--- title: "Example dataset with Confoundering" author: - name: "Jakob Wirbel and Georg Zeller" affiliation: "EMBL Heidelberg" email: "georg.zeller@embl.de" date: "Date last modified: 2020-11-11" output: BiocStyle::html_document vignette: > %\VignetteIndexEntry{SIAMCAT confounder example} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{ASCII} --- ```{r,include=FALSE} knitr::opts_chunk$set(collapse=TRUE) ``` # About This Vignette Here, we demonstrate the standard workflow of the `SIAMCAT` package using as an example the dataset from [Nielsen et al. _Nat Biotechnol_ 2014](https://www.ncbi.nlm.nih.gov/pubmed/24997787). This dataset contains samples from patients with inflammatory bowel disease and from controls. More importantly, these samples have been collected in two different countries, Spain and Denmark. Together with technical differences between these samples, this introduces a potent confounding factor into the data. Here we are going to explore how `SIAMCAT` would identify the confounding factor and what the results would be if you account for the confounder or not. ## Setup First, we load the packages needed to perform the analyses. ```{r setup, warning=FALSE, message=FALSE} library("tidyverse") library("SIAMCAT") library("ggpubr") ``` # Preparations There are two different ways to access the data for our example dataset. On the one hand, it is available through the `curatedMetagenomicData` R package. However, using it here would create many more dependencies for the `SIAMCAT` package. Therefore, we here use data available through the EMBL cluster. In the `SIAMCAT` paper, we performed the presented analyses on the datasets available through `curatedMetagenomicData`. If you want to reproduce the analysis from the `SIAMCAT` paper, you can execute the code chunks in the `curatedMetageomicData` section, otherwise execute the code in the mOTUs2 section. ## curatedMetagenomicsData First, we load the package: ```{r curateMGD, eval=FALSE} library("curatedMetagenomicData") ``` ### Metadata The data are part of the `combined_metadata` ```{r get_metadata, eval=FALSE} meta.nielsen.full <- combined_metadata %>% filter(dataset_name=='NielsenHB_2014') ``` One thing we have to keep in mind are repeated samples per subject (see also the **Machine learning pitfalls** vignette). ```{r reason_meta_clean_2, eval=FALSE} print(length(unique(meta.nielsen.full$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. ```{r clean_metadata_1, eval=FALSE} 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]+$')) %>% mutate(visit=str_remove(visit, '_')) %>% mutate(visit=as.numeric(visit)) %>% mutate(visit=case_when(is.na(visit)~0, TRUE~visit)) %>% group_by(subjectID) %>% filter(visit==min(visit)) %>% ungroup() %>% mutate(Sample_ID=sampleID) %>% mutate(Group=case_when(disease=='healthy'~'CTR', TRUE~disease_subtype)) ``` Now, we can restrict our metadata to samples with `UC` and healthy control samples: ```{r clean_metadata_4, eval=FALSE} meta.nielsen <- meta.nielsen %>% filter(Group %in% c('UC', 'CTR')) ``` As a last step, we can adjust the column names for the metadata so that they agree with the data available from the EMBL cluster. Also, we add rownames to the dataframe since `SIAMCAT` needs rownames to match samples across metadata and features. ```{r clean_metadata_3, eval=FALSE} meta.nielsen <- meta.nielsen %>% mutate(Country=country) meta.nielsen <- as.data.frame(meta.nielsen) rownames(meta.nielsen) <- meta.nielsen$sampleID ``` ### Taxonomic Profiles We can load the taxonomic profiles generated with MetaPhlAn2 via the _curatedMetagenomicsData_ R package. ```{r tax_profiles, eval=FALSE} x <- 'NielsenHB_2014.metaphlan_bugs_list.stool' feat <- curatedMetagenomicData(x=x, dryrun=FALSE) feat <- feat[[x]]@assayData$exprs ``` The MetaPhlAn2 profiles contain information on different taxonomic levels. Therefore, we want to restrict them to species-level profiles. In a second step, we convert them into relative abundances (summing up to 1) instead of using the percentages (summing up to 100) that MetaPhlAn2 outputs. ```{r clean_tax_profiles, eval=FALSE} feat <- feat[grep(x=rownames(feat), pattern='s__'),] feat <- feat[grep(x=rownames(feat),pattern='t__', invert = TRUE),] feat <- t(t(feat)/100) ``` The feature names are very long and may be a bit un-wieldy for plotting later on, so we shorten them to only the species name: ```{r clean_feature_names, eval=FALSE} rownames(feat) <- str_extract(rownames(feat), 's__.