initialize_esvd: Initialize eSVD

In linnykos/eSVD2: eSVD2 for performing the eSVD-DE for cohort-wide differential expression analysis

Initialize eSVD

Description

For each gene, this function estimates two ridge-regression penalized GLMs (using the Poisson model) – one using the case_control_variable and one without, and both sets of coefficients as well as the p-value (according to a deviance test) is returned. This p-value is on the log10-scale.

Usage

initialize_esvd(
  dat,
  covariates,
  metadata_individual,
  bool_intercept = F,
  case_control_variable = NULL,
  k = 30,
  lambda = 0.01,
  library_size_variable = "Log_UMI",
  offset_variables = "Log_UMI",
  metadata_case_control = NULL,
  verbose = 0
)

Arguments

dat	Dataset (either matrix or dgCMatrix) where the n rows represent cells and p columns represent genes. The rows and columns of the matrix should be named.
covariates	matrix object with n rows with the same rownames as dat where the columns represent the different covariates. Notably, this should contain only numerical columns (i.e., all categorical variables should have already been split into numerous indicator variables), and all the columns in covariates will (strictly speaking) be included in the eSVD matrix factorization model.
metadata_individual	factor vector of length n that denotes which cell originates from which individual.
bool_intercept	Boolean on whether or not an intercept will be included as a covariate.
case_control_variable	A string of the column name of covariates which depicts the case-control status of each cell. Notably, this should be a binary variable where a 1 is hard-coded to describe case, and a 0 to describe control.
k	Number of latent dimensions.
lambda	Penalty of the mixed_effect_variables when using glmnet::glmnet to initialize the coefficients.
library_size_variable	A string of the variable name (which must be in covariates) of which variable denotes the sequenced (i.e., observed) library size.
offset_variables	A vector of strings depicting which column names in covariate will be set to have a coefficient of 1 automatically (i.e., there will be no estimation of their coefficient).
metadata_case_control	(Optional) vector of length n with values strictly 0 or 1 that denotes if a cell is from cases or controls. By default, this is set to NULL since the code will extract this information from covariates.
verbose	Integer

Value

eSVD object with elements dat, covariates, initial_Reg and param

linnykos/eSVD2 documentation built on July 17, 2024, 12:01 a.m.