About This Vignette

In this vignette, we want to demonstrate how SIAMCAT can facilitate metagenomic meta-analyses, focussing both on association testing and ML workflows. As an example, we use five different studies of Crohn’s disease (CD), since we have taxonomic profiles from five different metagenomic datasets available. Those studies are:

  1. metaHIT
  2. Lewis et al. 2015
  3. He et al. 2017
  4. Franzosa et al. 2019
  5. HMP2

Setup

First, we load the data for all studies, which are available for download from the EMBL cluster. The raw data have been preprocessed and taxonomically profiled with mOTUs2 and were then aggregated at genus level.

# base url for data download
data.location <- 'https://www.embl.de/download/zeller/'
# datasets
datasets <- c('metaHIT', 'Lewis_2015', 'He_2017', 'Franzosa_2019', 'HMP2')
# metadata
meta.all <- read_tsv(paste0(data.location, 'CD_meta/meta_all.tsv'))
# features
feat <- read.table(paste0(data.location, 'CD_meta/feat_genus.tsv'), 
                   check.names = FALSE, stringsAsFactors = FALSE, quote = '', 
                   sep='\t')
feat <- as.matrix(feat)
# check that metadata and features agree
stopifnot(all(colnames(feat) == meta.all$Sample_ID))

Let us have a look at the distribution of groups across the studies

table(meta.all$Study, meta.all$Group)
##                
##                  CD CTR
##   Franzosa_2019  88  56
##   He_2017        49  53
##   HMP2          583 357
##   Lewis_2015    294  25
##   metaHIT        21  71

Some of the studies contain more than one sample for the same subject. For example, the HMP2 publication focussed on the longitudinal aspect of CD. Therefore. we want to take this into account when training and evaluating the machine learning model (see the vignette about Machine learning pitfalls) and when performing the association testing. Thus, it will be convenient to create a second metadata table containing a single entry for each individual.

meta.ind <- meta.all %>% 
  group_by(Individual_ID) %>% 
  filter(Timepoint==min(Timepoint)) %>% 
  ungroup()

Compare Associations

Compute Associations with SIAMCAT

To test for associations, we can encapsulate each dataset into a different SIAMCAT object and use the check.associations function:

assoc.list <- list()
for (d in datasets){
  
  # filter metadata and convert to dataframe
  meta.train <- meta.ind %>% 
    filter(Study==d) %>% 
    as.data.frame()
  rownames(meta.train) <- meta.train$Sample_ID
  
  # create SIAMCAT object
  sc.obj <- siamcat(feat=feat, meta=meta.train, label='Group', case='CD')
  # test for associations
  sc.obj <- check.associations(sc.obj, detect.lim = 1e-05,
                               feature.type = 'original',
                               fn.plot = paste0('./assoc_plot_', 
                                                d, '.pdf'))
  # extract the associations and save them in the assoc.list
  temp <- associations(sc.obj)
  temp$genus <- rownames(temp)
  assoc.list[[d]] <- temp %>% 
    select(genus, fc, auc, p.adj) %>% 
    mutate(Study=d)
}
# combine all associations
df.assoc <- bind_rows(assoc.list)
df.assoc <- df.assoc %>% filter(genus!='unclassified')
head(df.assoc)
##                    genus fc auc p.adj   Study
## 1 159730 Thermovenabulum  0 0.5   NaN metaHIT
## 2     42447 Anaerobranca  0 0.5   NaN metaHIT
## 3  1562 Desulfotomaculum  0 0.5   NaN metaHIT
## 4     60919 Sanguibacter  0 0.5   NaN metaHIT
## 5      357 Agrobacterium  0 0.5   NaN metaHIT
## 6 392332 Geoalkalibacter  0 0.5   NaN metaHIT

Plot Heatmap for Interesting Genera

Now, we can compare the associations stored in the df.assoc tibble. For example, we can extract features which are very strongly associated with the label (single-feature AUROC > 0.75 or < 0.25) in at least one of the studies and plot the generalized fold change as heatmap.

genera.of.interest <- df.assoc %>% 
  group_by(genus) %>% 
  summarise(m=mean(auc), n.filt=any(auc < 0.25 | auc > 0.75), 
            .groups='keep') %>% 
  filter(n.filt) %>% 
  arrange(m)

