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hmm.prototypes.R
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hmm.prototypes.R
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#
# HMM model
#
library(rqhmm)
# Create HMM with 3 states and 2 observation slots
#
hmm3states2slotsDiscrete <- function() {
# Data:
#
# E1: Gene start predictions
# E2: Gene body predictions
# states: PNEG, PG.S, PG.B :: total 3
n.states = 3
PNEG = 1
PG.S = 2
PG.B = 3
# valid transitions
vtbl = NULL
# valid transitions
vtbl = matrix(data=0, nrow=n.states, ncol=n.states)
vtbl[PNEG, PNEG] = 1
vtbl[PNEG, PG.S] = 2
vtbl[PG.S, PG.S] = 1
vtbl[PG.S, PG.B] = 2
vtbl[PG.B, PG.B] = 1
vtbl[PG.B, PNEG] = 2
# transition groups
# tgrps = list(2:n.states) # share size distribution; it's a poor fit for all, but want to avoid getting a tiny 'T' state
# tgrps = list(c(2,4)) # must exclude PG.B=3 state as it has a different number of transitions
tgrps = NULL
# emission groups
nSlots = 2
# egrps = NULL
egrps = new.emission.groups(n.states, nSlots)
egrps = add.emission.groups(egrp, group=c(nSlots, 2:3))
# egrps = add.emission.groups(egrps, states = c(2, 3, 4), slots = c(2, 2, 2)) # share GROseq over T, P and D [scaled]
tdist = "discrete"
hmm <- new.qhmm(list(rep(1, nSlots), NULL),
vtbl,
rep("discrete", n.states),
rep(list(rep(tdist, nSlots)), n.states),
transition.groups = tgrps,
emission.groups = egrps)
# set initial parameters
set.initial.probs.qhmm(hmm, c(1, rep(0, n.states - 1))) # start with background
# Transition and emission probabilities should be set randomly,
# so that we can have multiple start points for the em algoithm
# set.seed(1)
# set transition parameters
# Give a very high probability to self transitions
# set.transition.params.qhmm(hmm, PNEG, c(0.98, 0.02))
# set.transition.params.qhmm(hmm, PG.B, c(0.98, 0.02))
# randomly generating n numbers that sum to 1
# (where n is the number of possible state transitions)
# https://stackoverflow.com/questions/11003967/generate-3-random-number-that-sum-to-1-in-r
# rowSums(tm>0)[i] gives the number of transitions for state i
# diff(c(0, sort(runif(n-1)), 1)) gives n random numbers that sum to 1
# (this gives an error if n < 1, so makes sure eash state always has at least one transition)
# diff(c(0, sort(runif((rowSums(tm>0)[i])-1)), 1)) gives us a random number for each state, all adding to 1
for (i in 1:n.states) {
set.transition.params.qhmm(hmm, i, diff(c(0, sort(runif((rowSums(vtbl>0)[i])-1)), 1)))
}
# Set emission parameters & options
# (initial probabilities based on distribution of predictions in each of the observation slots)
# plusGenestartParams = c(0.04, 0.96)
# plusGenestartParams.reverse = c(0.96, 0.04)
# set.emission.params.qhmm(hmm, 1:n.states, plusGenestartParams.reverse, slot = 1)
# set.emission.params.qhmm(hmm, PG.S, plusGenestartParams, slot = 1)
# plusGenebodyParams = c(0.17, 0.83)
# plusGenebodyParams.reverse = c(0.83, 0.17)
# set.emission.params.qhmm(hmm, PNEG, plusGenebodyParams.reverse, slot = 1)
# set.emission.params.qhmm(hmm, PG.B, plusGenebodyParams, slot = 1)
# plusGeneendParams = c(0.11, 0.89)
# plusGeneendParams.reverse = c(0.89, 0.11)
# set.emission.params.qhmm(hmm, 1:n.states, plusGeneendParams.reverse, slot = 3)
# set.emission.params.qhmm(hmm, PG.E, plusGeneendParams, slot = 3)
# plusNeg = c(0.29, 0.71)
# plusNeg.reverse = c(0.71, 0.29)
# set.emission.params.qhmm(hmm, 1:n.states, plusNeg.reverse, slot = 4)
# set.emission.params.qhmm(hmm, PNEG, plusNeg, slot = 4)
# Alternatively, set emission params randomly
# (see notes above for what diff(c(0, sort(runif(n - 1)), 1)) is doing)
nEmissionVals = 2 # number of possible emission values (0 and 1 in this case)
