| Title: | Haplotype-Aware Hidden Markov Model for RNA |
| Version: | 1.0.0 |
| Description: | Haplotype-aware Hidden Markov Model for RNA (HaHMMR) is a method for detecting copy number variations (CNVs) from bulk RNA-seq data. Additional examples, documentations, and details on the method are available at https://github.com/kharchenkolab/hahmmr/. |
| Depends: | R (≥ 4.1.0) |
| Imports: | data.table, dplyr, GenomicRanges, ggplot2, glue, IRanges, methods, patchwork, Rcpp, stringr, tibble, zoo |
| Suggests: | ggrastr, testthat |
| LinkingTo: | Rcpp, RcppArmadillo, roptim |
| NeedsCompilation: | yes |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.3 |
| LazyData: | true |
| Packaged: | 2023-10-25 14:22:02 UTC; tenggao |
| Author: | Teng Gao  [aut,
cre],
Evan Biederstedt [aut],
Peter Kharchenko [aut] |
| Maintainer: | Teng Gao <tgaoteng@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2023-10-25 18:00:10 UTC |
centromere regions (hg19)
Description
centromere regions (hg19)
Usage
acen_hg19
Format
An object of class tbl_df (inherits from tbl, data.frame) with 22 rows and 3 columns.
centromere regions (hg38)
Description
centromere regions (hg38)
Usage
acen_hg38
Format
An object of class tbl_df (inherits from tbl, data.frame) with 22 rows and 3 columns.
Analyze allele profile
Description
Analyze allele profile
Usage
analyze_allele(
bulk,
t = 1e-05,
theta_min = 0.08,
gamma = 20,
nu = 0.5,
r = 0.015,
hmm = "S5",
fit_theta = FALSE,
fit_gamma = FALSE,
theta_start = 0.05,
verbose = TRUE
)
Arguments
bulk |
dataframe Bulk allele profile |
t |
numeric Transition probability |
theta_min |
numeric Minimum allele fraction |
gamma |
numeric Overdispersion parameter |
nu |
numeric Phase switch rate |
r |
numeric Alternative allele count bias |
hmm |
character HMM model to use (S3 or S5) |
fit_theta |
logical Whether to fit theta_min |
fit_gamma |
logical Whether to fit gamma |
theta_start |
numeric Starting value for theta_min |
verbose |
logical Whether to print progress |
Value
dataframe Bulk allele profile with CNV states
Examples
bulk_example = analyze_allele(bulk_example, hmm = 'S5')
Analyze allele and expression profile
Description
Analyze allele and expression profile
Usage
analyze_joint(
bulk,
t = 1e-05,
gamma = 20,
theta_min = 0.08,
logphi_min = 0.25,
hmm = "S15",
nu = 1,
min_genes = 10,
r = 0.015,
theta_start = 0.05,
exclude_neu = TRUE,
fit_gamma = FALSE,
fit_theta = FALSE,
verbose = TRUE
)
Arguments
bulk |
dataframe Bulk allele and expression profile |
t |
numeric Transition probability |
gamma |
numeric Overdispersion parameter |
theta_min |
numeric Minimum allele fraction |
logphi_min |
numeric Minimum log2 fold change |
hmm |
character HMM model to use (S7 or S15) |
nu |
numeric Phase switch rate |
min_genes |
integer Minimum number of genes per segment |
r |
numeric Alternative allele count bias |
theta_start |
numeric Starting value for theta_min |
exclude_neu |
logical Whether to exclude neutral segments in retest |
fit_gamma |
logical Whether to fit gamma |
fit_theta |
logical Whether to fit theta_min |
verbose |
logical Whether to print progress |
Value
dataframe Bulk allele and expression profile with CNV states
Examples
bulk_example = analyze_joint(bulk_example, hmm = 'S15')
Annotate genetic distance between markers
Description
Annotate genetic distance between markers
Usage
annot_cm(bulk, genetic_map)
Arguments
bulk |
dataframe Pseudobulk profile |
genetic_map |
dataframe Genetic map |
Value
dataframe Annotated pseudobulk profile
Annotate copy number segments after HMM decoding
Description
Annotate copy number segments after HMM decoding
