--- title: "Tools for microbiome marker identification" author: - name: Yang Cao affiliation: Department of Environmental Medicine, Tianjin Institute of Environmental and Operational Medicine email: caoyang.name@gmail.com output: BiocStyle::html_document: toc: true bibliography: vignette.bib vignette: > %\VignetteIndexEntry{Tools for microbiome marker identification} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>", message = FALSE, warning = FALSE, fig.align = "center", crop = NULL ) library(BiocStyle) ``` # Introduction It is well established that the microbiome play a key role in human health and disease, due to its function such as host nutrition production (e.g. short-chain fatty acids, SCFA), defense against pathogens, and development of immunity [@gilbert2018current]. The microbiome provide novel biomarkers for many disease, and characterizing biomarkers based on microbiome profiles has great potential for translational medicine and precision medicine [@manor2020health]. Differential analysis (DA) is a widely used approach to identify biomarkers. To date, a number of methods have been developed for microbiome marker discovery based on metagenomic profiles, e.g. simple statistical analysis methods STAMP [@parks2014stamp], RNA-seq based methods such as edgeR [@robinson2010edger] and DESeq2 [@love2014moderated], metagenomeSeq [@paulson2013differential], and Linear Discriminant Analysis Effect Size (LEfSe) [@segata2011metagenomic]. However, all of these methods have its own advantages and disadvantages, and none of them is considered standard or universal. Moreover, the programs/softwares for different DA methods may be development using different programming languages, even in different operating systems. Here, we have developed an all-in-one R/Bioconductor package [`microbiomeMarker`](https://yiluheihei.github.io/microbiomeMarker) that integrates commonly used differential analysis methods as well as three machine learning-based approaches (Logistic regression, Random forest, and Support vector machine) to facilitate the identification of microbiome markers. # Installation Install the package from Bioconductor directly: ```{r install-bioc,eval=FALSE} if (!requireNamespace("BiocManager", quietly = TRUE)) { install.packages("BiocManager") } BiocManager::install("microbiomeMarker") ``` Or install the development version of the package from [Github](https://github.com/yiluheihei/microbiomeMarker). ```{r install-gh,eval=FALSE} if (!requireNamespace("remotes", quietly = TRUE)) { install.packages("remotes") } remotes::install_github("yiluheihei/microbiomeMarker") ``` # Package loading Load the `microbiomeMarker` into the R session: ```{r load} library(microbiomeMarker) ``` # Data structure ## Input phyloseq-class object `r Biocpkg("phyloseq")` is the most popular [Biocondcutor](https://bioconductor.org/) package used by the microbiome research community, and `phyloseq-class` objects are a great data-standard for microbiome data in R. Therefore, the core functions in `microbiomeMarker` take `phyloseq-class` object as input. Conveniently, `microbiomeMarker` provides features to import external metagenomic abundance profiles from two popular microbiome analysis pipelines, [qiime2](http://qiime.org/) [@bolyen2019reproducible] and [dada2](https://benjjneb.github.io/dada2) [@callahan2016dada2], and return a `phyloseq-class` object. ### Import from dada2 The output of the [dada2](https://benjjneb.github.io/dada2) pipeline is a feature table of amplicon sequence variants (an ASV table): A matrix with rows corresponding to samples and columns to ASVs, in which the value of each entry is the number of times that ASV was observed in that sample. This table is analogous to the traditional OTU table. Conveniently, taxa names are saved as ```{r import-dada2} seq_tab <- readRDS( system.file( "extdata", "dada2_seqtab.rds", package = "microbiomeMarker" ) ) tax_tab <- readRDS( system.file( "extdata", "dada2_taxtab.rds", package = "microbiomeMarker" ) ) sam_tab <- read.table( system.file( "extdata", "dada2_samdata.txt", package = "microbiomeMarker" ), sep = "\t", header = TRUE, row.names = 1 ) ps <- import_dada2(seq_tab = seq_tab, tax_tab = tax_tab, sam_tab = sam_tab) ps ``` ### Import from qiime2 [qiime2](http://qiime.org/) is the most widely used software for metagenomic analysis. User can import the feature table, taxonomic table, phylogenetic tree, representative sequence and sample metadata from qiime2 using `import_qiime2()`. ```{r import-qiime2,message=FALSE} otuqza_file <- system.file( "extdata", "table.qza", package = "microbiomeMarker" ) taxaqza_file <- system.file( "extdata", "taxonomy.qza", package = "microbiomeMarker" ) sample_file <- system.file( "extdata", "sample-metadata.tsv", package = "microbiomeMarker" ) treeqza_file <- system.file( "extdata", "tree.qza", package = "microbiomeMarker" ) ps <- import_qiime2( otu_qza = otuqza_file, taxa_qza = taxaqza_file, sam_tab = sample_file, tree_qza = treeqza_file ) ps ``` ### Other import functions reexport from phyloseq Moreover, `microbiomeMarker` reexports three import functions from `r Biocpkg("phyloseq")`, including `import_biom()`, `import_qiime()` and `import_mothur()`, to help users to import abundance data from [biom file](http://biom-format.org/), [qiime1](http://www.qiime.org/), and [mothur](http://www.mothur.org/). More details on these three import functions can be see from [here](https://joey711.github.io/phyloseq/import-data.html#the_import_family_of_functions). Users can also import the external files into `phyloseq-class` object manually. For more details on how to create `phyloseq-class` object from manually imported data, please see [this tutorial](http://joey711.github.io/phyloseq/import-data.html#manual). ## Output microbiomeMaker-class object The object class used by the `microbiomeMarker` package to store the result of microbiome marker analysis (also referred as DA) is the `microbiomeMarker-class` object. The `microbiomeMarker-class` extends the `phyloseq-class` by adding three custom slots: - `marker_table`: also a new S4 class to store the markers, which is inherit from `data.frame`. Rows represent the microbiome markers and variables represents feature of the marker, such as feature names, effect size and p value. - `norm_method`: normalization method. - `diff_method`: DA method. Once users have a `microbiomeMarker-class` object, many accessor functions are available to query aspects of the data set. The function name and its purpose can be seen [here](https://yiluheihei.github.io/microbiomeMarker/reference/index.html#section-microbiome-marker). # Diferential analysis A number of methods have been developed for identifying differentially metagenomic features. `microbiomeMarker` provides the most commonly used DA methods which can be divided into three main categories: a) simple statistical tests; b) RNA-seq based methods; c) metagenomic based methods. All the names of DA functions in `microbiomeMarker` are prefixed with `run_` (the `run_*` family of functions). By default, all the methods will perform DA on all levels of features (`taxa_rank = "all"` in DA functions) like LEfSe [@segata2011metagenomic], therefore, the corrected p value in the result (var `padj` in the `marker_table` object) may be over-corrected. Users can change the para `taxa_rank` to a specific level of interest, and the DA will only perform in the specified level. For simplicity, DA on a specific level of feature is not contained in this vignette. ## Normalization It is critical to normalize the metagenomic data to eliminate artifactual bias in the original measurements prior to DA [@weiss2017normalization]. Here in `microbiomeMarker`, we provides seven popular normalization methods, including: - `rarefy`: random subsampling counts to the smallest library size in the data set. - `TSS`: total sum scaling, also referred to as "relative abundance", the abundances were normalized by dividing the corresponding sample library size. - `TMM`: trimmed mean of m-values. First, a sample is chosen as reference. The scaling factor is then derived using a