GenomeAdapt (version 1.0.0)

zscores_qvals: Calculating (q)p-values from multiple factors/scores

Description

Converting the Z-scores to (q)p-values. This function calibrates the p-value/q-values considering multiple scores based on Mahalanobis Distance/componentwise method.

Usage

zscores_qvals(x, outlier.method = "mahalanobis",
estim = "pairwiseGK", pval.coret.method = "bonferroni")

Arguments

x

The GenomeAdapt object containing the locus scores

outlier.method

The methods used to detect the outliers, "mahalanobis" or "componentwise", default is mahalanobis

estim

Method used to estimate Mahalanobis distance. The choices are : "mcd" for the Fast MCD algorithm of Rousseeuw and Van Driessen, "weighted" for the Reweighted MCD, "donostah" for the Donoho-Stahel projection based estimator, "M" for the constrained M estimator provided by Rocke, "pairwiseQC" for the orthogonalized quadrant correlation pairwise estimator, and "pairwiseGK" for the Orthogonalized Gnanadesikan-Kettenring pairwise estimator. The default "auto" selects from "donostah", "mcd", and "pairwiseQC" with the goal of producing a good estimate in a reasonable amount of time.

pval.coret.method

Correction method for p-values,choices are "holm", "hochberg", "hommel", "bonferroni", "BH", "BY","fdr", "none".The adjustment methods include the Bonferroni correction ("bonferroni") in which the p-values are multiplied by the number of comparisons. Less conservative corrections are also included by Holm (1979) ("holm"), Hochberg (1988) ("hochberg"), Hommel (1988) ("hommel"), Benjamini & Hochberg (1995) ("BH" or its alias "fdr"), and Benjamini & Yekutieli (2001) ("BY"), respectively. A pass-through option ("none") is also included.

Value

A dataframe with p-values, adjusted p-values and q-values.

pvals

A dataframe containing 3 columns, including the p-values, q-values, and adjusted p-values for all loci

chr

The chromsomes for the dataset

Details

Calculating (q)p-values from GenomeAdapt to identify the outlier loci

References

Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B, 57, 289-300. http://www.jstor.org/stable/2346101.

R. A. Maronna and R. H. Zamar (2002) Robust estimates of location and dispersion of high-dimensional datasets. Technometrics 44 (4), 307-317.

Capblancq, T., Luu, K., Blum, M. G., & Bazin, E. (2018). Evaluation of redundancy analysis to identify signatures of local adaptation. Molecular Ecology Resources, 18(6), 1223-1233.

Examples

# NOT RUN {
##---- Do genome scan ----
HapmapScan=GenomeAdapt.gds(genfile = SNPRelate::snpgdsExampleFileName(),
method="EIGMIX",num.thread = 1L, autosome.only=TRUE,
remove.monosnp=TRUE, maf=0.01, missing.rate=0.1)

## estimating the q-values from genome scan

# }
# NOT RUN {
Hapmapqval=zscores_qvals(HapmapScan)
# }
# NOT RUN {
# }