Description Usage Arguments Details Value Examples
Simulate MAP data set:
1 2 3 4 | data_generation(G = 100, n_control = 10, n_treat = 10, n_rep = 3,
k_real = 4, sigma2_r = rep(1, 2), sigma1_2_r = 1,
sigma2_2_r = c(3, 2), mu1_r = 4, phi_g_r = rep(1, 100),
p_k_real = c(0.7, 0.1, 0.1, 0.1), x = x)
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G |
Number of genes to simulate |
n_control |
Number of time points in control group |
n_treat |
Number of time points in treatment group |
n_rep |
Number of replicates in both group |
k_real |
Always be 4 |
sigma2_r |
Variance parameter for τ_{g} |
sigma1_2_r |
Variance parameter for η_{g1} |
sigma2_2_r |
Variance parameter for η_{g2} |
mu1_r |
Mean parameter for η_{g1} |
phi_g_r |
Dispersion parameter |
p_k_real |
True proportion for each mixture component |
x |
Time structured design for the simulated data |
The vector of read counts for gene g, treatment group i, replicate j, at time point t,Y_{gij}(t), follows a Negative Binomial distribution parameterized mean λ_{gi} and φ_g, where E[Y_{gij}(t)] = λ_{gi}(t). λ_{gi}(t) is further modeled as λ_{gi}(t) = S_{ij} \exp[η_{g1}I_{i = 2} + B'(t)η_{g2}I_{i = 2} + B'(t)τ_{g}]. We have B'(t) are design matrix, which is constructed by 2 orthorgonal polynomial bases.
t = 1,..., n_treat (or n_control if control group);
j = 1,..., n_rep;
g = 1,...,G; and
[η_{g1}, η_{g2}, τ_{g}] ~ 4-component gausssian mixture model
Y1 Simulated data
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | library(matlib)
n_basis = 2
n_control = 10
n_treat = 10
n_rep = 3
tt_treat = c(1:n_treat)/n_treat
nt = length(tt_treat)
ind_t = sort(sample(c(1:nt), n_control))
tt = tt_treat[ind_t]
tttt = c(rep(tt, n_rep), rep(tt_treat, n_rep))
z = x = matrix(0, length(tttt), n_basis)
z[,1] = 1.224745*tttt
z[,2] = -0.7905694 + 2.371708*tttt^2
x[,1] = z[,1] - Proj(z[,1], rep(1, length(tttt)))
x[,2] = z[,2] - Proj(z[,2], rep(1, length(tttt))) - Proj(z[,2], x[,1])
Y1 = data_generation(G = 100,
n_control = n_control,
n_treat = n_treat,
n_rep = n_rep,
k_real = 4,
sigma2_r = rep(1, 2),
sigma1_2_r = 1,
sigma2_2_r = c(3,2),
mu1_r = 4,
phi_g_r = rep(1, 100),
p_k_real = c(0.7, 0.1, 0.1, 0.1),
x = x)
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