In biobakery/MMUPHin: Meta-analysis Methods with Uniform Pipeline for Heterogeneity in Microbiome Studies

knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(cache = FALSE)

Introduction

MMUPHin is an R package implementing meta-analysis methods for microbial community profiles. It has interfaces for: a) covariate-controlled batch and study effect adjustment, b) meta-analytic differential abundance testing, and meta-analytic discovery of c) discrete (cluster-based) or d) continuous unsupervised population structure.

Overall, MMUPHin enables the normalization and combination of multiple microbial community studies. It can then help in identifying microbes, genes, or pathways that are differential with respect to combined phenotypes. Finally, it can find clusters or gradients of sample types that reproduce consistently among studies.

Installation

MMUPHin is a Bioconductor package and can be installed via the following command.

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

This vignette is intended to provide working examples for all four functionalities of MMUPHin.

MMUPHin can be loaded into R with the first library() command shown below. We also load some utility packages for data manipulation and visualization.

library(MMUPHin)
library(magrittr)
library(dplyr)
library(ggplot2)

Input data

As input, MMUPHin requires a properly formatted collection of microbial community studies, with both feature abundances and accompanying metadata. Here we use the five published colorectal cancer (CRC) stool metagenomic studies, incorporated in @thomas2019metagenomic. Data for the studies are already conveniently packaged and accessible through the Biocondcutor package r BiocStyle::Biocpkg("curatedMetagenomicData"), though additional wranglings are needed to format input for MMUPHin.

Importantly, MMUPHin asks that feature abundances be provided as a feature-by-sample matrix, and the metadata be provided as a data frame. The two objects shoud agree on sample IDs, that is, colname of the feature abundance matrix and rowname of the metadata data frame must agree. Many popular 'omic data classes in R already enforce this standard, such as ExpressionSet from r BiocStyle::Biocpkg("Biobase"), or phyloseq from r BiocStyle::Biocpkg("phyloseq").

To minimize users' efforts in loading data to run the examples, we have properly formatted the five studies for easy access. The feature abundances and metadata can be loaded with the following code chunk. For the interested user, the commented out scripts were used for accessing data directly from r BiocStyle::Biocpkg("curatedMetagenomicData") and formatting. It might be worthwhile to read through these as they perform many of the common tasks for preprocessing microbial feature abundance data in R, including sample/feature subsetting, normalization, filtering, etc.

data("CRC_abd", "CRC_meta")
CRC_abd[1:5, 1, drop = FALSE]

CRC_meta[1, 1:5]

table(CRC_meta$studyID)

Performing batch (study) effect adjustment with adjust_batch

adjust_batch aims to correct for technical study/batch effects in microbial feature abundances. It takes as input the feature-by-sample abundance matrix, and performs batch effect adjustment given provided batch and optional covariate variables. It returns the batch-adjusted abundance matrix. Check help(adjust_batch) for additional details and options. This figure from the paper (figure 2b) shows the desired effect of the batch adjustment: batch effects are removed, making the effect of the biological variable of interest easier to detect.

knitr::include_graphics('../man/figures/adj_batch_example.png')

Here we use adjust_batch to correct for differences in the five studies, while controlling for the effect of CRC versus control (variable study_condition in CRC_meta).

adjust_batch returns a list of multiple outputs. The part we are most interested in now is the adjusted abundance table, which we assign to CRC_abd_adj.

fit_adjust_batch <- adjust_batch(feature_abd = CRC_abd,
                                 batch = "studyID",
                                 covariates = "study_condition",
                                 data = CRC_meta,
                                 control = list(verbose = FALSE))

CRC_abd_adj <- fit_adjust_batch$feature_abd_adj

One way to evaluate the effect of batch adjustment is to assess the total variability in microbial profiles attributable to study differences, before and after adjustment. This is commonly known as a PERMANOVA test [@tang2016permanova], and can be performed with the adonis function in r BiocStyle::CRANpkg("vegan"). The inputs to adonis() are indices of microbial compositional dissimilarity.

library(vegan, quietly = TRUE)

D_before <- vegdist(t(CRC_abd))
D_after <- vegdist(t(CRC_abd_adj))

set.seed(1)
fit_adonis_before <- adonis(D_before ~ studyID, data = CRC_meta)
fit_adonis_after <- adonis(D_after ~ studyID, data = CRC_meta)
print(fit_adonis_before$aov.tab)
print(fit_adonis_after$aov.tab)

We can see that, before study effect adjustment, study differences can expalin a total of r round(fit_adonis_before$aov.tab["studyID", "R2"] * 100, digits = 2)% of the variability in microbial abundance profiles, whereas after adjustment this was reduced to r round(fit_adonis_after$aov.tab["studyID", "R2"] * 100, digits = 2)%, though the effect of study is significant in both cases.

