### Install SpatialExperiment from Bioconductor

Source: https://github.com/drighelli/spatialexperiment/blob/devel/README.md

Install the release version of the SpatialExperiment package using BiocManager. This is the recommended installation method for most users.

```R
install.packages("BiocManager")
BiocManager::install("SpatialExperiment")
```

--------------------------------

### Install SpatialExperiment from GitHub

Source: https://github.com/drighelli/spatialexperiment/blob/devel/README.md

Install the latest development version of the SpatialExperiment package directly from GitHub. This may require manual installation of some dependency packages.

```R
remotes::install_github("drighelli/SpatialExperiment")
```

--------------------------------

### Constructing a SpatialExperiment Object

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

Demonstrates creating a SpatialExperiment object using spatial coordinates from colData, a separate matrix, or by integrating full image data from 10x Genomics Visium files.

```r
library(SpatialExperiment)

# Method 1: Provide spatial coordinates via colData column names
n <- 26
y <- matrix(nrow = n, ncol = n)
cd <- DataFrame(x = seq(n), y = seq(n), sample_id = "sample01")

spe <- SpatialExperiment(
    assays = list(counts = y),
    colData = cd,
    spatialCoordsNames = c("x", "y")
)

# Method 2: Provide spatial coordinates as a separate matrix
xy <- as.matrix(cd[, c("x", "y")])

spe <- SpatialExperiment(
    assays = list(counts = y),
    colData = cd["sample_id"],
    spatialCoords = xy
)

# Full constructor with image data
dir <- system.file(
    file.path("extdata", "10xVisium", "section1", "outs"),
    package = "SpatialExperiment")

# Read count data using DropletUtils
fnm <- file.path(dir, "raw_feature_bc_matrix")
sce <- DropletUtils::read10xCounts(fnm)

# Read image data
img <- readImgData(
    path = file.path(dir, "spatial"),
    sample_id = "foo")

# Read spatial coordinates from tissue positions file
fnm <- file.path(dir, "spatial", "tissue_positions_list.csv")
xyz <- read.csv(fnm, header = FALSE,
    col.names = c("barcode", "in_tissue", "array_row", "array_col",
                  "pxl_row_in_fullres", "pxl_col_in_fullres"))

# Construct SpatialExperiment with images
spe <- SpatialExperiment(
    assays = list(counts = assay(sce)),
    rowData = DataFrame(symbol = rowData(sce)$Symbol),
    colData = DataFrame(xyz),
    spatialCoordsNames = c("pxl_col_in_fullres", "pxl_row_in_fullres"),
    imgData = img,
    sample_id = "foo")

spe
```

--------------------------------

### colData Access with $

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

Demonstrates convenient column access from colData using the $ operator with auto-completion.

```APIDOC
## colData Access with $

### Description
The `SpatialExperiment` class supports convenient column access from `colData` using the `$` operator with auto-completion in RStudio.

### Method
Not specified (operator usage in R)

### Endpoint
Not applicable (R object manipulation)

### Parameters
No explicit parameters for the `$` operator itself, but it accesses columns within the `spe` object's `colData`.

### Request Example
```r
# Access sample_id column
unique(spe$sample_id)

# Access in_tissue column
table(spe$in_tissue)

# Access array coordinates
head(spe$array_row)
head(spe$array_col)

# Replace sample_id (must map uniquely to existing values)
new_ids <- spe$sample_id
new_ids[new_ids == "section1"] <- "sample_A"
spe$sample_id <- new_ids

# Note: sample_id is protected and cannot be removed
spe$sample_id <- NULL  # Has no effect
```

### Response
#### Success Response (200)
Access to columns within the `colData` of the `SpatialExperiment` object (`spe`). Modifications to columns are reflected in the object.

#### Response Example
```r
# Output of unique(spe$sample_id)
# [1] "section1" "section2"

