computational-metabolomics/struct
Statistics in R Using Class-based Templates

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In computational-metabolomics/struct: Statistics in R Using Class-based Templates 

knitr::opts_chunk$set(
    dpi=72,fig.width=5,fig.height=5.5
)
set.seed(57475)

Introduction

The aim of this vignette is to use struct to implement a small set of exemplar objects (i.e. class-based templates) that can be used to conduct exploratory and statistical analysis of a multivariate dataset (e.g. Metabolomics or other omics). A more extensive and advanced use of struct templates is provided in the structToolbox package, which included quality filters, normalisation, scaling, univariate and multivariate statistics and machine learning methods.

Getting started

The latest version of struct compatible with your current R version can be installed using BiocManager.

# install BiocManager if not present
if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")

# install structToolbox and dependencies
BiocManager::install("struct")

# install ggplot if not already present
 if (!require('ggplot2')) {
  install.packages('ggplot2')
}

The ggplot2 package is also needed for this vignette.

suppressPackageStartupMessages({
  # load the package
  library(struct)

  # load ggplot
  library(ggplot2)
})

struct helper functions {#helpers}

'struct' provides a number of helper functions that can be used to create a new struct object from the command line, or in a script:

Examples of using these functions to create new struct objects based on the provided templates are included in section \@ref(model).

Creating a new struct object

To create a new struct object use the set_struct_obj() function. There are several inputs which are described below.

Note that slots listed in params and outputs will be accessible using dollar syntax and are intended to be get/set by the user, while slots named in private are only available internally to the developer.

Changing the default methods

All struct objects have some default methods that are intended to be "overloaded" i.e. replaced to provide functionality specific to the new object being implemented. The helper function set_obj_method() provides this functionality with the following inputs:

Changing the default show output

All struct objects have a default show output, which prints the name and description for an object.

If you want to provide additional information when printing an object then you can overload the show method using the set_obj_show() function, which has the following inputs:

The string output from the extra_string function will be appended to the default show output.

Class-based templates and struct objects

struct provides S4 classes, which can be thought of as extendable templates, for a number of different types of objects. These objects are fundamental components of a data analysis workflow and include:

Each of the templates has a similar structure and a number of template-specific methods defined. They have been designed with the development of data analysis workflows in mind and make it easy to incorporate new methods into the existing framework.

All struct objects have a number of common fields or "slots", which are defined in the base struct_class object:

All struct objects have a show method defined which summarises the object.

S = struct_class()
S

Slots can be set by including them as named inputs when the object is created.

S = struct_class(name = 'Example',
                 description = 'An example struct object')
S

Methods have also been defined so that the value for slots can be set and retrieved using dollar syntax.

# set the name
S$name = 'Basic example'

# get the name
S$name

In addition to these publicly accessible slots two additional hidden, or internal, slots are defined. These slots are not intended for general access and therefore cannot be accessed using dollar syntax.

Use of these slots will be covered in section \@ref(modelobj) as they are only applicable when extending the base struct_class template.

DatasetExperiment objects

The DatasetExperiment object is an extension of SummarizedExperiment. It is used to hold measured data and the relevant meta data, such as group labels or feature/variable annotations. All struct objects expect the data to be in the DatasetExperiment format, with samples in rows and variables/features in columns. As well as the default slots, DatasetExperiment objects also have the following additional slots:

As for all struct objects these slots, as well as the slots from the base class can be assigned during object creation.

DE = DatasetExperiment(
  data = iris[,1:4],
  sample_meta=iris[,5,drop=FALSE],
  variable_meta=data.frame('idx'=1:4,row.names=colnames(data)),
  name = "Fisher's iris dataset",
  description = 'The famous one')
DE

A formal version of the iris dataset is included in the struct package.

DE = iris_DatasetExperiment()
DE

Because DatasetExperiment extends SummarizedExperiment it inherits functionality such as subsetting, nrow, ncol, etc.

# number of columns
ncol(DE)

# number of rows
nrow(DE)

# subset the 2nd and 3rd column
Ds = DE[,c(2,3)]
Ds

The slots are also accessible using dollar syntax.