Usage
annot_segs(bulk, var = "cnv_state")
Arguments
bulk |
dataframe Pseudobulk profile |
Value
a pseudobulk dataframe
Laplace approximation of the posterior of expression fold change phi
Description
Laplace approximation of the posterior of expression fold change phi
Usage
approx_phi_post(
Y_obs,
lambda_ref,
d,
alpha = NULL,
beta = NULL,
mu = NULL,
sig = NULL,
lower = 0.2,
upper = 10,
start = 1
)
Arguments
Y_obs |
numeric vector Gene expression counts |
lambda_ref |
numeric vector Reference expression levels |
d |
numeric Total library size |
alpha |
numeric Shape parameter of the gamma distribution |
beta |
numeric Rate parameter of the gamma distribution |
mu |
numeric Mean of the normal distribution |
sig |
numeric Standard deviation of the normal distribution |
lower |
numeric Lower bound of phi |
upper |
numeric Upper bound of phi |
start |
numeric Starting value of phi |
Value
numeric MLE of phi and its standard deviation
Find optimal theta of a CNV segment using forward-backward; uses a 2-state allele HMM
Description
Find optimal theta of a CNV segment using forward-backward; uses a 2-state allele HMM
Usage
approx_theta_post_s2(
pAD,
DP,
R,
p_s,
upper = 0.499,
start = 0.25,
gamma = 20,
r = 0.015
)
Find optimal theta of a chromosome using forward-backward; uses a 3-state allele HMM
Description
Find optimal theta of a chromosome using forward-backward; uses a 3-state allele HMM
Usage
approx_theta_post_s3(
pAD,
DP,
R,
p_s,
t,
upper = 0.45,
start = 0.05,
gamma = 20,
r = 0.015
)
example pseudobulk dataframe
Description
example pseudobulk dataframe
Usage
bulk_example
Format
An object of class tbl_df (inherits from tbl, data.frame) with 10321 rows and 58 columns.
Calculate LLR for an allele HMM
Description
Calculate LLR for an allele HMM
Usage
calc_allele_LLR(pAD, DP, R, p_s, theta_mle, theta_0 = 0, gamma = 20, r = 0.015)
Arguments
pAD |
numeric vector Phased allele depth |
DP |
numeric vector Total allele depth |
R |
numeric vector Allelic bias direction |
p_s |
numeric vector Phase switch probabilities |
theta_mle |
numeric MLE of imbalance level theta (alternative hypothesis) |
theta_0 |
numeric Imbalance level in the null hypothesis |
gamma |
numeric Dispersion parameter for the Beta-Binomial allele model |
Value
numeric Log-likelihood ratio
Calculate allele likelihoods for 2-state allele HMM
Description
Calculate allele likelihoods for 2-state allele HMM
Usage
calc_allele_lik_s2(pAD, DP, R, p_s, theta, gamma = 20, r = 0.015)
Arguments
pAD |
integer vector Paternal allele counts |
DP |
integer vector Total alelle counts |
R |
numeric vector Variant mapping bias direction |
p_s |
numeric vector Phase switch probabilities |
theta |
numeric Haplotype imbalance |
gamma |
numeric Overdispersion in the allele-specific expression |
Value
numeric vector Allele likelihoods
Calculate allele likelihoods for 3-state allele HMM
Description
Calculate allele likelihoods for 3-state allele HMM
Usage
calc_allele_lik_s3(pAD, DP, R, p_s, t, theta, gamma = 20, r = 0.015)
Arguments
pAD |
integer vector Paternal allele counts |
DP |
integer vector Total alelle counts |
p_s |
numeric vector Phase switch probabilities |
theta |
numeric Haplotype imbalance |
gamma |
numeric Overdispersion in the allele-specific expression |
Calculate LLR for an expression HMM
Description
Calculate LLR for an expression HMM
Usage
calc_exp_LLR(
Y_obs,
lambda_ref,
d,
phi_mle,
mu = NULL,
sig = NULL,
alpha = NULL,
beta = NULL
)
Arguments
Y_obs |
numeric vector Gene expression counts |
lambda_ref |
numeric vector Reference expression levels |
d |
numeric vector Total library size |
