Simulated coverage-QTL example

Simulated cis-QTL data on a power-of-two grid of positions: n = 574 (the full susieR::N3finemapping$X[, 1:100] LD scaffold), p = 100 SNPs, T = 128 evenly-spaced positions. Two causal SNPs at positions 25 and 75 carry smooth random per-position effects sampled from the IBSS wavelet prior, matching the smooth-function class fSuSiE assumes. The same shape applies to RNA-seq exon-body coverage, ATAC-seq peak coverage, WGBS / ChIP-seq read counts on a window, or any per-position coverage / read-count assay. Used by vignette("fsusie_intro").

Usage

coverage_example

Format

A list with components

X

n x p genotype matrix (n = 574, p = 100) sliced from susieR::N3finemapping$X[, 1:100].

Y

n x T per-position response matrix (T = 128).

pos

length-T integer vector of positions.

causal_snps

integer vector c(25, 75) of column indices in X that carry true effects.

causal_betas

length(causal_snps) x T matrix of true per-position effects.

description

free-text description.

Source

Simulated. See data-raw/make_data.R.

Examples

# \donttest{
data(coverage_example)
fit <- fsusie(coverage_example$Y, coverage_example$X,
              pos = coverage_example$pos,
              L = 15, L_greedy = 5, verbose = TRUE)
#> iter          ELBO       delta   sigma2      mem      V  extras
#>    1   -102841.2670           -   [0.998, 0.998, 0.999]   0.18 GB  [1.00e+00, 1.00e+00, 1.00e+00, 1.00e+00, 1.00e+00]  pi_null=[1.00, 1.00]
#> iter   2: max|dPIP|=1.54e-09, V=[1.00e+00, 1.00e+00, 1.00e+00, 1.00e+00, 1.00e+00] -- converged [mem: 0.18 GB]
#> [L_greedy] 1 round, greedy_lbf_cutoff=0.100, final L=5
#> round  L     min(lbf)   action
#> 1      5     0.000      saturated
fit_s <- mf_post_smooth(fit, method = "TI")
mfsusie_plot(fit_s)

# }