Tools for microbiome marker identification
25 April 2023
Contents
1 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 (Gilbert et al. 2018). The microbiome provide novel biomarkers for many disease, and characterizing biomarkers based on microbiome profiles has great potential for translational medicine and precision medicine (Manor et al. 2020).
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
(Parks et al. 2014), RNA-seq based methods such as edgeR (Robinson, McCarthy, and Smyth 2010) and
DESeq2 (Love, Huber, and Anders 2014), metagenomeSeq (Paulson et al. 2013), and
Linear Discriminant Analysis Effect Size (LEfSe) (Segata et al. 2011).
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
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.
2 Installation
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")3 Package loading
Load the microbiomeMarker into the R session:
library(microbiomeMarker)4 Data structure
4.1 Input phyloseq-class object
phyloseq is the most popular
Biocondcutor 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 (Bolyen et al. 2019) and
dada2 (Callahan et al. 2016), and return a
phyloseq-class object.
4.1.1 Import from dada2
The output of the 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
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
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 232 taxa and 20 samples ]
#> sample_data() Sample Data: [ 20 samples by 4 sample variables ]
#> tax_table() Taxonomy Table: [ 232 taxa by 6 taxonomic ranks ]
#> refseq() DNAStringSet: [ 232 reference sequences ]4.1.2 Import from qiime2
qiime2 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().
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
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 770 taxa and 34 samples ]
#> sample_data() Sample Data: [ 34 samples by 9 sample variables ]
#> tax_table() Taxonomy Table: [ 770 taxa by 7 taxonomic ranks ]
#> phy_tree() Phylogenetic Tree: [ 770 tips and 768 internal nodes ]4.1.3 Other import functions reexport from phyloseq
Moreover, microbiomeMarker reexports three import functions from
phyloseq, including import_biom(), import_qiime() and
import_mothur(), to help users to import abundance data from
biom file, qiime1, and
mothur. More details on these three import functions
can be see from here.
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.
4.2 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 fromdata.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.
5 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 (Segata et al. 2011),
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.
5.1 Normalization
It is critical to normalize the metagenomic data to eliminate artifactual bias
in the original measurements prior to DA (Weiss et al. 2017). 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.
# take tss as example
norm_tss(ps)
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 770 taxa and 34 samples ]
#> sample_data() Sample Data: [ 34 samples by 9 sample variables ]
#> tax_table() Taxonomy Table: [ 770 taxa by 7 taxonomic ranks ]
#> phy_tree() Phylogenetic Tree: [ 770 tips and 768 internal nodes ]
normalize(ps, method = "TSS")
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 770 taxa and 34 samples ]
#> sample_data() Sample Data: [ 34 samples by 9 sample variables ]
#> tax_table() Taxonomy Table: [ 770 taxa by 7 taxonomic ranks ]
#> phy_tree() Phylogenetic Tree: [ 770 tips and 768 internal nodes ]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.
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
)5.2 Simple statitical tests
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.
data(enterotypes_arumugam)
tg_welch <- run_test_two_groups(
enterotypes_arumugam,
group = "Gender",
method = "welch.test"
)
# three significantly differential genera (marker)
tg_welch
#> microbiomeMarker-class inherited from phyloseq-class
#> normalization method: [ TSS ]
#> microbiome marker identity method: [ welch.test ]
#> marker_table() Marker Table: [ 3 microbiome markers with 5 variables ]
#> otu_table() OTU Table: [ 244 taxa and 39 samples ]
#> sample_data() Sample Data: [ 39 samples by 9 sample variables ]
#> tax_table() Taxonomy Table: [ 244 taxa by 1 taxonomic ranks ]
# details of result of the three markers
head(marker_table(tg_welch))
#> feature enrich_group ef_diff_mean
#> marker1 p__Firmicutes|g__Heliobacterium M -8.542172e-06
#> marker2 p__Firmicutes|g__Parvimonas M -1.339857e-05
#> marker3 p__Firmicutes|g__Peptostreptococcus M -6.695045e-05
#> pvalue padj
#> marker1 0.02940341 0.02940341
#> marker2 0.03281399 0.03281399
#> marker3 0.01714937 0.01714937Function run_test_multiple_groups() is constructed for statistical
differential test for multiple groups.