phi_mle |
numeric MLE of expression fold change phi (alternative hypothesis) |
mu |
numeric Mean parameter for the PLN expression model |
sig |
numeric Dispersion parameter for the PLN expression model |
alpha |
numeric Hyperparameter for the gamma poisson model (not used) |
beta |
numeric Hyperparameter for the gamma poisson model (not used) |
Value
numeric Log-likelihood ratio
Calculate the transition matrix for 15-state joint HMM
Description
Calculate the transition matrix for 15-state joint HMM
Usage
calc_trans_mat_s15(t, p_s, w, states_cn, states_phase)
Arguments
t |
numeric; CNV state transition probability |
p_s |
numeric; phase switch probability |
w |
numeric; relative abundance of states |
states_cn |
character; CNV states |
states_phase |
character; haplotype phase states |
Value
array; transition matrix
Calculate the transition matrix for 7-state joint HMM
Description
Calculate the transition matrix for 7-state joint HMM
Usage
calc_trans_mat_s7(t, p_s, w, states_cn, states_phase)
Arguments
t |
numeric; CNV state transition probability |
p_s |
numeric; phase switch probability |
w |
numeric; relative abundance of states |
states_cn |
character; CNV states |
states_phase |
character; haplotype phase states |
Value
array; transition matrix
Check if columns are present in dataframe
Description
Check if columns are present in dataframe
Usage
check_cols(df, cols)
Arguments
df |
dataframe Dataframe |
cols |
character vector Column names |
Value
dataframe Dataframe
Check the format of a count matrix
Description
Check the format of a count matrix
Usage
check_matrix(count_mat)
Arguments
count_mat |
matrix Count matrix |
Value
matrix Count matrix
chromosome sizes (hg19)
Description
chromosome sizes (hg19)
Usage
chrom_sizes_hg19
Format
An object of class data.table (inherits from data.frame) with 22 rows and 2 columns.
chromosome sizes (hg38)
Description
chromosome sizes (hg38)
Usage
chrom_sizes_hg38
Format
An object of class data.table (inherits from data.frame) with 22 rows and 2 columns.
classify alleles using viterbi and forward-backward
Description
classify alleles using viterbi and forward-backward
Usage
classify_alleles(bulk)
Arguments
bulk |
dataframe Pesudobulk profile |
Value
dataframe Pesudobulk profile
Combine allele and expression pseudobulks
Description
Combine allele and expression pseudobulks
Usage
combine_bulk(allele_bulk, exp_bulk, gtf)
Arguments
allele_bulk |
dataframe Bulk allele profile |
exp_bulk |
dataframe Bulk expression profile |
gtf |
dataframe Transcript gtf |
Value
dataframe Pseudobulk allele and expression profile
Beta-binomial distribution density function A distribution is beta-binomial if p, the probability of success, in a binomial distribution has a beta distribution with shape parameters alpha > 0 and beta > 0 For more details, see extraDistr::dbbinom
Description
Beta-binomial distribution density function A distribution is beta-binomial if p, the probability of success, in a binomial distribution has a beta distribution with shape parameters alpha > 0 and beta > 0 For more details, see extraDistr::dbbinom
Usage
dbbinom(x, size, alpha = 1, beta = 1, log = FALSE)
Arguments
x |
vector of quantiles |
size |
number of trials (zero or more) |
alpha |
numeric (default=1) |
beta |
numeric (default=1) |
log |
boolean (default=FALSE) |
Value
numeric Probability density values
Examples
dbbinom(1, 1, 1, 1)
example allele count dataframe
Description
example allele count dataframe
Usage
df_allele_example
Format
An object of class data.table (inherits from data.frame) with 9957 rows and 11 columns.
