This vignette demonstrates how to use OME-TIFF files generated by the NanoString GeoMx® instrument to enhance the data visualization for a NanoString GeoMx experiment. SpatialOmicsOverlay was specifically made to visualize and analyze the free-handed nature of Region of Interest (ROI) selection in a GeoMx experiment, and the immunofluorescent-guided segmentation process. The overlay from the instrument is recreated in the R environment allowing for plotting overlays with data like ROI type or gene expression.
In this vignette, we will be walking through a mouse brain dataset from the Spatial Organ Atlas
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("GeomxTools")
BiocManager::install("SpatialOmicsOverlay")
library(SpatialOmicsOverlay)
library(GeomxTools)
Open Chrome and access your GeoMx workspace remotely:
In the GeoMx Scan Workspace, click the “Export Image” tab and export the image as an OME-TIFF. This will have to be done for each individual scan.
Files needed:
Reading in the SpatialOverlay object can be done with or without the image. We will start without the image as that can be added later.
If outline = TRUE
, only ROI outline points are saved.
This decreases memory needed and figure rendering time downstream. If
ANY ROIs are segmented in the study, outline will be FALSE. In this
particular example, there are segmented ROIs, so we set
outline = FALSE
.
In this example, we are downloading a TIFF image from AWS S3 but this variable is simply the file path to a OME-TIFF.
This function will be downloading a 13 GB file and will keep a 4 GB file in BiocFileCache. This download will take ~15 minutes but you will only have to download once
tifFile <- downloadMouseBrainImage()
tifFile
## [1] "/home/biocbuild/.cache/R/SpatialOmicsOverlay/1aeac61796ceb4_mu_brain_004.ome.tiff"
muBrainLW <- system.file("extdata", "muBrain_LabWorksheet.txt",
package = "SpatialOmicsOverlay")
muBrain <- readSpatialOverlay(ometiff = tifFile, annots = muBrainLW,
slideName = "D5761 (3)", image = FALSE,
saveFile = FALSE, outline = FALSE)
The readSpatialOverlay function is a wrapper to walk through all of
the necessary steps to store the OME-TIFF file components. This function
automates XML extraction & parsing, image extraction, and coordinate
generation. These functions can also be run separately if desired
(xmlExtraction
, parseScanMetadata
,
parseOverlayAttrs
, addImageOmeTiff
,
createCoordFile
).
If you are getting a
java.lang.OutOfMemoryError: Java heap space
when running
readSpatialOverlay()
, restart your R session and try
increasing the maximum heap size by supplying the -Xmx parameter before
the Java Virtual Machine is initialized used by a dependency package
RBioFormats. Run the code below before loading any other libraries. RBioFormats
source
options(java.parameters = "-Xmx4g")
library(RBioFormats)
SpatialOverlay objects hold data specific to the image and the ROIs. Here are a couple of functions to access the most important parts.
#full object
muBrain
## SpatialOverlay
## Slide Name: D5761 (3)
## Overlay Data: 93 samples
## Overlay Names: DSP-1012999073013-A-A02 ... DSP-1012999073013-A-H09 ( 93 total )
## Scan Metadata
## Panels: TAP Mouse Whole Transcriptome Atlas
## Segmentation: Segmented
## Outline: FALSE
#sample names
head(sampNames(muBrain))
## [1] "DSP-1012999073013-A-A02" "DSP-1012999073013-A-A03"
## [3] "DSP-1012999073013-A-A04" "DSP-1012999073013-A-A05"
## [5] "DSP-1012999073013-A-A06" "DSP-1012999073013-A-A07"
#slide name
slideName(muBrain)
## [1] "D5761 (3)"
#metadata of ROI overlays
#Height, Width, X, Y values are in pixels
head(meta(overlay(muBrain)))
ROILabel | Sample_ID | Height | Width | X | Y | Segmentation |
---|---|---|---|---|---|---|
001 | DSP-1012999073013-A-A02 | 751 | 751 | 10363 | 15008 | Geometric |
002 | DSP-1012999073013-A-A03 | 1239 | 782 | 14346 | 16630 | Geometric |
003 | DSP-1012999073013-A-A04 | 1272 | 902 | 15664 | 16603 | Geometric |
004 | DSP-1012999073013-A-A05 | 1119 | 1276 | 13707 | 16135 | Geometric |
005 | DSP-1012999073013-A-A06 | 1054 | 1124 | 15932 | 16228 | Geometric |
006 | DSP-1012999073013-A-A07 | 751 | 751 | 10756 | 14259 | Geometric |
#coordinates of each ROI
head(coords(muBrain))
sampleID | ycoor | xcoor |
---|---|---|
DSP-1012999073013-A-A02 | 15383 | 10363 |
DSP-1012999073013-A-A02 | 15356 | 10364 |
DSP-1012999073013-A-A02 | 15357 | 10364 |
DSP-1012999073013-A-A02 | 15358 | 10364 |
DSP-1012999073013-A-A02 | 15359 | 10364 |
DSP-1012999073013-A-A02 | 15360 | 10364 |
After parsing, ROIs can be plotted without the image in the object. These plots are the highest resolution versions since there is no scaling down to the image size, and might take a little time to render. If the image is attached to the object, coordinates are automatically scaled down to the image size and plotted as if they are on top of the image.
