merge

Merge branch 'suite' of https://github.com/drieslab/Giotto_website into suite # Conflicts: # vignettes/spatial_genes.Rmd
drieslab · Oct 25, 2024 · 2635240 · 2635240
2 parents f857d6c + d552eda
commit 2635240
Show file tree

Hide file tree

Showing 5 changed files with 157 additions and 36 deletions.
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -56,19 +56,22 @@ Style Guide. These guidelines are preferences and strongly encouraged!
     -   We follow the BioConductor styling. You can set this up easily
         by installing *biocthis* and *styler.*
 
-        ```{r, eval=FALSE}
-        # package installations
-        BiocManager::install("biocthis")
-        install.packages("styler")
 
-        # styling a file
-        b_style <- biocthis::bioc_style()
-        styler::style_file(path = "[???]", transformers = b_style)
+```{r, eval=FALSE}
+# package installations
+BiocManager::install("biocthis")
+install.packages("styler")
+
+# styling a file
+b_style <- biocthis::bioc_style()
+styler::style_file(path = "[???]", transformers = b_style)
+
+# styling the active package (may lead to lots of conflicts)
+# !! This should only be done be core devs with a lot of caution and forewarning !!
+styler::style_pkg(transformers = b_style)
+```
+
 
-        # styling the active package (may lead to lots of conflicts)
-        # !! This should only be done be core devs with a lot of caution and forewarning !!
-        styler::style_pkg(transformers = b_style)
-        ```
 
     -   setting your default indent size to be 4 spaces instead of 2 is
         also recommended.
@@ -333,7 +336,7 @@ vignette: >
 ---
 ```
 
-- 2. Absolutely no eval=TRUE for example code.
+- 2. Absolutely no `eval=TRUE` for example code.
 
 To save time when rendering the website, all chunks should not evaluate the code. Image results should be
 included via linking or a `knitr` chunk of this style:

diff --git a/vignettes/core_functions.Rmd b/vignettes/core_functions.Rmd
@@ -38,8 +38,9 @@ g <- setGiotto(g, data_list)
 ```
 
 
-# Spatial Aggregation
-For use with raw subcellular data. Find overlapped feature information and convert to a matrix
+# Spatial Aggregation (subcellular data only)
+For use with raw subcellular data that has spatial boundaries/polygon annotations and spatial features. Find overlapped feature information and convert to a matrix.
+Other examples can be found in the [object creation](object_creation.html) tutorial.
 ```{r, eval=FALSE}
 # overlap of feature points with polygons
 g <- calculateOverlap(g)
@@ -86,15 +87,15 @@ activeSpatUnit(g) <- "aggregate"
 activeFeatType(g) <- "rna"
 ```
 
-# Spatial Centroid Calculation
+# Spatial Centroid Calculation (polygon annotations only)
 
 ```{r, eval=FALSE}
 g <- addSpatialCentroidLocations(g, poly_info = "aggregate")
 ``` 
 
 # Standard workflow
 
-For more detail, see the standard workflow vignette
+For more detail, see the [standard workflow](general_workflow.html) vignette
 ```{r, eval=FALSE}
 g <- filterGiotto(g,
     expression_threshold = 1,
@@ -143,10 +144,44 @@ dim(g)
 # feature metadata
 fDataDT(g)
 
+# cell metadata
+pDataDT(g)
+
 # get specific values from cell metadata
 g$leiden_clus
 
-# cell metadata
+# set values directly into cell metadata
+# this is safest when directly using values from another metadata column
+# since cell_IDs and their ordering may not as expected.
+g$leiden_clus2 <- sprintf("clus_%s", g$leiden_clus)
+force(g$leiden_clus2)
+
+# annotate based on another column
+g <- annotateGiotto(g, 
+    name = "region",
+    cluster_column = "leiden_clus",
+    annotation_vector = c(
+        "1" = "a",
+        "2" = "b",
+        "3" = "c",
+        "4" = "a",
+        "5" = "d",
+        "6" = "b"
+    )
+)
+spatPlot2D(g, cell_color = "region")
+
+# merge in table of values to metadata
+ann <- data.frame(
+    cell_ID = spatIDs(g),
+    random = rnorm(ncol(g))
+)
+force(ann)
+g <- addCellMetadata(g,
+    new_metadata = ann,
+    by_column = TRUE,
+    column_cell_ID = "cell_ID"
+)
 pDataDT(g)
 ```
 