Returns the density for the Poisson lognormal distribution with parameters mu and sig
Description
Returns the density for the Poisson lognormal distribution with parameters mu and sig
Usage
dpoilog(x, mu, sig, log = FALSE)
Arguments
x |
vector of integers, the observations |
mu |
mean of lognormal distribution |
sig |
standard deviation of lognormal distribution |
log |
boolean Return the log density if TRUE (default=FALSE) |
Value
numeric Probability density values
Examples
p = dpoilog(1, 1, 1)
filter for mutually expressed genes
Description
filter for mutually expressed genes
Usage
filter_genes(count_mat, lambdas_ref, gtf, verbose = FALSE)
Arguments
count_mat |
dgCMatrix Gene expression counts |
lambdas_ref |
named numeric vector A reference expression profile |
gtf |
dataframe Transcript gtf |
Value
vector Genes that are kept after filtering
fit gamma maximum likelihood
Description
fit gamma maximum likelihood
Usage
fit_gamma(AD, DP, r = 0.015, start = 20)
Arguments
AD |
numeric vector Variant allele depth |
DP |
numeric vector Total allele depth |
r |
numeric Degree of allele bias |
Value
a fit
Fit MLE of log-normal Poisson model
Description
Fit MLE of log-normal Poisson model
Usage
fit_lnpois_cpp(Y_obs, lambda_ref, d)
Arguments
Y_obs |
Vector of observed counts |
lambda_ref |
Vector of reference rates |
d |
integer Total depth |
Value
NumericVector MLE estimates of mu and sigma
Fit a reference profile from multiple references using constrained least square
Description
Fit a reference profile from multiple references using constrained least square
Usage
fit_ref_sse(Y_obs, lambdas_ref, gtf, min_lambda = 2e-06, verbose = FALSE)
Arguments
Y_obs |
vector |
lambdas_ref |
matrix |
gtf |
dataframe |
Value
fitted expression profile
Forward-backward algorithm for allele HMM
Description
Forward-backward algorithm for allele HMM
Usage
forward_back_allele(hmm)
Arguments
hmm |
HMM object; expect variables x (allele depth), d (total depth), logPi (log transition prob matrix), delta (prior for each state), alpha (alpha for each state), beta (beta for each state), states (states), p_s (phase switch probs) |
Value
numeric matrix; posterior probabilities
Examples
forward_back_allele(pre_likelihood_hmm)
genome gap regions (hg19)
Description
genome gap regions (hg19)
Usage
gaps_hg19
Format
An object of class data.table (inherits from data.frame) with 28 rows and 3 columns.
genome gap regions (hg38)
Description
genome gap regions (hg38)
Usage
gaps_hg38
Format
An object of class data.table (inherits from data.frame) with 30 rows and 3 columns.
example gene expression counts matrix
Description
example gene expression counts matrix
Usage
gene_counts_example
Format
An object of class matrix (inherits from array) with 1758 rows and 1 columns.
Generate alphabetical postfixes
Description
Generate alphabetical postfixes
Usage
generate_postfix(n)
Arguments
n |
vector of integers |
Value
vector of alphabetical postfixes
Aggregate into pseudobulk alelle profile
Description
Aggregate into pseudobulk alelle profile
Usage
get_allele_bulk(df_allele, gtf, genetic_map = NULL, nu = 0.5, min_depth = 0)
Arguments
df_allele |
dataframe Single-cell allele counts |
gtf |
dataframe Transcript gtf |
genetic_map |
dataframe Genetic map |
nu |
numeric Phase switch rate |
min_depth |
integer Minimum coverage to filter SNPs |
Value
dataframe Pseudobulk allele profile
Examples
bulk_example = get_allele_bulk(
df_allele = df_allele_example,
gtf = gtf_hg38)
Make a 2-state allele HMM - no transitions to netural state
Description
Make a 2-state allele HMM - no transitions to netural state
Usage
get_allele_hmm_s2(pAD, DP, R, p_s, theta, gamma = 20, r = 0.015)
Arguments
pAD |
integer vector Paternal allele counts |
DP |
integer vector Total alelle counts |
p_s |
