spatial

DESCRIPTION

@@ -14,6 +14,7 @@ Depends:
 LazyData: true
 Imports: 
     fields,
+    FNN,
     ggplot2,
     Matrix,
     methods,

NAMESPACE

@@ -8,6 +8,7 @@ export(run.DOT.lowresolution)
 export(setup.ref)
 export(setup.srt)
 exportClasses(Dot)
+importFrom(FNN,get.knn)
 importFrom(Matrix,colMeans)
 importFrom(Matrix,colSums)
 importFrom(Matrix,rowMeans)

R/DOT-package.R

@@ -2,6 +2,7 @@
 "_PACKAGE"
 
 ## usethis namespace: start
+#' @importFrom FNN get.knn
 #' @importFrom Matrix colMeans
 #' @importFrom Matrix colSums
 #' @importFrom Matrix rowMeans

R/dot.R

@@ -239,7 +239,7 @@ setup.srt <- function(srt_data, srt_coords = NULL, th.spatial = 0.84, th.nonspat
 
   if(th.spatial > 0)
   {
-    s$P <- get_pairs(s, th.spatial, th.nonspatial, nrow(s$X), verbose = verbose)
+    s$P <- get_pairs(s, th.spatial, th.nonspatial, nrow(s$X), radius = radius, verbose = verbose)
   }
 
   gc()
@@ -439,7 +439,7 @@ run.DOT.lowresolution <- function(object, ratios_weight = 0,
   l_sp <- l_I * sparsity_coef / max_size
   l_C <- inner_params[3] * S / C
 
-  if(is.null(object@srt$P))
+  if(is.null(object@srt$P) || nrow(object@srt$P) == 0)
   {
     l_S <- 0
   }else

R/process.R

@@ -159,32 +159,18 @@ get_de_genes <- function(ref_centroids, ref_ratios, max_genes, verbose = FALSE)
 #' An internal method for computing the spatial neighborhood radius
 #' @param coordinates Coordinates of spots
 #' @param neighbors An internal parameter
-#' @param sample_size An internal parameter
 #' @param th.quantile An internal parameter
 #' @return A numeric value
 #' @keywords internal
 #' @noRd
 #'
-find_radius <- function(coordinates, neighbors = 8, sample_size = 2000, th.quantile = 0.9)
+find_radius <- function(coordinates, neighbors = 8, th.quantile = 0.9)
 {
-  N <- nrow(coordinates)
-  if(N > sample_size)
-  {
-    sample_spots <- sample(1:N, sample_size)
-    dists <- fields::rdist(coordinates[sample_spots, ], coordinates)
-  }else
-  {
-    dists <- as.matrix(stats::dist(coordinates))
-  }
-  min_dists <- matrix(NA, nrow = nrow(dists), ncol = neighbors)
-  for(r in 1:nrow(dists))
-  {
-    min_dists[r, ] <- sort(dists[r, which(dists[r,]>0)])[1:neighbors]
-  }
+  knn <- FNN::get.knn(coordinates, neighbors)
 
-  mid_dists <- as.vector(min_dists)
+  knn <- as.vector(knn$nn.dist)
 
-  radius <- as.numeric(stats::quantile(min_dists, th.quantile)) # 99%
+  radius <- as.numeric(stats::quantile(knn, th.quantile)) # 90%
 
   return(radius)
 }
@@ -208,8 +194,7 @@ find_spatial_pairs <- function(nrm_X, coordinates, radius = "auto", neighbors =
   message("Computing spatial radius")
   if(radius == "auto")
   {
-    radius <- find_radius(coordinates, neighbors = neighbors,
-                          sample_size = 1000, th.quantile = th.quantile)
+    radius <- find_radius(coordinates, neighbors = neighbors, th.quantile = th.quantile)
     radius <- radius * 1.05
     # print(radius)
     gc()
@@ -282,9 +267,11 @@ get_pairs <- function(srt, th.spatial, th.nonspatial, max_size, radius = 'auto',
     srtp <- rbind(srtp, extra_pairs)
   }
 
-  if(nrow(srtp) == 0 & verbose)
+  if(nrow(srtp) == 0)
   {
-    message("No pairs satisfied the thresholds.")
+    if(verbose)
+      message("No pairs satisfied the thresholds.")
+
     srtp <- NULL
   }
 
