CNVMetrics: Copy Number Variant Metrics

calculateOverlapMetric

R Documentation

Calculate metric using overlapping amplified/deleted regions

Description

This function calculates a specific metric, as specified by the user, using overlapping regions of specific state between to samples. The metric is calculated for each state separately. When more than 2 samples are present, the metric is calculated for each sample pair. By default, the function is calculating metrics for the AMPLIFICATION and DELETION states. However, the user can specify the list of states to be analyzed.

Usage

calculateOverlapMetric(
  segmentData,
  states = c("AMPLIFICATION", "DELETION"),
  method = c("sorensen", "szymkiewicz", "jaccard"),
  nJobs = 1
)

Arguments

`segmentData`	a `GRangesList` that contains a collection of genomic ranges representing copy number events, including amplified/deleted status, from at least 2 samples. All samples must have a metadata column called '`state`' with a state, in an character string format, specified for each region (ex: DELETION, LOH, AMPLIFICATION, NEUTRAL, etc.).
`states`	a `vector` of `character` string with at least one entry. The strings are representing the states that will be analyzed. Default: c('`AMPLIFICATION`', '`DELETION`').
`method`	a `character` string representing the metric to be used. This should be (an unambiguous abbreviation of) one of "sorensen", "szymkiewicz" or "jaccard". Default: "sorensen".
`nJobs`	a single positive `integer` specifying the number of worker jobs to create in case of distributed computation. Default: `1` and always `1` for Windows.

Details

The two methods each estimate the overlap between paired samples. They use different metrics, all in the range [0, 1] with 0 indicating no overlap. The NA is used when the metric cannot be calculated.

The available metrics are (written for two GRanges):

sorensen:

This metric is calculated by dividing twice the size of the intersection by the sum of the size of the two sets. With this metric, an overlap metric value of 1 is only obtained when the two samples are identical.

szymkiewicz:

This metric is calculated by dividing the size of the intersection by the size of the smallest set. With this metric, if one set is a subset of the other set, the overlap metric value is 1.

jaccard:

This metric is calculated by dividing the size of the intersection by the size of the union of the two sets. With this metric, an overlap metric value of 1 is only obtained when the two samples are identical.

Value

an object of class "CNVMetric" which contains the calculated metric. This object is a list where each entry corresponds to one state specified in the 'states' parameter. Each entry is a matrix:

state a lower-triangular matrix with the results of the selected metric on the amplified regions for each paired samples. The value NA is present when the metric cannot be calculated. The value NA is also present in the top-triangular section, as well as the diagonal, of the matrix.

The object has the following attributes (besides "class" equal to "CNVMetric"):

metric the metric used for the calculation.
names the names of the two matrix containing the metrics for the amplified and deleted regions.

Author(s)

Astrid Deschênes, Pascal Belleau

References

Sørensen, Thorvald. n.d. “A Method of Establishing Groups of Equal Amplitude in Plant Sociology Based on Similarity of Species and Its Application to Analyses of the Vegetation on Danish Commons.” Biologiske Skrifter, no. 5: 1–34.

Vijaymeena, M. K, and Kavitha K. 2016. “A Survey on Similarity Measures in Text Mining.” Machine Learning and Applications: An International Journal 3 (1): 19–28. doi: https://doi.org/10.5121/mlaij.2016.3103

Jaccard, P. (1912), The Distribution of the Flora in the Alpine Zone. New Phytologist, 11: 37-50. doi: https://doi.org/10.1111/j.1469-8137.1912.tb05611.x

Examples


## Load required package to generate the samples
require(GenomicRanges)

## Create a GRangesList object with 3 samples
## The stand of the regions doesn't affect the calculation of the metric
demo <- GRangesList()
demo[["sample01"]] <- GRanges(seqnames="chr1",
    ranges=IRanges(start=c(1905048, 4554832, 31686841, 32686222),
    end=c(2004603, 4577608, 31695808, 32689222)), strand="*",
    state=c("AMPLIFICATION", "AMPLIFICATION", "DELETION", "LOH"))

demo[["sample02"]] <- GRanges(seqnames="chr1",
    ranges=IRanges(start=c(1995066, 31611222, 31690000, 32006222),
    end=c(2204505, 31689898, 31895666, 32789233)),
    strand=c("-", "+", "+", "+"),
    state=c("AMPLIFICATION", "AMPLIFICATION", "DELETION", "LOH"))

## The amplified region in sample03 is a subset of the amplified regions
## in sample01
demo[["sample03"]] <- GRanges(seqnames="chr1",
    ranges=IRanges(start=c(1906069, 4558838),
    end=c(1909505, 4570601)), strand="*",
    state=c("AMPLIFICATION", "DELETION"))

## Calculating Sorensen metric for both AMPLIFICATION and DELETION
calculateOverlapMetric(demo, method="sorensen", nJobs=1)

## Calculating Szymkiewicz-Simpson metric on AMPLIFICATION only
calculateOverlapMetric(demo, states="AMPLIFICATION", method="szymkiewicz",
    nJobs=1)

## Calculating Jaccard metric on LOH only
calculateOverlapMetric(demo, states="LOH", method="jaccard", nJobs=1)

adeschen/CNVMetrics documentation built on July 19, 2023, 10:24 p.m.