After we extracted the genera, we plot them:

df.assoc %>% 
  # take only genera of interest
  filter(genus %in% genera.of.interest$genus) %>% 
  # convert to factor to enforce an ordering by mean AUC
  mutate(genus=factor(genus, levels = rev(genera.of.interest$genus))) %>% 
  # convert to factor to enforce ordering again
  mutate(Study=factor(Study, levels = datasets)) %>% 
  # annotate the cells in the heatmap with stars
  mutate(l=case_when(p.adj < 0.01~'*', TRUE~'')) %>%  
  ggplot(aes(y=genus, x=Study, fill=fc)) + 
    geom_tile() + 
    scale_fill_gradient2(low = '#3B6FB6', high='#D41645', mid = 'white', 
                         limits=c(-2.7, 2.7), 
                         name='Generalized\nfold change') + 
    theme_minimal() + 
    geom_text(aes(label=l)) +
    theme(panel.grid = element_blank()) + 
    xlab('') + ylab('') +
    theme(axis.text = element_text(size=6))

Study as Confounding Factor

Additionally, we can check how differences between studies might influence the variance of specific genera. To do so, we create a singel SIAMCAT object which holds the complete datasets and then we run the check.confounder function.

df.meta <- meta.ind %>% 
  as.data.frame()
rownames(df.meta) <- df.meta$Sample_ID
sc.obj <- siamcat(feat=feat, meta=df.meta, label='Group', case='CD')
## + starting create.label
## Label used as case:
##    CD
## Label used as control:
##    CTR
## + finished create.label.from.metadata in 0.001 s
## + starting validate.data
## +++ checking overlap between labels and features
## + Keeping labels of 504 sample(s).
## +++ checking sample number per class
## +++ checking overlap between samples and metadata
## + finished validate.data in 0.071 s
check.confounders(sc.obj, fn.plot = './confounder_plot_cd_meta.pdf',
                  feature.type='original')
## Finished checking metadata for confounders, results plotted to: ./confounder_plot_cd_meta.pdf

The resulting variance plot shows that some genera are strongly impacated by differences between studies, other genera not so much. Of note, the genera that vary most with the label (CD vs controls) do not show a lot of variance across studies.

ML Meta-analysis

Train LASSO Models

Lastly, we can perform the machine learning (ML) meta-analysis: we first train one model for each datasets and then apply it to the other datasets using the holdout testing functionality of SIAMCAT. For datasets with repeated samples across subjects, we block the cross-validation for subjects in order not to bias the results (see also the vignette about Machine learning pitfalls).

# create tibble to store all the predictions
auroc.all <- tibble(study.train=character(0),
                    study.test=character(0),
                    AUC=double(0))
# and a list to save the trained SIAMCAT objects
sc.list <- list()
for (i in datasets){
  # restrict to a single study
  meta.train <- meta.all %>% 
    filter(Study==i) %>% 
    as.data.frame()
  rownames(meta.train) <- meta.train$Sample_ID
  
  ## take into account repeated sampling by including a parameters
  ## in the create.data.split function
  ## For studies with repeated samples, we want to block the
  ## cross validation by the column 'Individual_ID'
  block <- NULL
  if (i %in% c('metaHIT', 'Lewis_2015', 'HMP2')){
    block <- 'Individual_ID'
    if (i == 'HMP2'){ 
      # for the HMP2 dataset, the number of repeated sample per subject need 
      # to be reduced, because some subjects have been sampled 20 times, 
      # other only 5 times
      meta.train <- meta.all %>% 
        filter(Study=='HMP2') %>% 
        group_by(Individual_ID) %>% 
        sample_n(5, replace = TRUE) %>% 
        distinct() %>% 
        as.data.frame()
      rownames(meta.train) <- meta.train$Sample_ID
    }
  }
  # create SIAMCAT object
  sc.obj.train <- siamcat(feat=feat, meta=meta.train, 
                          label='Group', case='CD')
  # normalize features
  sc.obj.train <- normalize.features(sc.obj.train, norm.method = 'log.std',
                                     norm.param=list(log.n0=1e-05, 
                                                     sd.min.q=0),
                                     feature.type = 'original')
  # Create data split
  sc.obj.train <- create.data.split(sc.obj.train,
                                    num.folds = 10, num.resample = 10,
                                    inseparable = block)
  # train LASSO model
  sc.obj.train <- train.model(sc.obj.train, method='lasso')
  