@@ -262,6 +297,9 @@ joinGiottoObjects(list(g, g), gobject_names = c("a", "b"), dry_run = TRUE)
 j <- joinGiottoObjects(list(g, g), gobject_names = c("a", "b"))
 ```
 
+For a more information, see the object [splitting and joining](split_join.html) tutorial
+
+
 ## Splitting objects
 
 ```{r, eval=FALSE}

diff --git a/vignettes/dimension_reduction.Rmd b/vignettes/dimension_reduction.Rmd
@@ -59,7 +59,7 @@ These methods are typically used early in the analysis pipeline after filtering,
 
 **Features to use**
 
-Which features to include when calculating these dimension reductions has a large effect on the information extracted. Highly variable features will focus on variation from features with the largest expression variation. Spatially variable features will focus on features with spatially organized expression. However, when there are not many features (only hundreds of features), it is a better idea to include all features than to use a subset.
+Which features to include when calculating these dimension reductions has a large effect on the information extracted. Only using [highly variable features](hvf.html) will focus on variation from features with the largest expression variation, maximizing expression-based separation of clusters. [Spatially variable features](spatial_genes.html) will focus on features with spatially organized expression, resulting in clusters that better map to spatial organization. However, when there are not many features (only hundreds of features), it is a better idea to include all features than to use a subset.
 
 **Centering and Scaling**
 

diff --git a/vignettes/networks.Rmd b/vignettes/networks.Rmd
@@ -178,6 +178,82 @@ dimPlot2D(mer, show_NN_network = TRUE, nn_network_to_use = "kNN", network_name =
 knitr::include_graphics("images/networks/5_kNN_dim.png")
 ```
 
+
+# API function
+
+`createNetwork()` exported from *GiottoClass* allows creation of any of the above network types, starting from a `matrix` of nodes. These nodes can be n-dimensional expression information, dimension reduction, or spatial coordinates. The output is either `igraph` if `as.igraph = TRUE` and `data.table` otherwise.
+
+```{r, eval=FALSE}
+topo <- expand.grid(x = 1:nrow(volcano),
+                    y = 1:ncol(volcano))
+topo$z <- c(volcano)
+topo <- as.matrix(topo)
+
+createNetwork(topo, type = "kNN", k = 8) # returns as igraph
+```
+
+```
+IGRAPH 64720af DNW- 5307 42456 -- 
++ attr: name (v/c), weight (e/n), distance (e/n)
++ edges from 64720af (vertex names):
+ [1] 1 ->88  2 ->89  3 ->90  4 ->91  5 ->92  6 ->7   7 ->6   8 ->94  9 ->95  10->97  11->97 
+[12] 12->99  13->12  14->13  15->102 16->102 17->103 18->19  19->18  20->21  21->22  22->21 
+[23] 23->24  24->25  25->26  26->25  27->115 28->29  29->28  30->31  31->30  32->31  33->120
+[34] 34->121 35->36  36->124 37->38  38->37  39->40  40->41  41->40  42->43  43->42  44->45 
+[45] 45->46  46->45  47->134 48->49  49->48  50->51  51->52  52->51  53->52  54->141 55->56 
+[56] 56->57  57->56  58->59  59->60  60->59  61->62  62->63  63->62  64->65  65->64  66->67 
+[67] 67->66  68->69  69->68  70->69  71->158 72->159 73->160 74->161 75->162 76->163 77->165
+[78] 78->79  79->78  80->81  81->80  82->83  83->170 84->85  85->172 86->87  87->86  88->1  
++ ... omitted several edges
+```
+
+```{r, eval=FALSE}
+createNetwork(topo, type = "kNN", k = 8, as.igraph = FALSE) # return as data.table
+```
+
+```
+        from    to    weight distance
+       <int> <int>     <num>    <num>
+    1:     1    88 0.5000000 1.000000
+    2:     2    89 0.5000000 1.000000
+    3:     3    90 0.5000000 1.000000
+    4:     4    91 0.5000000 1.000000
+    5:     5    92 0.5000000 1.000000
+   ---                               
+42452:  5303  5130 0.3090170 2.236068
+42453:  5304  5306 0.3333333 2.000000
+42454:  5305  5303 0.3333333 2.000000
+42455:  5306  5131 0.3090170 2.236068
+42456:  5307  5131 0.2612039 2.828427
+```
+
+```{r, eval=FALSE}
+# default weight for kNN was 1 / (d + 1)
+# use a custom weight function
+createNetwork(topo,
+    type = "kNN", 
+    k = 8, 
+    as.igraph = FALSE,
+    weight_fun = function(d) 1 / (d)^2
+)
+```
+
+```
+        from    to weight distance
+       <int> <int>  <num>    <num>
+    1:     1    88  1.000 1.000000
+    2:     2    89  1.000 1.000000
+    3:     3    90  1.000 1.000000
+    4:     4    91  1.000 1.000000
+    5:     5    92  1.000 1.000000
+   ---                            
+42452:  5303  5130  0.200 2.236068
+42453:  5304  5306  0.250 2.000000
+42454:  5305  5303  0.250 2.000000
+42455:  5306  5131  0.200 2.236068
+42456:  5307  5131  0.125 2.828427
+```
+
 # Session Info
 ```{r, eval=FALSE}
 sessionInfo()