# get data frame 
head(DE$data)

# sample meta
head(DE$sample_meta)

Note that although technically it is possible to set data, sample_meta and variable_meta using dollar syntax, it is usually better to create a new DatasetExperiment and assign them during creation of the object due to the strict definition of a SummarizedExperiment.

model objects {#modelobj}

Model objects are the most commonly used template as they are the main building block of data analysis workflows. They can be used to implement data processing methods for quality filtering, normalisation, and scaling, as well as methods for statistics and machine learning (e.g. classification, regression and clustering).

model objects have three unique slots defined, but they are all related to model sequences, so they are discussed in detail in section \@ref(modelseq).

model objects also have four methods that are used to actually carry out the intended data analysis method.

For the base model template these methods function only as placeholder; they should be defined as part of extending the template, as described in the next section.

Model objects also make use of the .params and .outputs slots of the base class to allow flexibility when extending the template, as shown in the next section.

using the model template {#model}

The model object is intended as a template for creating new objects. In programming terms it is intended to be inherited by new objects derived from it. Methods have been defined so that they can be overloaded with new functionality.

In structToolbox a number of model objects have been defined using setClass and setMethod, which is the preferred way to extend the templates in a package.

struct also makes it possible to define new model objects as you go, which we will demonstrate here. We will define two new objects, one for mean centring and one for Principal Component Analysis. More complete versions of these objects are available as part of the structToolbox package.

The first step is to define the new model objects using the set_struct_object() function (see section \@ref(helpers)). 

# mean centre object
mean_centre = set_struct_obj(
  class_name = 'mean_centre', 
  struct_obj = 'model',       
  params = character(0),            
  outputs = c(centred = 'DatasetExperiment',
              mean = 'numeric'),
  private = character(0),
  prototype=list(predicted = 'centred')
)

# PCA object
PCA = set_struct_obj(
  class_name = 'PCA', 
  struct_obj = 'model',       
  params = c(number_components = 'numeric'),                
  outputs = c(scores = 'DatasetExperiment',
              loadings = 'data.frame'),
  private = character(0),
  prototype = list(number_components = 2,
                  ontology = 'OBI:0200051',
                  predicted = 'scores')
)

The objects we have created (mean_centre and PCA) both extend the model object template. mean_centre is a fairly basic model with a single output slot centred while the PCA object has inputs, outputs and sets a some default values.

The new objects can be initialised like other struct, with named input parameter values.

M = mean_centre()
M

P = PCA(number_components=4)
P

The new objects currently have the default methods, which need to be replaced using the either setMethod or the helper function set_obj_method() to provide the desired functionality.

We will need to define model_train and model_predict for both of our new objects. We will start with the mean_centre object.

# mean centre training
set_obj_method(
  class_name = 'mean_centre',
  method_name = 'model_train',
  definition = function(M,D) {
    # calculate the mean of all training data columns
    m = colMeans(D$data)
    # assign to output slot
    M$mean = m
    # always return the modified model object
    return(M)
  }
)

# mean_centre prediction
set_obj_method(
  class_name = 'mean_centre',
  method_name = 'model_predict',
  definition = function(M,D) {
    # subtract the mean from each column of the test data
    D$data = D$data - rep(M$mean, rep.int(nrow(D$data), ncol(D$data)))
    # assign to output
    M$centred = D
    # always return the modified model object
    return(M)
  }
)

The mean_centre object can now be used with DatasetExperiment objects.

# create instance of object
M = mean_centre()

# train with iris data
M = model_train(M,iris_DatasetExperiment())

# print to mean to show the function worked
M$mean

# apply to iris_data
M = model_predict(M,iris_DatasetExperiment())

# retrieve the centred data and show that the column means are zero
colMeans(M$centred$data)

Now we define methods for the PCA object.