numeric vector Phase switch probabilities |
theta |
numeric Haplotype imbalance |
gamma |
numeric Overdispersion in the allele-specific expression |
Value
HMM object
Make a 3-state allele HMM - allow transitions to netural state
Description
Make a 3-state allele HMM - allow transitions to netural state
Usage
get_allele_hmm_s3(pAD, DP, R, p_s, t, theta_min, gamma = 20, r = 0.015)
Arguments
pAD |
integer vector Paternal allele counts |
DP |
integer vector Total alelle counts |
p_s |
numeric vector Phase switch probabilities |
t |
numeric Transition probability between haplotype states |
theta_min |
numeric Haplotype imbalance |
gamma |
numeric Overdispersion in the allele-specific expression |
r |
numeric Variant mapping bias |
Value
HMM object
Produce combined bulk expression and allele profile
Description
Produce combined bulk expression and allele profile
Usage
get_bulk(
count_mat,
lambdas_ref,
df_allele,
gtf,
genetic_map = NULL,
min_depth = 0,
nu = 1,
verbose = TRUE
)
Arguments
count_mat |
matrix Gene expression counts |
lambdas_ref |
matrix Reference expression profiles |
df_allele |
dataframe Allele counts |
gtf |
dataframe Transcript gtf |
genetic_map |
dataframe Genetic map |
min_depth |
integer Minimum coverage to filter SNPs |
nu |
numeric Phase switch rate |
verbose |
logical Whether to print progress |
Value
dataframe Pseudobulk gene expression and allele profile
Examples
bulk_example = get_bulk(
count_mat = gene_counts_example,
lambdas_ref = ref_hca,
df_allele = df_allele_example,
gtf = gtf_hg38)
Aggregate into bulk expression profile
Description
Aggregate into bulk expression profile
Usage
get_exp_bulk(count_mat, lambdas_ref, gtf, verbose = FALSE)
Arguments
count_mat |
dgCMatrix Gene expression counts |
lambdas_ref |
matrix Reference expression profiles |
gtf |
dataframe Transcript gtf |
Value
dataframe Pseudobulk gene expression profile
Helper function to calculate transition porbabilities for 15-state joint HMM cn/phase are sclars, only p_s is vectorized
Description
Helper function to calculate transition porbabilities for 15-state joint HMM cn/phase are sclars, only p_s is vectorized
Usage
get_trans_probs_s15(t, p_s, w, cn_from, phase_from, cn_to, phase_to)
Arguments
t |
numeric; CNV state transition probability |
p_s |
numeric; phase switch probability |
w |
numeric; relative abundance of states |
cn_from |
character; originating CNV state |
phase_from |
character; originating haplotype phase state |
cn_to |
character; destination CNV state |
phase_to |
character; destination haplotype phase state |
Value
numeric; transition probability
Helper function to calculate transition porbabilities for 7-state joint HMM cn/phase are sclars, only p_s is vectorized
Description
Helper function to calculate transition porbabilities for 7-state joint HMM cn/phase are sclars, only p_s is vectorized
Usage
get_trans_probs_s7(t, p_s, w, cn_from, phase_from, cn_to, phase_to)
Arguments
t |
numeric; CNV state transition probability |
p_s |
numeric; phase switch probability |
w |
numeric; relative abundance of states |
cn_from |
character; originating CNV state |
phase_from |
character; originating haplotype phase state |
cn_to |
character; destination CNV state |
phase_to |
character; destination haplotype phase state |
Value
numeric; transition probability
gene model (hg19)
Description
gene model (hg19)
Usage
gtf_hg19
Format
An object of class data.table (inherits from data.frame) with 26841 rows and 5 columns.
gene model (hg38)
Description
gene model (hg38)
Usage
gtf_hg38
Format
An object of class data.table (inherits from data.frame) with 26807 rows and 5 columns.
gene model (mm10)
Description
gene model (mm10)
Usage
gtf_mm10
Format
An object of class data.table (inherits from data.frame) with 30336 rows and 5 columns.
