---
title: "Getting started with NetCoupler"
date: "`r Sys.Date()`"
bibliography: references.bib
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Getting started with NetCoupler}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
The goal of NetCoupler is to estimate causal links between a set of -omic (e.g.
metabolomics, lipidomics) or other high-dimensional data and an external variable,
such as a disease outcome, an exposure, or both. The NetCoupler-algorithm,
initially formulated during Clemens' PhD thesis [@Wittenbecher2017], links a
conditional dependency network with an external variable (i.e. an outcome or
exposure) to identify network-independent associations between the network
variables and the external variable, classified as direct effects.
<!-- TODO: Add link to the description vignette -->
A typical use case we have in mind would be if a researcher might be interested
in exploring potential pathways that exist between a health exposure like red
meat consumption, its impact on the metabolic profile, and the subsequent impact
on an outcome like type 2 diabetes incidence.
So for instance, you want to ask questions to get answers that look like the
figure below.
```{r}
#| echo = FALSE,
#| fig.cap = "The structure of questions that NetCoupler aims to help answers or explore."
knitr::include_graphics("aim-output.png")
```
The input for NetCoupler includes:
1. Standardized metabolic or other high-dimensional data.
2. Exposure or outcome data.
3. Network estimating method (default is the PC algorithm [@Colombo2014] from
the [pcalg](https://CRAN.R-project.org/package=pcalg) package).
3. Modeling method (e.g. linear regression with `lm()`), including confounders
to adjust for.
<!-- TODO: Add figure demonstrating NetCoupler -->
The final output is the modeling results along with the results from
NetCoupler's classification. Results can then be displayed as a joint network
model in graphical format.
There are a few key assumptions to consider before using NetCoupler for your
own research purposes.
<!-- TODO: Add link to assumptions of PC-algorithm -->
1. -omics data is the basis for the network. We haven't tested this on non-omics
datasets, so can't guarantee it works as intended.
1. The variables used for the metabolic network are numerical
1. Metabolic data should have a theoretical network underlying it.
1. Missing data are not used in any of the NetCoupler processes.
<!-- TODO: Add other assumptions -->
## Overall package framework
NetCoupler has several frameworks in mind:
- Works with [magrittr](https://magrittr.tidyverse.org/) `%>%` or base R
`|>` operator.
- Works with [tidyselect](https://tidyselect.r-lib.org/) helpers (e.g.
`starts_with()`, `contains()`).
- Is auto-complete friendly (e.g. start function names with `nc_`).
- Inputs and outputs of functions are
[tibbles](https://tibble.tidyverse.org/)/dataframes or [tidygraph tibbles](https://tidygraph.data-imaginist.com/).
- Generic modeling approach by using model and settings as function argument
inputs.
- This allows flexibility with what model can be used (e.g. linear regression,
cox models).
- Almost all functionality of modeling in R is available here, for instance
handling of missing data or of categorical variables.
<!-- - Works with the [tidymodel](https://www.tidymodels.org/) approach, such as by -->
<!-- making use of [parsnip](https://parsnip.tidymodels.org/) functions to make the -->
<!-- models. -->
## Workflow
The general workflow for using NetCoupler revolves around several main
functions, listed below as well as visualized in the figure below:
- `nc_standardize()`: The algorithm in general, but especially the network
estimation method, is sensitive to the values and distribution of the variables.
Scaling the variables by standardizing, mean-centering, and natural log
transforming them are important to obtaining more accurate estimations.
- `nc_estimate_network()`: Estimate the connections between metabolic variables
as a undirected graph based on dependencies between variables. This network is
used to identify metabolic variables that are connected to each other as
neighbours.
- We plan on implementing other network estimators aside from the PC-algorithm
at some point in the future.
- `nc_estimate_exposure_links()` and `nc_estimate_outcome_links()`: Uses the
standardized data and the estimated network to classify the conditionally
independent relationship between each metabolic variable and an external
variable (e.g. an outcome or an exposure) as either being a direct, ambiguous,
or no effect relationship.
- Setting the threshold for classifying effects as direct, ambigious, or none
is done through the argument `classify_option_list`. See the help documentation
of the estimating functions for more details. For larger datasets, with more
sample size and variables included in the network, we *strongly* recommend
lowering the threshold used to reduce the risk of false positives.
- `nc_join_links()`: **Not implemented yet.** Join together the exposure- and outcome-side estimated links.
- `nc_plot_network()`: **Not implemented yet.** Visualize the connections
estimated from `nc_estimate_network()`.
- `nc_plot_links()`: **Not implemented yet.** Plots the output results from
either `nc_estimate_exposure_links()`, `nc_estimate_outcome_links()`, or
`nc_join_links()`.
```{r}
#| echo = FALSE,
#| out.width = "60%",
#| fig.cap = "NetCoupler functions and their input and ouput. Input and output
#| objects are the light gray boxes, while the light blue boxes are the
#| currently available functions, and the light orange boxes are functions
#| planned to be developed."
knitr::include_graphics("nc-diagram-io.png", dpi = 144)
```
## Simple example
The below is an example using a simulated dataset for demonstrating NetCoupler.
For more examples, particularly on how to use with different models,
check out the `vignette("examples")`.
### Estimating the metabolic network
For estimating the network, it's (basically) required to standardize
the metabolic variables before inputting into `nc_estimate_network()`.
This function also log-transforms and scales
(mean-center and z-score normalize) the values of the metabolic variables.
