opt_esvd.default: Optimize eSVD for matrices or sparse matrices.

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

Optimize eSVD for matrices or sparse matrices.

Description

Optimize eSVD for matrices or sparse matrices.

Usage

## Default S3 method:
opt_esvd(
  input_obj,
  x_init,
  y_init,
  z_init = NULL,
  covariates = NULL,
  family = "poisson",
  l2pen = 0.1,
  library_multipler = rep(1, nrow(input_obj)),
  max_iter = 100,
  nuisance_vec = rep(NA, ncol(input_obj)),
  offset_variables = NULL,
  tol = 1e-06,
  verbose = 0,
  ...
)

Arguments

input_obj	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.
x_init	Initial matrix of the cells' latent vectors that is n rows and k columns. The row names should be the same as input_obj.
y_init	Initial matrix of the genes' latent vectors that is p rows and k columns. The row names should be the same as the column names of input_obj.
z_init	Initial matrix of the genes' coefficient vectors that is p rows and ncol(covariates) columns. The row names should be the same as the column names of input_obj, and the column names should be the same as covariates.
covariates	matrix object with n rows with the same rownames as input_obj 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).
family	String among "gaussian", "curved_gaussian", "exponential", "poisson", "neg_binom", "neg_binom2", or "bernoulli". Notably, with exception of "neg_binom2", all the other families are parameterized such that eSVD is fitting the dot product to be the canonical parameter of these expoential-family distributions. For "neg_binom2", the dot product is the log-mean of the distribution (i.e., similar to the canonical parameterization of the Poisson family).
l2pen	Small positive number for the amount of penalization for both the cells' and the genes' latent vectors as well as the coefficients.
library_multipler	Vector of positive numerics of length n. It is the multiplier such that the variance of cell i's entries is the mean of cell i's entries times the square-root of cell i's value in library_multipler (entry-wise). This is used as an alternative interpretation of how library-size affects a cell's gene expression (instead of using the library size as a covariate to be regressed out).
max_iter	Positive integer for number of iterations.
nuisance_vec	Vector of non-negative numerics (or NA's) of length p, representing each gene's nuisance parameter when using an exponential-family distribution that requires one. It is used only when family is "curved_gaussian" or "neg_binom" or "neg_binom2".
offset_variables	A vector of strings depicting which column names in input_obj$covariate be treated as an offset during the optimization (i.e., their coefficients will not change throughout the optimization).
tol	Small positive number to differentiate between zero and non-zero.
verbose	Integer
...	Additional parameters

Value

a list with elements x_mat, y_mat, z_mat, library_multiplier, loss, nuisance_vec and param.

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