---
title: "Evaluation of Bioinformatics Metrics with evaluomeR"
author:
- name: "José Antonio Bernabé-DÃaz"
affiliation: &dis Departamento de Informática y Sistemas, Universidad de Murcia, IMIB-Arrixaca, 30100, Murcia, Spain
- name: "Manuel Franco"
affiliation: &deio Departamento de EstadÃstica e Investigación Operativa, Universidad de Murcia, 30100, Murcia, Spain
- name: "Juana-MarÃa Vivo"
affiliation: *deio
- name: "Manuel Quesada-MartÃnez"
affiliation: &cio Center of Operations Research (CIO), Miguel Hernández University of Elche, 03202, Elche, Spain
- name: "Astrid Duque-Ramos"
affiliation: &ds Departamento de Sistemas, Facultad de IngenierÃas, Universidad de Antioquia, MedellÃn, 050010, Colombia
- name: "Jesualdo Tomás Fernández-Breis"
affiliation: *dis
package: evaluomeR
date: "2020-04-13"
bibliography: ../inst/REFERENCES.bib
biblio-style: apsr
link-citations: yes
abstract: >
R package **evaluomeR** how-to guide
output:
BiocStyle::html_document:
toc_float: false
vignette: >
%\VignetteIndexEntry{Evaluation of Bioinformatics Metrics with evaluomeR}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r style, include=FALSE, results='hide'}
BiocStyle::markdown()
library(kableExtra)
library(magrittr)
library(SummarizedExperiment)
```
# Introduction #
The **evaluomeR** package permits to evaluate the reliability of bioinformatic
metrics by analysing the stability and goodness of the classifications of such
metrics. The method takes the measurements of the metrics for the dataset and
evaluates the reliability of the metrics according to the following analyses:
Correlations, Stability and Goodness of classifications.
* **Correlations**: Calculation of Pearson correlation coefficient between
every pair of metrics available in order to quantify their interrelationship
degree. The score is in the range [-1,1].
- Perfect correlations: -1 (inverse), and 1 (direct).
* **Stability**: This analysis permits to estimate whether the clustering is
meaningfully affected by small variations in the sample
[@milligan1996measuring]. First, a clustering using the k-means algorithm is
carried out. The value of K can be provided by the user. Then, the stability
index is the mean of the Jaccard coefficient [@jaccard1901distribution]
values of a number of bootstrap replicates. The values are in the range [0,1],
having the following meaning:
- Unstable: [0, 0.60[.
- Doubtful: [0.60, 0.75].
- Stable: ]0.75, 0.85].
- Highly Stable: ]0.85, 1].
* **Goodness of classifications**: The goodness of the classifications are
assessed by validating the clusters generated. For this purpose, we use the
Silhouette width as validity index. This index computes and compares the
quality of the clustering outputs found by the different metrics, thus
enabling to measure the goodness of the classification for both instances and
metrics. More precisely, this goodness measurement provides an assessment of
how similar an instance is to other instances from the same cluster and
dissimilar to the rest of clusters. The average on all the instances
quantifies how the instances appropriately are clustered. Kaufman and
Rousseeuw [@kaufman2009finding] suggested the interpretation of the global
Silhouette width score as the effectiveness of the clustering structure. The
values are in the range [0,1], having the following meaning:
- There is no substantial clustering structure: [-1, 0.25].
- The clustering structure is weak and could be artificial: ]0.25, 0.50].
- There is a reasonable clustering structure: ]0.50, 0.70].
- A strong clustering structure has been found: ]0.70, 1].
# Installation #
The installation of **evaluomeR** package is performed via Bioconductor:
```{r installation, eval=FALSE}
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("evaluomeR")
```
## Prerequisites ##
The package **evaluomeR** depends on the following CRAN packages for the
calculus: *cluster* [@cluster2018], *corrplot*
[@corrplot2017]. Moreover, this package also depends on *grDevices*,
*graphics*, *stats* and *utils* from R Core [@rcore] for plotting and on the
Bioconductor packages *SummarizedExperiment* [@summarizedExperiment], *MultiAssayExperiment* [@multiAssayExperiment] for
input/output data.