Meta-analytical differential abundance testing with lm_meta

One of the most common meta-analysis goals is to combine association effects across batches/studies to identify consistent overall effects. lm_meta provides a straightforward interface to this task, by first performing regression analysis in individual batches/studies using the well-validated r BiocStyle::Biocpkg("Maaslin2") package, and then aggregating results with established fixed/mixed effect models, realized via the r BiocStyle::CRANpkg("vegan") package. Here, we use lm_meta to test for consistently differential abundant species between CRC and control samples across the five studies, while controlling for demographic covariates (gender, age, BMI). Again, lm_meta returns a list of more than one components. meta_fits provides the final meta-analytical testing results. See help(lm_meta) for the meaning of other components.

fit_lm_meta <- lm_meta(feature_abd = CRC_abd_adj,
                       batch = "studyID",
                       exposure = "study_condition",
                       covariates = c("gender", "age", "BMI"),
                       data = CRC_meta,
                       control = list(verbose = FALSE))

meta_fits <- fit_lm_meta$meta_fits

You'll note that this command produces a few error messages. This is because for one of the studies the specified covariates were not measured. As the message states, these covariates are not included when analyzing data from that study. We also note that batch correction is standard but not necessary for the analysis here.

We can visualize the significant (FDR q < 0.05) species associated with CRC in these studies/samples. Comparing them with Figure 1b of @thomas2019metagenomic, we can see that many of the significant species do agree.

meta_fits %>% 
  filter(qval.fdr < 0.05) %>% 
  arrange(coef) %>% 
  mutate(feature = factor(feature, levels = feature)) %>% 
  ggplot(aes(y = coef, x = feature)) +
  geom_bar(stat = "identity") +
  coord_flip()

Identifying discrete population structures with discrete_discover

Clustering analysis of microbial profiles can help identify meaningful discrete population subgroups [@ravel2011vaginal], but must be carried out carefully with validations to ensure that the identified structures are consistent [@koren2013guide]. discrete_discover provides the functionality to use prediction strength [@tibshirani2005cluster] to evaluate discrete clustering structures within individual microbial studies, as well as a "generalized prediction strength" to evaluate their reproducibility in other studies. These jointly provide meta-analytical evidence for (or against) identifying discrete population structures in microbial profiles. Check help(discrete_discover) to see more details on the method and additional options. We can contrast discrete structure (clusters) with continuous structure (which will be overviewed in the next section):

knitr::include_graphics('../man/figures/structure_types.PNG')

The gut microbiome is known to form gradients rather than discrete clusters [@koren2013guide]. Here we use discrete_discover to evaluate clustering structures among control samples in the five stool studies. It's worth noting that this function takes a distance/dissimilarity matrix as an input, rather than the abundance table.

control_meta <- subset(CRC_meta, study_condition == "control")
control_abd_adj <- CRC_abd_adj[, rownames(control_meta)]

D_control <- vegdist(t(control_abd_adj))
fit_discrete <- discrete_discover(D = D_control,
                                  batch = "studyID",
                                  data = control_meta,
                                  control = list(k_max = 8,
                                                 verbose = FALSE))

The internal_mean and internal_sd components of fit_discrete are matrices that provide internal evaluation statistics (prediction strength) for each batch (column) and evaluated number of clusters (row). Similarly, external_mean and external_sd provide external evaluation statistics ( generalized prediction strenght). Evidence for existence of discrete structures would be a "peaking" of the mean statistics at a particular cluster number. Here, for easier examination of such a pattern, we visualize the results for the largest study, ZellerG_2014. Note that visualization for all studies are by default automatically generated and saved to the output file "diagnostic_discrete.pdf".

study_id = "ZellerG_2014.metaphlan_bugs_list.stool"

internal <- data.frame(K = 2:8,
                       statistic = fit_discrete$internal_mean[, study_id],
                       se = fit_discrete$internal_se[, study_id],
                       type = "internal")

external <- data.frame(K = 2:8,
                       statistic = fit_discrete$external_mean[, study_id],
                       se = fit_discrete$external_se[, study_id],
                       type = "external")

rbind(internal, external) %>% 
  ggplot(aes(x = K, y = statistic, color = type)) +
  geom_point(position = position_dodge(width = 0.5)) + 
  geom_line(position = position_dodge(width = 0.5)) +
  geom_errorbar(aes(ymin = statistic - se, ymax = statistic + se),
                position = position_dodge(width = 0.5), width = 0.5) +
  ggtitle("Evaluation of discrete structure in control stool microbiome (ZellerG_2014)")

