---
title: "Holdout Testing with SIAMCAT"
author:
-   name: "Jakob Wirbel, Konrad Zych, and Georg Zeller"
    affiliation: "EMBL Heidelberg"
    email: "georg.zeller@embl.de"
date: "Date last modified: 2018-09-24"
output: BiocStyle::html_document
vignette: >
    %\VignetteIndexEntry{SIAMCAT holdout testing}
    %\VignetteEngine{knitr::rmarkdown}
    %\VignetteEncoding{ASCII}
---

# Introduction

One of the functionalities of the `SIAMCAT` package is the training of
statistical machine learning models on metagenomics data. In this vignette,
we demonstrate how such a model can be built on one dataset and then be applied
on another, similarly processed holdout dataset. This might be of interest
when comparing data from two different studies on the same disease.

In this vignette, we look at two datasets from studies on colorectal cancer
(CRC). The first study from
[Zeller et al.](http://europepmc.org/abstract/MED/25432777) investigated
metagenomic markers for CRC in a population in France, while the second study
from [Yu et al.](https://europepmc.org/abstract/MED/26408641) used samples
from China for the same goal. Both datasets were profiled with the same
[taxonomic profiling tool](https://github.com/motu-tool/mOTUs_v2), yielding
the same taxonomic identifiers, which is required for holdout testing.

# Load the Data

The datasets can be found on the web repository for public metagenomics
datasets from the
[Zeller group](https://www.embl.de/research/units/scb/zeller/index.html).

```{r start, message=FALSE, warning=FALSE}
library(SIAMCAT)

# this is data from Zeller et al., Mol. Syst. Biol. 2014
fn.feat.fr  <-
    'https://www.embl.de/download/zeller/FR-CRC/FR-CRC-N141_tax-ab-specI.tsv'
fn.meta.fr  <-
    'https://www.embl.de/download/zeller/FR-CRC/FR-CRC-N141_metadata.tsv'

# this is the external dataset from Yu et al., Gut 2017
fn.feat.cn  <-
    'https://www.embl.de/download/zeller/CN-CRC/CN-CRC-N128_tax-ab-specI.tsv'
fn.meta.cn  <-
    'https://www.embl.de/download/zeller/CN-CRC/CN-CRC-N128_metadata.tsv'
```

First of all, we build a `SIAMCAT` object using the data from the French study
in the same way that we have seen before in the main `SIAMCAT` vignette.

```{r siamcat_fr}
# features
# be vary of the defaults in R!!!
feat.fr  <- read.table(fn.feat.fr, sep='\t', quote="",
    check.names = FALSE, stringsAsFactors = FALSE)
# the features are counts, but we want to work with relative abundances
feat.fr.rel <- prop.table(as.matrix(feat.fr), 2)

# metadata
meta.fr  <- read.table(fn.meta.fr, sep='\t', quote="",
    check.names=FALSE, stringsAsFactors=FALSE)

# create SIAMCAT object
siamcat.fr <- siamcat(feat=feat.fr.rel, meta=meta.fr,
    label='Group', case='CRC')
```

We can load the data from the Chinese study in a similar way and also create a
`SIAMCAT` object for the holdout dataset.

```{r siamcat_cn}
# features
feat.cn  <- read.table(fn.feat.cn, sep='\t', quote="",
    check.names = FALSE)
feat.cn.rel <- prop.table(as.matrix(feat.cn), 2)

# metadata
meta.cn  <- read.table(fn.meta.cn, sep='\t', quote="",
    check.names=FALSE, stringsAsFactors = FALSE)

# SIAMCAT object
siamcat.cn <- siamcat(feat=feat.cn.rel, meta=meta.cn,
        label='Group', case='CRC')
```

# Model Building on the French Dataset

## Preprocessing

With the French dataset, we perform the complete process of model building
within `SIAMCAT`, including data preprocessing steps like data validation,
filtering, and data normalization.

```{r preprocessing_fr}
siamcat.fr <- filter.features(
    siamcat.fr,
    filter.method = 'abundance',
    cutoff = 0.001,
    rm.unmapped = TRUE,
    verbose=2
)

siamcat.fr <- normalize.features(
    siamcat.fr,
    norm.method = "log.std",
    norm.param = list(log.n0 = 1e-06, sd.min.q = 0.1),
    verbose = 2
)
```
## Model Training

Now, we can build the statistical model. We use the same parameters as in
the main `SIAMCAT` vignette, where the process is explained in more detail.

```{r build_model_fr, results='hide'}
siamcat.fr <-  create.data.split(
    siamcat.fr,
    num.folds = 5,
    num.resample = 2
)

siamcat.fr <- train.model(
    siamcat.fr,
    method = "lasso"
)
```


## Predictions

Finally, we can make predictions for each cross-validation fold and evaluate
the predictions as seen in the main `SIAMCAT` vignette.

```{r predict_evaluate_fr, results='hide'}
siamcat.fr <- make.predictions(siamcat.fr)

siamcat.fr <-  evaluate.predictions(siamcat.fr)
```

# Application on the Holdout Dataset

Now that we have successfully built the model for the French dataset, we can
apply it to the Chinese holdout dataset. First, we will normalize the Chinese
dataset with the same parameters that we used for the French dataset in order
to make the data comparable. For that step, we can use the frozen normalization
functionality in the `normalize.features` function in `SIAMCAT`. We supply to
the function all normalization parameters saved in the `siamcat.fr` object,
which can be accessed using the `norm_params` accessor.

## Frozen Normalization

```{r normalize_cn}

siamcat.cn <- normalize.features(siamcat.cn,
    norm.param=norm_params(siamcat.fr),
    feature.type='original',
    verbose = 2)

```


## Holdout Predictions

Next, we apply the trained model to predict the holdout dataset.

```{r predict_cn, results='hide'}
siamcat.cn <- make.predictions(
    siamcat = siamcat.fr,
    siamcat.holdout = siamcat.cn,
    normalize.holdout = FALSE)
```

Note that the `make.predictions` function can also take care of the
normalization of the holdout dataset.

```{r alternative_pipeline_cn, eval=FALSE}
## Alternative Code, not run here
siamcat.cn <- siamcat(feat=feat.cn.rel, meta=meta.cn,
    label='Group', case='CRC')
siamcat.cn <- make.predictions(siamcat = siamcat.fr,
    siamcat.holdout = siamcat.cn,
    normalize.holdout = TRUE)
```

Again, we have to evaluate the predictions:

```{r eval_cn, message=FALSE}
siamcat.cn <- evaluate.predictions(siamcat.cn)
```

# Model Evaluation

Now, we can compare the performance of the classifier on the original and
the holdout dataset by using the `model.evaluation.plot` function. Here,
we can supply several `SIAMCAT` objects for which the model evaluation will be
plotted in the same plot. Note that we can supply the objects as named objects
in order to print the names in the legend.

```{r eval_plot, eval=FALSE}
model.evaluation.plot('FR-CRC'=siamcat.fr,
    'CN-CRC'=siamcat.cn,
    colours=c('dimgrey', 'orange'))
```

![](./eval_plot_holdout.png)

# Session Info

```{r session_info}
sessionInfo()
```
