R/CNVMetricsMethods.R
calculateOverlapMetric.RdThis 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.
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.).
a vector of character string with at least one
entry. The strings are representing the states that will be analyzed.
Default: c('AMPLIFICATION', 'DELETION').
a character string representing the metric to be used.
This should be (an unambiguous abbreviation of) one of "sorensen",
"szymkiewicz" or "jaccard". Default: "sorensen".
a single positive integer specifying the number of
worker jobs to create in case of distributed computation.
Default: 1 and always 1 for Windows.
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.
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.
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
## 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)
#> CNV Metrics
#> Metric:
#> sorensen
#>
#> AMPLIFICATION:
#> sample01 sample02 sample03
#> sample01 NA NA NA
#> sample02 0.04647582 NA NA
#> sample03 0.05465532 0 NA
#>
#>
#> DELETION:
#> sample01 sample02 sample03
#> sample01 NA NA NA
#> sample02 0.0541291 NA NA
#> sample03 0.0000000 0 NA
#>
## Calculating Szymkiewicz-Simpson metric on AMPLIFICATION only
calculateOverlapMetric(demo, states="AMPLIFICATION", method="szymkiewicz",
nJobs=1)
#> CNV Metrics
#> Metric:
#> szymkiewicz
#>
#> AMPLIFICATION:
#> sample01 sample02 sample03
#> sample01 NA NA NA
#> sample02 0.07796751 NA NA
#> sample03 1.00000000 0 NA
#>
## Calculating Jaccard metric on LOH only
calculateOverlapMetric(demo, states="LOH", method="jaccard", nJobs=1)
#> CNV Metrics
#> Metric:
#> jaccard
#>
#> LOH:
#> sample01 sample02 sample03
#> sample01 NA NA NA
#> sample02 0.003832636 NA NA
#> sample03 NA NA NA
#>