# Output of table(spe$in_tissue)
# FALSE  TRUE
#    99    99

# Output of head(spe$array_row)
# [1] 0 1 0 1 2 0
```
```

--------------------------------

### Importing Visium Data via VisiumIO

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

Recommended approach for importing 10x Genomics Visium data using the VisiumIO package.

```r
library(VisiumIO)

# Get sample directories
dir <- list.dirs(system.file(
    file.path("extdata", "10xVisium"),
    package = "SpatialExperiment"),
    recursive = FALSE, full.names = TRUE)

sample_ids <- basename(dir)

# Create TENxVisiumList and import
vl <- TENxVisiumList(
    sampleFolders = dir,
    sample_ids = sample_ids,
    processing = "raw",      # "raw" or "filtered"
    images = "lowres"        # image resolution
)

spe <- import(vl)

spe
```

--------------------------------

### Access Image Content and Sources

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

Retrieve raster objects for visualization or file paths/URLs for image sources.

```r
# Get raster object for plotting
img <- imgRaster(spe, sample_id = "section1", image_id = "lowres")
plot(img)

# Get raster for first available image
img <- imgRaster(spe)
class(img)
# [1] "raster"

# Get image source path/URL
imgSource(spe, sample_id = "section1", image_id = "lowres")
# [1] "/path/to/tissue_lowres_image.png"

# For remote images, get cached file path
imgSource(spe, sample_id = "section1", image_id = "lowres", path = TRUE)
```

--------------------------------

### Loading 10x Genomics Visium Data (Deprecated)

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

Loads Visium data using the deprecated read10xVisium function. This method is maintained for backward compatibility.

```r
# Define sample directories
dir <- system.file(
    file.path("extdata", "10xVisium"),
    package = "SpatialExperiment")

sample_ids <- c("section1", "section2")
samples <- file.path(dir, sample_ids, "outs")

# Load multiple Visium samples
spe <- read10xVisium(
    samples = samples,
    sample_id = sample_ids,
    type = "sparse",      # "sparse" or "HDF5"
    data = "raw",         # "raw" or "filtered"
    images = "lowres",    # "lowres", "hires", "detected", "aligned"
    load = FALSE          # FALSE stores path, TRUE loads into memory
)

spe

# Tabulate spots mapped to tissue per sample
table(
    in_tissue = colData(spe)$in_tissue,
    sample_id = colData(spe)$sample_id)
```

--------------------------------

### Combine SpatialExperiment Samples

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

Merge multiple SpatialExperiment objects column-wise, ensuring sample identifiers and image data are synchronized.

```r
# Create two separate SPE objects
spe1 <- spe2 <- spe

# Method 1: Let cbind handle duplicate sample_ids (appends indices)
spe_combined <- cbind(spe1, spe2)
unique(spe_combined$sample_id)
# [1] "section1.1" "section2.1" "section1.2" "section2.2"

# Method 2: Pre-assign unique sample identifiers (preferred)
spe1 <- spe2 <- spe
spe1$sample_id <- paste(spe1$sample_id, "A", sep = ".")
spe2$sample_id <- paste(spe2$sample_id, "B", sep = ".")

spe_combined <- cbind(spe1, spe2)
unique(spe_combined$sample_id)
# [1] "section1.A" "section2.A" "section1.B" "section2.B"

# Image data is also combined
imgData(spe_combined)
#      sample_id image_id                   data scaleFactor
# 1   section1.A   lowres LoadedSpatialImage   0.03462326
# 2   section2.A   lowres LoadedSpatialImage   0.03462326
# 3   section1.B   lowres LoadedSpatialImage   0.03462326
# 4   section2.B   lowres LoadedSpatialImage   0.03462326

# Tabulate combined data
cd <- colData(spe_combined)
table(in_tissue = cd$in_tissue, sample_id = cd$sample_id)
```

