About This Vignette

Here, we demonstrate the standard workflow of the SIAMCAT package using as an example the dataset from Nielsen et al. Nat Biotechnol 2014. This dataset contains samples from patients with inflammatory bowel disease and from controls.
More importantly, these samples have been collected in two different countries, Spain and Denmark. Together with technical differences between these samples, this introduces a potent confounding factor into the data. Here we are going to explore how SIAMCAT would identify the confounding factor and what the results would be if you account for the confounder or not.

Setup

First, we load the packages needed to perform the analyses.

Preparations

There are two different ways to access the data for our example dataset. On the one hand, it is available through the curatedMetagenomicsData R package. However, using it here would create many more dependencies for the SIAMCAT package.
Therefore, we here use data available through the EMBL cluster.

In the SIAMCAT paper, we performed the presented analyses on the datasets available through curatedMetagenomicsData. If you want to reproduce the analysis from the SIAMCAT paper, you can execute the code chunks in the curatedMetageomicsData section, otherwise execute the code in the mOTUs2 section.

curatedMetagenomicsData

First, we load the package:

library("curatedMetagenomciData")

Metadata

The data are part of the combined_metadata

meta.nielsen.full <- combined_metadata %>%
  filter(dataset_name=='NielsenHB_2014')

One thing we have to keep in mind are repeated samples per subject (see also the Machine learning pitfalls vignette).

print(length(unique(meta.nielsen.full$subjectID)))
print(nrow(meta.nielsen.full))

Some subjects (but not all) had been sampled multiple times. Therefore, we want to remove repeated samplings for the same subject, since the samples would otherwise not be indepdenent from another.

The visit number is encoded in the sampleID. Therefore, we can use this information to extract when the samples have been taken and use only the first visit for each subject.

meta.nielsen <- meta.nielsen.full %>%
  select(sampleID, subjectID, study_condition, disease_subtype,
         disease, age, country, number_reads, median_read_length, BMI) %>%
  mutate(visit=str_extract(sampleID, '_[0-9]+$')) %>%
  mutate(visit=str_remove(visit, '_')) %>%
  mutate(visit=as.numeric(visit)) %>%
  mutate(visit=case_when(is.na(visit)~0, TRUE~visit)) %>%
  group_by(subjectID) %>%
  filter(visit==min(visit)) %>%
  ungroup() %>%
  mutate(Sample_ID=sampleID) %>%
  mutate(Group=case_when(disease=='healthy'~'CTR',
                         TRUE~disease_subtype))

Now, we can restrict our metadata to samples with UC and healthy control samples:

meta.nielsen <- meta.nielsen %>%
  filter(Group %in% c('UC', 'CTR'))

As a last step, we can adjust the column names for the metadata so that they agree with the data available from the EMBL cluster. Also, we add rownames to the dataframe since SIAMCAT needs rownames to match samples across metadata and features.

meta.nielsen <- meta.nielsen %>%
  mutate(Country=country)
meta.nielsen <- as.data.frame(meta.nielsen)
rownames(meta.nielsen) <- meta.nielsen$sampleID

Taxonomic Profiles

We can load the taxonomic profiles generated with MetaPhlAn2 via the curatedMetagenomicsData R package.

x <- 'NielsenHB_2014.metaphlan_bugs_list.stool'
feat <- curatedMetagenomicData(x=x, dryrun=FALSE)
feat <- feat[[x]]@assayData$exprs

The MetaPhlAn2 profiles contain information on different taxonomic levels. Therefore, we want to restrict them to species-level profiles. In a second step, we convert them into relative abundances (summing up to 1) instead of using the percentages (summing up to 100) that MetaPhlAn2 outputs.

feat <- feat[grep(x=rownames(feat), pattern='s__'),]
feat <- feat[grep(x=rownames(feat),pattern='t__', invert = TRUE),]
feat <- t(t(feat)/100)

The feature names are very long and may be a bit un-wieldy for plotting later on, so we shorten them to only the species name:

rownames(feat) <- str_extract(rownames(feat), 's__.*$')

mOTUs2 Profiles

Both metadata and features are available through the EMBL cluster:

