Merge pull request #78 from JunxiangXu/main

some typo and format error for spat proteomics and co registration

02_session4.Rmd

@@ -70,16 +70,16 @@ list.files(paste0(download_dir,'/Lunaphore'))
 ```
 We provide a way to extract meta data information directly from ome.tiffs. Please note that different platforms may store the meta data such as channel information in a different format, we will probably need to change the node names of the ome-XML.
 ```{r eval=FALSE}
-img_path <- paste0(download_dir,"Lunaphore/Lunaphore_example.ome.tiff")
+img_path <- paste0(download_dir,"/Lunaphore/Lunaphore_example.ome.tiff")
 img_meta <- ometif_metadata(img_path, node = "Channel", output = "data.frame")
 img_meta
 ```
 
-However, sometimes a cropping could result in a loss of ome-XML information from the ome.tiff file. That way, we can use a different strategy to parse the xml information seperately and get channel information from it.  
+However, sometimes a simple ometiff file manipulation like cropping could result in a loss of ome-XML information from the ome.tiff file. That way, we can use a different strategy to parse the xml information seperately and get channel information from it.  
 ```{r eval=FALSE}
 ##Get channel information
-Luna = paste0(download_dir,"Lunaphore/Lunaphore_example.ome.tiff")
-xmldata = xml2::read_xml(paste0(download_dir,"Lunaphore/Lunaphore_sample_metadata.xml"))
+Luna = paste0(download_dir,"/Lunaphore/Lunaphore_example.ome.tiff")
+xmldata = xml2::read_xml(paste0(download_dir,"/Lunaphore/Lunaphore_sample_metadata.xml"))
 node <- xml2::xml_find_all(xmldata, "//d1:Channel", ns = xml2::xml_ns(xmldata))
 channel_df <- as.data.frame(Reduce("rbind", xml2::xml_attrs(node)))
 channel_df
@@ -159,6 +159,7 @@ knitr::include_graphics("img/02_session4/mesmer_overlap.png")
 ```
 
 ### Using Giotto wrapper of Cellpose to perform segmentation
+Here, we create a mini example by cropping the image to a smaller area. Note that crop()  is probably easier to use to directly crop image, unless cropping the image when the image is inside of a giotto object.
 ```{r eval = F}
 gimg_cropped <- cropGiottoLargeImage(giottoLargeImage = gimg_DAPI, crop_extent = terra::ext(zoom))
 writeGiottoLargeImage(gimg_cropped, filename = paste0(download_dir,'/Lunaphore/DAPI_forcellpose.tiff'),overwrite =  T)

03_session2.Rmd

@@ -1,6 +1,7 @@
 # Spatial multi-modal analysis
 
 George Chen
+
 Junxiang Xu
 
 August 7th 2024
@@ -227,13 +228,13 @@ Currently _giotto_ image objects are not fully compatible with .ome.tif files. _
 10X Genomics Released a comprehensive dataset on 2022. To capture spatial structure by complementing different spatial resolutions and modalities across different assays, they provided a dataset with Xenium in situ transcriptomics data, together with Visium on closely adjacent sections. Additional IF staining was also performed on the Xenium slides. For more information, please refer to the pre-release dataset [page](https://www.10xgenomics.com/products/xenium-in-situ/preview-dataset-human-breast) as well as the [publication](https://www.nature.com/articles/s41467-023-43458-x).
 
 - Visium 
-  -- H&E Histology
-  -- 55um spot level expression with transcriptome coverage
+  - H&E Histology
+  - 55um spot level expression with transcriptome coverage
 - Xenium
-  -- H&E Histology
-  -- IF image staining DAPI, HER2 and CD20
-  -- in situ transcripts
-  -- cooresponding centroid locations
+  - H&E Histology
+  - IF image staining DAPI, HER2 and CD20
+  - in situ transcripts
+  - cooresponding centroid locations
 