*$') ``` ## mOTUs2 Profiles Both metadata and features are available through the EMBL cluster: ```{r load_motus} # base url for data download data.loc <- 'https://zenodo.org/api/files/d81e429c-870f-44e0-a44a-2a4aa541b6c1/' ## metadata meta.nielsen <- read_tsv(paste0(data.loc, 'meta_Nielsen.tsv')) # 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$Sample_ID ## features feat <- read.table(paste0(data.loc, 'metaHIT_motus.tsv'), stringsAsFactors = FALSE, sep='\t', check.names = FALSE, quote = '', comment.char = '') feat <- feat[,colSums(feat) > 0] feat <- prop.table(as.matrix(feat), 2) ``` # SIAMCAT Workflow (without Confounders) ## The SIAMCAT Object Now, we have everything ready to create a `SIAMCAT` object which stores the feature matrix, the meta-variables, and the label. Here, the label is created using the information in the metadata. To demonstrate the normal `SIAMCAT` workflow, we remove the confounding factor by only looking at samples from Spain. Below, we have a look what would have happened if we had not removed them. ```{r siamcat} # remove Danish samples meta.nielsen.esp <- meta.nielsen[meta.nielsen$Country == 'ESP',] sc.obj <- siamcat(feat=feat, meta=meta.nielsen.esp, label='Group', case='UC') ``` ## Filtering Now, we can filter feature with low overall abundance and prevalence. ```{r feature_filtering} sc.obj <- filter.features(sc.obj, cutoff=1e-04, filter.method = 'abundance') sc.obj <- filter.features(sc.obj, cutoff=0.05, filter.method='prevalence', feature.type = 'filtered') ``` ## Association Plot The `check.assocation` function calculates the significance of enrichment and metrics of association (such as generalized fold change and single-feature AUROC). ```{r assoc_plot, message=FALSE, warning=FALSE} sc.obj <- check.associations(sc.obj, log.n0 = 1e-06, alpha=0.1) association.plot(sc.obj, fn.plot = './association_plot_nielsen.pdf', panels = c('fc')) ``` ![](./association_plot_nielsen.png) ## Confounder Analysis We can also check the supplied meta-variables for potential confounding. ```{r check_confounders, warning=FALSE} check.confounders(sc.obj, fn.plot = './confounders_nielsen.pdf') ``` ![](./confounders_nielsen.png) 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) ```{r ml_workflow, eval=FALSE} 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). ```{r model_eval_plot, eval=FALSE} model.evaluation.plot(sc.obj, fn.plot = './eval_plot_nielsen.pdf') ## Plotted evaluation of predictions successfully to: ./eval_plot_nielsen.pdf ``` ![](./eval_plot_nielsen.png) ### 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. ```{r model_interpretation_plot, eval=FALSE} model.interpretation.plot(sc.obj, consens.thres = 0.8, fn.plot = './interpret_nielsen.pdf') ## Successfully plotted model interpretation plot to: ./interpret_nielsen.pdf ``` ![](./interpretation_nielsen.png) # Confounder Analysis As already mentioned above, the Nielsen dataset contains samples from both Spain and Denmark. How would `SIAMCAT` have alerted us to this? ```{r confounders} table(meta.nielsen$Group, meta.nielsen$Country) ``` ## Country Confounder First, we create a `SIAMCAT` object again, this time including the Danish controls: ```{r conf_start} sc.obj.full <- siamcat(feat=feat, meta=meta.nielsen, label='Group', case='UC') sc.obj.full <- filter.features(sc.obj.full, cutoff=1e-04, filter.method = 'abundance') sc.obj.full <- filter.features(sc.obj.full, cutoff=0.05, filter.method='prevalence', feature.type = 'filtered') ``` The confounder plot would show us that the meta-variable "country" might be problematic: ```{r conf_country, eval=FALSE} check.confounders(sc.obj.full, fn.plot = './confounders_dnk.pdf') ``` ![](./confounders_dnk.png) ## Association Testing First, we can use `SIAMCAT` to test for associations including the Danish samples. ```{r assoc_plot_2, warning=FALSE, message=FALSE} sc.obj.full <- check.associations(sc.obj.full, log.n0 = 1e-06, alpha=0.1) ``` 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. ```{r conf_assoc_plot, warning=FALSE} 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='none') + 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. ```{r dorea_plot} # 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. ```{r ml_workflow_dnk, eval=FALSE} 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. ```{r eval_plot_comp, eval=FALSE} 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 ``` ![](./eval_plot_dnk.png) 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. ```{r ml_workflow_country, eval=FALSE} 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 ```{r session_info} sessionInfo() ```