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JEFworks-Lab · Jan 30, 2025 · 5e2cb2e · 5e2cb2e
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diff --git a/README.md b/README.md
@@ -1,17 +1,13 @@
-# Cell-type Relationship Analysis Workflow Done Across Distances
+## Cell-type Relationship Analysis Workflow Done Across Distances
 
 `CRAWDAD` enables the characterization of spatial cell-type relationships across different length scales.
 
 ## Overview
 
 `CRAWDAD` is a statistical framework that uses cell-type labeled spatial omics data to identify the colocalization or separation of cell types at different length scales. CRAWDAD identifies the spatial relationship of cell types in the tissue and the length scale in which they reach significance. It identifies groups of cells with similar spatial relationships patterns. Also, CRAWDAD subsets the cell types based on their proximity to others. Lastly, CRAWDAD compares multiple tissue samples using their cell-type spatial relationship patterns. Therefore, CRAWDAD is a powerful tool for tissue characterization and comparison.
 
-\
 <img src="https://github.com/JEFworks/CRAWDAD/blob/main/docs/img/overview_github.png?raw=true"/>
 
-\
-More information can be found in our publication: [**Characterizing cell-type spatial relationships across length scales in spatially resolved omics data.** Rafael dos Santos Peixoto, Brendan F. Miller, Maigan A. Brusko, Gohta Aihara, Lyla Atta, Manjari Anant, Mark A. Atkinson, Todd M. Brusko, Clive H. Wasserfall, Jean Fan.](https://doi.org/10.1038/s41467-024-55700-1)
-
 ## Installation
 
 To install `CRAWDAD`, we recommend using `remotes`:
@@ -21,55 +17,222 @@ require(remotes)
 remotes::install_github('JEFworks-Lab/CRAWDAD')
 ```
 
-## Example
-
-
+## Visualization of cells in spleen pkhl and ksfb samples
 
