% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/integration.R
\name{online_iNMF-deprecated}
\alias{online_iNMF-deprecated}
\title{\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#deprecated}{\figure{lifecycle-deprecated.svg}{options: alt='[Deprecated]'}}}{\strong{[Deprecated]}} Perform online iNMF on scaled datasets}
\arguments{
\item{object}{\code{liger} object with data stored in HDF5 files. Should
normalize, select genes, and scale before calling.}

\item{X_new}{List of new datasets for scenario 2 or scenario 3. Each list
element should be the name of an HDF5 file.}

\item{projection}{Perform data integration by shared metagene (W) projection
(scenario 3). (default FALSE)}

\item{W.init}{Optional initialization for W. (default NULL)}

\item{V.init}{Optional initialization for V (default NULL)}

\item{H.init}{Optional initialization for H (default NULL)}

\item{A.init}{Optional initialization for A (default NULL)}

\item{B.init}{Optional initialization for B (default NULL)}

\item{k}{Inner dimension of factorization--number of metagenes (default 20).
A value in the range 20-50 works well for most analyses.}

\item{lambda}{Regularization parameter. Larger values penalize
dataset-specific effects more
strongly (ie. alignment should increase as lambda increases). We recommend
always using the default value except
possibly for analyses with relatively small differences (biological
replicates, male/female comparisons, etc.)
in which case a lower value such as 1.0 may improve reconstruction quality.
(default 5.0).}

\item{max.epochs}{Maximum number of epochs (complete passes through the
data). (default 5)}

\item{miniBatch_max_iters}{Maximum number of block coordinate descent (HALS
algorithm) iterations to perform for each update of W and V (default 1).
Changing this parameter is not  recommended.}

\item{miniBatch_size}{Total number of cells in each minibatch (default 5000).
This is a reasonable default, but a smaller value such as 1000 may be
necessary for analyzing very small datasets. In general, minibatch size
should be no larger than the number of cells in the smallest dataset.}

\item{h5_chunk_size}{Chunk size of input hdf5 files (default 1000). The chunk
size should be no larger than the batch size.}

\item{seed}{Random seed to allow reproducible results (default 123).}

\item{verbose}{Print progress bar/messages (TRUE by default)}
}
\value{
\code{liger} object with H, W, V, A and B slots set.
}
\description{
\bold{Please turn to \code{\link{runOnlineINMF}} or
\code{\link{runIntegration}}}.

Perform online integrative non-negative matrix factorization to represent
multiple single-cell datasets in terms of H, W, and V matrices. It optimizes
the iNMF objective function using online learning (non-negative least squares
for H matrix, hierarchical alternating least squares for W and V matrices),
where the number of factors is set by k. The function allows online learning
in 3 scenarios: (1) fully observed datasets; (2) iterative refinement using
continually arriving datasets; and (3) projection of new datasets without
updating the existing factorization. All three scenarios require fixed memory
independent of the number of cells.

For each dataset, this factorization produces an H matrix (cells by k), a V
matrix (k by genes), and a shared W matrix (k by genes). The H matrices
represent the cell factor loadings. W is identical among all datasets, as it
represents the shared components of the metagenes across datasets. The V
matrices represent the dataset-specific components of the metagenes.
}