  
  ## apply trained models to other datasets
  
  # loop through datasets again
  for (i2 in datasets){
    if (i == i2){
      # make and evaluate cross-validation predictions (on the same dataset)
      sc.obj.train <- make.predictions(sc.obj.train)
      sc.obj.train <- evaluate.predictions(sc.obj.train)
      auroc.all <- auroc.all %>% 
        add_row(study.train=i, study.test=i,
                AUC=eval_data(sc.obj.train)$auroc %>% as.double())
    } else {
      # make and evaluate on the external datasets
      # use meta.ind here, since we want only one sample per subject!
      meta.test <- meta.ind %>% 
        filter(Study==i2) %>%
        as.data.frame()
      rownames(meta.test) <- meta.test$Sample_ID
      sc.obj.test <- siamcat(feat=feat, meta=meta.test,
                             label='Group', case='CD')
      # make holdout predictions
      sc.obj.test <- make.predictions(sc.obj.train, 
                                      siamcat.holdout = sc.obj.test)
      sc.obj.test <- evaluate.predictions(sc.obj.test)
      auroc.all <- auroc.all %>% 
        add_row(study.train=i, study.test=i2,
                AUC=eval_data(sc.obj.test)$auroc %>% as.double())
    }
  }
  # save the trained model
  sc.list[[i]] <- sc.obj.train
}

After we trained and applied all models, we can calculate the test average for each dataset:

test.average <- auroc.all %>% 
  filter(study.train!=study.test) %>% 
  group_by(study.test) %>% 
  summarise(AUC=mean(AUC), .groups='drop') %>% 
  mutate(study.train="Average")

Now that we have the AUROC values, we can plot them into a nice heatmap:

# combine AUROC values with test average
bind_rows(auroc.all, test.average) %>% 
  # highlight cross validation versus transfer results
  mutate(CV=study.train == study.test) %>%
  # for facetting later
  mutate(split=case_when(study.train=='Average'~'Average', 
                         TRUE~'none')) %>% 
  mutate(split=factor(split, levels = c('none', 'Average'))) %>% 
  # convert to factor to enforce ordering
  mutate(study.train=factor(study.train, levels = c(datasets, 'Average'))) %>% 
  mutate(study.test=factor(study.test, levels=c(rev(datasets),'Average'))) %>% 
  ggplot(aes(y=study.test, x=study.train, fill=AUC, size=CV, color=CV)) +
    geom_tile() + theme_minimal() +
    # text in tiles
    geom_text(aes_string(label="format(AUC, digits=2)"), 
              col='white', size=2)+
    # color scheme
    scale_fill_gradientn(colours=rev(c('darkgreen','forestgreen', 
                                       'chartreuse3','lawngreen', 
                                       'yellow')), limits=c(0.5, 1)) +
    # axis position/remove boxes/ticks/facet background/etc.
    scale_x_discrete(position='top') + 
    theme(axis.line=element_blank(), 
          axis.ticks = element_blank(), 
          axis.text.x.top = element_text(angle=45, hjust=.1), 
          panel.grid=element_blank(), 
          panel.border=element_blank(), 
          strip.background = element_blank(), 
          strip.text = element_blank()) + 
    xlab('Training Set') + ylab('Test Set') + 
    scale_color_manual(values=c('#FFFFFF00', 'grey'), guide=FALSE) + 
    scale_size_manual(values=c(0, 1), guide=FALSE) + 
    facet_grid(~split, scales = 'free', space = 'free')

Investigate Feature Weights

Now that we the trained models (and we saved them in the sc.list object), we can also extract the model weights using SIAMCAT and compare to the associations we computed above.

weight.list <- list()
for (d in datasets){
  sc.obj.train <- sc.list[[d]]
  # extract the feature weights out of the SIAMCAT object
  temp <- feature_weights(sc.obj.train)
  temp$genus <- rownames(temp)
  # save selected info in the weight.list
  weight.list[[d]] <- temp %>% 
    select(genus, median.rel.weight, mean.rel.weight, percentage) %>% 
    mutate(Study=d) %>% 
    mutate(r.med=rank(-abs(median.rel.weight)), 
           r.mean=rank(-abs(mean.rel.weight)))
}
# combine all feature weights into a single tibble
df.weights <- bind_rows(weight.list)
df.weights <- df.weights %>% filter(genus!='unclassified')

Using this, we can plot another heatmap with the weights, focussing on the genera of interest for which we plotted the associations as heatmap above.

# compute absolute feature weights
abs.weights <- df.weights %>% 
  group_by(Study) %>% 
  summarise(sum.median=sum(abs(median.rel.weight)),
            sum.mean=sum(abs(mean.rel.weight)),
            .groups='drop')

df.weights %>% 
  full_join(abs.weights) %>% 
  # normalize by the absolute model size
  mutate(median.rel.weight=median.rel.weight/sum.median) %>% 
  # only include genera of interest
  filter(genus %in% genera.of.interest$genus) %>% 
  # highlight feature rank for the top 20 features
  mutate(r.med=case_when(r.med > 20~NA_real_, TRUE~r.med)) %>%
  # enforce the correct ordering by converting to factors again
  mutate(genus=factor(genus, levels = rev(genera.of.interest$genus))) %>% 
  mutate(Study=factor(Study, levels = datasets)) %>% 
  ggplot(aes(y=genus, x=Study, fill=median.rel.weight)) + 
    geom_tile() + 
    scale_fill_gradientn(colours=rev(
      c('#007A53', '#009F4D', "#6CC24A", 'white',
        "#EFC06E", "#FFA300", '#BE5400')), 
      limits=c(-0.15, 0.15)) +
    theme_minimal() + 
    geom_text(aes(label=r.med), col='black', size= 2) +
    theme(panel.grid = element_blank()) + 
    xlab('') + ylab('') +
    theme(axis.text = element_text(size=6))
## Joining, by = "Study"

