summix_local: summix_local

In hendriau/Summix: Summix2: A suite of methods to estimate, adjust, and leverage substructure in genetic summary data

summix_local

Description

Estimates local substructure mixture proportions in genetic summary data; Also performs a selection scan (optional) that identifies potential regions of selection along the given chromosome.

Usage

summix_local(
  data,
  reference,
  observed,
  goodness.of.fit = TRUE,
  type = "variants",
  algorithm = "fastcatch",
  minVariants = 0,
  maxVariants = 0,
  maxWindowSize = 0,
  minWindowSize = 0,
  windowOverlap = 200,
  maxStepSize = 1000,
  diffThreshold = 0.02,
  NSimRef = NULL,
  override_fit = FALSE,
  override_removeSmallAnc = FALSE,
  selection_scan = FALSE,
  position_col = "POS",
  nSimSE = 1000
)

Arguments

data	a data frame of the observed group and reference group allele frequencies for N genetic variants on a single chromosome. Must contain a column specifying the genetic variant positions.
reference	a character vector of the column names for K reference groups.
observed	a character value that is the column name for the observed group.
goodness.of.fit	an option to override the default scaled objective to return the raw loss from slsqp
type	user choice of how to define window size; options "variants" and "bp" are available where "variants" defines window size as the number of variants in a given window and "bp" defines window size as the number of base pairs in a given window. Default is "variants".
algorithm	user choice of algorithm to define local substructure blocks; options "fastcatch" and "windows" are available. "windows" uses a fixed window in a sliding windows algorithm. "fastcatch" allows dynamic window sizes. The "fastcatch" algorithm is recommended- though it is computationally slower. Default is "fastcatch".
minVariants	Used if algorithm = "fastcatch" and type = "variants". A numeric value that specifies the minimum number of genetic variants allowed to define a given window.
maxVariants	Used if type = "variants". A numeric value that specifies the maximum number of genetic variants allowed to define a given window.
maxWindowSize	Used if type = "bp". A numeric value that defines the maximum allowed window size by the number of base pairs in a given window.
minWindowSize	Used if algorithm = "fastcatch" and type = "bp". A numeric value that specifies the minimum number of base pairs allowed to define a given window.
windowOverlap	Used if algorithm = "windows". A numeric value that defines the number of variants or the number of base pairs that overlap between the given sliding windows. Default is 200.
maxStepSize	a numeric value that defines the maximum gap in base pairs between two consecutive genetic variants within a given window. Default is 1000.
diffThreshold	Used if algorithm = "fastcatch". A numeric value that defines the percent difference threshold to mark the end of a local substructure block. Default is 0.02.
NSimRef	Used if f selection_scan = TRUE. A numeric vector of the sample sizes for each of the K reference groups that is in the same order as the reference parameter. This is used in a simulation framework that calculates within local substructure block standard error.
override_fit	default is FALSE. If set as TRUE, the user will override the auto-stop of summix_local() that occurs if the global goodness of fit value is greater than 1.5 (indicating a poor fit of the reference data to the observed data).
override_removeSmallAnc	default is FALSE. If set as TRUE, the user will override the automatic removal of reference ancestries with <2% global proportions – this is not recommended.
selection_scan	user option to perform a selection scan on the given chromosome. Default is FALSE. If set as TRUE, a test statistic will be calculated for each local substructure block. Note: the user can expect extended computation time if this option is set as TRUE.
position_col	a character value that is the column name for the genetic variants positions. Default is "POS".
nSimSE	user choice of number of internal simulations to run to calculate standard error of estimates. Default is 1000.

Value

data frame with a row for each local substructure block and the following columns:

goodness.of.fit: scaled objective reflecting the fit of the reference data. Values between 0.5-1.5 are considered moderate fit and should be used with caution. Values greater than 1.5 indicate poor fit, and users should not perform further analyses using summix

iterations: number of iterations for SLSQP algorithm

time: time in seconds of SLSQP algorithm

filtered: number of SNPs not used in estimation due to missing values

K columns of mixture proportions of reference groups input into the function

nSNPs: number of SNPs in the given local substructure block

Author(s)

Hayley Wolff (Stoneman), [e-mail]

Audrey Hendricks, [e-mail]

References

https://github.com/hendriau/Summix2

See Also

https://github.com/hendriau/Summix2 for further documentation.

Examples

data(ancestryData)
results <- summix_local(data = ancestryData,
                        reference = c("reference_AF_afr",
                                      "reference_AF_eas",
                                      "reference_AF_eur",
                                      "reference_AF_iam",
                                      "reference_AF_sas"),
                        NSimRef = c(704,787,741,47,545),
                        observed="gnomad_AF_afr",
                        goodness.of.fit = TRUE,
                        type = "variants",
                        algorithm = "fastcatch",
                        minVariants = 150,
                        maxVariants = 250,
                        maxStepSize = 1000,
                        diffThreshold = .02,
                        override_fit = FALSE,
                        override_removeSmallAnc = TRUE,
                        selection_scan = FALSE,
                        position_col = "POS")
print(results$results)

hendriau/Summix documentation built on Nov. 13, 2024, 6:53 a.m.