weighted trimmed mean over the differences of the log-transformed gene-count fold-change between the sample and the reference. - `RLE`: relative log expression, RLE uses a pseudo-reference calculated using the geometric mean of the gene-specific abundances over all samples. The scaling factors are then calculated as the median of the gene counts ratios between the samples and the reference. - `CSS`: cumulative sum scaling, calculates scaling factors as the cumulative sum of gene abundances up to a data-derived threshold. - `CLR`: centered log-ratio normalization. - `CPM`: pre-sample normalization of the sum of the values to 1e+06. We can use `norm_*()` family of functions or a wrapper function `normalize` to normalize the original metagenomic abundance data. ```{r norm} # take tss as example norm_tss(ps) normalize(ps, method = "TSS") ``` --------------- ***Note***: all the DA functions provides a para to specify the normalization method. We emphasize that users should specify the normalization method in the DA functions rather than using these normalization functions directly. If you use normalize data first and then perform DA, you should set the `norm_method` manually. We recommend to use the default normalization methods for the corresponding DA methods, e.g. "CPM" for LEfSe and "CSS" for metagenomeSeq, and the default values of `norm` in the DA functions is set as their default normalization methods. ```{r norm-note,eval=FALSE} data(kostic_crc) mm_test <- normalize(kostic_crc, method = "CPM") %>% run_lefse( wilcoxon_cutoff = 0.01, norm = "none", # must be "none" since the input has been normalized group = "DIAGNOSIS", kw_cutoff = 0.01, multigrp_strat = TRUE, lda_cutoff = 4 ) # equivalent to run_lefse( wilcoxon_cutoff = 0.01, norm = "CPM", group = "DIAGNOSIS", kw_cutoff = 0.01, multigrp_strat = TRUE, lda_cutoff = 4 ) ``` ## Simple statitical tests {#simple-stat} In practice, simple statitical tests such as t-test (for two groups comparison) and Kruskal-Wallis rank sum test (for multiple groups comparison) are frequently used for metagenomic differential analysis. STAMP [parks2014stamp] is a widely-used graphical software package that provides "best pratices" in choose appropriate statistical methods for metagenomic analysis. Here in `microbiomeMarker`, `t-test`, Welch’s `t-test`, and White’s non-parametric `t-test` are provided for two groups comparison, and ANOVA and Kruskal–Wallis test for multiple groups comparisons. We can use `test_two_groups()` to perform simple statistical differential test between two groups. ```{r two-group-test} data(enterotypes_arumugam) tg_welch <- run_test_two_groups( enterotypes_arumugam, group = "Gender", method = "welch.test" ) # three significantly differential genera (marker) tg_welch # details of result of the three markers head(marker_table(tg_welch)) ``` Function `run_test_multiple_groups()` is constructed for statistical differential test for multiple groups. ```{r multi-group-test} # three groups ps <- phyloseq::subset_samples( enterotypes_arumugam, Enterotype %in% c("Enterotype 3", "Enterotype 2", "Enterotype 1") ) mg_anova <- run_test_multiple_groups( ps, group = "Enterotype", method = "anova" ) # 24 markers mg_anova head(marker_table(mg_anova)) ``` Moreover, a wrapper of `run_test_two_groups()` and `run_test_multiple_groups()` named `run_simple_stat()` is provided for simple statistical differential analysis. ## RNA-seq based DA methods Some models developed specifically for RNA-Seq data have been proposed for metagenomic differential analysis. Three popular methods, including DESeq2 [@love2014moderated] (`run_deseq2()`), edgeR [@robinson2010edger] (`run_edger()`), and Voom [@law2014voom] (`run_limma_voom()`) are provided in `microbiomeMarker`. Here we take edgeR method as an example. ```{r edger} # contrast must be specified for two groups comparison data(pediatric_ibd) mm_edger <- run_edger( pediatric_ibd, group = "Class", pvalue_cutoff = 0.1, p_adjust = "fdr" ) mm_edger # multiple groups data(cid_ying) cid <- phyloseq::subset_samples( cid_ying, Consistency %in% c("formed stool", "liquid", "semi-formed") ) mm_edger_mg <- run_edger( cid, group = "Consistency", method = "QLFT", pvalue_cutoff = 0.05, p_adjust = "fdr" ) mm_edger_mg ``` ## metagenomic based methods Five methods, LEfSe [@segata2011metagenomic], metagenomeSeq [@paulson2013differential], ALDEx2 [@fernandes2014unifying], ANCOM [@mandal2015analysis], and ANCOMBC [@lin2020analysis], which were developed specifically for microbiome data (contain many more zeros that RNA-seq data), are also provided in our package. All these methods have greater power to detect differentially features than simple statistical tests by incorporating more sensitive tests. Curently, LEfSe is the most popular tool for microbiome biomarker discovery. Here we take LEfSe method for example: ```{r lefse} data(kostic_crc) kostic_crc_small <- phyloseq::subset_taxa( kostic_crc, Phylum %in% c("Firmicutes") ) mm_lefse <- run_lefse( kostic_crc_small, wilcoxon_cutoff = 0.01, group = "DIAGNOSIS", kw_cutoff = 0.01, multigrp_strat = TRUE, lda_cutoff = 4 ) mm_lefse head(marker_table(mm_lefse)) ``` ## Supervised machine learning methods Given that supervised learning (SL) methods can be used to predict differentiate samples based on there metagenomic profiles efficiently [@knights2011supervised]. `microbiomeMarker` also provides three SL classification models, random forest, logistic regression, and support vector machine, to identify microbiome biomarkers. In addition, the feature importance score for each marker will be provided too. Here we take random forest for example: ```{r rf} # must specify the importance para for random forest set.seed(2021) # small example phyloseq object for test ps_small <- phyloseq::subset_taxa( enterotypes_arumugam, Phylum %in% c("Firmicutes", "Bacteroidetes") ) mm_lr <- run_sl( ps_small, group = "Gender", nfolds = 2, nrepeats = 1, taxa_rank = "Genus", top_n = 15, norm = "TSS", method = "LR", ) marker_table(mm_lr) ``` **Please note that SL methods can be biased for data with sample size due to the model overfitting. Thus, we advise users to use these SL methods with caution for a smaller dataset.** ## Pair-wise comparison of multiple groups All the DE methods in ***microbiomeMarker***, except for simple statistical tests for two groups comparison (`test_mulitple_groups()`), can be used for multiple groups comparison, that is to find markers that differ between any of the groups by analyze all groups at once. Users can perform post-hoc test to identify which pairs of groups may differ from each other using `run_posthoc_test()`. Apparently, the mutliple groups comparison will result in a larger number of genes than the individual pair-wise comparisons. ```{r post-hoc-test} pht <- run_posthoc_test(ps, group = "Enterotype") pht # 24 significantly differential genera markers <- marker_table(mg_anova)$feature markers # take a marker "p__Bacteroidetes|g__Bacteroides" # for example, we will show "p__Bacteroidetes|g__Bacteroides" differ from # between Enterotype 2-Enterotype 1 and Enterotype 3-Enterotype 2. extract_posthoc_res(pht, "p__Bacteroidetes|g__Bacteroides")[[1]] ``` In addition, for the five linear models-based methods, including edgeR, DESeq2, metagenoSeq, limma-voom, and ANCOMBC, users can perform pair-wise comparisons by setting the argument `contrast`, a two length character in which the first element is the reference level (donominator of the logFC) and the second element is used as baseline (numerator for fold change). For more details on `contrast` argument, please see the help page of the corresponding functions. Here we take limma-voom method as example: ```{r pair-wise-linear} # comparison between Enterotype 3 and Enterotype 2 mm_lv_pair <- run_limma_voom( ps, "Enterotype", contrast = c("Enterotype 3", "Enterotype 2"), pvalue_cutoff = 0.05, p_adjust = "fdr" ) mm_lv_pair head(marker_table(mm_lv_pair)) ``` # Visualization In `microbiomeMarker`, users can visualize the microbiome biomarker in different ways, such as box plot, bar plot, dot plot, heatmap, and cladogram. Except for