# Using evaluomeR #
## Creating an input SummarizedExperiment ##
The input is a `SummarizedExperiment` object. The assay contained in
`SummarizedExperiment` must follow a certain structure, see Table
\@ref(tab:table): A valid header must be specified. The first column of the
header is the ID or name of the instance of the dataset (e.g., ontology,
pathway, etc.) on which the metrics are measured. The other columns of the
header contains the names of the metrics. The rows contains the measurements
of the metrics for each instance in the dataset.
ID | MetricNameA | MetricNameB | MetricNameC | ... |
--------- | ----------- | ----------- | ----------- | --- |
instance1 | 1.2 | 6.4 | 0.5 | ... |
instance2 | 2.4 | 5.4 | 0.8 | ... |
instance3 | 1.9 | 8.9 | 1.1 | ... |
: (\#tab:table) Example of an input assay from a `SummarizedExperiment` for
the **evaluomeR** package.
## Using input sample data from evaluomeR ##
In our package we provide three different sample input data:
* **ontMetrics**: Structural ontology metrics, 19 metrics measuring
structural aspects of bio-ontologies have been analysed on two different
corpora of ontologies: OBO Foundry and AgroPortal [@ontoeval].
* **rnaMetrics**: RNA quality metrics for the assessment of gene expression
differences, 2 quality metrics from 16 aliquots of a unique batch of RNA
Samples. The metrics are Degradation Factor (DegFact) and RNA Integrity Number
(RIN) [@imbeaud2005towards].
* **bioMetrics**: Metrics for biological pathways, 2 metrics that
quantitative characterizations of the importance of regulation in biochemical
pathway systems, including systems designed for applications in synthetic
biology or metabolic engineering. The metrics are reachability and efficiency
[@davis2018metrics].
The user shall run the `data` built-in method to load **evaluomeR** sample input
data. This requires to provide the descriptor of the desired dataset. The
datasets availables can take the following values: "ontMetrics", "rnaMetrics" or
"bioMetrics".
```{r sample-input, message=FALSE}
library(evaluomeR)
data("ontMetrics")
data("rnaMetrics")
data("bioMetrics")
```
## Correlations ##
We provide the `metricsCorrelations` function to evaluate the correlations among the
metrics defined in the `SummarizedExperiment`:
```{r correlations-1, echo=TRUE}
library(evaluomeR)
data("rnaMetrics")
correlationSE <- metricsCorrelations(rnaMetrics, margins = c(4,4,12,10))
# Access the correlation matrix via its first assay:
# assay(correlationSE,1)
```
## Stability analysis ##
The calculation of the stability indices is performed by `stability` and
`stabilityRange` functions.
### Stability ###
The stability index analysis is performed by the `stability` function. For
instance, running a stability analysis for the metrics of `rnaMetrics` with a
number of `100` bootstrap replicates with a k-means cluster whose `k` is 2
(note that `k` must be inside [2,15] range):
```{r stability-1, results='hide', echo=TRUE, message=FALSE}
stabilityData <- stability(rnaMetrics, k=2, bs = 100)
```
The `stability` function returns the `stabilityData` object, a
`ExperimentList` that contains the several assays such as the stability mean or the mean, betweenss, totss, tot.swithinss and anova values from the `kmeans` clustering:
```{r stability-0-assay, echo=TRUE}
stabilityData
```
The stability indices plots shown when `getImages = TRUE` are generated with the values of the stability mean:
```{r stability-1-assay, results='hide', echo=TRUE, eval=FALSE}
assay(stabilityData, "stability_mean")
```
```{r stability-1-table, results='asis', echo=FALSE}
data <- assay(stabilityData, "stability_mean")
kable(data) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
```
The plot represents the stability mean from each metric for a given `k` value.
This mean is calculated by performing the average of every stability index
from `k`ranges [1,k] for each metric.
### Stability range ### {#sec:stabilityrange}
The `stabilityRange` function is an iterative method of `stability` function.
It performs a stability analysis for a range of `k` values (`k.range`).
For instance, analyzing the stability of `rnaMetrics` in range [2,4], with
`bs=100`:
```{r stabilityRange-1, results='hide', echo=TRUE, message=FALSE}
stabilityRangeData = stabilityRange(rnaMetrics, k.range=c(2,4), bs = 100)
```
Two kind of graphs are plotted in `stabilityRange` function. The first type
(titled as "*St. Indices for k=X across metrics*") shows, for every `k` value,
the stability indices across the metrics. The second kind (titled as
*St. Indices for metric 'X' in range [x,y]*), shows a plot of the behaviour of
each metric across the `k` range.