for (i in 1:n.states) {
for (slotNum in 1:nSlots) {
# set.emission.params.qhmm(hmm, i, c(0.1, 0.1), slot = slotNum) # dgamma params
set.emission.params.qhmm(hmm, i, diff(c(0, sort(runif(nEmissionVals - 1)), 1)), slot = slotNum)
set.emission.option.qhmm(hmm, i, "offset", 0, slot = slotNum)
}
}
return(hmm)
}
# Create HMM with 2 states and 1 observation slot
#
hmm2states1slotDiscrete <- function() {
# Data:
#
# E1: Gene body predictions
# states: PG.B, PNEG :: total 2
n.states = 2
PNEG = 1
PG.B = 2
# valid transitions
vtbl = NULL
# valid transitions
vtbl = matrix(data=0, nrow=n.states, ncol=n.states)
vtbl[PNEG, PNEG] = 1
vtbl[PNEG, PG.B] = 2
vtbl[PG.B, PG.B] = 1
vtbl[PG.B, PNEG] = 2
# transition groups
# tgrps = list(2:n.states) # share size distribution; it's a poor fit for all, but want to avoid getting a tiny 'T' state
# tgrps = list(c(2,4)) # must exclude PG.B=3 state as it has a different number of transitions
tgrps = NULL
# emission groups
nSlots = 1
# egrps = new.emission.groups(n.states, nSlots)
# egrps = add.emission.groups(egrps, states = c(2, 3, 4), slots = c(2, 2, 2)) # share GROseq over T, P and D [scaled]
egrps = NULL
tdist = "discrete"
hmm <- new.qhmm(list(rep(1, nSlots), NULL),
vtbl,
rep("discrete", n.states),
rep(list(rep(tdist, nSlots)), n.states),
transition.groups = tgrps,
emission.groups = egrps)
# set initial parameters
set.initial.probs.qhmm(hmm, c(1, rep(0, n.states - 1))) # start with background
# Transition and emission probabilities should be set randomly,
# so that we can have multiple start points for the em algoithm
# set.seed(1)
# set transition parameters
# Give a very high probability to self transitions
# set.transition.params.qhmm(hmm, PNEG, c(0.98, 0.02))
# set.transition.params.qhmm(hmm, PG.B, c(0.98, 0.02))
# randomly generating n numbers that sum to 1
# (where n is the number of possible state transitions)
# https://stackoverflow.com/questions/11003967/generate-3-random-number-that-sum-to-1-in-r
# rowSums(tm>0)[i] gives the number of transitions for state i
# diff(c(0, sort(runif(n-1)), 1)) gives n random numbers that sum to 1
# (this gives an error if n < 1, so makes sure eash state always has at least one transition)
# diff(c(0, sort(runif((rowSums(tm>0)[i])-1)), 1)) gives us a random number for each state, all adding to 1
for (i in 1:n.states) {
set.transition.params.qhmm(hmm, i, diff(c(0, sort(runif((rowSums(vtbl>0)[i])-1)), 1)))
}
# Set emission parameters & options
# (initial probabilities based on distribution of predictions in each of the observation slots)
# plusGenestartParams = c(0.04, 0.96)
# plusGenestartParams.reverse = c(0.96, 0.04)
# set.emission.params.qhmm(hmm, 1:n.states, plusGenestartParams.reverse, slot = 1)
# set.emission.params.qhmm(hmm, PG.S, plusGenestartParams, slot = 1)
# plusGenebodyParams = c(0.17, 0.83)
# plusGenebodyParams.reverse = c(0.83, 0.17)
# set.emission.params.qhmm(hmm, PNEG, plusGenebodyParams.reverse, slot = 1)
# set.emission.params.qhmm(hmm, PG.B, plusGenebodyParams, slot = 1)
# plusGeneendParams = c(0.11, 0.89)
# plusGeneendParams.reverse = c(0.89, 0.11)
# set.emission.params.qhmm(hmm, 1:n.states, plusGeneendParams.reverse, slot = 3)
# set.emission.params.qhmm(hmm, PG.E, plusGeneendParams, slot = 3)
# plusNeg = c(0.29, 0.71)
# plusNeg.reverse = c(0.71, 0.29)
# set.emission.params.qhmm(hmm, 1:n.states, plusNeg.reverse, slot = 4)
# set.emission.params.qhmm(hmm, PNEG, plusNeg, slot = 4)
# Alternatively, set emission params randomly
# (see notes above for what diff(c(0, sort(runif(n - 1)), 1)) is doing)
nEmissionVals = 2 # number of possible emission values (0 and 1 in this case)
for (i in 1:n.states) {
for (slotNum in 1:nSlots) {
set.emission.params.qhmm(hmm, i, diff(c(0, sort(runif(nEmissionVals - 1)), 1)), slot = slotNum)
set.emission.option.qhmm(hmm, i, "offset", 0, slot = slotNum)
}
}
return(hmm)
}
# NOTE: I would like to do the HMM with a 'scaled' neg. binomial
# but I have not implemented that yet, so first run will
# use a DGamma instead
#
# 1. no RNA info (use TSS prior)
#
# state paths:
# B -> T -> PP -> D -> B
# -> B [optional link]
#
# state #pro dist
# B [1] [1]
# T [2] [2]
# PP [3] [2]
# D [4] [2]
#
# #pro: [1] background level (Poisson)
# [2] body level (NegBinom | DGamma)
# [3] fixed scale up of body level (NegBinom | DGamma)
# [4] fixed scale down of body level (NegBinom | DGamma)
#
# dist: [1] background distances (Geom0)
# [2] body distances (Geom0)
#
splithmm1.hmm <- function(scaleTP, scalePD, with.shortcut = FALSE, use.negbinom = FALSE) {
N = 4
# B, T, P, D
# valid transitions
vtbl = NULL
tnames = c("autocorr_covar", "autocorr", "autocorr", "autocorr")
if (!with.shortcut) {
#
# no link from T to B directly
#
vtbl = rbind(
c(1, 2, 0, 0), # B to: B or T
c(0, 1, 2, 0), # T to: T or P
c(0, 0, 1, 2), # P to: P or D
c(2, 0, 0, 1)) # D to: D or B
} else {
#
# no link from T to B directly
#
vtbl = rbind(
c(1, 2, 0, 0), # B to: B or T
c(3, 1, 2, 0), # T to: T or B or P
c(0, 0, 1, 2), # P to: P or D
c(2, 0, 0, 1)) # D to: D or B
}
tdist = "dgamma"
if (use.negbinom)
tdist = "neg_binomial"
# transition groups
tgrps = list(2:N) # share size distribution; it's a poor fit for all, but want to avoid getting a tiny 'T' state
if (with.shortcut)
tgrps = list(3:N) # must exclude T=2 state as it has a different number of transitions
# emission groups
egrps = new.emission.groups(N, 2)
egrps = add.emission.groups(egrps, states = c(2, 3, 4), slots = c(2, 2, 2)) # share GROseq over T, P and D [scaled]
hmm = new.qhmm(list(c(1, 1), 1), # covar: TSS signal
vtbl,
tnames,
list(c("geometric", "poisson"), # B
c("geometric", tdist), # T
c("geometric", tdist), # P
c("geometric", tdist)), # D
transition.groups = tgrps,
emission.groups = egrps)
# set initial parameters
set.initial.probs.qhmm(hmm, c(1, 0, 0, 0)) # start with background
# set transitions
set.transition.params.qhmm(hmm, 2:N, 0.99)
# set emissions
geom.base = 0
# background
set.emission.params.qhmm(hmm, 1, 1/3000, slot = 1)
set.emission.option.qhmm(hmm, 1, "base", geom.base, slot = 1)
set.emission.params.qhmm(hmm, 1, 0.1, slot = 2) # lambda
# transcribed
for (i in 2:N) {
set.emission.params.qhmm(hmm, i, 1/10, slot = 1)
set.emission.option.qhmm(hmm, i, "base", geom.base, slot = 1)
}
if (use.negbinom) {
set.emission.params.qhmm(hmm, 2:N, c(10, 0.1), slot = 2)
# set scale factors
stop("not implemented!")
