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Find CNVs from deviants

Source: R/detect.R
cnv.Rd

Categorize deviant and non-deviant into "singlets" and "duplicates" based on the statistical approaches specified by the user. The intersection of all the stats provided will be used in the categorization. If one would like to use the intersection of at least two stats, this can be specified in the n.ints

Usage

cnv(
  data,
  test = c("z.het", "z.05", "z.all", "chi.het", "chi.05", "chi.all"),
  filter = c("intersection", "kmeans"),
  WGS = TRUE,
  ft.threshold = 0.05,
  plot = TRUE,
  verbose = TRUE,
  ...
)

Arguments

data

A data frame of allele information generated with the function allele.info

test

vector of characters. Type of test to be used for significance. See details

filter

character. Type of filter to be used for filtering CNVs. default kmeans. See details.

WGS

logical. test parameter. See details WGS is a test parameter to include or exclude coefficient of variance (cv) in kmeans. For data sets with more homogeneous depth distribution, excluding cv improves CNV detection. If you're not certain about this, use TRUE which is the default.

ft.threshold

confidence interval for filtering default = 0.05

plot

logical. Plot the detection of duplicates. default TRUE

verbose

logical. show progress

...

other arguments to be passed to plot

Value

Returns a data frame of SNPs with their detected duplication status

Details

SNP deviants are detected with both excess of heterozygosity according to HWE and deviant SNPs where depth values fall outside of the normal distribution are detected using the following methods:

  • Z-score test \(Z_{x} = \sum_{i=1}^{n} Z_{i}\); \(Z_{i} = \frac{\left ( (N_{i}\times p)- N_{Ai} \right )}{\sqrt{N_{i}\times p(1-p)}}\)

  • chi-square test \(X_{x}^{2} = \sum_{i-1}^{n} X_{i}^{2}\); \(X_{i}^{2} = (\frac{(N_{i}\times p - N_{Ai})^2}{N_{i}\times p} + \frac{(N_{i}\times (1 - p)- (N_{i} - N_{Ai}))^2}{N_{i}\times (1-p)})\)

See references for more details on the methods

Users can pick among Z-score for heterozygotes (z.het, chi.het), all allele combinations (z.all, chi.all) and the assumption of no probe bias p=0.5 (z.05, chi.05)

filter will determine whether the intersection or kmeans clustering of the provided tests should be used in filtering CNVs. The intersection uses threshold values for filtering and kmeans use unsupervised clustering. Kmeans clustering is recommended if one is uncertain about the threshold values.

Author

Piyal Karunarathne

Examples

if (FALSE) data(alleleINF)
DD<-cnv(alleleINF)
#> categorizing putative duplicates with 
#>  excess of heterozygotes z.all & chi.all