## Goodness of classifications ##
There are two methods to calculate the goodness of classifications: `quality`
and `qualityRange`.
### Quality ###
This method plots how the metrics behave for the current `k` value, according
to the average silhouette width. Also, it will plot how the clusters are
grouped for each metric (one plot per metric).
For instance, running a quality analysis for the two metrics of `rnaMetrics`
dataset, being `k=4`:
```{r quality-1, results='hide', eval=TRUE, echo=TRUE, message=FALSE}
qualityData = quality(rnaMetrics, k = 4)
```
The data of the first plot titled as "*Qual. Indices for k=4 across metrics*"
according to *Silhouette avg. width*, is stored in *Avg_Silhouette_Width*
column from the first assay of the `SummarizedExperiment`, `qualityData`. The
other three plots titled by their metric name display the input rows grouped
by colours for each cluster, along with their Silhouette width scores.
The variable `qualityData` contains information about the clusters of each
metric: The average silhouette width per cluster, the overall average
sihouette width (taking into account all the clusters) and the number of
individuals per cluster:
```{r quality-1-assay, results='hide', eval=FALSE, echo=TRUE}
assay(qualityData,1)
```
```{r quality-1-table, results='asis', echo=FALSE}
data <- assay(qualityData,1)
kable(data) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
scroll_box(width = "100%")
```
### Quality range ###
The `qualityRange` function is an iterative method that uses the same
functionality of `quality` for a range of values (`k.range`), instead for one
unique `k` value. This methods allows to analyse the goodness of the
classifications of the metric for different values of the range.
In the next example we will keep using the `rnaMetrics` dataset, and a
`k.range` set to [4,6].
```{r quality-range-1, results='hide', eval=TRUE, echo=TRUE, message=FALSE}
k.range = c(4,6)
qualityRangeData = qualityRange(rnaMetrics, k.range)
```
The `qualityRange` function also returns two kind of plots, as seen in
[Stability range](#sec:stabilityrange) section. One for each `k` in the
`k.range`, showing the quality indices (goodness of the classification) across
the metrics, and a second type of plot to show each metric with its respective
quality index in each `k` value.
The `qualityRangeData` object returned by `qualityRange` is a `ExperimentList` from
`MultiAssayExperiment`, which is a list of `SummarizedExperiment`
objects whose size is `diff(k.range)+1`. In the example shown above, the size of
`qualityRangeData` is 3, since the array length would contain the dataframes from
`k=4` to `k=6`.
```{r quality-range-2, eval=TRUE, echo=TRUE}
diff(k.range)+1
length(qualityRangeData)
```
The user can access a specific dataframe for a given `k` value in three
different ways: by dollar notation, brackets notation or using our wrapper
method `getDataQualityRange`. For instance, if the user wishes to retrieve the
dataframe which contains information of `k=5`, being the `k.range` [4,6]:
```{r quality-range-3, eval=FALSE, echo=TRUE}
k5Data = qualityRangeData$k_5
k5Data = qualityRangeData[["k_5"]]
k5Data = getDataQualityRange(qualityRangeData, 5)
assay(k5Data, 1)
```
```{r quality-range-table, results='asis', echo=FALSE}
data <- assay(qualityRangeData$k_5, 1)
kable(data) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
scroll_box(width = "100%", height = "150px")
```
Once the user believes to have found a proper `k` value, then the user can run
the `quality` function to see further silhouette information on the plots.
## General functionality ##
In this section we describe a series of parameters that are shared among our
analysis functions: `metricsCorrelations`, `stability`, `stabilityRange`, `quality`
and `qualityRange`.
### Disabling plotting ###
The generation of the images can be disabled by setting to `FALSE` the
parameter `getImages`:
```{r general-func-noplot, eval=FALSE, echo=TRUE}
stabilityData <- stability(rnaMetrics, k=5, bs = 50, getImages = FALSE)
```
This prevents from generating any graph, performing only the calculus. By
default `getImages` is set to `TRUE`.
