Name: assisted selection of biomarkers by linear discriminant analysis effect size (lefse) in microbiome data
Uploaded: 2022-05-16T14:00:00
Description: Watch this Scientific Journal Video about Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data at JoVE.com

This work was supported by a grant from Fundamental Research Funds for the Central Public Welfare Research Institutes (TKS170205) and Foundation for Development of Science and Technology, and Tianjin Research Institute for Water Transport Engineering (TIWTE), M.O.T. (KJFZJJ170201).

NOTE: The protocol was sourced and modified from the research of Segata et al.3. The method is provided at https://bitbucket.org/biobakery/biobakery/wiki/lefse.
1. Preparation of input file for analysis
<ol>
	<li>Prepare the input file (Table 1) of LEfSe, which could be easily generated by many workflows8 or previous protocols9 with the original files (sample file and corresponding species annotation fil...

<ol>
	<li>Bolyen, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reproducible,+interactive,+scalable+and+extensible+microbiome+data+science+using+QIIME+2.">Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2.</a> Nature Biotechnology. 37 (8), 852-857 (2019).</li><li>Knight, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Best+practices+for+analysing+microbiomes.">Best practices for analysing microbiomes.</a> Nature Reviews. Microbiology. 16 (7), 410-422 (2018).</li><li>Segata, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Metagenomic+biomarker+discovery+and+explanation.">Metagenomic biomarker discovery and explanation.</a> Genome Biology. 12 (6), 60 (2011).</li><li>. Biobakery / lefse [tool] Available from: <a target="_blank" href="https://bitbucket.org/biobakery/biobakery/wiki/lefse">https://bitbucket.org/biobakery/biobakery/wiki/lefse</a> (2019)</li><li>Kruskal, W. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+nonparametric+test+for+the+several+sample+problem.">A nonparametric test for the several sample problem.</a> The Annals of Mathematical Statistics. 23 (4), 525-540 (1952).</li><li>Wilcoxon, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Individual+comparisons+by+ranking+methods.">Individual comparisons by ranking methods.</a> Biometrics Bulletin. 1 (6), 80-83 (1945).</li><li>Fisher, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+use+of+multiple+measurements+in+taxonomic+problems.">The use of multiple measurements in taxonomic problems.</a> Annals of Eugenics. 7 (1), 179-188 (1936).</li><li>Liu, Y. X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+practical+guide+to+amplicon+and+metagenomic+analysis+of+microbiome+data.">A practical guide to amplicon and metagenomic analysis of microbiome data.</a> Protein and Cell. 41 (7), 1-16 (2020).</li><li>Shahi, S. K., Zarei, K., Guseva, N. V., Mangalam, A. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microbiota+analysis+using+two-step+PCR+and+next-generation+16S+rRNA+gene+sequencing.">Microbiota analysis using two-step PCR and next-generation 16S rRNA gene sequencing.</a> Journal of Visualized Experiments: JoVE. (152), e59980 (2019).</li><li>Gr&#252;ning, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bioconda:+sustainable+and+comprehensive+software+distribution+for+the+life+sciences.">Bioconda: sustainable and comprehensive software distribution for the life sciences.</a> Nature Methods. 15 (7), 475-476 (2018).</li><li>Blankenberg, D., Chilton, J., Coraor, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Galaxy+external+display+applications:+closing+a+dataflow+interoperability+loop.">Galaxy external display applications: closing a dataflow interoperability loop.</a> Nature Methods. 17 (2), 123-124 (2020).</li><li>Langille, M. G. I., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Predictive+functional+profiling+of+microbial+communities+using+16S+rRNA+marker+gene+sequences.">Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences.</a> Nature Biotechnology. 31 (9), 814-821 (2013).</li><li>Shilei, Z., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reservoir+water+stratification+and+mixing+affects+microbial+community+structure+and+functional+community+composition+in+a+stratified+drinking+reservoir.">Reservoir water stratification and mixing affects microbial community structure and functional community composition in a stratified drinking reservoir.</a> Journal of Environmental Management. 267, 110456 (2020).</li></ol>

Here, the protocol for the identification and characterization of biomarkers within different groups is described. This protocol can easily be adapted for other sample types, such as OTUs of microorganisms. The statistical method by LEfSe can find the characteristic microorganisms in each group (default is LDA &#62;2), that is, the microorganisms that are more abundant in this group relative to the others12. LEfSe is available in both native and web Linux versions where users can also perform LEfS...

Biomarkers are biological characteristics that can be measured and can indicate some phenomena such as infection, disease, or environment. Among them, functional biomarkers may be specific biological functions of single species or common to some species, such as gene, protein, metabolite and pathways. Besides, taxonomic biomarkers indicate an unusual species, a group of organisms (kingdom, phylum, class, order, family, genus, species), the Amplicon Sequence Varient (ASV)1, or the Operational Taxonomic Unit (OTU)2. In order to find biomarkers more quickly and accurately, a tool for analyzing the biological data is necessary. The differences between classes can be explained by LEfSe coupled with standard tests for statistical significance and additional tests encoding biological consistency and effect relevance3. LEfSe is available as a galaxy module, a conda formula, a docker image, and included in bioBakery (VM and cloud)4. Generally, the analysis of microbial diversity often uses a non-parametric test for the uncertain distribution of a sample community. The rank sum test is a non-parametric test method, which uses the rank of samples to replace the value of samples. According to the difference of sample groups, it can be divided into two samples with the Wilcoxon rank sum test and into multiple samples with the Kruskal-Wallis test5,6. Notably, when there are significant differences among multiple groups of samples, a rank-sum test of pairwise comparison of multiple samples should be performed. LDA (which stands for Linear Discriminant Analysis) invented by Ronald Fisher in 1936, is a type of supervised learning, also known as Fisher&#8217;s Linear Discriminant7. It is a classic and popular algorithm in the current field of machine learning data mining.
Here, the LEfSe assay has been optimized by Conda and Galaxy servers. Three groups of 16S rRNA gene sequences are analyzed to demonstrate the significant differences between different groups with LDA scores of microbial communities and visualization results.

