snp_fastImpute: Fast imputation

In bigsnpr: Analysis of Massive SNP Arrays

Fast imputation

Description

Fast imputation algorithm based on local XGBoost models.

Usage

snp_fastImpute(
  Gna,
  infos.chr,
  alpha = 1e-04,
  size = 200,
  p.train = 0.8,
  n.cor = nrow(Gna),
  seed = NA,
  ncores = 1
)

Arguments

Gna	A FBM.code256 (typically ⁠<bigSNP>$genotypes⁠). You can have missing values in these data.
infos.chr	Vector of integers specifying each SNP's chromosome. Typically ⁠<bigSNP>$map$chromosome⁠.
alpha	Type-I error for testing correlations. Default is 1e-4.
size	Number of neighbor SNPs to be possibly included in the model imputing this particular SNP. Default is 200.
p.train	Proportion of non missing genotypes that are used for training the imputation model while the rest is used to assess the accuracy of this imputation model. Default is 0.8.
n.cor	Number of rows that are used to estimate correlations. Default uses them all.
seed	An integer, for reproducibility. Default doesn't use seeds.
ncores	Number of cores used. Default doesn't use parallelism. You may use bigstatsr::nb_cores().

Value

An FBM with

• the proportion of missing values by SNP (first row),

• the estimated proportion of imputation errors by SNP (second row).

See Also

snp_fastImputeSimple()

Examples

## Not run: 

fake <- snp_attachExtdata("example-missing.bed")
G <- fake$genotypes
CHR <- fake$map$chromosome
infos <- snp_fastImpute(G, CHR)
infos[, 1:5]

# Still missing values
big_counts(G, ind.col = 1:10)
# You need to change the code of G
# To make this permanent, you need to save (modify) the file on disk
fake$genotypes$code256 <- CODE_IMPUTE_PRED
fake <- snp_save(fake)
big_counts(fake$genotypes, ind.col = 1:10)

# Plot for post-checking
## Here there is no SNP with more than 1% error (estimated)
pvals <- c(0.01, 0.005, 0.002, 0.001); colvals <- 2:5
df <- data.frame(pNA = infos[1, ], pError = infos[2, ])

# base R
plot(subset(df, pNA > 0.001), pch = 20)
idc <- lapply(seq_along(pvals), function(i) {
  curve(pvals[i] / x, from = 0, lwd = 2,
        col = colvals[i], add = TRUE)
})
legend("topright", legend = pvals, title = "p(NA & Error)",
       col = colvals, lty = 1, lwd = 2)

# ggplot2
library(ggplot2)
Reduce(function(p, i) {
  p + stat_function(fun = function(x) pvals[i] / x, color = colvals[i])
}, x = seq_along(pvals), init = ggplot(df, aes(pNA, pError))) +
  geom_point() +
  coord_cartesian(ylim = range(df$pError, na.rm = TRUE)) +
  theme_bigstatsr()

## End(Not run)

bigsnpr documentation built on Sept. 30, 2024, 9:18 a.m.