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Title:

Bayesian Hierarchical Models for Single-Cell Protein Data

Version:

1.0.0

Description:

Bayesian Hierarchical beta-binomial models for modeling cell population to predictors/exposures. This package utilizes 'runjags' to run Gibbs sampling with parallel chains. Options for different covariances/relationship structures between parameters of interest.

License:

MIT + file LICENSE

Encoding:

UTF-8

RoxygenNote:

7.3.2

Imports:

coda, runjags, VGAM, matlib

Depends:

R (≥ 3.5), rjags

Suggests:

knitr, rmarkdown

VignetteBuilder:

knitr

LazyData:

true

NeedsCompilation:

Packaged:

2025-09-30 21:02:27 UTC; cjsakitis

Author:

Chase Sakitis [aut, cre], Brooke Fridley [aut]

Maintainer:

Chase Sakitis <cjsakitis@cmh.edu>

Repository:

CRAN

Date/Publication:

2025-10-07 18:00:12 UTC

Bayesian Immune Cell Abundance Model (BICAM)

Description

Bayesian Immune Cell Abundance Model (BICAM)

Usage

BICAM(
  dat,
  M,
  adapt,
  burn,
  it,
  thin = 1,
  ran_eff = 1,
  chains = 4,
  cores = 4,
  v0_mu_logit = 0.01,
  ncov = 1,
  model = "Unstr",
  dis = NULL,
  tree = NULL,
  treelevels = NULL
)

Arguments

dat

data frame with dataset (proper setup displayed in tutorial)

M

number of cell types/parameters of interest

adapt

number of adaptation iterations (for compiling model)

burn

number of burn-in iterations

it

number of sampling iterations (after burn-in)

thin

number of thinning samples

ran_eff

indicate whether to use random subject effect (repeated measurements)

chains

number of chains to run

cores

number of cores

v0_mu_logit

anticipated proportion of cell types/parameters

ncov

number of covariates input into the model

model

covariance model selection

dis

distance matrix for Exp. Decay model

tree

tree-structured covariance matrix for Tree and Scaled Tree models

treelevels

list of matrices for multilevel, tree-structured covariance matrix for TreeLevels model

Value

A list of inputs and results

Examples

data(dat)
BICAM(dat,2,1500,250,250)

Example dataset: dat

Description

A sample dataset used for demonstrating the function.

Usage

dat

Format

A data frame with 10 rows and 5 columns:

suid: Subject ID's
total: Total number of trials
stage: Binary predictor variable (0/1)
M1: Count data for Marker 1
M2: Count data for Marker 2

Source

Imported from CSV and saved as RData

Examples

data(dat)
head(dat)