Merge pull request #254 from jr-leary7/dev

Dev

Author: jr-leary7

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: scLANE
 Type: Package
 Title: Model Gene Expression Dynamics with Spline-Based NB GLMs, GEEs, & GLMMs
-Version: 0.8.4
+Version: 0.8.5
 Authors@R: c(person(given = c("Jack", "R."), family = "Leary", email = "j.leary@ufl.edu", role = c("aut", "cre"), comment = c(ORCID = "0009-0004-8821-3269")), 
              person(given = "Rhonda", family = "Bacher", email = "rbacher@ufl.edu", role = c("ctb", "fnd"), comment = c(ORCID = "0000-0001-5787-476X")))
 Description: Our scLANE model uses truncated power basis spline models to build flexible, interpretable models of single cell gene expression over pseudotime or latent time. 

NEWS.md

@@ -1,3 +1,9 @@
+# Changes in v0.8.5
+
++ Minor bug fixes. 
++ More improved matrix operations using `RcppEigen`. 
++ Improved support for `cell_data_set` objects from `monocle3`. 
+
 # Changes in v0.8.4
 
 + Minor bug fixes. 

R/getFittedValues.R

@@ -14,6 +14,7 @@
 #' @param size.factor.offset (Optional) An offset to be used to rescale the fitted values. Can be generated easily with \code{\link{createCellOffset}}. No need to provide if the GEE backend was used. Defaults to NULL.
 #' @param log1p.norm (Optional) Should log1p-normalized versions of expression & model predictions be returned as well? Defaults to TRUE.
 #' @param cell.meta.data (Optional) A data.frame of metadata values for each cell (celltype label, subject characteristics, tissue type, etc.) that will be included in the result table. Defaults to NULL.
+#' @param is.gee Was the GEE mode used to fit the models? Defaults to FALSE. 
 #' @param id.vec (Optional) A vector of subject IDs used in fitting GEE or GLMM models. Defaults to NULL.
 #' @param ci.alpha (Optional) The pre-specified Type I Error rate used in generating (\eqn{1 - \alpha})\% CIs. Defaults to good old 0.05.
 #' @param filter.lineage (Optional) A character vector of lineages to filter out before generating the final plot. Should be letters, i.e. lineage "A" or "B". Defaults to NULL.
@@ -35,6 +36,7 @@ getFittedValues <- function(test.dyn.res = NULL,
                             size.factor.offset = NULL,
                             log1p.norm = TRUE,
                             cell.meta.data = NULL,
+                            is.gee = FALSE, 
                             id.vec = NULL,
                             ci.alpha = 0.05,
                             filter.lineage = NULL) {
@@ -44,6 +46,9 @@ getFittedValues <- function(test.dyn.res = NULL,
   if (inherits(expr.mat, "SingleCellExperiment")) {
     expr.mat <- BiocGenerics::counts(expr.mat)[genes, , drop = FALSE]
     expr.mat <- as.matrix(expr.mat)
+  } else if (inherits(expr.mat, "cell_data_set")) {
+    expr.mat <- BiocGenerics::counts(expr.mat)[genes, , drop = FALSE]
+    expr.mat <- as.matrix(expr.mat)
   } else if (inherits(expr.mat, "Seurat")) {
     expr.mat <- Seurat::GetAssayData(expr.mat,
                                      slot = "counts",
@@ -104,7 +109,7 @@ getFittedValues <- function(test.dyn.res = NULL,
                                                     scLANE_pred = exp(scLANE_pred_link),
                                                     scLANE_ci_ll = exp(scLANE_pred_link - Z * scLANE_se_link),
                                                     scLANE_ci_ul = exp(scLANE_pred_link + Z * scLANE_se_link))
-                           if (!is.null(size.factor.offset)) {
+                           if (!is.null(size.factor.offset) & !is.gee) {
                              gene_df <- dplyr::mutate(gene_df,
                                                       dplyr::across(c(rna, scLANE_pred, scLANE_ci_ll, scLANE_ci_ul), \(m) m * size_factor))
                            }

R/sortObservations.R

@@ -4,14 +4,14 @@
 #' @author Jack R. Leary
 #' @importFrom dplyr arrange
 #' @description Since the GEE & GLMM modes require data to be sorted by sample ID and pseudotime, this function provides a simple way to do so for a range of inputs.
-#' @param expr.mat Either a \code{SingleCellExperiment} or \code{Seurat} object from which counts can be extracted, or a matrix of integer-valued counts with genes as rows & cells as columns. Defaults to NULL.
+#' @param expr.mat Either a \code{SingleCellExperiment}, \code{Seurat}, or \code{cell_data_set} object from which cell-level metadata can be extracted, or a matrix of integer-valued counts with genes as rows & cells as columns. Defaults to NULL.
 #' @param pt.vec A vector of pseudotime values used to sort the observations. May contain NA values. Defaults to NULL.
 #' @param id.vec A vector of subject IDs used to sort the observations. Defaults to NULL.
 #' @return An object of the same class as the input \code{expr.mat}, but sorted by sample ID & pseudotime.
 #' @details
 #' \itemize{
 #' \item If the input is a matrix, it is assumed that the columns are cells - and are named as such - and the rows are genes.
-#' \item If the input is a Seurat object, sorting requires converting to \code{SingleCellExperiment} object first, then ordering, then converting back toa \code{Seurat} object. Some information might be lost, so it is recommended not to overwrite your original \code{Seurat} object.
+#' \item If the input is a \code{Seurat} object, sorting requires converting to \code{SingleCellExperiment} object first, then ordering, then converting back to a \code{Seurat} object. Some information might be lost, so it is recommended not to overwrite your original \code{Seurat} object.
 #' }
 #' @export
 #' @examples
@@ -36,7 +36,7 @@ sortObservations <- function(expr.mat = NULL,
                             ID,
                             PT)
   # sort object by cells
-  if (inherits(expr.mat, "SingleCellExperiment") || inherits(expr.mat, "matrix") || inherits(expr.mat, "dgCMatrix")) {
+  if (inherits(expr.mat, "SingleCellExperiment") || inherits(expr.mat, "cell_data_set") || inherits(expr.mat, "matrix") || inherits(expr.mat, "dgCMatrix")) {
     expr.mat <- expr.mat[, subj_df$Cell]
   } else if (inherits(expr.mat, "Seurat")) {
     warning("Ordering a Seurat object requires conversion to SingleCellExperiment, and some information might be lost.")