# base url for data download
data.location <- 'https://www.embl.de/download/zeller/metaHIT/'
## metadata
meta.nielsen <- read_tsv(paste0(data.location, 'meta_Nielsen.tsv'))
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   Sample_ID = col_character(),
##   Individual_ID = col_character(),
##   Country = col_character(),
##   Sampling_day = col_double(),
##   Gender = col_character(),
##   Age = col_double(),
##   BMI = col_double(),
##   Group = col_character(),
##   Library_Size = col_double()
## )
# also here, we have to remove repeated samplings and CD samples
meta.nielsen <- meta.nielsen %>%
  filter(Group %in% c('CTR', 'UC')) %>%
  group_by(Individual_ID) %>%
  filter(Sampling_day==min(Sampling_day)) %>%
  ungroup() %>%
  as.data.frame()
rownames(meta.nielsen) <- meta.nielsen$Sample_ID

## features
feat <- read.table(paste0(data.location, 'metaHIT_motus.tsv'), 
                   stringsAsFactors = FALSE, sep='\t',
                   check.names = FALSE, quote = '', comment.char = '')
feat <- feat[,colSums(feat) > 0]
feat <- prop.table(as.matrix(feat), 2)

SIAMCAT Workflow (without Confounders)

The SIAMCAT Object

Now, we have everything ready to create a SIAMCAT object which stores the feature matrix, the meta-variables, and the label. Here, the label is created using the information in the metadata.
To demonstrate the normal SIAMCAT workflow, we remove the confounding factor by only looking at samples from Spain. Below, we have a look what would have happened if we had not removed them.

# remove Danish samples
meta.nielsen.esp <- meta.nielsen[meta.nielsen$Country == 'ESP',]
sc.obj <- siamcat(feat=feat, meta=meta.nielsen.esp, label='Group', case='UC')
## + starting create.label
## Label used as case:
##    UC
## Label used as control:
##    CTR
## + finished create.label.from.metadata in 0.02 s
## + starting validate.data
## +++ checking overlap between labels and features
## + Keeping labels of 128 sample(s).
## +++ checking sample number per class
## +++ checking overlap between samples and metadata
## + finished validate.data in 0.374 s

Filtering

Now, we can filter feature with low overall abundance and prevalence.

sc.obj <- filter.features(sc.obj, cutoff=1e-04,
                          filter.method = 'abundance')
## Features successfully filtered
sc.obj <- filter.features(sc.obj, cutoff=0.05,
                          filter.method='prevalence',
                          feature.type = 'filtered')
## Features successfully filtered

Association Plot

The check.assocation function calculates the significance of enrichment and metrics of association (such as generalized fold change and single-feautre AUROC).

sc.obj <- check.associations(sc.obj, detect.lim = 1e-06,
                             alpha=0.1, max.show = 20,
                             plot.type = 'quantile.rect',
                             panels = c('fc'),
                             fn.plot = './association_plot_nielsen.pdf')

Confounder Analysis

We can also check the supplied meta-variables for potential confounding.

check.confounders(sc.obj, fn.plot = './confounders_nielsen.pdf')
## ++ remove metadata variables, since all subjects have the same value
##  Country
## Finished checking metadata for confounders, results plotted to: ./confounders_nielsen.pdf

The function produces one plot for each meta-variable. Here, we show only the example of the body mass index (BMI). The BMI distributions look very similar for both controls and UC cases, so it is unlikely that the BMI would confound the analyses.

Machine Learning Workflow

The machine learning workflow can be easily implemented in SIAMCAT. It contains the following steps:

  • Feature normalization
  • Data splitting for cross-validation
  • Model training
  • Making model predictions (on left-out data)
  • Evaluating model predictions (using AUROC and AUPRC)
sc.obj <- normalize.features(sc.obj, norm.method = 'log.std',
                             norm.param = list(log.n0=1e-06, sd.min.q=0))
## Features normalized successfully.
sc.obj <- create.data.split(sc.obj, num.folds = 5, num.resample = 5)
## Features splitted for cross-validation successfully.
sc.obj <- train.model(sc.obj, method='lasso')
## Trained lasso models successfully.
sc.obj <- make.predictions(sc.obj)
## Made predictions successfully.
sc.obj <- evaluate.predictions(sc.obj)
## Evaluated predictions successfully.