} else {
set.emission.params.qhmm(hmm, 2:N, c(1, 1), slot = 2) # gamma params
set.emission.option.qhmm(hmm, 3, "scale_private", scaleTP, slot = 2)
set.emission.option.qhmm(hmm, 4, "scale_private", scalePD, slot = 2)
}
return(hmm)
}
# 2. w/ RNA info (use TSS prior)
# B -> U
# -> FEXON -> INTRON -> EXON -> INTRON
# -> PPAUSE -> PDECAY -> B
#
# state #pro dist #rna
# B [1] [1] [1]
# U [2] [2] [1]
# FEXON [2] [2] [2]
# INTRON [3] [2] [1]
# EXON [3] [2] [2]
# PPAUSE [4] [2] [1]
# PDECAY [5] [2] [1]
#
# #pro: [1] background level (Poisson)
# [2] start level (NegBinom | DGamma)
# [3] body level (NegBinom | DGamma)
# [4] fixed scale up of body level (NegBinom | DGamma)
# [5] fixed scale down of body level (NegBinom | DGamma)
#
# dist: [1] background distances (Geom0)
# [2] body distances (Geom0)
#
# #rna: [1] background level (Poisson)
# [2] exon level (NegBinom | DGamma)
#
splithmm2.hmm <- function(scaleTP, scalePD, use.negbinom = FALSE) {
N = 7
# B, U, F, I, E, P, D
# valid transitions
vtbl = NULL
tnames = c("autocorr_covar", rep("autocorr", N - 1))
vtbl = rbind(
c(1, 2, 3, 0, 0, 0, 0), # B to: B or U or F
c(2, 1, 0, 0, 0, 0, 0), # U to: U or B
c(0, 0, 1, 2, 0, 0, 0), # F to: F or I
c(0, 0, 0, 1, 2, 0, 0), # I to: I or E
c(0, 0, 0, 2, 1, 3, 0), # E to: E or I or P
c(0, 0, 0, 0, 0, 1, 2), # P to: P or D
c(2, 0, 0, 0, 0, 0, 1)) # D to: D or B
tdist = "dgamma"
if (use.negbinom)
tdist = "neg_binomial"
# emission groups
egrps = new.emission.groups(N, 3)
# distances
egrps = add.emission.groups(egrps, states = 2:7, slots = rep(1, 6)) # share distance distribution between all non-background states
# pro/gro
egrps = add.emission.groups(egrps, states = 4:7, slots = rep(2, 4)) # share GROseq over E, I, P, D (last two scaled versions)
egrps = add.emission.groups(egrps, states = 2:3, slots = c(2, 2)) # share GROseq over U and F
# rna
egrps = add.emission.groups(egrps, states = c(1, 2, 4, 6, 7), slots = c(3, 3, 3, 3, 3)) # share background RNAseq (B, U, I, P, D)
egrps = add.emission.groups(egrps, states = c(3, 5), slots = c(3, 3)) # share exon RNAseq (F, E)
hmm = new.qhmm(list(c(1, 1, 1), 1), # covar: TSS signal
vtbl,
tnames,
list(c("geometric", "poisson", "poisson"), # B
c("geometric", tdist, "poisson"), # U
c("geometric", tdist, tdist), # F
c("geometric", tdist, "poisson"), # I
c("geometric", tdist, tdist), # E
c("geometric", tdist, "poisson"), # P
c("geometric", tdist, "poisson")), # D
transition.groups = list(c(3, 5)), # share size distribution between F and E (both exons)
emission.groups = egrps)
# set initial parameters
set.initial.probs.qhmm(hmm, c(1, rep(0, N - 1))) # start with background
# set transitions
set.transition.params.qhmm(hmm, 2:N, 0.99)
# set emissions
geom.base = 0
# background
set.emission.params.qhmm(hmm, 1, 1/3000, slot = 1)
set.emission.option.qhmm(hmm, 1, "base", geom.base, slot = 1)
set.emission.params.qhmm(hmm, 1, 0.1, slot = 2) # lambda
set.emission.params.qhmm(hmm, 1, 0.1, slot = 3) # lambda
# transcribed
# dist
for (i in 2:N) {
set.emission.params.qhmm(hmm, i, 1/10, slot = 1)
set.emission.option.qhmm(hmm, i, "base", geom.base, slot = 1)
}
# pro/gro
if (use.negbinom) {
set.emission.params.qhmm(hmm, 2:N, c(10, 0.1), slot = 2)
# set scale factors
stop("not implemented!")
} else {
set.emission.params.qhmm(hmm, 2:N, c(1, 1), slot = 2) # gamma params
set.emission.option.qhmm(hmm, 6, "scale_private", scaleTP, slot = 2)
set.emission.option.qhmm(hmm, 7, "scale_private", scalePD, slot = 2)
}
# rna
set.emission.params.qhmm(hmm, c(2,4,6,7) , 0.1, slot = 3) # lambda
if (use.negbinom) {
set.emission.params.qhmm(hmm, c(3, 5), c(10, 0.1), slot = 3)
} else {
set.emission.params.qhmm(hmm, c(3, 5), c(1, 1), slot = 3) # gamma params
}
return(hmm)
}
#
# 3. three state HMM
#
# B -> T -> D
#
# state paths:
# B -> T -> D -> B
# -> B (optional link)
#
# state #pro dist
# B [1] [1]
# T [2] [2]
# D [3] [2]
#
# #pro: [1] background level (Poisson)
# [2] body level (NegBinom | DGamma)
# [3] fixed scale down of body level (NegBinom | DGamma)
#
# dist: [1] background distances (Geom0)
# [2] body distances (Geom0)
#
splithmm3.hmm <- function(scaleTD, with.shortcut = FALSE, no.egrps = FALSE, poisson.decay = FALSE, use.negbinom = FALSE) {
