bMIND: The bMIND algorithm to estimate sample-level...

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

The bMIND algorithm to estimate sample-level cell-type-specific expression

Description

It calculates the Bayesian estimates of sample- and cell-type-specific (CTS) gene expression, via MCMC. For all input, dimension names are recommended if applicable.

Usage

bMIND(
  bulk,
  frac = NULL,
  sample_id = NULL,
  ncore = NULL,
  profile = NULL,
  covariance = NULL,
  nu = 50,
  V_fe = NULL,
  nitt = 1300,
  burnin = 300,
  thin = 1,
  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). We recommend log2-transformed data for better performance, except when using Bisque to estimate cell type fractions, raw count is expected for Bisque. If the max(bulk) > 50, bulk will be transformed to log2(count per million + 1) before running bMIND.
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 for providing sample/subject-level CTS estimates. 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. If not specified (NULL), the bulk mean will be supplied.
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. If not specified (NULL), bulk variance / sum(colMeans(frac)^2) will be supplied.
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.
V_fe	hyper-parameter for the covariance matrix of fixed-effects. The default is 0.5 * Identity matrix.
nitt	number of MCMC iterations.
burnin	burn-in iterations for MCMC.
thin	thinning interval for MCMC.
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., those genes with constant expression)

A	the deconvolved cell-type-specific gene expression (gene x cell type x sample).
SE	the standard error of cell-type-specific gene expression (gene x cell type x sample).
Sigma_c	the covariance matrix for the deconvolved cell-type-specific expression (gene x cell type x cell type).
mu	the estimated profile matrix (gene x cell type).
frac	the estimated cell type fractions (sample x cell type) if fractions are not provided.

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)

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

# with provided cell type fractions
deconv1 = bMIND(bulk, frac = frac, ncore = 12)

# set.seed(1)
# data(signature)
# bulk = matrix(rnorm(300 * ncol(bulk), 10), ncol = ncol(bulk))
# rownames(bulk) = rownames(signature)[1:nrow(bulk)]
# colnames(bulk) = 1:ncol(bulk)

## without provided cell type fractions: use built-in deconvolution methods NNLS or Bisque to estimate fractions
# deconv2 = bMIND(bulk, signature = signature[, -6], ncore = 12)

# 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. Note that covariance matrix is required to be positive-definite.

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