update object_creation tutorial

vignettes/object_creation.Rmd

@@ -35,7 +35,9 @@ results_directory = paste0(getwd(),'/gobject_results/')
 # alternatively, "/path/to/store/the/results/"
 
 # Download osmFISH dataset to data_directory
-getSpatialDataset(dataset = 'osmfish_SS_cortex', directory = data_directory, method = 'wget')
+getSpatialDataset(dataset = 'osmfish_SS_cortex', 
+                  directory = data_directory, 
+                  method = 'wget')
 ```
 
 
@@ -53,22 +55,19 @@ Here, creating a `giotto` object with the minimum requirements is shown in two e
 osm_exprs = paste0(data_directory, "osmFISH_prep_expression.txt")
 osm_locs = paste0(data_directory, "osmFISH_prep_cell_coordinates.txt")
 
-minimum_gobject1 = createGiottoObject(expression = osm_exprs,
-                                      spatial_locs = osm_locs)
+minimum_gobject1 <- createGiottoObject(expression = osm_exprs,
+                                       spatial_locs = osm_locs)
 
 # Example 2.
 # Create a Giotto object using objects already loaded into workspace
 expression_matrix = readExprMatrix(path = osm_exprs) # fast method to read expression matrix
 cell_locations = data.table::fread(file = osm_locs)
 
-minimum_gobject2 = createGiottoObject(expression = expression_matrix,
-                                      spatial_locs = cell_locations)
+minimum_gobject2 <- createGiottoObject(expression = expression_matrix,
+                                       spatial_locs = cell_locations)
 ```
 
-
-
-Expression file formatting
-
+**Expression file formatting**
 
 Expression count matrices accepted by *Giotto* should have columns correlating to individual cells (**cell IDs**) and rows to individual features (**feature IDs**).
 
@@ -78,9 +77,7 @@ Expression count matrices accepted by *Giotto* should have columns correlating t
 already incorporated as the rownames.
 
 
-
-Locations file formatting
-
+**Locations file formatting**
 
 Numerical columns will be interpreted in order of x, y, and (optionally) z coordinate. The first non-numerical column will be taken as **cell IDs**
 
@@ -96,10 +93,10 @@ custom_matrix = expression_matrix * 0.5
 # Provide multiple expression matrices at once to the Giotto Object
 # If these matrices are stored in files rather than in the workspace,
 # file paths may be provided instead of variables
-multi_expr_gobject = createGiottoObject(expression = list(raw = expression_matrix,
-                                                          scaled = scaled_matrix,
-                                                          custom = custom_matrix),
-                                        spatial_locs = cell_locations)
+multi_expr_gobject <- createGiottoObject(expression = list(raw = expression_matrix,
+                                                           scaled = scaled_matrix,
+                                                           custom = custom_matrix),
+                                         spatial_locs = cell_locations)
 ```
 
 
@@ -150,19 +147,20 @@ instrs = createGiottoInstructions(python_path = my_python_path,
                                   width = 9)
 
 # Create Giotto object
-custom_gobject = createGiottoObject(expression = osm_exprs,
-                                    spatial_locs = osm_locs,                                      
-                                    instructions = instrs)
+custom_gobject <- createGiottoObject(expression = osm_exprs,
+                                     spatial_locs = osm_locs,
+                                     instructions = instrs)
 
 # Add field annotations as cell metadata
 metadata = data.table::fread(file = meta_path)
-custom_gobject = addCellMetadata(custom_gobject, 
-                                 new_metadata = metadata,
-                                 by_column = T, 
-                                 column_cell_ID = 'CellID')
+
+custom_gobject <- addCellMetadata(custom_gobject, 
+                                  new_metadata = metadata,
+                                  by_column = T, 
+                                  column_cell_ID = 'CellID')
 
 # Show the Giotto instructions associated with the Giotto object
-instructions(custom_gobject)
+showGiottoInstructions(custom_gobject)
 ```
 
 
@@ -175,14 +173,17 @@ The `giottoInstructions` may be changed, or completely replaced:
 
 ```{r, eval=FALSE}
 # Change a specific previously set parameter, e.g. change dpi = 200 to dpi = 300
-instructions(custom_gobject, 'dpi') = 300
+custom_gobject <- changeGiottoInstructions(custom_gobject, 
+                                           params = 'dpi',
+                                           new_values = 300)
 
 # Observe that the instructions have changed
 instructions(custom_gobject, 'dpi')
 
 # Create new instructions using a named list
 sub_results_directory = paste0(results_directory, 'specific_results/')
 my_python_path = instructions(custom_gobject, 'python_path')
+
 new_instrs = list(python_path = my_python_path,
                   show_plot = TRUE,
                   return_plot = FALSE,
@@ -199,7 +200,7 @@ new_instrs = list(python_path = my_python_path,
 instructions(custom_gobject) = new_instrs
 
 # Observe that the instructions have changed
-instructions(custom_gobject)
+showGiottoInstructions(custom_gobject)
 ```
 