N = 3
if (poisson.decay & !no.egrps)
stop("poisson.decay requires no.egrps")
# B, T, D
# valid transitions
vtbl = NULL
tnames = c("autocorr_covar", "autocorr", "autocorr")
if (!with.shortcut) {
#
# no link from T to B directly
#
vtbl = rbind(
c(1, 2, 0), # B to: B or T
c(0, 1, 2), # T to: T or D
c(2, 0, 1)) # D to: D or B
} else {
#
# no link from T to B directly
#
vtbl = rbind(
c(1, 2, 0), # B to: B or T
c(3, 1, 2), # T to: T or D or B
c(2, 0, 1)) # D to: D or B
}
tdist = "dgamma"
if (use.negbinom)
tdist = "neg_binomial"
ddist = tdist
if (poisson.decay)
ddist = "poisson"
# transition groups
tgrps = list(2:N) # share size distribution; it's a poor fit for all, but want to avoid getting a tiny 'T' state
if (with.shortcut)
tgrps = NULL # must exclude T=2 state as it has a different number of transitions
# emission groups
egrps = NULL
if (!no.egrps) {
egrps = new.emission.groups(N, 2)
egrps = add.emission.groups(egrps, states = c(2, 3), slots = c(2, 2)) # share GROseq over T and D [scaled]
egrps = add.emission.groups(egrps, states = c(2, 3), slots = c(1, 1)) # share distance over T and D
}
hmm = new.qhmm(list(c(1, 1), 1), # covar: TSS signal
vtbl,
tnames,
list(c("geometric", "poisson"), # B
c("geometric", tdist), # T
c("geometric", ddist)), # D
transition.groups = tgrps,
emission.groups = egrps)
# set initial parameters
set.initial.probs.qhmm(hmm, c(1, 0, 0)) # start with background
# set transitions
set.transition.params.qhmm(hmm, 2:N, 0.99)
# set emissions
geom.base = 0
# background
set.emission.params.qhmm(hmm, 1, 1/3000, slot = 1)
set.emission.option.qhmm(hmm, 1, "base", geom.base, slot = 1)
set.emission.params.qhmm(hmm, 1, 0.1, slot = 2) # lambda
# transcribed
for (i in 2:N) {
set.emission.params.qhmm(hmm, i, 1/10, slot = 1)
set.emission.option.qhmm(hmm, i, "base", geom.base, slot = 1)
}
if (use.negbinom) {
if (!no.egrps) {
set.emission.params.qhmm(hmm, 2:N, c(10, 0.1), slot = 2)
# set scale factors
stop("not implemented!")
} else {
if (poisson.decay) {
set.emission.params.qhmm(hmm, 2, c(10, 0.1), slot = 2)
set.emission.params.qhmm(hmm, 3, 0.1, slot = 2)
} else {
set.emission.params.qhmm(hmm, 2, c(10, 0.1), slot = 2)
set.emission.params.qhmm(hmm, 2, c(0.1, 0.1), slot = 2)
}
}
} else {
if (!no.egrps) {
set.emission.params.qhmm(hmm, 2:N, c(1, 1), slot = 2) # gamma params
set.emission.option.qhmm(hmm, 3, "scale_private", scaleTD, slot = 2)
} else {
if (poisson.decay) {
set.emission.params.qhmm(hmm, 2, c(1, 1), slot = 2) # gamma params
set.emission.params.qhmm(hmm, 3, 0.1, slot = 2) # gamma params
} else {
set.emission.params.qhmm(hmm, 2, c(1, 1), slot = 2) # gamma params
set.emission.params.qhmm(hmm, 3, c(1, 0.1), slot = 2) # gamma params
}
}
}
return(hmm)
}
#
# 4. five state HMM
#
# B -> I -> P -> T -> D
#
# state paths:
# B -> I -> P -> T -> D -> B
# -....-> T -> D -> B [skippable pause]
# -> B (optional link)
#
# state #pro dist
# B [1] [1]
# I [2] [2]
# P [3] [2]
# T [4] [3]
# D [5] [4]
#
# #pro: [1] background level (Poisson)
# [2] initiation level (Poisson)
# [4] pause level (Poisson)
# [4] body level (NegBinom | DGamma)
# [5] decay level (Poisson)
#
# dist: [1] background distances (Geom0)
# [2] initiation/pause distances (Geom0)
# [3] body distances (Geom0)
# [4] decay distances (Geom0)
#
splithmm4.hmm <- function(with.shortcut = TRUE, no.egrps = FALSE, use.negbinom = FALSE) {
N = 5
# B, I, P, T, D
# valid transitions
vtbl = NULL
tnames = c("autocorr_covar", rep("autocorr", N - 1))
if (!with.shortcut) {
#
# no link from T to B directly
#
vtbl = rbind(
c(1, 2, 0, 0, 0), # B to: B or I
c(0, 1, 2, 3, 0), # I to: I or P or T
c(0, 0, 1, 2, 0), # P to: P or T
c(0, 0, 0, 1, 2), # T to: T or D
c(2, 0, 0, 0, 1)) # D to: D or B
} else {
#
# no link from T to B directly
#
vtbl = rbind(
c(1, 2, 0, 0, 0), # B to: B or I
c(0, 1, 2, 3, 0), # I to: I or P or T
c(0, 0, 1, 2, 0), # P to: P or T
c(3, 0, 0, 1, 2), # T to: T or D or B
c(2, 0, 0, 0, 1)) # D to: D or B
}
tdist = "dgamma"
if (use.negbinom)
tdist = "neg_binomial"
ddist = "poisson"
