R/randomWalkBySolve.R

In netSmooth: Network smoothing for scRNAseq

setGeneric(
    name = "randomWalkBySolve",
    def = function(E, A, alpha, normalizeAjdMatrix) {
        standardGeneric("randomWalkBySolve")
    }
)

#' Smooth data on graph by solving the linear equation
#' (I - alpha*A) * E_sm = E * (1-alpha)
#'
#'
#' @param E      initial data matrix [NxM]
#' @param A      adjacency matrix of graph to network smooth on
#'               will be column-normalized.
#' @param alpha  smoothing coefficient (1 - restart probability of
#'                random walk)
#' @return network-smoothed gene expression
#' @keywords internal
setMethod("randomWalkBySolve",
    signature(E='matrix'), function(E, A, alpha,
    normalizeAjdMatrix=c('rows','columns')) {
    normalizeAjdMatrix <- match.arg(normalizeAjdMatrix)
    if(normalizeAjdMatrix=='rows') Anorm <- l1NormalizeRows(A)
    else if(normalizeAjdMatrix=='columns') Anorm <- l1NormalizeColumns(A)
    eye <- diag(dim(A)[1])
    AA <- eye - alpha*Anorm
    BB <- (1-alpha) * E
    return(solve(AA, BB))
    }
)

setMethod("randomWalkBySolve",
    signature(E='Matrix'),
    function(E, A, alpha,
    normalizeAjdMatrix=c('rows','columns')) {
    normalizeAjdMatrix <- match.arg(normalizeAjdMatrix)
    if(normalizeAjdMatrix=='rows') Anorm <- l1NormalizeRows(A)
    else if(normalizeAjdMatrix=='columns') Anorm <- l1NormalizeColumns(A)
    eye <- diag(dim(A)[1])
    AA <- eye - alpha*Anorm
    BB <- (1-alpha) * E
    return(Matrix::solve(AA, BB))
    }
)