bmind_de: Cell type-specific differential expression analysis with...

In randel/MIND: Using Tissue Expression to Estimate Sample-/Subject- And Cell-Type-Specific Gene Expression via Deconvolution

Cell type-specific differential expression analysis with prior derived from single-cell data

Description

It conducts Bayesian testing of cell-type-specific (CTS) differential gene expression analysis, via MCMC.

Usage

bmind_de(
  bulk,
  frac = NULL,
  sample_id = NULL,
  ncore = NULL,
  profile = NULL,
  covariance = NULL,
  profile_co = NULL,
  covariance_co = NULL,
  profile_ca = NULL,
  covariance_ca = NULL,
  y = NULL,
  covariate = NULL,
  covariate_bulk = NULL,
  covariate_cts = NULL,
  np = F,
  nu = 50,
  nitt = 1300,
  burnin = 300,
  thin = 1,
  max_samp = 1e+06,
  frac_method = NULL,
  sc_count = NULL,
  sc_meta = NULL,
  signature = NULL,
  signature_case = NULL,
  case_bulk = NULL
)

Arguments

bulk	bulk gene expression (gene x sample).
frac	sample-specific cell type fraction (sample x cell type). If not specified (NULL), it will be estimated by non-negative least squares (NNLS) by providing signature matrix or Bisque by providing single-cell reference.
sample_id	sample/subject ID vector. The default is that sample ID will be automatically provided for sample-level bMIND analysis, otherwise subject ID should be provided for subject-level bMIND analysis. Note that the subject ID will be sorted in the output and different sample_id would produce slightly different results in MCMCglmm.
ncore	number of cores to run in parallel. The default is all available cores.
profile	prior profile matrix (gene by cell type). Gene names should be in the same order of bulk, and cell type names should be in the same order as frac.
covariance	prior covariance array (gene by cell type by cell type). Gene names should be in the same order of bulk, and cell type names should be in the same order as frac. The default is 0.5 * Identity matrix for covariance of fixed effects.
profile_co	prior profile matrix (gene by cell type) for controls.
covariance_co	prior covariance array (gene by cell type by cell type) for controls.
profile_ca	prior profile matrix (gene by cell type) for cases.
covariance_ca	prior covariance array (gene by cell type by cell type) for cases.
y	binary (0-1) outcome/phenotype vector for CTS DE analysis (0 for controls, 1 for cases). Should be the same length and order as sample_id or sort(unique(sample_id)) and row names of covariate.
covariate	matrix for covariates to be adjusted in deconvolution model.
covariate_bulk	colnames of covariate denoting variables that affect bulk expression
covariate_cts	colnames of covariate denoting variables that affect CTS expression
np	option to use non-informative prior. Default is FALSE
nu	hyper-parameter for the prior covariance matrix. The larger the nu, the higher the certainty about the information in covariance, and the more informative is the distribution. The default is 50.
nitt	number of MCMC iterations.
burnin	burn-in iterations for MCMC.
thin	thinning interval for MCMC.
max_samp	max number of posterior samples to generate in testing. An adaptive procedure is used to increase nitt for those genes with p-values = 1/number of posterior samples.
frac_method	method to be used for estimating cell type fractions, either 'NNLS' or 'Bisque'. **All arguments starting from this one will be used to estimate cell-type fractions only, if those fractions are not pre-estimated.**
sc_count	sc/snRNA-seq raw count as reference for Bisque to estimate cell type fractions.
sc_meta	meta data frame for sc/snRNA-seq reference. A binary (0-1) column of 'case' is expected to indicate case/control status.
signature	signature matrix for NNLS to estimate cell type fractions. Log2 transformation is recommended.
signature_case	signature matrix from case samples for NNLS to estimate cell type fractions. Log2 transformation is recommended. If this is provided, signature will be treated as signature matrix for unaffected controls.
case_bulk	case/control status vector for bulk data when using case/control reference to estimate the cell type fractions for case/control subjects separately.

Value

A list containing the output of the bMIND algorithm (some genes with error message in MCMCglmm will not be outputted, e.g., with constant expression)

coef	the estimated coefficients matrix (gene x variables).
frac	the estimated cell type fractions (sample x cell type) if fractions are not provided.
pval	the p-values of CTS-DE testing (gene x cell type).
qval	the q-values of CTS-DE testing by BH FDR adjustment (gene x cell type).

Examples

data(example)
bulk = t(na.omit(apply(example$X, 1, as.vector)))
frac = na.omit(apply(example$W, 3, as.vector))
colnames(bulk) = rownames(frac) = 1:nrow(frac)
y = rbinom(n = nrow(frac), size = 1, prob = 0.5)
covariate = data.frame(c1 = rnorm(length(y)), c2 = rnorm(length(y)))

bulk[1:5, 1:5]
head(frac)

# CTS-DE (np = TRUE: use non-informative prior)
deconv = bmind_de(bulk, frac, y = y, covariate = covariate, covariate_bulk = 'c1', covariate_cts = 'c2', np = T)

# If you have informative prior, use get_prior() in https://randel.github.io/MIND/reference/get_prior.html to generate prior `profile` and `covariance` matrices. You can do this sepately for cases (profile_ca, covariance_ca) and controls (profile_co, covariance_co). Note that covariance matrix is required to be positive-definite.
deconv = bmind_de(bulk, frac, np = FALSE)

randel/MIND documentation built on May 6, 2023, 7:45 a.m.