 ``` r
+
 library(crawdad)
 library(tidyverse)
-## load the spleen data of the pkhl sample 
+library(dplyr)
+library(ggplot2)
+## Load the spleen data of the pkhl sample 
 data('pkhl')
-## convert dataframe to spatial points (SP)
-cells <- crawdad::toSF(pos = pkhl[,c("x", "y")], cellTypes = pkhl$celltypes)
-## define the scales to analyze the data
-scales <- c(100, 200, 300, 400, 500, 600, 700, 800, 900, 1000)
-## shuffle cells to create null background
-shuffle_list <- crawdad:::makeShuffledCells(cells,
-                                            scales = scales,
-                                            perms = 3,
-                                            ncores = 7,
-                                            seed = 1,
-                                            verbose = TRUE)
-## calculate the zscore for the cell-type pairs at different scales
-results <- crawdad::findTrends(cells,
-                               neighDist = 50,
-                               shuffleList = shuffle_list,
-                               ncores = 7,
-                               verbose = TRUE,
-                               returnMeans = FALSE)
-dat <- crawdad::meltResultsList(results, withPerms = TRUE)
-## calculate the zscore for the multiple-test correction
-zsig <- correctZBonferroni(dat)
-## summary visualization
-vizColocDotplot(dat, zSigThresh = zsig, zScoreLimit = 2*zsig, 
+## Load the spleen data of the ksfb sample 
+data('ksfb')
+
+
+all_celltypes <- unique(c(pkhl$celltype, ksfb$celltype))
+colors <- rainbow(length(all_celltypes))
+names(colors) <- all_celltypes
+
+## Plot 1: pkhl dataset
+p1 <- pkhl %>% 
+  ggplot(aes(pkhl$x, pkhl$y, color = pkhl$celltype)) +
+  geom_point(size = .01) +
+  scale_color_manual(values = colors) +
+  guides(colour = guide_legend(override.aes = list(size = 1))) + 
+  labs(title = 'pkhl') + 
+  theme_minimal()
+
+## Plot 2: ksfb dataset
+p2 <- ksfb %>% 
+  ggplot(aes(ksfb$x, ksfb$y, color = ksfb$celltype)) +
+  geom_point(size = .01) +
+  scale_color_manual(values = colors) +
+  guides(colour = guide_legend(override.aes = list(size = 1))) + 
+  labs(title = 'ksfb') + 
+  theme_minimal()
+
+options(repr.plot.width = 20, repr.plot.height = 9)
+grid.arrange(p1, p2, nrow = 1)
+```
+<img src="https://github.com/adinajailova/CRAWDAD/blob/main/docs/img/visualization.png?raw=true"/>
+
+## Determine neighboring distance
+
+For choosing neighboring distance for CRAWDAD analysis, please refer to the [CRAWDAD documentation](https://github.com/JEFworks-Lab/CRAWDAD/blob/main/docs/choosing_neighDist.md).
+
+## Individual sample analysis
+
+```r
+## pkhl sample
+
+## Define the scales to analyze the data
+scales1 <- c(100, 200, 300, 400, 500, 600, 700, 800, 900, 1000)
+## Shuffle cells to create null background
+shuffle_list1 <- crawdad:::makeShuffledCells(cells1,
+                                             scales = scales1,
+                                             perms = 3,
+                                             ncores = 11,
+                                             seed = 1,
+                                             verbose = TRUE)
+## Calculate the z-score for the cell-type pairs at different scales
+results1 <- crawdad::findTrends(cells1,
+                                neighDist = 50,
+                                shuffleList = shuffle_list1,
+                                ncores = 7,
+                                verbose = TRUE,
+                                returnMeans = FALSE)
+dat_pkhl <- crawdad::meltResultsList(results1, withPerms = TRUE)
+
+## Calculate the z-score for the multiple-test correction
+zsig1 <- correctZBonferroni(dat_pkhl)
+## Summary visualization
+vizColocDotplot(dat_pkhl, zSigThresh = zsig1, zScoreLimit = 2*zsig1, 
+                dotSizes = c(3,15)) +
+  theme(axis.text.x = element_text(angle = 35, h = 0))
+
+## ksfb sample
+
+## Define the scales to analyze the data
+scales2 <- c(100, 200, 300, 400, 500, 600, 700, 800, 900, 1000)
+## Shuffle cells to create null background
+shuffle_list2 <- crawdad:::makeShuffledCells(cells2,
+                                             scales = scales2,
+                                             perms = 3,
+                                             ncores = 7,
+                                             seed = 1,
+                                             verbose = TRUE)
+## Calculate the z-score for the cell-type pairs at different scales
+results2 <- crawdad::findTrends(cells2,
+                                neighDist = 50,
+                                shuffleList = shuffle_list2,
+                                ncores = 7,
+                                verbose = TRUE,
+                                returnMeans = FALSE)
+dat_ksfb <- crawdad::meltResultsList(results2, withPerms = TRUE)
+
+## Calculate the z-score for the multiple-test correction
+zsig2 <- correctZBonferroni(dat_ksfb)
+## Summary visualization
+
+vizColocDotplot(dat_ksfb, zSigThresh = zsig2, zScoreLimit = 2*zsig2, 
                 dotSizes = c(3,15)) +
   theme(axis.text.x = element_text(angle = 35, h = 0))
 ```
 
-<img src="https://github.com/JEFworks/CRAWDAD/blob/main/docs/img/coloc.png?raw=true" height="510"/>
+<img src="https://github.com/adinajailova/CRAWDAD/blob/main/docs/img/CRAWDAD.png?raw=true"/>
+
+## Multiple sample analysis
+```r
+## Add id to datasets
+dat_pkhl$id <- 'pkhl'
+dat_ksfb$id <- 'ksfb'
+## Calculate auc
+auc_samples <- calculateAUC(list(dat_pkhl, dat_ksfb))
+
+options(repr.plot.width = 10, repr.plot.height = 9)
+vizVarianceSamples(auc_samples)
+```
+<img src="https://github.com/adinajailova/CRAWDAD/blob/main/docs/img/variance.png?raw=true" height="510"/>
+
+## Visualize trends for one cell-type pair
+```r
+reference_var <- 'Ki67 proliferating'
+neighbor_var <- 'Fol B cells'
+
+d <- dplyr::bind_rows(list(dat_ksfb, dat_pkhl))
+d %>%
+  filter(reference == reference_var) %>%
+  filter(neighbor == neighbor_var) %>%
+  vizTrends(lines = TRUE, withPerms = TRUE, zSigThresh = zsig1)
 
-``` r
-## visualize trend for one cell-type pair
-dat %>% 
-  mutate(id = 'pkhl') %>% 
-  filter(reference == 'Podoplanin') %>% 
-  filter(neighbor == 'CD4 Memory T cells') %>% 
-  vizTrends(lines = TRUE, withPerms = TRUE, zSigThresh = zsig)
 ```
+<img src="https://github.com/adinajailova/CRAWDAD/blob/main/docs/img/Ki67 proliferating-Fol B cells_relationships_plot.png?raw=true" height="510"/>
 