diff --git a/vignettes/spatial_genes.Rmd b/vignettes/spatial_genes.Rmd
@@ -46,16 +46,18 @@ g <- GiottoData::loadGiottoMini("visium")
 # Create a spatial network
 
 ```{r, eval=FALSE}
-g <- createSpatialNetwork(gobject = g,
-                          method = "kNN", 
-                          k = 6,
-                          maximum_distance_knn = 400,
-                          name = "spatial_network")
-
-spatPlot2D(gobject = g,  
-           show_network= TRUE,
-           network_color = "blue", 
-           spatial_network_name = "spatial_network")
+g <- createSpatialNetwork(g,
+    method = "kNN", 
+    k = 6,
+    maximum_distance_knn = 400,
+    name = "spatial_network"
+)
+
+spatPlot2D(g,  
+    show_network = TRUE,
+    network_color = "blue", 
+    spatial_network_name = "spatial_network"
+)
 ```
 
 ```{r, echo=FALSE, out.width="60%", fig.align='center'}
@@ -70,10 +72,11 @@ This step may take a few minutes to run.
 
 ```{r, eval=FALSE}
 ranktest <- binSpect(g, 
-                     bin_method = "rank",
-                     calc_hub = TRUE, 
-                     hub_min_int = 5,
-                     spatial_network_name = "spatial_network")
+    bin_method = "rank",
+    calc_hub = TRUE, 
+    hub_min_int = 5,
+    spatial_network_name = "spatial_network"
+)
 ```
 
 ## Visualize top results
@@ -82,10 +85,11 @@ Plot the scaled expression of genes with the highest probability of being spatia
 
 ```{r, eval=FALSE}
 spatFeatPlot2D(g, 
-               expression_values = "scaled",
-               feats = ranktest$feats[1:6], 
-               cow_n_col = 2, 
-               point_size = 2)
+    expression_values = "scaled",
+    feats = ranktest$feats[1:6], 
+    cow_n_col = 2, 
+    point_size = 2
+)
 ```
 
 ```{r, echo=FALSE, out.width="80%", fig.align='center'}