 The goal of creating this multi-modal dataset is to register all the modalities listed above to the same coordinate system as Xenium in situ transcripts as the coordinate represents a certain micron distance.
 ```{r eval=FALSE}
@@ -247,14 +248,14 @@ destfile <- file.path(download_dir,'Multimodal_registration.zip')
 if (!dir.exists(download_dir)) { dir.create(download_dir, recursive = TRUE) }
 download.file('https://zenodo.org/records/13208139/files/Multimodal_registration.zip?download=1', destfile = destfile)
 unzip(paste0(download_dir,'/Multimodal_registration.zip'), exdir = download_dir)
-Xenium_dir <- paste0(download_dir,'/Multimodal_registration/Xenium/')
-Visium_dir <- paste0(download_dir,'/Multimodal_registration/Visium/')
+Xenium_dir <- paste0(download_dir,'/Xenium/')
+Visium_dir <- paste0(download_dir,'/Visium/')
 ```
 
 ### Target Coordinate system
 Xenium transcripts, polygon information and corresponding centroids are output from the Xenium instrument and are in the same coordinate system from the raw output. We can start with checking the centroid information as a representation of the target coordinate system.
 ```{r eval=FALSE}
-xen_cell_df <- read.csv(paste0(Xenium_dir,"cells.csv.gz"))
+xen_cell_df <- read.csv(paste0(Xenium_dir,"/cells.csv.gz"))
 xen_cell_pl <- ggplot2::ggplot() + ggplot2::geom_point(data = xen_cell_df, ggplot2::aes(x = x_centroid , y = y_centroid),size = 1e-150,,color = 'orange') + ggplot2::theme_classic()
 xen_cell_pl
 ```
@@ -329,7 +330,7 @@ knitr::include_graphics("img/03_session2/Visium_resgister_check.png")
 #### Create Pseudo Visium dataset for comparison
 Giotto provides a way to create different shapes on certain locations, we can use that to create a pseudo-visium polygons to aggregate transcripts or image intensities. To do that, we will need the centroid locations, which can be get using getSpatialLocations(). and also the radius information to create circles. We know that Visium certer to center distance is 100um and spot diameter is 55um, thus we can estimate the radius from certer to center distance. And we can use a spatial network created by nearest neighbor = 2 to capture the distance.
 ```{r eval=FALSE}
-V_final <- createSpatialNetwork(V_final, k = 1,method = 'kNN')
+V_final <- createSpatialNetwork(V_final, k = 1)
 spat_network <- getSpatialNetwork(V_final,output = 'networkDT')
 spatPlot2D(V_final, 
            show_network = T,
@@ -358,7 +359,7 @@ knitr::include_graphics("img/03_session2/Visium_polygon.png")
 
 Create Xenium object with pseudo Visium polygon. To save run time, example shown here only have MS4A1 and ERBB2 genes to create Giotto points
 ```{r eval = FALSE}
-xen_transcripts <- data.table::fread(paste0(Xenium_dir,'Xen_2_genes.csv.gz'))
+xen_transcripts <- data.table::fread(paste0(Xenium_dir,'/Xen_2_genes.csv.gz'))
 gpoints <- createGiottoPoints(xen_transcripts)
 Xen_obj <-createGiottoObjectSubcellular(gpoints = list('rna' = gpoints),
                                         gpolygons = list('visium' = pseudo_visium_poly))
@@ -432,8 +433,8 @@ knitr::include_graphics("img/03_session2/Visium_spatfeatplot.png")
 ### Register post-Xenium H&E and IF image
 For Xenium instrument output, Giotto provide a convenience function to load the output from the Xenium ranger output. Note that 10X created the affine image alignment file by applying rotation, scale at (0,0) of the top left corner and translation last. Thus, it will look different than the affine matrix created from landmarks above. In this example, we used a 0.05X compressed ometiff and the alignment file is also create by first rescale at 20X, then apply the affine matrix provided by 10X Genomics.
 ```{r eval = F}
-HE_xen <- read10xAffineImage(file = paste0(Xenium_dir, "HE_ome_compressed.tiff"),
-                           imagealignment_path = paste0(Xenium_dir,"Xenium_he_imagealignment.csv"),
+HE_xen <- read10xAffineImage(file = paste0(Xenium_dir, "/HE_ome_compressed.tiff"),
+                           imagealignment_path = paste0(Xenium_dir,"/Xenium_he_imagealignment.csv"),
                            micron = 0.2125)
 plot(HE_xen)
 ```
@@ -479,7 +480,7 @@ Xen_obj <- addGiottoLargeImage(gobject = Xen_obj,
 