# 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
#> microbiomeMarker-class inherited from phyloseq-class
#> normalization method: [ TSS ]
#> microbiome marker identity method: [ anova ]
#> marker_table() Marker Table: [ 24 microbiome markers with 5 variables ]
#> otu_table() OTU Table: [ 238 taxa and 32 samples ]
#> sample_data() Sample Data: [ 32 samples by 9 sample variables ]
#> tax_table() Taxonomy Table: [ 238 taxa by 1 taxonomic ranks ]
head(marker_table(mg_anova))
#> feature enrich_group ef_eta_squared
#> marker1 p__Bacteroidetes Enterotype 1 0.5821619
#> marker2 p__Unclassified Enterotype 3 0.4497271
#> marker3 p__Actinobacteria|g__Scardovia Enterotype 2 0.2196652
#> marker4 p__Bacteroidetes|g__Alistipes Enterotype 3 0.2001541
#> marker5 p__Bacteroidetes|g__Bacteroides Enterotype 1 0.7633661
#> marker6 p__Bacteroidetes|g__Parabacteroides Enterotype 1 0.2582573
#> pvalue padj
#> marker1 3.196070e-06 3.196070e-06
#> marker2 1.731342e-04 1.731342e-04
#> marker3 2.742042e-02 2.742042e-02
#> marker4 3.922758e-02 3.922758e-02
#> marker5 8.396825e-10 8.396825e-10
#> marker6 1.314233e-02 1.314233e-02Moreover, a wrapper of run_test_two_groups() and run_test_multiple_groups()
named run_simple_stat() is provided for simple statistical differential
analysis.
5.3 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
(Love, Huber, and Anders 2014) (run_deseq2()), edgeR (Robinson, McCarthy, and Smyth 2010)
(run_edger()), and Voom (Law et al. 2014) (run_limma_voom()) are provided in
microbiomeMarker.
Here we take edgeR method as an example.
# 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
#> microbiomeMarker-class inherited from phyloseq-class
#> normalization method: [ TMM ]
#> microbiome marker identity method: [ edgeR: LRT ]
#> marker_table() Marker Table: [ 34 microbiome markers with 5 variables ]
#> otu_table() OTU Table: [ 786 taxa and 43 samples ]
#> sample_data() Sample Data: [ 43 samples by 2 sample variables ]
#> tax_table() Taxonomy Table: [ 786 taxa by 1 taxonomic ranks ]
# 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
#> microbiomeMarker-class inherited from phyloseq-class
#> normalization method: [ TMM ]
#> microbiome marker identity method: [ edgeR: QLFT ]
#> marker_table() Marker Table: [ 325 microbiome markers with 5 variables ]
#> otu_table() OTU Table: [ 669 taxa and 413 samples ]
#> sample_data() Sample Data: [ 413 samples by 6 sample variables ]
#> tax_table() Taxonomy Table: [ 669 taxa by 1 taxonomic ranks ]5.4 metagenomic based methods
Five methods, LEfSe (Segata et al. 2011), metagenomeSeq (Paulson et al. 2013), ALDEx2 (Fernandes et al. 2014), ANCOM (Mandal et al. 2015), and ANCOMBC (Lin and Peddada 2020), 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:
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
#> microbiomeMarker-class inherited from phyloseq-class
#> normalization method: [ CPM ]
#> microbiome marker identity method: [ lefse ]
#> marker_table() Marker Table: [ 12 microbiome markers with 5 variables ]
#> otu_table() OTU Table: [ 276 taxa and 177 samples ]
#> sample_data() Sample Data: [ 177 samples by 71 sample variables ]
