Description Usage Arguments Value Author(s) References Examples

This function reconstructs DNA methylation values from raw measurements. It iteratively implements the group fused lars to smooth related-by-location methylation values and the constrained least squares to remove probe affinity effect across multiple sequences. It also contains a criterion-based method (AIC or BIC) for selecting the tuning paramter.

1 |

`Y` |
An observed matrix (p x n) of methylation values (beta values); p is the number of probes and n is the number of samples; |

`wts` |
A pre-specified vector of weights. By default, we use the probe index-dependent weight scheme, $wts_i = sqrt(p / i / (p - i))$ for $i = 1, ... , p$; |

`steps` |
Limit the number of steps taken. One can use this option to perform early stopping. |

`ans.aic` |
A list corresponds to the AIC, containing estimated beta values, estimated probed effects, estimated change-point locations, residual sum of squares, and degree of freedom. |

`ans.bic` |
A list corresponds to the BIC, containing estimated beta values, estimated probed effects, estimated change-point locations, residual sum of squares, and degree of freedom. |

Tao Wang, Mengjie Chen

paper under review

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
p <- 80
n <- 40
K <- 2
k <- K - 1
cp <- numeric()
L <- c(0, floor(p / K) * (1 : k), p)
cp <- floor(p / K) * (1 : k) + 1
## phi0: probe effects; theta0: true methylation values; part: partition of probe indices
phi0 <- runif(p, 0.5, 2.0)
theta0 <- matrix(0, p, n)
part <- list()
for (s in 1 : K) {
part[[s]] <- (L[s] + 1) : L[s + 1]
phi0[part[[s]]] <- phi0[part[[s]]] / sqrt(mean(phi0[part[[s]]]^2))
}
theta0[part[[1]], ] <- rep(1, length(part[[1]]))
theta0[part[[2]], ] <- rep(1, length(part[[2]]))
error <- matrix(runif(p * n, 0, 0.1), p, n)
Y <- theta0 * phi0 + error
fit <- MBAmethyl(Y, steps = 10)
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