Description Usage Arguments Value Examples
Optimisation is done by solving a L1-penalized least-square problem thanks to the glmnet package
1 | get.Zt(Y, lambda, W, WtWm1)
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Y |
a matrix with n rows (number of samples) and J columns (number of segments) |
lambda |
a positive scalar tuning the penalty level for fusion |
W |
a matrix with n rows (number of samples) and K columns (number of subclones) |
WtWm1 |
a K x K square matrix (with K the number of subclones), precomputed to save time |
The transpose of Z, where Z a matrix with K rows (number of subclones) and J columns (number of segments)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | J <- 11L ## Number of segments
K <- 4L ## Number of subclones
n <- 4L ## Number of samples
Z <- matrix(1, nrow = K, ncol = J)
Z[2, 2] <- 2
Z[3, 5:6] <- 2
Z[4, 9:10] <- 2
W <- matrix(0, nrow = n, ncol = K)
W[1,1] <- W[2,2] <- W[3,3] <- W[4,4] <- 1
WtWm1 <- tcrossprod(backsolve(qr.R(qr(W)), x=diag(K)))
E <- matrix(rnorm(n*J, sd = 0.1), nrow = n, ncol = J)
Y <- W %*% Z + E
Zthat <- c3co:::get.Zt(Y, lambda = 0.01, W = W, WtWm1 = WtWm1)
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