#> tax_table() Taxonomy Table: [ 276 taxa by 1 taxonomic ranks ]
head(marker_table(mm_lefse))
#> feature
#> marker1 k__Bacteria|p__Firmicutes|c__Clostridia|o__Clostridiales|f__Ruminococcaceae
#> marker2 k__Bacteria|p__Firmicutes|c__Clostridia|o__Clostridiales|f__Ruminococcaceae|g__Faecalibacterium
#> marker3 k__Bacteria|p__Firmicutes|c__Clostridia|o__Clostridiales|f__Ruminococcaceae|g__Faecalibacterium|s__Faecalibacterium_s__
#> marker4 k__Bacteria|p__Firmicutes|c__Clostridia
#> marker5 k__Bacteria|p__Firmicutes|c__Clostridia|o__Clostridiales
#> marker6 k__Bacteria|p__Firmicutes|c__Clostridia|o__Clostridiales|f__Ruminococcaceae|g__Ruminococcaceae_g__
#> enrich_group ef_lda pvalue padj
#> marker1 Healthy 5.013283 7.154793e-05 7.154793e-05
#> marker2 Healthy 4.866423 5.914547e-04 5.914547e-04
#> marker3 Healthy 4.864530 6.043983e-04 6.043983e-04
#> marker4 Healthy 4.649214 7.176046e-04 7.176046e-04
#> marker5 Healthy 4.649214 7.176046e-04 7.176046e-04
#> marker6 Healthy 4.289835 6.990210e-03 6.990210e-035.5 Supervised machine learning methods
Given that supervised learning (SL) methods can be used to predict
differentiate samples based on there metagenomic profiles efficiently
(Knights et al. 2011). 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:
# 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)
#> feature enrich_group ef_imp
#> marker1 Dyadobacter M 100.00000
#> marker2 Paenibacillus M 80.09623
#> marker3 Weissella M 52.90190
#> marker4 Bacillales F 49.21685
#> marker5 Zunongwangia F 47.67223
#> marker6 Macrococcus M 46.78631
#> marker7 Heliobacterium M 42.77684
#> marker8 Gemella M 40.85011
#> marker9 Syntrophothermus M 36.87214
#> marker10 Geobacillus M 33.29326
#> marker11 Symbiobacterium M 32.72664
#> marker12 Desulfitobacterium F 30.36170
#> marker13 Thermoanaerobacter F 29.51966
#> marker14 Porphyromonadaceae F 28.72514
#> marker15 Syntrophomonas M 25.87942Please 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.
5.6 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.
pht <- run_posthoc_test(ps, group = "Enterotype")
pht
#> postHocTest-class object
#> Pairwise test result of 238 features, DataFrameList object, each DataFrame has five variables:
#> comparisons : pair groups to test which separated by '-'
#> diff_mean: difference in mean proportions
#> pvalue : post hoc test p values
#> ci_lower : lower confidence interval
#> ci_upper : upper confidence interval
#> Posthoc multiple comparisons of means using tukey method
# 24 significantly differential genera
markers <- marker_table(mg_anova)$feature
markers
#> p__Bacteroidetes p__Unclassified
#> "p__Bacteroidetes" "p__Unclassified"
#> p__Actinobacteria|g__Scardovia p__Bacteroidetes|g__Alistipes
#> "p__Actinobacteria|g__Scardovia" "p__Bacteroidetes|g__Alistipes"
#> p__Bacteroidetes|g__Bacteroides p__Bacteroidetes|g__Parabacteroides
#> "p__Bacteroidetes|g__Bacteroides" "p__Bacteroidetes|g__Parabacteroides"
#> p__Bacteroidetes|g__Prevotella p__Firmicutes|g__Bulleidia
#> "p__Bacteroidetes|g__Prevotella" "p__Firmicutes|g__Bulleidia"