# transition groups
# NOT valid!!
# tgrps = list(2:3, 4:5) # share size distribution; it's a poor fit for all, but want to avoid getting a tiny 'T' state
# if (with.shortcut)
# tgrps = list(2:3) # must exclude T=4 state as it has a different number of transitions
tgrps = list(4:5)
if (with.shortcut)
tgrps = NULL # must exclude T=4 state as it has a different number of transitions
# emission groups
egrps = NULL
if (!no.egrps) {
egrps = new.emission.groups(N, 2)
egrps = add.emission.groups(egrps, states = c(2, 3), slots = c(1, 1)) # share distance over I and P
egrps = add.emission.groups(egrps, states = c(2, 5), slots = c(2, 2)) # share reads over I and D
}
hmm = new.qhmm(list(c(1, 1), 1), # covar: TSS signal
vtbl,
tnames,
list(c("geometric", "poisson"), # B
c("geometric", "poisson"), # I
c("geometric", "poisson"), # P
c("geometric", tdist), # T
c("geometric", ddist)), # D
transition.groups = tgrps,
emission.groups = egrps)
# set initial parameters
set.initial.probs.qhmm(hmm, c(1, rep(0, N - 1))) # start with background
# set transitions
set.transition.params.qhmm(hmm, 2:3, 0.50)
set.transition.params.qhmm(hmm, 4:N, 0.99)
# set emissions
geom.base = 0
# background
set.emission.params.qhmm(hmm, 1, 1/3000, slot = 1)
set.emission.option.qhmm(hmm, 1, "base", geom.base, slot = 1)
set.emission.params.qhmm(hmm, 1, 0.1, slot = 2) # lambda
# initiation
set.emission.params.qhmm(hmm, 2, 1/10, slot = 1)
set.emission.option.qhmm(hmm, 2, "base", geom.base, slot = 1)
set.emission.params.qhmm(hmm, 2, 0.2, slot = 2) # lambda
# pause
set.emission.params.qhmm(hmm, 3, 1/10, slot = 1)
set.emission.option.qhmm(hmm, 3, "base", geom.base, slot = 1)
set.emission.params.qhmm(hmm, 3, 10, slot = 2) # lambda
# transcribed
for (i in 4:N) {
set.emission.params.qhmm(hmm, i, 1/10, slot = 1)
set.emission.option.qhmm(hmm, i, "base", geom.base, slot = 1)
}
if (use.negbinom) {
set.emission.params.qhmm(hmm, 4, c(10, 0.1), slot = 2)
set.emission.params.qhmm(hmm, 5, 0.1, slot = 2)
} else {
set.emission.params.qhmm(hmm, 4, c(1, 1), slot = 2) # gamma params
set.emission.params.qhmm(hmm, 5, 0.1, slot = 2) # gamma params
}
return(hmm)
}
# 5. three state HMM
#
# B -> T -> D
#
# state paths:
# B -> T -> D -> B
# -> B (optional link)
#
# state #pro dist
# B [1] [1]
# T [2] [2]
# D [3] [2]
#
# #pro: [1] background level (Poisson)
# [2] body level (NegBinom | DGamma)
# [3] fixed scale down of body level (NegBinom | DGamma)
#
# dist: [1] background distances (Geom0)
# [2] body distances (Geom0)
#
splithmm5.hmm <- function(scaleTD = 1, with.shortcut = FALSE, no.egrps = FALSE, poisson.decay = FALSE, use.negbinom = FALSE) {
N = 3
if (poisson.decay & !no.egrps)
stop("poisson.decay requires no.egrps")
# B, T, D
# valid transitions
vtbl = NULL
tnames = c("autocorr_covar", "autocorr", "autocorr")
if (!with.shortcut) {
#
# no link from T to B directly
#
vtbl = rbind(
c(1, 2, 0), # B to: B or T
c(0, 1, 2), # T to: T or D
c(2, 0, 1)) # D to: D or B
} else {
#
# no link from T to B directly
#
vtbl = rbind(
c(1, 2, 0), # B to: B or T
c(3, 1, 2), # T to: T or D or B
c(2, 0, 1)) # D to: D or B
}
tdist = "dgamma"
if (use.negbinom)
tdist = "neg_binomial"
ddist = tdist
if (poisson.decay)
ddist = "poisson"