Model Evaluation Plot

The model evaluation plot will produce one plot with the ROC curve and another one with the precision-recall curve (not shown here).

model.evaluation.plot(sc.obj, fn.plot = './eval_plot_nielsen.pdf')
## Plotted evaluation of predictions successfully to: ./eval_plot_nielsen.pdf

Model Interpretation Plot

The model interpretation plot can give you additional information about the trained machine learning model. It will show you:

  • the feature importance as barplot,
  • the feature robustness (in how many of the models in the repeated cross-validation this feature has been selected into the model),
  • the normalized feature abundances across samples as heatmap,
  • the optional metadata as heatmap below, and
  • a boxplot showing the proportion of the model weight that is explained by
    the selected features.
model.interpretation.plot(sc.obj, consens.thres = 0.8,
                          fn.plot = './interpretation_nielsen.pdf')
## Successfully plotted model interpretation plot to: ./interpretation_nielsen.pdf

Confounder Analysis

As already mentioned above, the Nielsen dataset contains samples from both Spain and Denmark. How would SIAMCAT have alerted us to this?

table(meta.nielsen$Group, meta.nielsen$Country)
##      
##       DNK ESP
##   CTR 177  59
##   UC    0  69

Country Confounder

First, we create a SIAMCAT object again, this time including the Danish controls:

sc.obj.full <- siamcat(feat=feat, meta=meta.nielsen,
                       label='Group', case='UC')
## + starting create.label
## Label used as case:
##    UC
## Label used as control:
##    CTR
## + finished create.label.from.metadata in 0.003 s
## + starting validate.data
## +++ checking overlap between labels and features
## + Keeping labels of 291 sample(s).
## + Removed 14 samples from the label object...
## +++ checking sample number per class
## +++ checking overlap between samples and metadata
## + finished validate.data in 0.511 s
sc.obj.full <- filter.features(sc.obj.full, cutoff=1e-04,
                               filter.method = 'abundance')
## Features successfully filtered
sc.obj.full <- filter.features(sc.obj.full, cutoff=0.05,
                               filter.method='prevalence',
                               feature.type = 'filtered')
## Features successfully filtered

The confounder plot would show us that the meta-variable “country” might be problematic:

check.confounders(sc.obj.full, fn.plot = './confounders_dnk.pdf')

Association Testing

First, we can use SIAMCAT to test for associations including the Danish samples.

sc.obj.full <- check.associations(sc.obj.full, detect.lim = 1e-06,
                                  alpha=0.1, max.show = 20,
                                  plot.type = 'quantile.rect',
                                  fn.plot = './association_plot_dnk.pdf')

Confounders can lead to biases in association testing. After using SIAMCAT to test for associations in both datasets (one time including the Danish samples, the other time restricted to samples from Spain only), we can extract the association metrics from both SIAMCAT objects and compare them in a scatter plot.

assoc.sp <- associations(sc.obj)
assoc.sp$species <- rownames(assoc.sp)
assoc.sp_dnk <- associations(sc.obj.full)
assoc.sp_dnk$species <- rownames(assoc.sp_dnk)

df.plot <- full_join(assoc.sp, assoc.sp_dnk, by='species')
df.plot %>%
  mutate(highlight=str_detect(species, 'formicigenerans')) %>%
  ggplot(aes(x=-log10(p.adj.x), y=-log10(p.adj.y), col=highlight)) +
    geom_point(alpha=0.3) +
    xlab('Spanish samples only\n-log10(q)') +
    ylab('Spanish and Danish samples only\n-log10(q)') +
    theme_classic() +
    theme(panel.grid.major = element_line(colour='lightgrey'),
          aspect.ratio = 1.3) +
    scale_colour_manual(values=c('darkgrey', '#D41645'), guide=FALSE) +
    annotate('text', x=0.7, y=8, label='Dorea formicigenerans')

This result shows that several species are only signficant if the Danish control samples are included, but not when considering only the Spanish samples.