-<img src="https://github.com/JEFworks/CRAWDAD/blob/main/docs/img/trend.png?raw=true" height="325"/>
+
+```r
 
+reference_var <- 'B cells, red pulp'
+neighbor_var <- 'Fol B cells'
+
+d <- dplyr::bind_rows(list(dat_ksfb, dat_pkhl))
+d %>%
+  filter(reference == reference_var) %>%
+  filter(neighbor == neighbor_var) %>%
+  vizTrends(lines = TRUE, withPerms = TRUE, zSigThresh = zsig1)
+
+```
+
+<img src="https://github.com/adinajailova/CRAWDAD/blob/main/docs/img/B cells, red pulp-Fol B cells_relationships_plot.png?raw=true" height="510"/>
+
+
+## Visualize cell-type relationships
+```r
+plot_celltypes <- function(celltypes) {
+  options(repr.plot.width = 18, repr.plot.height = 9)
+
+  ## Combine unique cell types from both datasets
+  all_celltypes <- unique(c(pkhl$celltypes, ksfb$celltypes))
+
+  ## Define consistent colors for all datasets
+  possible_colors <- c('darkblue', 'red', 'cyan', 'yellow', 'maroon')
+  colors <- setNames(
+    rep('#F0F0F0', length(all_celltypes)),
+    all_celltypes
+  )
+  for (i in seq_along(celltypes)) {
+    if (i <= length(possible_colors)) {
+      colors[celltypes[i]] <- possible_colors[i]
+    }
+  }
+
+  ## Plot for pkhl
+  p1 <- pkhl %>%
+    mutate(celltype_highlight = celltypes %in% celltypes) %>%
+    arrange(celltype_highlight, celltypes) %>%
+    ggplot() +
+    geom_point(aes(x, y, color = celltypes), size = .01) +
+    scale_color_manual(values = colors) +
+    guides(colour = guide_legend(override.aes = list(size = 1))) + 
+    labs(title = 'pkhl') + 
+    theme_minimal()
+
+  ## Plot for ksfb
+  p2 <- ksfb %>%
+    mutate(celltype_highlight = celltypes %in% celltypes) %>%
+    arrange(celltype_highlight, celltypes) %>%
+    ggplot() +
+    geom_point(aes(x, y, color = celltypes), size = .01) +
+    scale_color_manual(values = colors) +
+    guides(colour = guide_legend(override.aes = list(size = 1))) + 
+    labs(title = 'ksfb') + 
+    theme_minimal()
+
+  ## Arrange plots
+  grid.arrange(p1, p2, nrow = 1)
+}
+
+## Example usage
+reference_cell <- 'Ki67 proliferating'
+neighbor_cell <- 'Fol B cells'
+
+plot_celltypes(c(reference_cell, neighbor_cell))
+```
+<img src="https://github.com/adinajailova/CRAWDAD/blob/main/docs/img/Ki67 proliferating_Fol B cells_plot.png?raw=true"/>
+
+
+
+```r
+reference_cell <- 'B cells, red pulp'
+neighbor_cell <- 'Fol B cells'
+
+plot_celltypes(c(reference_cell, neighbor_cell))
+```
+
+<img src="https://github.com/adinajailova//CRAWDAD/blob/main/docs/img/B cells, red pulp_Fol B cells_plot.png?raw=true"/>
+
+
 More details can be found in the tutorials.
 
 ## Tutorials
@@ -82,3 +245,4 @@ More details can be found in the tutorials.
 
 CRAWDAD was tested on R version 4.3.0 (2023-04-21) -- "Already Tomorrow".
 Installation time is 1 minute in a Mac Pro Computer.
+
diff --git a/docs/img/B cells, red pulp-Fol B cells_relationships_plot.png b/docs/img/B cells, red pulp-Fol B cells_relationships_plot.png
diff --git a/docs/img/B cells, red pulp_Fol B cells_plot.png b/docs/img/B cells, red pulp_Fol B cells_plot.png
diff --git a/docs/img/CRAWDAD.png b/docs/img/CRAWDAD.png
diff --git a/docs/img/Ki67 proliferating-Fol B cells_relationships_plot.png b/docs/img/Ki67 proliferating-Fol B cells_relationships_plot.png
diff --git a/docs/img/Ki67 proliferating_Fol B cells_plot.png b/docs/img/Ki67 proliferating_Fol B cells_plot.png
diff --git a/docs/img/variance.png b/docs/img/variance.png
diff --git a/docs/img/visualization.png b/docs/img/visualization.png