Type: Package
Title: Estimating Dynamic Correlation
Version: 0.1.1
Description: Implementations for two different Bayesian models of differential co-expression. scdeco.cop() fits the bivariate Gaussian copula model from Zichen Ma, Shannon W. Davis, Yen-Yi Ho (2023) <doi:10.1111/biom.13701>, while scdeco.pg() fits the bivariate Poisson-Gamma model from Zhen Yang, Yen-Yi Ho (2022) <doi:10.1111/biom.13457>.
Imports: MASS, rjags, msm
License: GPL (≥ 3)
Encoding: UTF-8
RoxygenNote: 7.3.2
URL: https://github.com/YenYiHo-Lab/scDECO
BugReports: https://github.com/YenYiHo-Lab/scDECO/issues
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2025-04-02 21:58:28 UTC; abussing
Author: Anderson Bussing [aut, cre], Yen-Yi Ho [aut, ths], Zichen Ma [aut], Zhen Yang [aut]
Maintainer: Anderson Bussing <abussing@email.sc.edu>
Repository: CRAN
Date/Publication: 2025-04-02 22:20:02 UTC

Copula dynamic correlation fitting function

Description

Copula dynamic correlation fitting function

Usage

scdeco.cop(
  y,
  x,
  marginals,
  w = NULL,
  n.mcmc = 10000,
  burn = 1000,
  thin = 1,
  offset1 = NULL,
  offset2 = NULL
)

Arguments

y

2-column matrix of observations

x

covariates

marginals

length-2 vector with strings of the two marginals

w

(optional)

n.mcmc

number of mcmc iterations to run

burn

how many of the mcmc iterations to burn

thin

how much to thin the mcmc iterations

offset1

(optional) offset for link(mu1)

offset2

(optional) offset for link(mu2)

Value

matrix with mcmc samples as rows and columns corresponding to the different parameters

Examples

n <- 1000
x.use = rnorm(n)
w.use = runif(n,-1,1)
eta1.use = c(-2.2, 0.7)
eta2.use = c(-2, 0.8)
beta1.use = c(1,0.5)
beta2.use = c(1,1)
alpha1.use = 7
alpha2.use = 3
tau.use = c(-0.2, .3)

marginals.use <- c("ZINB", "ZIGA")

y.use <- scdeco.sim.cop(marginals=marginals.use, x=x.use,
                    eta1.true=eta1.use, eta2.true=eta2.use,
                    beta1.true=beta1.use, beta2.true=beta2.use,
                    alpha1.true=alpha1.use, alpha2.true=alpha2.use,
                    tau.true=tau.use, w=w.use)
mcmc.out <- scdeco.cop(y=y.use, x=x.use, marginals=marginals.use, w=w.use,
                      n.mcmc=10, burn=0, thin=1) # n.mcmc=1000, burn=100, thin=5)

lowerupper <- t(apply(mcmc.out, 2, quantile, c(0.025, 0.5, 0.975)))
estmat <- cbind(lowerupper[,1],
                c(eta1.use, eta2.use, beta1.use, beta2.use, alpha1.use, alpha2.use, tau.use),
                lowerupper[,c(2,3)])
colnames(estmat) <- c("lower", "trueval", "estval", "upper")
estmat

ZENCO Poisson Gamma dynamic correlation fitting function

Description

ZENCO Poisson Gamma dynamic correlation fitting function

Usage

scdeco.pg(
  dat,
  b0,
  b1,
  adapt_iter = 100,
  update_iter = 100,
  coda_iter = 1000,
  coda_thin = 5,
  coda_burnin = 100
)

Arguments

dat

matrix containing expression values as first two columns and covariate as third column

b0

intercept of zinf parameter

b1

slope of zinf parameter

adapt_iter

number of adaptation iterations in the jags.model function

update_iter

update iterations in the update function

coda_iter

number of iterations for the coda.sample function

coda_thin

how much to thin the resulting MCMC output

coda_burnin

how many iterations to burn before beginning coda sample collection

Value

MCMC samples that have been adapted, burned, and thinned

Examples


phi1_use <- 4
phi2_use <- 4
phi3_use <- 1/7
mu1_use <- 15
mu2_use <- 15
mu3_use <- 7
b0_use <- -3
b1_use <- 0.1
tau0_use <- -2
tau1_use <- 0.4

simdat <- scdeco.sim.pg(N=1000, b0=b0_use, b1=b1_use,
                        phi1=phi1_use, phi2=phi2_use, phi3=phi3_use,
                        mu1=mu1_use, mu2=mu2_use, mu3=mu3_use,
                        tau0=tau0_use, tau1=tau1_use)

zenco_out <- scdeco.pg(dat=simdat,
                       b0=b0_use, b1=b1_use,
                       adapt_iter=1, # 500,
                       update_iter=1, # 500,
                       coda_iter=5, # 5000,
                       coda_thin=1, # 10,
                       coda_burnin=0) # 1000

boundsmat <- cbind(zenco_out$quantiles[,1],
                   c(1/phi1_use, 1/phi2_use, 1/phi3_use,
                   mu1_use, mu2_use, mu3_use,
                   tau0_use, tau1_use),
                   zenco_out$quantiles[,c(3,5)])

colnames(boundsmat) <- c("lower", "true", "est", "upper")

boundsmat

ZENCO fitting function when no secondary covariate is provided

Description

ZENCO fitting function when no secondary covariate is provided

Usage

scdeco.pg.noxc(
  dat,
  b0,
  b1,
  adapt_iter = 10000,
  update_iter = 5000,
  coda_iter = 50000,
  coda_thin = 20,
  coda_burnin = 10000
)

