evaluomeR 1.14.0

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 (Milligan and Cheng 1996). 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 (Jaccard 1901) 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 (Kaufman and Rousseeuw 2009) 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].

The installation of **evaluomeR** package is performed via Bioconductor:

```
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("evaluomeR")
```

The package **evaluomeR** depends on the following CRAN packages for the
calculus: *cluster* (Maechler et al. 2018), *corrplot*
(Wei and Simko 2017). Moreover, this package also depends on *grDevices*,
*graphics*, *stats* and *utils* from R Core (R Core Team 2018) for plotting and on the
Bioconductor packages *SummarizedExperiment* (Morgan et al. 2018), *MultiAssayExperiment* (Ramos et al. 2017) for
input/output data.

The input is a `SummarizedExperiment`

object. The assay contained in
`SummarizedExperiment`

must follow a certain structure, see Table
1: 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 | … |

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 (Franco et al. 2019).**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) (Imbeaud et al. 2005).**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 (Davis and Voit 2018).

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”.

```
library(evaluomeR)
data("ontMetrics")
data("rnaMetrics")
data("bioMetrics")
```

We provide the `metricsCorrelations`

function to evaluate the correlations among the
metrics defined in the `SummarizedExperiment`

:

```
library(evaluomeR)
data("rnaMetrics")
correlationSE <- metricsCorrelations(rnaMetrics, margins = c(4,4,12,10))
```

`## `

```
## Data loaded.
## Number of rows: 16
## Number of columns: 3
```

```
# Access the correlation matrix via its first assay:
# assay(correlationSE,1)
```

The calculation of the stability indices is performed by `stability`

and
`stabilityRange`

functions.

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):

`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:

`stabilityData`

```
## ExperimentList class object of length 9:
## [1] stability_mean: SummarizedExperiment with 2 rows and 2 columns
## [2] cluster_partition: SummarizedExperiment with 2 rows and 2 columns
## [3] cluster_mean: SummarizedExperiment with 2 rows and 2 columns
## [4] cluster_centers: SummarizedExperiment with 2 rows and 2 columns
## [5] cluster_size: SummarizedExperiment with 2 rows and 2 columns
## [6] cluster_betweenss: SummarizedExperiment with 2 rows and 2 columns
## [7] cluster_totss: SummarizedExperiment with 2 rows and 2 columns
## [8] cluster_tot.withinss: SummarizedExperiment with 2 rows and 2 columns
## [9] cluster_anova: SummarizedExperiment with 2 rows and 2 columns
```

The stability indices plots shown when `getImages = TRUE`

are generated with the values of the stability mean:

`assay(stabilityData, "stability_mean")`

Metric | Mean_stability_k_2 |
---|---|

RIN | 0.82540873015873 |

DegFact | 0.873916305916306 |

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.

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`

:

`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.

There are two methods to calculate the goodness of classifications: `quality`

and `qualityRange`

.

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`

:

`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:

`assay(qualityData,1)`

Metric | Cluster_1_SilScore | Cluster_2_SilScore | Cluster_3_SilScore | Cluster_4_SilScore | Avg_Silhouette_Width | Cluster_1_Size | Cluster_2_Size | Cluster_3_Size | Cluster_4_Size |
---|---|---|---|---|---|---|---|---|---|

RIN | 0.420502645502646 | 0.696597735248404 | 0.473928571428572 | 0.324731573607137 | 0.488264943810529 | 4 | 4 | 5 | 3 |

DegFact | 0.759196481622952 | 0.59496499852177 | 0.600198799385732 | 0.521618857725795 | 0.634170498361632 | 5 | 3 | 5 | 3 |

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].

```
k.range = c(4,6)
qualityRangeData = qualityRange(rnaMetrics, k.range)
```