The decreasing trend for both the internal and external statistics along with number of clusters (K) suggests that discrete structures cannot be well-established. To provide a positive example, we examine the two vaginal microbiome studies provided by r BiocStyle::Biocpkg("curatedMetagenomicData"), as the vaginal microbiome is known to have distinct subtypes [@ravel2011vaginal]. Again, we pre-formatted these datasets for easy access, but you can recreate them from r BiocStyle::Biocpkg("curatedMetagenomicData") with the commented out scripts.

data("vaginal_abd", "vaginal_meta")
D_vaginal <- vegdist(t(vaginal_abd))

fit_discrete_vag <- discrete_discover(D = D_vaginal,
                                      batch = "studyID",
                                      data = vaginal_meta,
                                      control = list(verbose = FALSE,
                                                     k_max = 8))

hmp_id = "HMP_2012.metaphlan_bugs_list.vagina"
data.frame(K = 2:8,
           statistic = fit_discrete_vag$internal_mean[, hmp_id],
           se = fit_discrete_vag$internal_se[, hmp_id]) %>% 
  ggplot(aes(x = K, y = statistic)) +
  geom_point() +
  geom_line() +
  geom_errorbar(aes(ymin = statistic - se, 
                    ymax = statistic + se), 
                width = 0.5) +
  ggtitle("Evaluation of discrete structure in vaginal microbiome (HMP_2012)")

We can see that for the vaginal microbiome, discrete_discover suggests the existence of five clusters. Here we examine only the internal metrics of HMP_2012 as the other study (FerrettiP_2018) has only r sum(vaginal_meta$studyID == "FerrettiP_2018.metaphlan_bugs_list.vagina") samples.

Identifying continuous population structures with continuous_discover

Population structure in the microbiome can manifest as gradients rather than discrete clusters, such as dominant phyla trade-off or disease-associated dysbiosis. continuous_discover provide functionality to identify such structures as well as to validate them with meta-analysis. We again evaluate these continuous structures in control samples of the five studies.

Much like adjust_batch and lm_meta, continuous_discover also takes as input feature-by-sample abundances. The control argument offers many tuning parameters and here we set one of them, var_perc_cutoff, to 0.5, which asks the method to include top principal components within each batch that in total explain at least 50% of the total variability in the batch. See help(continuous_discover) for more details on the tuning parameters and their interpretations.

fit_continuous <- continuous_discover(feature_abd = control_abd_adj,
                                      batch = "studyID",
                                      data = control_meta,
                                      control = list(var_perc_cutoff = 0.5,
                                                     verbose = FALSE))

We can visualize the identified continuous structure scores in at least two ways: first, to examine their top contributing microbial features, to get an idea of what the score is characterizing, and second, to overlay the continuous scores with an ordination visualization. Here we perform these visualizations on the first identified continuous score.

loading <- data.frame(feature = rownames(fit_continuous$consensus_loadings),
                      loading1 = fit_continuous$consensus_loadings[, 1])

loading %>%
    arrange(-abs(loading1)) %>%
    slice(1:20) %>%
    arrange(loading1) %>%
    mutate(feature = factor(feature, levels = feature)) %>%
    ggplot(aes(x = feature, y = loading1)) +
    geom_bar(stat = "identity") +
    coord_flip() +
    ggtitle("Features with top loadings")

mds <- cmdscale(d = D_control)
colnames(mds) <- c("Axis1", "Axis2")
as.data.frame(mds) %>% 
  mutate(score1 = fit_continuous$consensus_scores[, 1]) %>% 
  ggplot(aes(x = Axis1, y = Axis2, color = score1)) +
  geom_point() +
  coord_fixed()

From ordination we see that the first continuos score indeed represent strong variation across these stool samples. From the top loading features we can see that this score strongly represents a Bacteroidetes (the Bacteroides species) versus Firmicutes (the Ruminococcus species) tradeoff.

Sessioninfo

sessionInfo()

References

biobakery/MMUPHin documentation built on March 30, 2024, 4:50 a.m.