calculate joint likelihood of allele data
Description
calculate joint likelihood of allele data
Usage
l_bbinom(AD, DP, alpha, beta)
Arguments
AD |
numeric vector Variant allele depth |
DP |
numeric vector Total allele depth |
alpha |
numeric Alpha parameter of Beta-Binomial distribution |
beta |
numeric Beta parameter of Beta-Binomial distribution |
Value
numeric Joint log likelihood
Examples
l_bbinom(c(1, 2), c(1, 2), 1, 1)
calculate joint likelihood of a PLN model
Description
calculate joint likelihood of a PLN model
Usage
l_lnpois(Y_obs, lambda_ref, d, mu, sig, phi = 1)
Arguments
Y_obs |
numeric vector Gene expression counts |
lambda_ref |
numeric vector Reference expression levels |
d |
numeric Total library size |
mu |
numeric Global mean expression |
sig |
numeric Global standard deviation of expression |
phi |
numeric Fold change of expression |
Value
numeric Joint log likelihood
Examples
l_lnpois(c(1, 2), c(1, 2), 1, 1, 1)
Only compute total log likelihood from an allele HMM
Description
Only compute total log likelihood from an allele HMM
Usage
likelihood_allele(hmm)
Arguments
hmm |
HMM object; expect variables x (allele depth), d (total depth), logPi (log transition prob matrix), delta (prior for each state), alpha (alpha for each state), beta (beta for each state), states (states), p_s (phase switch probs) |
Value
numeric; total log likelihood
Examples
likelihood_allele(pre_likelihood_hmm)
logSumExp function
Description
logSumExp function
Usage
logSumExp(x)
Arguments
x |
NumericVector |
Value
double logSumExp of x
Plot a group of pseudobulk HMM profiles
Description
Plot a group of pseudobulk HMM profiles
Usage
plot_bulks(bulks, ..., ncol = 1, title = TRUE, title_size = 8)
Arguments
bulks |
dataframe Pseudobulk profiles annotated with "sample" column |
... |
additional parameters passed to plot_psbulk() |
ncol |
integer Number of columns |
title |
logical Whether to add titles to individual plots |
title_size |
numeric Size of titles |
Value
a ggplot object
Examples
p = plot_bulks(bulk_example)
Plot a pseudobulk HMM profile
Description
Plot a pseudobulk HMM profile
Usage
plot_psbulk(
bulk,
use_pos = TRUE,
allele_only = FALSE,
min_LLR = 5,
min_depth = 8,
exp_limit = 2,
phi_mle = TRUE,
theta_roll = FALSE,
dot_size = 0.8,
dot_alpha = 0.5,
legend = TRUE,
exclude_gap = TRUE,
genome = "hg38",
text_size = 10,
raster = FALSE
)
Arguments
bulk |
dataframe Pseudobulk profile |
use_pos |
logical Use marker position instead of index as x coordinate |
allele_only |
logical Only plot alleles |
min_LLR |
numeric LLR threshold for event filtering |
min_depth |
numeric Minimum coverage depth for a SNP to be plotted |
exp_limit |
numeric Expression logFC axis limit |
phi_mle |
logical Whether to plot estimates of segmental expression fold change |
theta_roll |
logical Whether to plot rolling estimates of allele imbalance |
dot_size |
numeric Size of marker dots |
dot_alpha |
numeric Transparency of the marker dots |
legend |
logical Whether to show legend |
exclude_gap |
logical Whether to mark gap regions and centromeres |
genome |
character Genome build, either 'hg38' or 'hg19' |
text_size |
numeric Size of text in the plot |
raster |
logical Whether to raster images |
Value
ggplot Plot of pseudobulk HMM profile
Examples
p = plot_psbulk(bulk_example)
HMM object for unit tests
Description
HMM object for unit tests
Usage
pre_likelihood_hmm
Format
An object of class list of length 10.
reference expression magnitudes from HCA
Description
reference expression magnitudes from HCA
Usage
ref_hca
Format
An object of class matrix (inherits from array) with 24756 rows and 12 columns.
reference expression counts from HCA
Description
reference expression counts from HCA
Usage
ref_hca_counts
Format
An object of class matrix (inherits from array) with 24857 rows and 12 columns.