As an example, we highlighted the species “Dorea formicigenerans” in the plot above. The test is not significant in the Spanish cohort, but is highly significant when the Danish samples are included.

# extract information out of the siamcat object
feat.dnk <- get.filt_feat.matrix(sc.obj.full)
label.dnk <- label(sc.obj.full)$label
country <- meta(sc.obj.full)$Country
names(country) <- rownames(meta(sc.obj.full))

df.plot <- tibble(dorea=log10(feat.dnk[str_detect(rownames(feat.dnk),
                                                  'formicigenerans'),
                                       names(label.dnk)] + 1e-05),
                  label=label.dnk, country=country) %>%
  mutate(label=case_when(label=='-1'~'CTR', TRUE~"UC")) %>%
  mutate(x_value=paste0(country, '_', label))

df.plot %>%
  ggplot(aes(x=x_value, y=dorea)) +
    geom_boxplot(outlier.shape = NA) +
    geom_jitter(width = 0.08, stroke=0, alpha=0.2) +
    theme_classic() +
    xlab('') +
    ylab("log10(Dorea formicigenerans)") +
    stat_compare_means(comparisons = list(c('DNK_CTR', 'ESP_CTR'),
                                          c('DNK_CTR', 'ESP_UC'),
                                          c('ESP_CTR', 'ESP_UC')))

Machine Learning

The results from the machine learning workflows can also be biased by the differences between countries, leading to exaggerated performance estimates.

sc.obj.full <- normalize.features(sc.obj.full, norm.method = 'log.std',
                                  norm.param = list(log.n0=1e-06, sd.min.q=0))
## Features normalized successfully.
sc.obj.full <- create.data.split(sc.obj.full, num.folds = 5, num.resample = 5)
## Features splitted for cross-validation successfully.
sc.obj.full <- train.model(sc.obj.full, method='lasso')
## Trained lasso models successfully.
sc.obj.full <- make.predictions(sc.obj.full)
## Made predictions successfully.
sc.obj.full <- evaluate.predictions(sc.obj.full)
## Evaluated predictions successfully.

When we compare the performance of the two different models, the model with the Danish and Spanish samples included seems to perform better (higher AUROC value). However, the previous analysis suggests that this performance estimate is biased and exaggerated because differences between Spanish and Danish samples can be very large.

model.evaluation.plot("Spanish samples only"=sc.obj,
                      "Danish and Spanish samples"=sc.obj.full,
                      fn.plot = './eval_plot_dnk.pdf')
## Plotted evaluation of predictions successfully to: ./eval_plot_dnk.pdf

To demonstrate how machine learning models can exploit this confounding factor, we can train a model to distinguish between Spanish and Danish control samples. As you can see, the model can distinguish between the two countries with almost perfect accuracy.

meta.nielsen.country <- meta.nielsen[meta.nielsen$Group=='CTR',]

sc.obj.country <- siamcat(feat=feat, meta=meta.nielsen.country,
                          label='Country', case='ESP')
sc.obj.country <- filter.features(sc.obj.country, cutoff=1e-04,
                              filter.method = 'abundance')
sc.obj.country <- filter.features(sc.obj.country, cutoff=0.05,
                              filter.method='prevalence',
                              feature.type = 'filtered')
sc.obj.country <- normalize.features(sc.obj.country, norm.method = 'log.std',
                                     norm.param = list(log.n0=1e-06,
                                                       sd.min.q=0))
sc.obj.country <- create.data.split(sc.obj.country,
                                    num.folds = 5, num.resample = 5)
sc.obj.country <- train.model(sc.obj.country, method='lasso')
sc.obj.country <- make.predictions(sc.obj.country)
sc.obj.country <- evaluate.predictions(sc.obj.country)