@@ -366,14 +353,14 @@ draw_maps <- function(spatial, weights, normalize = TRUE, ncol = 4, trans = 'ide
     ggplot2::scale_color_viridis_c(legend_title, option = viridis_option, trans = trans)+
     ggplot2::xlab("") + ggplot2::ylab("")+
     ggplot2::theme(axis.text.x = ggplot2::element_blank(),
-          axis.ticks.x = ggplot2::element_blank(),
-          axis.text.y = ggplot2::element_blank(),
-          axis.ticks.y = ggplot2::element_blank(),
-          panel.grid.major = ggplot2::element_blank(),
-          panel.grid.minor = ggplot2::element_blank(),
-          plot.background = ggplot2::element_rect(fill = "transparent", color = NA),
-          strip.background = ggplot2::element_rect(fill = 'transparent'),
-          panel.background = ggplot2::element_rect(fill = background)
+                   axis.ticks.x = ggplot2::element_blank(),
+                   axis.text.y = ggplot2::element_blank(),
+                   axis.ticks.y = ggplot2::element_blank(),
+                   panel.grid.major = ggplot2::element_blank(),
+                   panel.grid.minor = ggplot2::element_blank(),
+                   plot.background = ggplot2::element_rect(fill = "transparent", color = NA),
+                   strip.background = ggplot2::element_rect(fill = 'transparent'),
+                   panel.background = ggplot2::element_rect(fill = background)
     )+
     ggplot2::facet_wrap(~celltype, ncol = ncol)
 

README.md

@@ -1,23 +1,23 @@
 # DOT: Flexible Feature Transfer to Spatial Omics <img src="man/figures/DOT_icon.png" align="right" height = "139">
 
 <!-- badges: start -->
-[![R-CMD-check](https://github.com/saezlab/DOT/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/saezlab/DOT/actions/workflows/R-CMD-check.yaml)
+  [![R-CMD-check](https://github.com/saezlab/DOT/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/saezlab/DOT/actions/workflows/R-CMD-check.yaml)
 [![GitHub issues](https://img.shields.io/github/issues/saezlab/DOT)](https://github.com/saezlab/DOT/issues)
 
 <!-- badges: end -->
-
-## Overview
-
-`DOT` is a method for transferring cell features from a reference single-cell RNA-seq data to spots/cells in spatial omics. It operates by optimizing a combination of multiple objectives using a Frank-Wolfe algorithm to produce a high quality transfer. Apart from transferring cell types/states to spatial omics, `DOT` can be used for transferring other relevant categorical or continuous features from one set of omics to another, such as estimating the expression of missinng genes or transferring transcription factor/pathway activities.
+  
+  ## Overview
+  
+  `DOT` is a method for transferring cell features from a reference single-cell RNA-seq data to spots/cells in spatial omics. It operates by optimizing a combination of multiple objectives using a Frank-Wolfe algorithm to produce a high quality transfer. Apart from transferring cell types/states to spatial omics, `DOT` can be used for transferring other relevant categorical or continuous features from one set of omics to another, such as estimating the expression of missinng genes or transferring transcription factor/pathway activities.
 
 
 <p align="center" width="100%">
-    <img src="man/figures/overview.png" align="center" width="65%">
-</p>
-
-For more information about how this package has been used with real data and expected outputs, please check the following link:
-
-- [DOT's general usage](https://saezlab.github.io/DOT/articles/general.html)
+  <img src="man/figures/overview.png" align="center" width="65%">
+    </p>
+    
+    For more information about how this package has been used with real data and expected outputs, please check the following link:
+    
+    - [DOT's general usage](https://saezlab.github.io/DOT/articles/general.html)
 
 ## Installation
 `DOT` is an R package which you can install from [GitHub](https://github.com/) with:
@@ -38,6 +38,6 @@ Installation takes less than five minutes. The sample dataset provided can be ru
 Operating system tested on: macOS Monterey 12.4
 
 ## Citation
-If you use **DOT** for your research please cite the [following preprint](https://arxiv.org/abs/2301.01682): 
+If you use **DOT** for your research please cite the [following article](https://doi.org/10.1038/s41467-024-48868-z): 
 
-> Rahimi, Arezou, Luis A. Vale-Silva, Maria Faelth Savitski, Jovan Tanevski, and Julio Saez-Rodriguez. "DOT: A flexible multi-objective optimization framework for transferring features across single-cell and spatial omics." arXiv preprint arXiv:2301.01682 (2023).
+> Rahimi, A., Vale-Silva, L.A., Fälth Savitski, M. et al. DOT: a flexible multi-objective optimization framework for transferring features across single-cell and spatial omics. Nat Commun 15, 4994 (2024). https://doi.org/10.1038/s41467-024-48868-z.