Run a 3-state allele HMM
Description
Run a 3-state allele HMM
Usage
run_allele_hmm_s3(
pAD,
DP,
R,
p_s,
t = 1e-05,
theta_min = 0.08,
gamma = 20,
r = 0.015
)
Arguments
pAD |
integer vector Paternal allele counts |
DP |
integer vector Total alelle counts |
R |
numeric vector Variant mapping bias direction |
p_s |
numeric vector Phase switch probabilities |
t |
numeric Transition probability between copy number states |
theta_min |
numeric Minimum haplotype frequency deviation threshold |
gamma |
numeric Overdispersion in the allele-specific expression |
Value
character vector Decoded states
Run a 5-state allele-only HMM - two theta levels
Description
Run a 5-state allele-only HMM - two theta levels
Usage
run_allele_hmm_s5(
pAD,
DP,
p_s,
t = 1e-05,
theta_min = 0.08,
gamma = 20,
prior = NULL,
...
)
Arguments
pAD |
integer vector Paternal allele counts |
DP |
integer vector Total alelle counts |
p_s |
numeric vector Phase switch probabilities |
t |
numeric Transition probability between copy number states |
theta_min |
numeric Minimum haplotype frequency deviation threshold |
gamma |
numeric Overdispersion in the allele-specific expression |
prior |
numeric vector Prior probabilities for each state |
... |
Additional parameters |
Value
character vector Decoded states
Examples
with(bulk_example, {
run_allele_hmm_s5(pAD = pAD, DP = DP, R = R, p_s = p_s, theta_min = 0.08, gamma = 30)
})
Run 15-state joint HMM on a pseudobulk profile
Description
Run 15-state joint HMM on a pseudobulk profile
Usage
run_joint_hmm_s15(
pAD,
DP,
p_s,
Y_obs = 0,
lambda_ref = 0,
d_total = 0,
theta_min = 0.08,
theta_neu = 0,
bal_cnv = TRUE,
phi_del = 2^(-0.25),
phi_amp = 2^(0.25),
phi_bamp = phi_amp,
phi_bdel = phi_del,
mu = 0,
sig = 1,
r = 0.015,
t = 1e-05,
gamma = 18,
prior = NULL,
exp_only = FALSE,
allele_only = FALSE,
classify_allele = FALSE,
debug = FALSE,
...
)
Arguments
pAD |
integer vector Paternal allele counts |
DP |
integer vector Total alelle counts |
p_s |
numeric vector Phase switch probabilities |
Y_obs |
numeric vector Observed gene counts |
lambda_ref |
numeric vector Reference expression rates |
d_total |
integer Total library size for expression counts |
theta_min |
numeric Minimum haplotype imbalance threshold |
theta_neu |
numeric Haplotype imbalance threshold for neutral state |
bal_cnv |
logical Whether to include balanced CNV states |
phi_del |
numeric Expected fold change for deletion |
phi_amp |
numeric Expected fold change for amplification |
phi_bamp |
numeric Expected fold change for balanced amplification |
phi_bdel |
numeric Expected fold change for balanced deletion |
mu |
numeric Global expression bias |
sig |
numeric Global expression variance |
r |
numeric Variant mapping bias |
t |
numeric Transition probability between copy number states |
gamma |
numeric Overdispersion in the allele-specific expression |
prior |
numeric vector Prior probabilities for each state |
exp_only |
logical Whether to only use expression data |
allele_only |
logical Whether to only use allele data |
classify_allele |
logical Whether to classify allele states |
debug |
logical Whether to print debug messages |
... |
Additional parameters |
Value
character vector Decoded states
Examples
with(bulk_example, {
run_joint_hmm_s15(pAD = pAD, DP = DP, p_s = p_s, Y_obs = Y_obs, lambda_ref = lambda_ref,
d_total = na.omit(unique(d_obs)), mu = mu, sig = sig, t = 1e-5, gamma = 30, theta_min = 0.08)
})
Run 7-state joint HMM on a pseudobulk profile
Description
Run 7-state joint HMM on a pseudobulk profile
Usage
run_joint_hmm_s7(
pAD,
DP,
R,
p_s,
Y_obs,
lambda_ref,
d_total,
theta_min = 0.08,
phi_del = 2^(-0.25),
phi_amp = 2^(0.25),
t = 1e-05,
mu = 0,
sig = 1,
gamma = 20,
r = 0.015,
debug = FALSE
)
Arguments
pAD |
integer vector Paternal allele counts |
DP |