# PCA training
set_obj_method(
  class_name = 'PCA',
  method_name = 'model_train',
  definition = function(M,D) {

    # get number of components
    A = M$number_components

    # convert to matrix
    X=as.matrix(D$data)

    # do svd
    model=svd(X,A,A)

    # loadings
    P=as.data.frame(model$v)

    # prepare data.frame for output
    varnames=rep('A',1,A)
    for (i in 1:A) {
      varnames[i]=paste0('PC',i)
    }
    rownames(P)=colnames(X)
    colnames(P)=varnames
    output_value(M,'loadings')=P

    # set output
    M$loadings = P

    # always return the modified model object
    return(M)
  }
)

# PCA prediction
set_obj_method(
  class_name = 'PCA',
  method_name = 'model_predict',
  definition = function(M,D) {
    ## calculate scores using loadings

    # get number of components
    A = M$number_components

    # convert to matrix
    X=as.matrix(D$data)

    # get loadings
    P=M$loadings

    # calculate scores
    that=X%*%as.matrix(P)

    # convert scores to DatasetExperiment
    that=as.data.frame(that)
    rownames(that)=rownames(X)
    varnames=rep('A',1,A)
    for (i in 1:A) {
      varnames[i]=paste0('PC',i)
    }
    colnames(that)=varnames
    S=DatasetExperiment(
      data=that,
      sample_meta=D$sample_meta,
      variable_meta=varnames)

    # set output
    M$scores=S

    # always return the modified model object
    return(M)
  }
)

Like the mean_centre object the PCA object now has methods defined and can be used with DatasetExperiment objects.

# get the mean centred data
DC = M$centred

# train PCA model
P = model_apply(P,DC)

# get scores
P$scores

Note that because we defined model_train and model_predict for the PCA object we didn't need to explicitly define a model_apply method as it defaults to applying model_train and model_precict sequentially. For some methods defining a model_apply definition would be more appropriate (e.g. t-test) and model_train and model_predict left undefined.

model_seq objects {#modelseq}

Model sequences are a special kind of list defined by the struct package. They can be created by symbolically "adding" models together to form a sequence. We will do that here using the mean_centre() and PCA() objects we created.

# create model sequence
MS = mean_centre() + PCA(number_components = 2)

# print summary
MS

The model_train and model_predict methods for model sequences will automatically pass data along the list of models. model_apply works in the same way as for models and will use model_train and model_predict sequentially for the model sequence.

Unless specified the default output named in the 'predicted' slot of the object will be used as the data input for the next object in the sequence.

For the PCA example, the data is input into the mean_centre object, mean centring is applied, and then the centred output is used an input into the PCA object to calculate the scores and loadings.

# apply model sequence to iris_data
MS = model_apply(MS,iris_DatasetExperiment())

# get default output from sequence (PCA scores with 2 components)
predicted(MS)

To change the flow of data through a model sequence three slots can be set for each model:

There are examples of this advanced model sequence flow control in the vignettes of the structToolbox package.

iterator objects

Iterator objects are similar to model object templates in that they can be extended to have input parameters and outputs specified. They have some unique slots:

iterator objects are intended for resampling methods, where an input model is run multiple times by subsetting the data into training and test sets. Cross-validation and permutation tests are just two examples where iterator objects might be used.

The model to be run by the iterator can be get/set using the models method.

# create iterator
I = iterator()

# add PCA model sequence
models(I) = MS

# retrieve model sequence
models(I)

Models can also be 'nested' within an iterator by symbolically multiplying them by a model or model_seq. The implication is that the iterator will use the model multiple times. This can be combined with the symbolic adding of models to create sequences.

# alternative to assign models for iterators
I = iterator() * (mean_centre() + PCA())
models(I)

It is also possible to nest iterators within iterators e.g. to create a 'permuted cross_validation' using a similar approach.

I = iterator() * iterator() * (mean_centre() + PCA())

A run method is provided, which can be extended in a similar fashion to method_train etc to implement the desired functionality. It is up to the developer to ensure that the iterator can handle model, model_seq and iterator objects as input and as such iterator run methods can be quite complex. For brevity we do not demonstrate one here, but there are several examples in the structToolbox package and the corresponding vignettes.

metric objects

Metric objects calculate a scalar value by comparing true, known values to predicted outputs from a model, usually a classifier or a regression model. They are a required input for running iterators and may be calculated multiple times depending on the iterator. Metrics have a single unique slot.

The calculate method is provided for metric object templates, which can be extended using the same approach as for model_train etc. It takes three inputs:

The calculate method can be called manually, but usually it is called by an iterator during e.g. a cross-validation.

chart objects

Chart object templates are intended to produce a ggplot object from an input DatasetExperiment, model or iterator. The template has no unique slots but includes a chart_plot method to generate the ggplot object from the input object.