Arguments

dat

matrix containing expression values as first two columns and covariate as third column

b0

intercept of zinf parameter

b1

slope of zinf parameter

adapt_iter

number of adaptation iterations in the jags.model function

update_iter

update iterations in the update function

coda_iter

number of iterations for the coda.sample function

coda_thin

how much to thin the resulting MCMC output

coda_burnin

how many iterations to burn before beginning coda sample collection

Value

MCMC samples that have been adapted, burned, and thinned

ZENCO fitting function when secondary covariate is provided

Description

ZENCO fitting function when secondary covariate is provided

Usage

scdeco.pg.xc(
  dat,
  b0,
  b1,
  adapt_iter = 10000,
  update_iter = 5000,
  coda_iter = 50000,
  coda_thin = 20,
  coda_burnin = 10000
)

Arguments

dat

matrix containing expression values as first two columns and covariate as third column

b0

intercept of zinf parameter

b1

slope of zinf parameter

adapt_iter

number of adaptation iterations in the jags.model function

update_iter

update iterations in the update function

coda_iter

number of iterations for the coda.sample function

coda_thin

how much to thin the resulting MCMC output

coda_burnin

how many iterations to burn before beginning coda sample collection

Value

MCMC samples that have been adapted, burned, and thinned

Simulating from copula model

Description

Simulating from copula model

Usage

scdeco.sim.cop(
  marginals,
  x,
  eta1.true,
  eta2.true,
  beta1.true,
  beta2.true,
  alpha1.true,
  alpha2.true,
  tau.true,
  w = NULL
)

Arguments

marginals

provide vector of length 2 of which marginals to use

x

covariate matrix

eta1.true

zero-inflation parameters for marginal 1

eta2.true

zero-inflation parameters for marginal 2

beta1.true

mean coefficients for marginal 1

beta2.true

mean coefficients for marginal 2

alpha1.true

second parameter coefficients for marginal 1

alpha2.true

second parameter coefficients for marginal 2

tau.true

coefficients for correlation

w

(optional) covariate matrix for zero-inflation portion

Value

matrix with values simulated from copula model

Examples

n <- 2500
x.use = rnorm(n)
w.use = runif(n,-1,1)
eta1.use = c(-2.2, 0.7)
eta2.use = c(-2, 0.8)
beta1.use = c(1,0.5)
beta2.use = c(1,1)
alpha1.use = 7
alpha2.use = 3
tau.use = c(-0.2, .3)

marginals.use <- c("ZINB", "ZIGA")

y.use <- scdeco.sim.cop(marginals=marginals.use, x=x.use,
                    eta1.true=eta1.use, eta2.true=eta2.use,
                    beta1.true=beta1.use, beta2.true=beta2.use,
                    alpha1.true=alpha1.use, alpha2.true=alpha2.use,
                    tau.true=tau.use, w=w.use)

y.use[1:10,]

Simulating from ZENCO Model

Description

Simulating from ZENCO Model

Usage

scdeco.sim.pg(
  N,
  b0,
  b1,
  phi1,
  phi2,
  mu1,
  mu2,
  tau0,
  tau1,
  mu3,
  phi3,
  tau2 = NULL,
  tau3 = NULL,
  xc = NULL
)

Arguments

N

size of sample to be generated

b0

intercept of zinf parameter

b1

slope of zinf parameter

phi1

over-dispersion parameter of first marginal

phi2

over-dispersion parameter of second marginal

mu1

mean parameter of first marginal

mu2

mean parameter of second marginal

tau0

intercept of correlation

tau1

slope of of correlation

mu3

mean parameter of covariate vector

phi3

over-dispersion parameter of covariate vector

tau2

(optional) correlation coefficient on optional xc covariate vector

tau3

(optional) correlation coefficient on interaction between x3 and xc

xc

(optional) secondary covariate to be regressed

Value

a matrix with expressions as first two columns and covariates as remaining columns

Examples

phi1_use <- 4
phi2_use <- 4
phi3_use <- 1/6
mu1_use <- 15
mu2_use <- 15
mu3_use <- 7
b0_use <- 0.6882
b1_use <- -0.2995
tau0_use <- 0.07
tau1_use <- 0.05

simdat <- scdeco.sim.pg(N=1000, b0=b0_use, b1=b1_use,
                        phi1=phi1_use, phi2=phi2_use, phi3=phi3_use,
                        mu1=mu1_use, mu2=mu2_use, mu3=mu3_use,
                        tau0=tau0_use, tau1=tau1_use)
simdat[1:10,]