print(eval_data(sc.obj.country)$auroc)
## Area under the curve: 0.9701

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] ggpubr_0.4.0      SIAMCAT_1.11.0    phyloseq_1.32.0   mlr_2.18.0       
##  [5] ParamHelpers_1.14 forcats_0.5.0     stringr_1.4.0     dplyr_1.0.2      
##  [9] purrr_0.3.4       readr_1.4.0       tidyr_1.1.2       tibble_3.0.4     
## [13] ggplot2_3.3.2     tidyverse_1.3.0   BiocStyle_2.16.1 
## 
## loaded via a namespace (and not attached):
##   [1] colorspace_2.0-0    ggsignif_0.6.0      rio_0.5.16         
##   [4] ellipsis_0.3.1      rprojroot_1.3-2     XVector_0.28.0     
##   [7] fs_1.5.0            rstudioapi_0.12     farver_2.0.3       
##  [10] fansi_0.4.1         lubridate_1.7.9     xml2_1.3.2         
##  [13] PRROC_1.3.1         codetools_0.2-18    splines_4.0.2      
##  [16] knitr_1.30          ade4_1.7-16         jsonlite_1.7.1     
##  [19] pROC_1.16.2         gridBase_0.4-7      broom_0.7.2        
##  [22] cluster_2.1.0       dbplyr_2.0.0        BiocManager_1.30.10
##  [25] compiler_4.0.2      httr_1.4.2          backports_1.2.0    
##  [28] assertthat_0.2.1    Matrix_1.2-18       cli_2.1.0          
##  [31] htmltools_0.5.0     prettyunits_1.1.1   tools_4.0.2        
##  [34] igraph_1.2.6        gtable_0.3.0        glue_1.4.2         
##  [37] reshape2_1.4.4      LiblineaR_2.10-8    fastmatch_1.1-0    
##  [40] Rcpp_1.0.5          parallelMap_1.5.0   carData_3.0-4      
##  [43] Biobase_2.48.0      cellranger_1.1.0    pkgdown_1.6.1      
##  [46] vctrs_0.3.4         Biostrings_2.56.0   multtest_2.44.0    
##  [49] ape_5.4-1           nlme_3.1-150        iterators_1.0.13   
##  [52] xfun_0.19           openxlsx_4.2.3      rvest_0.3.6        
##  [55] lifecycle_0.2.0     XML_3.99-0.5        rstatix_0.6.0      
##  [58] beanplot_1.2        zlibbioc_1.34.0     MASS_7.3-53        
##  [61] scales_1.1.1        ragg_0.4.0          hms_0.5.3          
##  [64] parallel_4.0.2      biomformat_1.16.0   rhdf5_2.32.4       
##  [67] RColorBrewer_1.1-2  BBmisc_1.11         curl_4.3           
##  [70] yaml_2.2.1          gridExtra_2.3       memoise_1.1.0      
##  [73] stringi_1.5.3       S4Vectors_0.26.1    desc_1.2.0         
##  [76] corrplot_0.84       foreach_1.5.1       checkmate_2.0.0    
##  [79] permute_0.9-5       BiocGenerics_0.34.0 zip_2.1.1          
##  [82] shape_1.4.5         matrixStats_0.57.0  rlang_0.4.8        
##  [85] pkgconfig_2.0.3     systemfonts_0.3.2   evaluate_0.14      
##  [88] lattice_0.20-41     Rhdf5lib_1.10.1     labeling_0.4.2     
##  [91] tidyselect_1.1.0    plyr_1.8.6          magrittr_1.5       
##  [94] bookdown_0.21       R6_2.5.0            IRanges_2.22.2     
##  [97] generics_0.1.0      DBI_1.1.0           foreign_0.8-80     
## [100] pillar_1.4.6        haven_2.3.1         withr_2.3.0        
## [103] mgcv_1.8-33         abind_1.4-5         survival_3.2-7     
## [106] car_3.0-10          modelr_0.1.8        crayon_1.3.4       
## [109] rmarkdown_2.5       progress_1.2.2      grid_4.0.2         
## [112] readxl_1.3.1        data.table_1.13.2   vegan_2.5-6        
## [115] infotheo_1.2.0      reprex_0.3.0        digest_0.6.27      
## [118] textshaping_0.1.2   glmnet_4.0-2        stats4_4.0.2       
## [121] munsell_0.5.0