 Get the cell polygons, as Xenium and IF are both subcellular resolution
 ```{r eval=FALSE}
-cellpoly_dt <- data.table::fread(paste0(Xenium_dir,'cell_boundaries.csv.gz'))
+cellpoly_dt <- data.table::fread(paste0(Xenium_dir,'/cell_boundaries.csv.gz'))
 colnames(cellpoly_dt) <- c('poly_ID','x','y')
 cellpoly <- createGiottoPolygonsFromDfr(cellpoly_dt)
 Xen_obj <- addGiottoPolygons(Xen_obj,gpolygons = list('cell' = cellpoly))
@@ -595,7 +596,7 @@ Pin landmarks or use compounded affine transforms to register image usually prov
 
 Here, we provide an example of two compressed images to show the automatic alignment pipeline.
 ```{r eval = F}
-HE <- createGiottoLargeImage(paste0(Xenium_dir,'mini_HE.png'),negative_y = F)
+HE <- createGiottoLargeImage(paste0(Xenium_dir,'/mini_HE.png'),negative_y = F)
 plot(HE)
 ```
 
@@ -604,7 +605,7 @@ knitr::include_graphics("img/03_session2/mini_HE.png")
 ```
 
 ```{r eval = F}
-IF <- createGiottoLargeImage(paste0(Xenium_dir,'mini_IF.tif'),negative_y = F,flip_horizontal = T)
+IF <- createGiottoLargeImage(paste0(Xenium_dir,'/mini_IF.tif'),negative_y = F,flip_horizontal = T)
 terra::plotRGB(IF@raster_object,r=1, g=2, b=3,, stretch="lin")
 ```
 
@@ -614,11 +615,11 @@ knitr::include_graphics("img/03_session2/mini_IF.png")
 
 Now, we can use the automated transformation pipeline. Note that we will start with a path to the images, run the preprocessImageToMatrix() first to meet the requirement of estimateAutomatedImageRegistrationWithSIFT() function. The images will be preprocessed to gray scale. And for that purpose, we use the DAPI channel from the miniIF, and set invert = T for mini HE as HE image so that grayscle HE will have higher value for high intensity pixels. The function will output an estimation of the transform.
 ```{r eval = F}
-estimation <- estimateAutomatedImageRegistrationWithSIFT(x = preprocessImageToMatrix(paste0(Xenium_dir,'mini_IF.tif'),
+estimation <- estimateAutomatedImageRegistrationWithSIFT(x = preprocessImageToMatrix(paste0(Xenium_dir,'/mini_IF.tif'),
                                                                                      flip_horizontal = T,
                                                                                      use_single_channel = T,
                                                                                      single_channel_number = 3),
-                                                         y = preprocessImageToMatrix(paste0(Xenium_dir,'mini_HE.png'),
+                                                         y = preprocessImageToMatrix(paste0(Xenium_dir,'/mini_HE.png'),
                                                                                      invert = T),
                                                          plot_match = T,
                                                          max_ratio = 0.5,estimate_fun = 'Projective')
@@ -635,7 +636,6 @@ transformed <- affine(IF, mtx)
 To_see_overlay <- transformed@funs$realize_magick(size = 2e6)
 plot(HE)
 plot(To_see_overlay@raster_object[[2]], add=TRUE, alpha=0.5)
-SIFT_registration_overlay
 ```
 
 ```{r, echo=FALSE, out.width="80%", fig.align='center'}