CINmetrics

Description

Calculate all CINmetrics on a given dataframe

Usage

CINmetrics(
  cnvData,
  segmentMean_tai = 0.2,
  segmentMean_cna = (log(1.7, 2) - 1),
  segmentMean_base_segments = 0.2,
  segmentMean_break_points = 0.2,
  segmentMean_fga = 0.2,
  numProbes = NA,
  segmentDistance_cna = 0.2,
  minSegSize_cna = 10,
  genomeSize_fga = 2873203431
)

Arguments

Value

All Chromosomal INstability metrics

Examples

CINmetrics(cnvData = maskCNV_BRCA)

Copy Number Aberration

Description

Calculates the number of copy number aberrations

Usage

cna(
  cnvData,
  segmentMean = (log(1.7, 2) - 1),
  numProbes = NA,
  segmentDistance = 0.2,
  minSegSize = 10
)

Arguments

Details

Copy Number Aberrations (CNA) (Davidson JM, et al), are defined as a segment with copy number outside the pre-defined range of 1.7-2.3

(\log_2 1.7 -1) \le \bar{y}_{S_i} \le (\log_2 2.3 -1)

that is not contiguous with an adjacent independent CNA of identical copy number. For our purposes, we have adapted the range to be

|\bar{y}_{S_i}| \ge |\log_2 1.7|

, which is only slightly larger than the original. It is nearly identical to countingBreakPoints, except this one calculates breaks as adjacent segments that have a difference in segment means of \ge 0.2.

Total\ Copy\ Number\ Aberration = \sum^{R}_{i = 1} n_i \ where \ \bar{y}_{S_i}| \ge |\log_2{1.7}|, \ \bar{y}_{S_{i-1}} - \bar{y}_{S_i}| \ge 0.2, \ d_i \ge 10

Value

Number of copy number aberrations between segments

See Also

countingBreakPoints

Examples

cna(cnvData = maskCNV_BRCA)

countingBaseSegments

Description

Function for counting altered base segments

Usage

countingBaseSegments(cnvData, segmentMean = 0.2, numProbes = NA)

Arguments

Details

The Altered Base Segment calculation takes all the CNV data for a single patient and first filters it for a segmentation mean of > 0.2 and, if specified, the minimum number of probes covering that area. Then, it calculates the sums of the lengths of each segment for a particular patient and outputs that.

Number\ of\ Altered\ Bases = \sum^{R}_{i = 1} d_i\ where\ |\bar{y}_{S_i}| \ge 0.2

Value

Number of Base segments for each unique sample

Examples

countingBaseSegments(cnvData = maskCNV_BRCA)

countingBreakPoints

Description

The Break Point calculation takes all the CNV data for a single patient and first filters it for segmentation mean of > 0.2 and, if specified, the minimum number of probes covering that area. Then it counts the number of rows of data and multiplies it by 2. This represents the break points at the 5' and 3' ends of each segment.

Number\ of \ Break\ Points = \sum^{R}_{i = 1} (n_i \cdot 2)\ where\ |\bar{y}_{S_i}| \ge 0.2

Usage

countingBreakPoints(cnvData, segmentMean = 0.2, numProbes = NA)

Arguments

Value

Number of Break points for each unique sample

Fraction Genome Altered

Description

Fraction Genome Altered looks at the fraction of the genome that deviates from a diploid state fga calculates the fraction of the genome altered (FGA; [Chin SF, et. al.](https://www.ncbi.nlm.nih.gov/pubmed/17925008)), measured by taking the sum of the number of bases altered and dividing it by the genome length covered ($G$). Genome length covered was calculated by summing the lengths of each probe on the Affeymetrix 6.0 array. This calculation **excludes** sex chromosomes.

Fraction\ Genome\ Altered = \frac {\sum^{R}_{i = 1} d_i} {G} \ \ where\ |\bar{y}_{S_i}| \ge 0.2

Usage

fga(cnvData, segmentMean = 0.2, numProbes = NA, genomeSize = 2873203431)

Arguments

Value

Fraction of the genome altered

Examples

fga(cnvData = maskCNV_BRCA)

Breast Cancer Data from TCGA Data Release 25.0 GDC Product: Data Release Date: July 22, 2020 Masked Copy Number variation data for Breast Cancer for 10 unique samples selected randomly from TCGA

Description

Breast Cancer Data from TCGA Data Release 25.0 GDC Product: Data Release Date: July 22, 2020 Masked Copy Number variation data for Breast Cancer for 10 unique samples selected randomly from TCGA

Usage

data(maskCNV_BRCA)

Format

An object of class dataframe

Source

https://portal.gdc.cancer.gov/

References

Koboldt, D., Fulton, R., McLellan, M. et al. (2012) Nature 490, 61–70 https://www.nature.com/articles/nature11412

Examples

data(maskCNV_BRCA)
tai <- tai(maskCNV_BRCA)

Total Aberration Index

Description

Total Aberration Index calculation takes the sum of lengths of each segment times its segmentation mean for each sample and divides it by the sum of the lengths of each sample.

Usage

tai(cnvData, segmentMean = 0.2, numProbes = NA)

Arguments

Details

The Total Aberration Index (TAI) (Baumbusch LO, et. al.) is “a measure of the abundance of genomic size of copy number changes in a tumour". It is defined as a weighted sum of the segment means

Total\ Aberration\ Index = \frac {\sum^{R}_{i = 1} {d_i} \cdot |{\bar{y}_{S_i}}|} {\sum^{R}_{i = 1} {d_i}}\ \ where |\bar{y}_{S_i}| \ge |\log_2 1.7|

Value

Average of lengths weighted by segmentation mean for each unique sample

Examples

tai(cnvData = maskCNV_BRCA)

Modified Total Aberration Index

Description

Modified Total Aberration Index calculation takes the sum of lengths of each segment times its segmentation mean for each sample and divides it by the sum of the lengths of each sample.

Usage

taiModified(cnvData, segmentMean = 0, numProbes = NA)

Arguments

Details

Modified Total Aberration Index uses all sample values instead of those in aberrant copy number state, thus does not remove the directionality from the score.

Modified\ Total\ Aberration\ Index = \frac {\sum^{R}_{i = 1} {d_i} \cdot {\bar{y}_{S_i}}} {\sum^{R}_{i = 1} {d_i}}

Value

Average of lengths weighted by segmentation mean for each unique sample

See Also

tai

Examples

taiModified(cnvData = maskCNV_BRCA)