 
@@ -229,7 +230,11 @@ Options` tutorial for alternative methods to save plots.
 ```{r, eval=FALSE}
 # Plot according to Giotto Instructions (default)
 spatPlot(custom_gobject)
+```
+
+![](images/object_creation/0-spatPlot2D.jpg)
 
+```{r, eval=FALSE}
 # Plot clusters, create, and save to a new subdirectory, all while overwriting formatting
 spatPlot(custom_gobject, 
          cell_color = 'ClusterName', 
@@ -244,37 +249,65 @@ spatPlot(custom_gobject,
                            base_width = 9))
 ```
 
-
 ![](images/object_creation/cell_clusters.png)
 
 For a more in-depth look at the `giotto` object structure, take a look at the [introduction to giotto classes](./classes_intro.html)
 
 ## 2. Session Info
 
-
 ```{r, eval=FALSE}
 sessionInfo()
 ```
 
 ```{r, eval=FALSE}
-    R version 4.2.1 (2022-06-23)
-    Platform: x86_64-apple-darwin17.0 (64-bit)
-    Running under: macOS Big Sur ... 10.16
-
-    Matrix products: default
-    BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
-    LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
-
-    locale:
-    [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
-
-    attached base packages:
-    [1] stats     graphics  grDevices utils     datasets  methods   base     
-
-    loaded via a namespace (and not attached):
-     [1] compiler_4.2.1  fastmap_1.1.1   cli_3.6.1       tools_4.2.1    
-     [5] htmltools_0.5.5 rstudioapi_0.14 yaml_2.3.7      rmarkdown_2.21 
-     [9] knitr_1.42      xfun_0.39       digest_0.6.31   jsonlite_1.8.4 
-    [13] rlang_1.1.1     evaluate_0.21  
-
+R version 4.3.2 (2023-10-31)
+Platform: x86_64-apple-darwin20 (64-bit)
+Running under: macOS Sonoma 14.3.1
+
+Matrix products: default
+BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
+LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
+
+locale:
+[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
+
+time zone: America/New_York
+tzcode source: internal
+
+attached base packages:
+[1] stats     graphics  grDevices utils     datasets  methods   base     
+
+other attached packages:
+[1] GiottoData_0.2.7.0 GiottoUtils_0.1.5  Giotto_4.0.2       GiottoClass_0.1.3 
+
+loaded via a namespace (and not attached):
+ [1] tidyselect_1.2.0            farver_2.1.1                dplyr_1.1.4                
+ [4] GiottoVisuals_0.1.4         bitops_1.0-7                fastmap_1.1.1              
+ [7] SingleCellExperiment_1.24.0 RCurl_1.98-1.14             digest_0.6.34              
+[10] lifecycle_1.0.4             processx_3.8.3              terra_1.7-71               
+[13] magrittr_2.0.3              compiler_4.3.2              rlang_1.1.3                
+[16] tools_4.3.2                 utf8_1.2.4                  yaml_2.3.8                 
+[19] data.table_1.15.0           knitr_1.45                  S4Arrays_1.2.0             
+[22] labeling_0.4.3              reticulate_1.35.0           DelayedArray_0.28.0        
+[25] RColorBrewer_1.1-3          abind_1.4-5                 withr_3.0.0                
+[28] BiocGenerics_0.48.1         grid_4.3.2                  stats4_4.3.2               
+[31] fansi_1.0.6                 colorspace_2.1-0            ggplot2_3.4.4              
+[34] scales_1.3.0                gtools_3.9.5                SummarizedExperiment_1.32.0
+[37] cli_3.6.2                   rmarkdown_2.25              crayon_1.5.2               
+[40] ragg_1.2.7                  generics_0.1.3              remotes_2.4.2.1            
+[43] rstudioapi_0.15.0           rjson_0.2.21                zlibbioc_1.48.0            
+[46] parallel_4.3.2              XVector_0.42.0              matrixStats_1.2.0          
+[49] vctrs_0.6.5                 Matrix_1.6-5                jsonlite_1.8.8             
+[52] callr_3.7.3                 IRanges_2.36.0              S4Vectors_0.40.2           
+[55] systemfonts_1.0.5           magick_2.8.2                pak_0.7.1                  
+[58] glue_1.7.0                  codetools_0.2-19            ps_1.7.6                   
+[61] cowplot_1.1.3               gtable_0.3.4                GenomeInfoDb_1.38.6        
+[64] GenomicRanges_1.54.1        munsell_0.5.0               tibble_3.2.1               
+[67] pillar_1.9.0                htmltools_0.5.7             GenomeInfoDbData_1.2.11    
+[70] R6_2.5.1                    textshaping_0.3.7           evaluate_0.23              
+[73] lattice_0.22-5              Biobase_2.62.0              png_0.1-8                  
+[76] backports_1.4.1             SpatialExperiment_1.12.0    Rcpp_1.0.12                
+[79] SparseArray_1.2.4           checkmate_2.3.1             colorRamp2_0.1.0           
+[82] xfun_0.42                   MatrixGenerics_1.14.0       pkgconfig_2.0.3
 ```
+