Session Info

sessionInfo()
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.5
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] SIAMCAT_1.11.0    phyloseq_1.32.0   mlr_2.18.0        ParamHelpers_1.14
##  [5] forcats_0.5.0     stringr_1.4.0     dplyr_1.0.2       purrr_0.3.4      
##  [9] readr_1.4.0       tidyr_1.1.2       tibble_3.0.4      ggplot2_3.3.2    
## [13] tidyverse_1.3.0   BiocStyle_2.16.1 
## 
## loaded via a namespace (and not attached):
##   [1] colorspace_2.0-0    ellipsis_0.3.1      rprojroot_1.3-2    
##   [4] XVector_0.28.0      fs_1.5.0            rstudioapi_0.12    
##   [7] farver_2.0.3        fansi_0.4.1         lubridate_1.7.9    
##  [10] xml2_1.3.2          PRROC_1.3.1         codetools_0.2-18   
##  [13] splines_4.0.2       knitr_1.30          ade4_1.7-16        
##  [16] jsonlite_1.7.1      pROC_1.16.2         gridBase_0.4-7     
##  [19] broom_0.7.2         cluster_2.1.0       dbplyr_2.0.0       
##  [22] BiocManager_1.30.10 compiler_4.0.2      httr_1.4.2         
##  [25] backports_1.2.0     assertthat_0.2.1    Matrix_1.2-18      
##  [28] cli_2.1.0           htmltools_0.5.0     prettyunits_1.1.1  
##  [31] tools_4.0.2         igraph_1.2.6        gtable_0.3.0       
##  [34] glue_1.4.2          reshape2_1.4.4      LiblineaR_2.10-8   
##  [37] fastmatch_1.1-0     Rcpp_1.0.5          parallelMap_1.5.0  
##  [40] Biobase_2.48.0      cellranger_1.1.0    pkgdown_1.6.1      
##  [43] vctrs_0.3.4         Biostrings_2.56.0   multtest_2.44.0    
##  [46] ape_5.4-1           nlme_3.1-150        iterators_1.0.13   
##  [49] xfun_0.19           rvest_0.3.6         lifecycle_0.2.0    
##  [52] beanplot_1.2        zlibbioc_1.34.0     MASS_7.3-53        
##  [55] scales_1.1.1        ragg_0.4.0          hms_0.5.3          
##  [58] parallel_4.0.2      biomformat_1.16.0   rhdf5_2.32.4       
##  [61] RColorBrewer_1.1-2  BBmisc_1.11         curl_4.3           
##  [64] yaml_2.2.1          gridExtra_2.3       memoise_1.1.0      
##  [67] stringi_1.5.3       S4Vectors_0.26.1    desc_1.2.0         
##  [70] corrplot_0.84       foreach_1.5.1       checkmate_2.0.0    
##  [73] permute_0.9-5       BiocGenerics_0.34.0 shape_1.4.5        
##  [76] matrixStats_0.57.0  rlang_0.4.8         pkgconfig_2.0.3    
##  [79] systemfonts_0.3.2   evaluate_0.14       lattice_0.20-41    
##  [82] Rhdf5lib_1.10.1     labeling_0.4.2      tidyselect_1.1.0   
##  [85] plyr_1.8.6          magrittr_1.5        bookdown_0.21      
##  [88] R6_2.5.0            IRanges_2.22.2      generics_0.1.0     
##  [91] DBI_1.1.0           pillar_1.4.6        haven_2.3.1        
##  [94] withr_2.3.0         mgcv_1.8-33         survival_3.2-7     
##  [97] modelr_0.1.8        crayon_1.3.4        rmarkdown_2.5      
## [100] progress_1.2.2      grid_4.0.2          readxl_1.3.1       
## [103] data.table_1.13.2   vegan_2.5-6         infotheo_1.2.0     
## [106] reprex_0.3.0        digest_0.6.27       textshaping_0.1.2  
## [109] glmnet_4.0-2        stats4_4.0.2        munsell_0.5.0