heatmap, all these plots are generated using the most flexible and popular data visualization package `r CRANpkg("ggplot2")`. Therefore, these plots can be easily customized before they are generated using the build-in functions of `r CRANpkg("ggplot2")`, e.g. using `theme()` to modify the titles and labels. Heatmap is generated using a fantastic Bioconductor package `r Biocpkg("ComplexHeatmap")` package. ## Abundance box plot First of all, users can visualize the abundances of markers using box plots with function `plot_abundance()`. We emphasize a concern that the `group` para for `plot_abunance()` must be keep same with the `group` para in the differential analysis function. By default, `plot_abundance()` will plot all the markers, users can plot the specificity markers using para `markers`. ```{r plot-abundance} p_abd <- plot_abundance(mm_lefse, group = "DIAGNOSIS") p_abd # customize the plot with ggplot2, modify the fill color manually library(ggplot2) p_abd + scale_fill_manual(values = c("Healthy" = "grey", "Tumor" = "red")) ``` ## Heat map Moreover, users can also visualize the abundances of markers using heatmap, in which rows represents the markers and columns represents the samples. Like the above abundance box plot, users should pay attention to the para `group`, and control which markers to display by setting para `markers`. ```{r heatmap} plot_heatmap(mm_edger, transform = "log10p", group = "Class") ``` ## Bar plot or dot plot for effect size We also estimate the effect size to measure the magnitude the observed phenomenon due to each characterizing marker. `plot_ef_bar()` and `plot_ef_dot()` were used to show the bar and dot plot of the effect sizes of markers. ```{r ef-plot} # bar plot plot_ef_bar(mm_lefse) # dot plot plot_ef_dot(mm_lefse) ``` Different effect size measures can be calculated for different DA methods, e.g. `lda` (linear discriminant analysis) for LEfSe, `imp` (importance) for SL methods. `plot_ef_bar()` and `plot_ef_dot()` can set the axis label of effect size correctly without manual intervention. ```{r ef-plot-diff} # set the x axis to log2 Fold Change automatically without manual intervention plot_ef_bar(mm_edger) ``` ## Cladogram As mentioned above, the microbiome marker analysis will run on all levels of features by default. Users can plot a LEfSe cladogram using function `plot_cladogram()`. ```{r cladogram,fig.width=7,fig.height=7} plot_cladogram(mm_lefse, color = c(Healthy = "darkgreen", Tumor = "red")) + theme(plot.margin = margin(0, 0, 0, 0)) ``` ## AUC-ROC curve from SL methods ROC (receiver operating characteristic) curve can be used to show the prediction performance of the identified marker. And AUC (area under the ROC curve) measures the ability of the identified marker to classify the samples. `plot_sl_roc()` was provided to show ROC curve and AUC value to evaluate marker prediction performance. ```{r auc-roc} set.seed(2021) plot_sl_roc(mm_lr, group = "Gender") ``` ## Visualization for post-hoc test As shown in \@ref(simple-stat), post-hoc test can be used to identify which pairs of groups may differ from each other. `plot_postHocTest()` was provided to allow users visualize the post-hoc test result. ```{r plot-pht} p_pht <- plot_postHocTest(pht, feature = "p__Bacteroidetes|g__Bacteroides") p_pht ``` The pot-hoc plots were wrapped using `r CRANpkg("patchwork")`, and users can modifying the themes of all subplots using `&`. ```{r customize-p-pht} p_pht & theme_bw() ``` # Citation Kindly cite as follows: Yang Cao (2020). microbiomeMarker: microbiome biomarker analysis. R package version 0.0.1.9000. https://github.com/yiluheihei/microbiomeMarker. DOI: [10.5281/zenodo.3749415](https://doi.org/10.5281/zenodo.3749415). # Question 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 [how to make a great r reproducible example](https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example) and https://www.tidyverse.org/help/#reprex. # Session information {-} This vignette was created under the following conditions: ```{r} sessionInfo() ``` # References {-}