and what output you expect.

Please provide a minimal reproducible example. For more details on how to make a great minimal reproducible example, see https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example and https://www.tidyverse.org/help/#reprex.

Brief description of the problem

# insert minimal reprducible example here

We thanks all the developers of the methods integrated into our package.

lefse python script, The main lefse code are translated from lefse python script,
microbiomeViz, cladogram visualization of lefse is modified from microbiomeViz.
phyloseq, the main data structures used in microbiomeMarker are from or inherit from phyloseq-class in package phyloseq.
MicrobiotaProcess, function import_dada2() and import_qiime2() are modified from the MicrobiotaProcess::import_dada2().
qiime2R, import_qiime2() are refer to the functions in qiime2R.