While manipulating the figure, there is a low-resolution option for faster rendering times.
A scale bar is automatically calculated when plotting. This
functionality can be turned off using scaleBar = FALSE
.
Scale bars can be fully customized using corner
,
textDistance
, and variables that start with scaleBar:
scaleBarWidth
, scaleBarColor
, etc.
plotSpatialOverlay(overlay = muBrain, hiRes = FALSE, legend = FALSE)
colorBy
, by default, is Sample_ID but almost any
annotation or data can be added instead, including gene expression,
tissue morphology annotations, pathway score, etc. These annotations can
come from a data.frame, matrix, GeomxSet object, or vector. Below we
attach the gene expression for CALM1 from a GeomxSet object and color
the segments by that value.
muBrainAnnots <- readLabWorksheet(lw = muBrainLW, slideName = "D5761 (3)")
muBrainGeomxSet <- readRDS(unzip(system.file("extdata", "muBrain_GxT.zip",
package = "SpatialOmicsOverlay")))
muBrain <- addPlottingFactor(overlay = muBrain, annots = muBrainAnnots,
plottingFactor = "segment")
muBrain <- addPlottingFactor(overlay = muBrain, annots = muBrainGeomxSet,
plottingFactor = "Calm1")
muBrain <- addPlottingFactor(overlay = muBrain, annots = 1:length(sampNames(muBrain)),
plottingFactor = "ROILabel")
muBrain
## SpatialOverlay
## Slide Name: D5761 (3)
## Overlay Data: 93 samples
## Overlay Names: DSP-1012999073013-A-A02 ... DSP-1012999073013-A-H09 ( 93 total )
## Scan Metadata
## Panels: TAP Mouse Whole Transcriptome Atlas
## Segmentation: Segmented
## Plotting Factors:
## varLabels: segment Calm1 ROILabel
## Outline: FALSE
head(plotFactors(muBrain))
segment | Calm1 | ROILabel | |
---|---|---|---|
DSP-1012999073013-A-A02 | Full ROI | 1177 | 1 |
DSP-1012999073013-A-A03 | Full ROI | 765 | 2 |
DSP-1012999073013-A-A04 | Full ROI | 1045 | 3 |
DSP-1012999073013-A-A05 | Full ROI | 1730 | 4 |
DSP-1012999073013-A-A06 | Full ROI | 1119 | 5 |
DSP-1012999073013-A-A07 | Full ROI | 600 | 6 |
All generated figures are ggplot based so they can be easily customized using functions from that or similar grammar of graphs packages. For example, we can change the color scale to the viridis color palette.
Note: hiRes and outline figures use fill, lowRes uses color
plotSpatialOverlay(overlay = muBrain, hiRes = FALSE, colorBy = "Calm1",
scaleBarWidth = 0.3, scaleBarColor = "green") +
viridis::scale_color_viridis()+
ggplot2::labs(title = "Calm1 Expression in Mouse Brain")
Images can be added automatically using
readSpatialOverlay(image = TRUE)
or added after reading in
the object.
An OME-TIFF file is a pyramidal file, meaning that many sizes of an image are saved. The largest having the highest resolution and decreasing as the image gets smaller. Images are 1/2 the size as the previous resolution.
The res
variable says which resolution of the image to
extract. 1 = largest image and the higher values get smaller. Each
OME-TIFF has a different number of layers, with most having around 8. It
is suggested to use the smallest res
value, and highest
resolution, your environment can handle. This is a trial and error
process.
Using too big of an image will cause a java memory error. If this
error occurs, increase your res
value. Below is an example
of the error you will receive if the resolution is too high for your
system.
Error in .jcall("RBioFormats", "Ljava/lang/Object;", "readPixels", i, :
java.lang.NegativeArraySizeException: -2147483648
The resolution size will affect speed and image resolution through
the rest of the analysis. To check the smallest resolution size
available, for the fastest speeds, use checkValidRes()
.
For the rest of this tutorial we will be using res = 8 for
vignette size restrictions, but res 4-6 is recommended.
#lowest resolution = fastest speeds
checkValidRes(ometiff = tifFile)
## [1] 8
res <- 8
muBrain <- addImageOmeTiff(overlay = muBrain, ometiff = tifFile, res = res)
muBrain
## SpatialOverlay
## Slide Name: D5761 (3)
## Overlay Data: 93 samples
## Overlay Names: DSP-1012999073013-A-A02 ... DSP-1012999073013-A-H09 ( 93 total )
## Scan Metadata
## Panels: TAP Mouse Whole Transcriptome Atlas
## Segmentation: Segmented
## Plotting Factors:
## varLabels: segment Calm1 ROILabel
## Outline: FALSE
## Image: /home/biocbuild/.cache/R/SpatialOmicsOverlay/1aeac61796ceb4_mu_brain_004.ome.tiff
showImage(muBrain)