integer vector Total alelle counts |
R |
numeric vector Variant mapping bias direction |
p_s |
numeric vector Phase switch probabilities |
Y_obs |
numeric vector Observed gene counts |
lambda_ref |
numeric vector Reference expression rates |
d_total |
integer Total library size for expression counts |
theta_min |
numeric Minimum haplotype imbalance threshold |
phi_del |
numeric Expected fold change for deletion |
phi_amp |
numeric Expected fold change for amplification |
t |
numeric Transition probability between copy number states |
mu |
numeric Global expression bias |
sig |
numeric Global expression variance |
gamma |
numeric Overdispersion in the allele-specific expression |
r |
numeric Variant mapping bias |
debug |
logical Whether to print debug messages |
Value
character vector Decoded states
example CNV segments dataframe
Description
example CNV segments dataframe
Usage
segs_example
Format
An object of class data.table (inherits from data.frame) with 27 rows and 30 columns.
Smooth the segments after HMM decoding
Description
Smooth the segments after HMM decoding
Usage
smooth_segs(bulk, min_genes = 10)
Arguments
bulk |
dataframe Pseudobulk profile |
min_genes |
integer Minimum number of genes to call a segment |
Value
dataframe Pseudobulk profile
predict phase switch probablity as a function of genetic distance
Description
predict phase switch probablity as a function of genetic distance
Usage
switch_prob_cm(d, nu = 1, min_p = 1e-10)
Arguments
d |
numeric vector Genetic distance in cM |
nu |
numeric Phase switch rate |
min_p |
numeric Minimum phase switch probability |
Value
numeric vector Phase switch probability
Estimate of imbalance level theta in a segment
Description
Estimate of imbalance level theta in a segment
Usage
theta_hat_seg(major_count, minor_count)
Arguments
major_count |
vector of major allele count |
minor_count |
vector of minor allele count |
Value
estimate of theta
example VCF header
Description
example VCF header
Usage
vcf_meta
Format
An object of class character of length 65.
Viterbi algorithm for allele HMM
Description
Viterbi algorithm for allele HMM
Usage
viterbi_allele(hmm)
Arguments
hmm |
HMM object; expect variables x (allele depth), d (total depth), logPi (log transition prob matrix), delta (prior for each state), alpha (alpha for each state), beta (beta for each state), states (states), p_s (phase switch probs) |
Value
character vector Decoded states
Viterbi algorithm for joint HMM, can only handle one set of observations
Description
Viterbi algorithm for joint HMM, can only handle one set of observations
Usage
viterbi_joint(hmm)
Arguments
hmm |
HMM object; expect variables x (allele depth), d (total depth), y (expression count), l (cell total library size), lambda (reference expression rate), mu (global expression mean), sig (global expression standard deviation), logPi (log transition prob matrix), phi (expression fold change for each state), delta (prior for each state), alpha (alpha for each state), beta (beta for each state), states (states), p_s (phase switch probs) |
Value
character vector Decoded states
Generalized viterbi algorithm for joint HMM, can handle multiple sets of observations
Description
Generalized viterbi algorithm for joint HMM, can handle multiple sets of observations
Usage
viterbi_joint_mat(hmm)
Arguments
hmm |
HMM object; expect variables x (allele depth), d (total depth), y (expression count), l (cell total library size), lambda (reference expression rate), mu (global expression mean), sig (global expression standard deviation), logPi (log transition prob matrix), phi (expression fold change for each state), delta (prior for each state), alpha (alpha for each state), beta (beta for each state), states (states), p_s (phase switch probs) |
Value
character vector; decoded states