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			<td class="xl65" height="21" style="height:15.75pt;width:162pt" width="216">No materials used</td>
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assisted selection of biomarkers by linear discriminant analysis effect size (lefse) in microbiome data

There is growing attention toward closed biological genomes in the environment and in health. To explore and reveal the intergroup differences among different samples or environments, it is crucial to discover biomarkers with statistical differences among groups. The application of Linear discriminant analysis Effect Size (LEfSe) can help find good biomarkers. Based on the original genome data, quality control, and quantification of different sequences based on taxa or genes are carried out. First, the Kruskal-Wallis rank test was used to distinguish between specific differences among statistical and biological groups. Then, the Wilcoxon rank test was performed between the two groups obtained in the previous step to assess whether the differences were consistent. Finally, a linear discriminant analysis (LDA) was conducted to evaluate the influence of biomarkers on significantly different groups based on LDA scores. To sum up, LEfSe provided the convenience for identifying genomic biomarkers that characterize statistical differences among biological groups.

The LDA scores of microbial communities with significant differences in each group by analyzing the 16S rRNA gene sequences of three samples is shown in Figure 8. The color of the histogram represents different groups, while the length represents the LDA score, which is the influence of the species with significant differences between different groups. The histogram shows the species with significant differences whose LDA score is greater than the preset value. The default preset value is 2....

Watch this Scientific Journal Video about Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data at JoVE.com

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

LEfSe (LDA Effect Size) is a tool for high-dimensional biomarker mining to identify genomic features (such as genes, pathways, and taxonomies) that significantly characterize two or more groups in microbiome data.

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size (LEfSe) in Microbiome Data

There is growing attention toward closed biological genomes in the environment and in health. To explore and reveal the intergroup differences among ...

The application of linear discriminant analysis effect size can solve the problem of finding good biomarkers with statistical differences between biologic groups. Linear discriminant analysis effect size provides a convenient method for identifying genomic biomarkers to characterize statistical differences between biologic groups. Be sure to take care with each step of the procedure, because the outcome of each preceding step may effect the subsequent step.After generating a linear discriminant analysis effect size input file, run the commands as indicated to exclude the possibility of dependencies conflict and create a conda environment for linear discriminant analysis effect size. Use the commands as indicated to activate the created environment and to install linear discriminant analysis effect size with channel bioBakery. To format data for linear discriminant analysis effect size, run the command to format the original file to the internal linear discriminant analysis effect size format.To calculate the linear discriminant analysis effect size, run the command to perform a linear discriminant analysis and to generate the resulting data file. After the analysis, use the commands as indicated to plot the effect size of the biomarkers in a PDF file and to draw the species tree to display the biomarkers in a cladogram. To plot the differences of a single biomarker among different groups, use the command as indicated.All of the features can also be drawn using the command if desired. For online LEfSe analysis using the Galaxy server, navigate to the server. To upload the relevant files, click up and Choose local file to select the files.Then, select tabular format and click Start. To format the data for linear discriminant analysis effect size, click LEfSe and Format data for LEfSe, select the specific rows for class, and click Execute. To calculate the linear discriminant analysis effect size, click LEfSe and LDA Effect Size.Select the parameter values according to the analysis requirements, and click Execute. To plot the linear discriminant analysis effect size results, click LEfSe and Plot LEfSe Results and click Execute. To plot the cladogram, select the appropriate parameter values and click Plot Cladogram and Execute.To plot one feature, select the appropriate parameter values and click Plot One Feature and Execute. To plot differential features, select the appropriate parameter values and click Plot Differential Features and Execute button. Here, the linear discrimination analysis scores of microbial communities with significant differences in each group, determined by analyzing the 16 S-ribosomal RNA gene sequences of three samples, is shown.In this figure, the biomarkers with significant differences in species trees between different classification levels can be observed. The circles radiating from inside to the outside represent the classification levels from phylum to genus, with the diameter of each species circle representing the level of abundance of each classification. The species with no significant differences appear in yellow, and the significantly different species biomarkers are colored to match the corresponding groups.The corresponding species names of the biomarkers shown in the plot are listed here. Here, a representative of abundance bar plot for one biomarker that shows differences among the different groups according to the linear discriminant analysis effect size results is shown. The solid line represents the average relative abundance, the dotted line represents the median relative abundance, and each column represents the relative abundance of each sample in different groups.Principal component analysis can also be performed, as the dimensionality education is directly related to the data dimension, and the projected coordinate system is orthogonal. As the demand for high-dimensional data analysis increases, this method will aid in the exploration of biomarker features of interest.

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