# transition groups
tgrps = list(2:N) # share size distribution; it's a poor fit for all, but want to avoid getting a tiny 'T' state
if (with.shortcut)
tgrps = NULL # must exclude T=2 state as it has a different number of transitions
# emission groups
egrps = NULL
if (!no.egrps) {
egrps = new.emission.groups(N, 2)
egrps = add.emission.groups(egrps, states = c(2, 3), slots = c(2, 2)) # share GROseq over T and D [scaled]
egrps = add.emission.groups(egrps, states = c(2, 3), slots = c(1, 1)) # share distance over T and D
}
hmm = new.qhmm(list(c(1, 1), 1), # covar: TSS signal
vtbl,
tnames,
list(c("geometric", "poisson"), # B
c("geometric", tdist), # T
c("geometric", ddist)), # D
transition.groups = tgrps,
emission.groups = egrps)
# set initial parameters
set.initial.probs.qhmm(hmm, c(1, 0, 0)) # start with background
# set transitions
set.transition.params.qhmm(hmm, 2:N, 0.99)
# set emissions
geom.base = 0
# background
set.emission.params.qhmm(hmm, 1, 1/3000, slot = 1)
set.emission.option.qhmm(hmm, 1, "base", geom.base, slot = 1)
set.emission.params.qhmm(hmm, 1, 0.1, slot = 2) # lambda
# transcribed
for (i in 2:N) {
set.emission.params.qhmm(hmm, i, 1/10, slot = 1)
set.emission.option.qhmm(hmm, i, "base", geom.base, slot = 1)
}
if (use.negbinom) {
if (!no.egrps) {
set.emission.params.qhmm(hmm, 2:N, c(10, 0.1), slot = 2)
# set scale factors
stop("not implemented!")
} else {
if (poisson.decay) {
set.emission.params.qhmm(hmm, 2, c(10, 0.1), slot = 2)
set.emission.params.qhmm(hmm, 3, 0.1, slot = 2)
} else {
set.emission.params.qhmm(hmm, 2, c(10, 0.1), slot = 2)
set.emission.params.qhmm(hmm, 2, c(0.1, 0.1), slot = 2)
}
}
} else {
if (!no.egrps) {
set.emission.params.qhmm(hmm, 2:N, c(1, 1), slot = 2) # gamma params
set.emission.option.qhmm(hmm, 3, "scale_private", scaleTD, slot = 2)
} else {
if (poisson.decay) {
set.emission.params.qhmm(hmm, 2, c(1, 1), slot = 2) # gamma params
set.emission.params.qhmm(hmm, 3, 0.1, slot = 2) # gamma params
} else {
set.emission.params.qhmm(hmm, 2, c(1, 1), slot = 2) # gamma params
set.emission.params.qhmm(hmm, 3, c(1, 0.1), slot = 2) # gamma params
}
}
}
return(hmm)
}
# 6. 4 state HMM, 2 slots
#
# state paths:
# B -> T -> PP -> D -> B
# -> B [optional link]
#
# state #pro dist
# PNEG [1] [1]
# PG.S [2] [2]
# PG.B [3] [2]
# PG.E [4] [2]
#
# #pro: [1] background level (Poisson)
# [2] gene start level (NegBinom | DGamma)
# [3] body level (NegBinom | DGamma)
# [4] gene end level (NegBinom | DGamma)
#
# dist: [1] start predictions (Geom0)
# [2] body predictions (Geom0)
#
splithmm6.hmm <- function(scaleTP = 0.153, scalePD = 0.153, use.negbinom = FALSE) {
# PNEG, PG.S, PG.B, PG.E
n.states = 4
PNEG = 1
PG.S = 2
PG.B = 3
PG.E = 4
# valid transitions
vtbl = NULL
tnames = c(rep("autocorr", n.states))
# tnames = c("autocorr_covar", rep("autocorr", n.states - 1))
# valid transitions
vtbl = matrix(data=0, nrow=n.states, ncol=n.states)
vtbl[PNEG, PNEG] = 1
vtbl[PNEG, PG.S] = 2
vtbl[PG.S, PG.S] = 1
vtbl[PG.S, PG.B] = 2
vtbl[PG.B, PG.B] = 1
vtbl[PG.B, PG.E] = 2
vtbl[PG.B, PNEG] = 3
vtbl[PG.E, PG.E] = 1
vtbl[PG.E, PNEG] = 2