# Selecting the optimal value of k #
`evaluomeR` analyzes the behavior of the metrics in terms of stability and goodness of the clusters for a range of values of $k$. In case of wishing to select the optimal value for $k$ for a metric in a given dataset we have implemented the `getOptimalKValue` function, which returns a table stating which is the optimal value of `k` for each metric.
The algorithm works as follows: The highest stability and the highest goodness are obtained for the same value of $k$. In such case, that value would be the optimal one. On the other hand, the highest stability and the highest goodness are obtained for different values of $k$. In this case, additional criteria are needed. \textit{evaluomeR} does not currently aim at providing those criteria, but to provide the data that could permit the user to make decisions. In the use cases described in this paper, we will apply the following criteria for the latter case:
* If both values of $k$ provide at least stable classifications (value >0.75), then we select the value of $k$ that provides the largest Silhouette width. The same would happen if none provides stable classifications.
* If $k_1$ provides stable classifications and $k_2$ does not, we will select $k_1$ if the width of the Silhouette is at least reasonable.
* If $k_1$ provides stable classifications, $k_2$ does not, and the width of the Silhouette of $k_1$ is less than reasonable, then we will select the value of $k$ with the largest Silhouette width.
```{r getOptimalKValue, results='hide', eval=TRUE, echo=TRUE, message=FALSE}
stabilityData <- stabilityRange(data=ontMetrics, k.range=c(2,4),
bs=20, getImages = FALSE, seed=100)
qualityData <- qualityRange(data=ontMetrics, k.range=c(2,4),
getImages = FALSE, seed=100)
kOptTable <- getOptimalKValue(stabilityData, qualityData)
```
```{r getOptimalKValue-table, results='asis', echo=FALSE}
data <- kOptTable
kable(data) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
scroll_box(width = "100%", height = "150px")
```
Additionally, you can select another subset of `k.range` to delimit the range of the optimal `k`.
```{r getOptimalKValue-delimited, results='hide', eval=TRUE, echo=TRUE}
kOptTable <- getOptimalKValue(stabilityData, qualityData, k.range=c(3,4))
```
```{r getOptimalKValue-table-delimited, results='asis', echo=FALSE}
data <- kOptTable
kable(data) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
scroll_box(width = "100%", height = "150px")
```
# Metric analysis #
We provide a series of methods for a further analysis on the metrics. These methods are: `plotMetricsMinMax`, `plotMetricsBoxplot`, `plotMetricsCluster` and `plotMetricsViolin`.
The `plotMetricsMinMax` function plots the minimum, maximum and standard deviation of min/max points of the values of each metric:
```{r plotMetricsMinMax, results='hide', eval=TRUE, echo=TRUE}
plotMetricsMinMax(ontMetrics)
```
The `plotMetricsBoxplot` method boxplots the value of each metric:
```{r plotMetricsBoxplot, results='hide', eval=TRUE, echo=TRUE}
plotMetricsBoxplot(rnaMetrics)
```
Next, the `plotMetricsCluster` function clusters the values of the metrics by
using the euclidean distance and the method `ward.D2` from `hclust`:
```{r plotMetricsCluster, results='hide', eval=TRUE, echo=TRUE}
plotMetricsCluster(ontMetrics)
```
And finally the `plotMetricsViolin` function:
```{r plotMetricsViolin, results='hide', eval=TRUE, echo=TRUE}
plotMetricsViolin(rnaMetrics)
```
# Information #
## Contact ##
The source code is available at **github**. For bug/error reports please refer
to evaluomeR github issues [https://github.com/neobernad/evaluomeR/issues](https://github.com/neobernad/evaluomeR/issues).
## License ##
The package 'evaluomeR' is licensed under GPL-3.
## How to cite ##
Currently there is no literature for evaluomeR. Please cite the R package, the
github or the website. This package will be updated as soon as a citation is
available.
## Additional information ##
The evaluomeR functionality can also be access through a web
interface^[[Evaluome web ](http://sele.inf.um.es/evaluome/index.html)] an API
REST^[[API documentation](https://documenter.getpostman.com/view/1705269/RznBMfbB)].
## Session information ##
```{r sessionInfo, eval=TRUE}
sessionInfo()
```
## Bibliography ##