A pseudo_ribo dataset contains num
=25000 transcripts
generated by generate_ribo
with parameters:
eL <- c(20, 36, 20, 96, 10)
eH <- c(5, 45,30,15, 40,20,10, 45,30,15, 5, 90,75,30, 60,50,20, 90,75,30, 5)
v <- rep(10, 21)
E <- c(rep(1,num/5), rep(5,num/5), rep(10,num/5), rep(15,num/5), rep(20,num/5))
p1, p2 = 0.8
1 |
A list with 25000 trajectories. Each list has variables as follows:
a vector. Hidden chain z
a vector. Hidden chain z
a vector. Observed chain x
a 0-1 vector. 1 if next 3-base is stop codon
a vector. (rho, rho_u, delta)
a vector. mean parameter in gamma distribution for each state
a vector. Shape parameter in gamma distribution.
a scalar. Normalizing constant for the observed chain x
a vector. (theta_u, theta) transition probability used
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