As a simple example we create a chart for plotting scores from the PCA object we created earlier. A more comprehensive pca_scores_plot object is included in the structToolbox as well several others for plotting the outputs of different methods.

We use the helper function set_struct_object() to define the new chart object.

# new chart object
set_struct_obj(
  class_name = 'pca_scores_plot',
  struct_obj = 'chart',
  params = c(factor_name = 'character'),
  prototype = list(
    name = 'PCA scores plot',
    description = 'Scatter plot of the first two principal components',
    libraries = 'ggplot2'
  )
)

The chart_plot method for our new object can then be replaced using the set_obj_method() helper function.

# new chart_plot method
set_obj_method(
  class_name = 'pca_scores_plot',
  method_name = 'chart_plot',
  signature = c('pca_scores_plot','PCA'),
  definition = function(obj,dobj) {

    if (!is(dobj,'PCA')) {
      stop('this chart is only for PCA objects')
    }

    # get the PCA scores data
    S = dobj$scores$data

    # add the group labels
    S$factor_name = dobj$scores$sample_meta[[obj$factor_name]]

    # ggplot
    g = ggplot(data = S, aes_string(x='PC1',y='PC2',colour='factor_name')) +
      geom_point() + labs(colour = obj$factor_name) 

    # chart objects return the ggplot object
    return(g)
  }

)

Note that we set signature = c('pca_scores_plot', 'PCA') to indicate that only PCA objects should be accepted as the second input for this chart.

The new chart object is now ready to be used with PCA objects.

# create chart object
C = pca_scores_plot(factor_name = 'Species')

# plot chart using trained PCA object from model sequence
chart_plot(C,MS[2]) + theme_bw() # add theme

entity and enum objects

These struct classes are used to make input params and outputs more informative and more flexible. The are not intednded to be an extendable template in the same way as model objects.

By specifying the type or class of a slot as an entity or enum object you can provide additional information about the slot, such as a more informative name and a description. entity and enum slots have some unique slots:

Both objects also make use of the type slot to ensure the assigned value for a slot can only be of a certain class e.g. setting type = c('numeric', 'integer') will not allow the parameter value to be set to a character.

The name and description slots can be useful e.g. in report writing for providing a standard definition of a parameter.

enitity and enum objects have methods for getting/setting all slots. They are designed to work seamlessly with dollar syntax in the same way you would access the value of any non-entity slot.

As an example the number_components parameter for the PCA object could be defined as an entity object.

# define entity
npc = entity(
  name = 'Number of principal components',
  description = 'The number of principal components to calculate',
  type = c('numeric','integer'),
  value = 2,
  max_length = 1
)

# summary
npc

It can then be used with the set_struct_obj() function when creating the PCA object by using "entity" as the input type for params and including the entity object in the prototype for the object. It will function exactly as it did before, but has the additional information (name, description, etc) attached as well.

# PCA object
PCA = set_struct_obj(
  class_name = 'PCA', 
  struct_obj = 'model',       
  params = c(number_components = 'entity'),                
  outputs = c(scores = 'DatasetExperiment',
              loadings = 'data.frame'),
  private = character(0),
  prototype = list(number_components = npc,
                  ontology = 'OBI:0200051',
                  predicted = 'scores')
)

# create PCA model 
P = PCA(number_components = 3)

# get set value
P$number_components

# get description
param_obj(P,'number_components')$description

Ontology.

All struct objects have an ontology slot that can be assigned ontology term identifiers. struct makes use of the rols bioconductor package to access the Ontology lookup Service API and obtain names and definitions based on the provided identifiers. 

P = PCA()

# return a list of ontologies for PCA, including the input parameters and outputs
ontology(P)

# return the ontology id'd for PCA only (not inputs/outputs)
P$ontology

# get the ontology for a specific input
IN = param_obj(P,'number_components') # get as entity object
IN$ontology

NOTE: in versions prior to v1.5.1 stato classes were used. This has now been deprecated in favour of ontology and will be removed in a future release.

Session Info

sessionInfo()


computational-metabolomics/struct documentation built on March 27, 2024, 4:26 p.m.

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