#> p__Firmicutes|g__Catenibacterium p__Firmicutes|g__Catonella
#> "p__Firmicutes|g__Catenibacterium" "p__Firmicutes|g__Catonella"
#> p__Firmicutes|g__Holdemania p__Firmicutes|g__Lactobacillus
#> "p__Firmicutes|g__Holdemania" "p__Firmicutes|g__Lactobacillus"
#> p__Firmicutes|g__Macrococcus p__Firmicutes|g__Peptostreptococcus
#> "p__Firmicutes|g__Macrococcus" "p__Firmicutes|g__Peptostreptococcus"
#> p__Firmicutes|g__Ruminococcus p__Firmicutes|g__Selenomonas
#> "p__Firmicutes|g__Ruminococcus" "p__Firmicutes|g__Selenomonas"
#> p__Firmicutes|g__Streptococcus p__Firmicutes|g__Subdoligranulum
#> "p__Firmicutes|g__Streptococcus" "p__Firmicutes|g__Subdoligranulum"
#> p__Proteobacteria|g__Bartonella p__Proteobacteria|g__Brucella
#> "p__Proteobacteria|g__Bartonella" "p__Proteobacteria|g__Brucella"
#> p__Proteobacteria|g__Granulibacter p__Proteobacteria|g__Rhodospirillum
#> "p__Proteobacteria|g__Granulibacter" "p__Proteobacteria|g__Rhodospirillum"
#> p__Proteobacteria|g__Stenotrophomonas p__Unclassified|g__Unclassified
#> "p__Proteobacteria|g__Stenotrophomonas" "p__Unclassified|g__Unclassified"
# 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]]
#> DataFrame with 3 rows and 5 columns
#> comparisons diff_mean pvalue
#> <character> <numeric> <numeric>
#> Enterotype 2-Enterotype 1 Enterotype 2-Enterot.. -0.2813948 4.77015e-08
#> Enterotype 3-Enterotype 1 Enterotype 3-Enterot.. -0.2604547 1.63635e-09
#> Enterotype 3-Enterotype 2 Enterotype 3-Enterot.. 0.0209401 7.88993e-01
#> ci_lower ci_upper
#> <numeric> <numeric>
#> Enterotype 2-Enterotype 1 -0.3713469 -0.1914428
#> Enterotype 3-Enterotype 1 -0.3312286 -0.1896808
#> Enterotype 3-Enterotype 2 -0.0575765 0.0994567In 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:
# 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
#> microbiomeMarker-class inherited from phyloseq-class
#> normalization method: [ none ]
#> microbiome marker identity method: [ limma_voom ]
#> marker_table() Marker Table: [ 3 microbiome markers with 5 variables ]
#> otu_table() OTU Table: [ 238 taxa and 32 samples ]
#> sample_data() Sample Data: [ 32 samples by 9 sample variables ]
#> tax_table() Taxonomy Table: [ 238 taxa by 1 taxonomic ranks ]
head(marker_table(mm_lv_pair))
#> feature enrich_group ef_logFC pvalue
#> marker1 p__Bacteroidetes|g__Prevotella Enterotype.2 5.853735 1.051399e-12
#> marker2 p__Verrucomicrobia|g__Akkermansia Enterotype.3 -8.651612 3.104720e-04
#> marker3 p__Verrucomicrobia Enterotype.3 -8.299989 5.426191e-04
#> padj
#> marker1 2.502331e-10
#> marker2 3.694617e-02
#> marker3 4.304778e-026 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 ggplot2. Therefore, these
plots can be easily customized before they are generated using the build-in
functions of ggplot2, e.g. using theme() to modify the titles
and labels. Heatmap is generated using a fantastic Bioconductor package
ComplexHeatmap package.
6.1 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.
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"))
6.2 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.
plot_heatmap(mm_edger, transform = "log10p", group = "Class")