We do this because the network estimation algorithm can sometimes be finicky
about differences in variable numerical scale (mean of 1 vs mean of 1000).
```{r metabolic-standardize}
library(NetCoupler)
std_metabolic_data <- simulated_data %>%
nc_standardize(starts_with("metabolite"))
```
If you have potential confounders that you need to adjust for during the estimating
links phase of NetCoupler, you'll need to include these confounding variables
when standardizing the metabolic variables. You do this by regressing the confounding
variables on the metabolic variables by using the `regressed_on` argument
of `nc_standardize()`. This will automatically first standardize the variables,
run models on the metabolic variables that includes the confounding variables,
and then extract the residuals from the model which are then used to construct
the network. Here's an example:
```{r metabolic-standardize-residuals, eval=FALSE}
std_metabolic_data <- simulated_data %>%
nc_standardize(starts_with("metabolite"),
regressed_on = "age")
```
After that, you can estimate the network. The network is by default estimated
using the PC-algorithm. You can read more about it in the help page of the
`pc_estimate_undirected_graph()` internal function.
```{r create-network}
# Make partial independence network from metabolite data
metabolite_network <- std_metabolic_data %>%
nc_estimate_network(starts_with("metabolite"))
```
<!-- To see what the network looks like, -->
<!-- use the function `nc_plot_network()`. -->
<!-- ```{r visualize-metabolic-network, fig.width=5.6, fig.height=4.5} -->
<!-- std_metabolic_data %>% -->
<!-- nc_plot_network(metabolite_network) -->
<!-- ``` -->
<!-- While the plot is a bit crowded, it at least provides a base to start tidying up -->
<!-- from. -->
### Estimating exposure and outcome-side connections
For the exposure and outcome side,
you should standardize the metabolic variables,
but this time, we don't regress on the confounders
since they will be included in the models.
```{r standardize-data}
standardized_data <- simulated_data %>%
nc_standardize(starts_with("metabolite"))
```
Now you can estimate the outcome or exposure and identify direct effects
for either the exposure side (`exposure -> metabolite`)
or the outcome side (`metabolite -> outcome`).
For the exposure side,
the function identifies whether a link between the exposure
and an index node (one metabolic variable in the network) exists,
independent of potential confounders
and from neighbouring nodes (other metabolic variables linked to the index variable).
Depending on how consistent and strong the link is,
the effect is classified as "direct", "ambiguous", or "none".
<!-- For more details on the algorithm, see the `vignette("description")`. -->
In the example below, we specifically generated the simulated data so that
the exposure is associated with metabolites 1, 8, and 12.
And as we can see, those links have been correctly identified.
```{r example-use, cache=TRUE}
outcome_estimates <- standardized_data %>%
nc_estimate_outcome_links(
edge_tbl = as_edge_tbl(metabolite_network),
outcome = "outcome_continuous",
model_function = lm
)
outcome_estimates
exposure_estimates <- standardized_data %>%
nc_estimate_exposure_links(
edge_tbl = as_edge_tbl(metabolite_network),
exposure = "exposure",
model_function = lm
)
exposure_estimates
```
If you want to adjust for confounders and have already used `regressed_on` in
the `nc_standardize()` function, add confounders to `nc_estimate_outcome_links()`
or `nc_estimate_exposure_links()` with the `adjustment_vars` argument:
```{r estimation-adjustment, eval=FALSE}
outcome_estimates <- standardized_data %>%
nc_estimate_outcome_links(
edge_tbl = as_edge_tbl(metabolite_network),
outcome = "outcome_continuous",
model_function = lm,
adjustment_vars = "age"
)
```
<!-- TODO: More about when to adjust and why and how. -->
<!-- ### Plotting -->
<!-- To visualize the results of the linked network graph and the effect -->
<!-- classification, there are two functions to show the exposure and the outcome -->
<!-- plots. In general, these plot functions are currently mostly for exploratory -->
<!-- purposes and are too "busy" and crowded to be meaningful for presentation or -->
<!-- publication. However, these are good starting points for making prettier, -->
<!-- more legible plots. -->
<!-- ```{r plot-outcome-estimation-networks, fig.width=7, fig.height=6} -->
<!-- nc_plot_outcome_estimation( -->
<!-- standardized_data, -->
<!-- metabolite_network, -->
<!-- outcome_estimates -->
<!-- ) -->
<!-- ``` -->
<!-- ```{r plot-exposure-estimation-networks, fig.width=7, fig.height=6} -->
<!-- nc_plot_exposure_estimation( -->
<!-- standardized_data, -->
<!-- metabolite_network, -->
<!-- exposure_estimates -->
<!-- ) -->
<!-- ``` -->
## Slow code? Use parallel processing with future
If the analysis is taking a while, you can use the future package to speed things
up by implementing parallel processing. It's easy to use parallel processing with
NetCoupler since it uses the future package. By setting the "processing plan" with
`future::plan()` to `multisession`, NetCoupler will use parallel processing for its
computationally intensive component of the algorithm.
After you run your code, close up the parallel
processing by putting it back to normal with `plan(sequential)`. Using the future
package you can speed up the processing by almost 2.5 times.
```{r future-parallel-processing, eval=FALSE}
# You'll need to have furrr installed for this to work.
library(future)
plan(multisession)
outcome_estimates <- standardized_data %>%
nc_estimate_outcome_links(
edge_tbl = as_edge_tbl(metabolite_network),
outcome = "outcome_continuous",
model_function = lm
)
plan(sequential)
```
<!-- - complete cases for all variables used by netcoupler -->
## References