# if (!with.shortcut) {
# #
# # no link from T to B directly
# #
# vtbl = rbind(
# c(1, 2, 0, 0), # B to: B or T
# c(0, 1, 2, 0), # T to: T or P
# c(0, 0, 1, 2), # P to: P or D
# c(2, 0, 0, 1)) # D to: D or B
# } else {
# #
# # no link from T to B directly
# #
# vtbl = rbind(
# c(1, 2, 0, 0), # B to: B or T
# c(3, 1, 2, 0), # T to: T or B or P
# c(0, 0, 1, 2), # P to: P or D
# c(2, 0, 0, 1)) # D to: D or B
# }
# transition groups
# tgrps = list(2:n.states) # share size distribution; it's a poor fit for all, but want to avoid getting a tiny 'T' state
# tgrps = list(c(2,4)) # must exclude PG.B=3 state as it has a different number of transitions
tgrps = NULL
# emission groups
nSlots = 2
# egrps = new.emission.groups(n.states, nSlots)
# egrps = add.emission.groups(egrps, states = c(2, 3, 4), slots = c(2, 2, 2)) # share GROseq over T, P and D [scaled]
egrps = NULL
# tdist = "gamma"
tdist = "geometric"
hmm = new.qhmm(list(c(1, 1), NULL),
vtbl,
tnames,
list(c(tdist, tdist), # PNEG
c(tdist, tdist), # PG.S
c(tdist, tdist), # PG.B
c(tdist, tdist)), # PG.E
transition.groups = tgrps,
emission.groups = egrps)
# set initial parameters
set.initial.probs.qhmm(hmm, c(1, rep(0, n.states - 1))) # start with background
# set transitions
set.transition.params.qhmm(hmm, 2:n.states, 0.99)
# set emissions
# Geometric
geom.base = 0
# for (i in 1:N) {
# set.emission.params.qhmm(hmm, i, 1/3000, slot = 1)
# set.emission.option.qhmm(hmm, i, "base", geom.base, slot = 1)
#}
# background11
# set.emission.params.qhmm(hmm, 1, 1/3000, slot = 1)
# set.emission.option.qhmm(hmm, 1, "base", geom.base, slot = 1)
# Gamma
# set.emission.params.qhmm(hmm, 1, c(1.23, 5.68), slot = 1) # lambda
# set.emission.params.qhmm(hmm, 1, c(1.23, 5.68), slot = 2) # lambda
# transcribed
# for (i in 2:n.states) {
# set.emission.params.qhmm(hmm, i, 1/10, slot = 1)
# set.emission.option.qhmm(hmm, i, "base", geom.base, slot = 1)
# }
# set.emission.params.qhmm(hmm, 2:n.states, c(0.1, 0.1), slot = 2) # gamma params
# set.emission.option.qhmm(hmm, 3, "scale_private", scaleTP, slot = 2)
# set.emission.option.qhmm(hmm, 4, "scale_private", scalePD, slot = 2)
# set.emission.params.qhmm(hmm, 2, c(1.6, 0.3), slot = 1) # gamma params
# set.emission.params.qhmm(hmm, 3, c(1.6, 0.3), slot = 1) # gamma params
# set.emission.params.qhmm(hmm, 4, c(1.6, 0.3), slot = 1) # gamma params
# set.emission.params.qhmm(hmm, 2, c(1.6, 0.3), slot = 2) # gamma params
# set.emission.params.qhmm(hmm, 3, c(1.6, 0.3), slot = 2) # gamma params
# set.emission.params.qhmm(hmm, 4, c(1.6, 0.3), slot = 2) # gamma params
# set.emission.params.qhmm(hmm, 2, 0.1, slot = 2) # gamma params
# set.emission.params.qhmm(hmm, 3, 0.1, slot = 2) # gamma params
# set.emission.params.qhmm(hmm, 4, 0.1, slot = 2) # gamma params
# Set shape and scale params for gamma distribution
for (i in 2:n.states) {
for (slotNum in 1:nSlots) {
set.emission.params.qhmm(hmm, i, c(1.6, 14.7), slot = slotNum)
}
}
# Alternatively, set emission params randomly
# (see notes in hmm.parse.R for what diff(c(0, sort(runif(n - 1)), 1)) is doing)
# nEmissionVals = 2 # number of possible emission values (0 and 1 in this case)
# for (i in 1:n.states) {
# for (slotNum in 1:nSlots) {
# set.emission.params.qhmm(hmm, i, diff(c(0, sort(runif(nEmissionVals - 1)), 1)), slot = slotNum)
# # set.emission.option.qhmm(hmm, i, "offset", 0, slot = slotNum)
# }
# }
return(hmm)
}
# 7. 2 states HMM, 1 slot