--------------------------------

### Access colData with $ Operator

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

Demonstrates accessing columns within the `colData` of a `SpatialExperiment` object using the `$` operator for convenient data retrieval and modification. Note that `sample_id` is a protected column and cannot be removed.

```r
# Access sample_id column
unique(spe$sample_id)
# [1] "section1" "section2"

# Access in_tissue column
table(spe$in_tissue)
# FALSE  TRUE
#    99    99

# Access array coordinates
head(spe$array_row)
# [1] 0 1 0 1 2 0

head(spe$array_col)
# [1] 16 11 18 19 14 20

# Replace sample_id (must map uniquely to existing values)
new_ids <- spe$sample_id
new_ids[new_ids == "section1"] <- "sample_A"
spe$sample_id <- new_ids

# Note: sample_id is protected and cannot be removed
spe$sample_id <- NULL  # Has no effect
```

--------------------------------

### Mirror and Rotate Images

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

Functions to mirror and rotate images within a SpatialExperiment object.

```APIDOC
## Mirror Image Vertically

```r
spe_mirrored <- mirrorImg(spe, sample_id = "section1", image_id = "lowres", axis = "v")
```

## Visual Comparison of Transformations

```r
par(mfrow = c(1, 3))
plot(imgRaster(spe))
plot(imgRaster(rotateImg(spe, degrees = 90)))
plot(imgRaster(mirrorImg(spe, axis = "h")))
```
```

--------------------------------

### Image Transformations

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

Apply rotation and mirroring transformations to images stored in the SpatialExperiment.

```APIDOC
## rotateImg, mirrorImg - Image Transformations

Apply rotation and mirroring transformations to images stored in the `SpatialExperiment`.

### Rotate image 90 degrees clockwise (positive) or counter-clockwise (negative)
```r
spe_rotated <- rotateImg(spe,
    sample_id = "section1",
    image_id = "lowres",
    degrees = 90)
```

### Rotate all images
```r
spe_rotated <- rotateImg(spe,
    sample_id = TRUE,
    image_id = TRUE,
    degrees = -90)  # Counter-clockwise
```

### Mirror image horizontally
```r
spe_mirrored <- mirrorImg(spe,
    sample_id = "section1",
    image_id = "lowres",
    axis = "h")
```
```

--------------------------------

### Combining Multiple Samples

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

Function to combine multiple SpatialExperiment objects column-wise, synchronizing all data fields.

```APIDOC
## cbind - Combining Multiple Samples

Combine multiple `SpatialExperiment` objects column-wise, merging samples while synchronizing all data fields including `imgData`.

### Method 1: Let cbind handle duplicate sample_ids (appends indices)

```r
# Create two separate SPE objects
spe1 <- spe2 <- spe

spe_combined <- cbind(spe1, spe2)
unique(spe_combined$sample_id)
# [1] "section1.1" "section2.1" "section1.2" "section2.2"
```

### Method 2: Pre-assign unique sample identifiers (preferred)

```r
spe1 <- spe2 <- spe
spe1$sample_id <- paste(spe1$sample_id, "A", sep = ".")
spe2$sample_id <- paste(spe2$sample_id, "B", sep = ".")

spe_combined <- cbind(spe1, spe2)
unique(spe_combined$sample_id)
# [1] "section1.A" "section2.A" "section1.B" "section2.B"

# Image data is also combined
imgData(spe_combined)
#      sample_id image_id                   data scaleFactor
# 1   section1.A   lowres LoadedSpatialImage   0.03462326
# 2   section2.A   lowres LoadedSpatialImage   0.03462326
# 3   section1.B   lowres LoadedSpatialImage   0.03462326
# 4   section2.B   lowres LoadedSpatialImage   0.03462326

# Tabulate combined data
cd <- colData(spe_combined)
table(in_tissue = cd$in_tissue, sample_id = cd$sample_id)
```
```

--------------------------------

### Read Image Data for Visium

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

Reads images and scale factors from 10x Genomics Visium spatial directories into a valid imgData DataFrame.

```APIDOC
## readImgData - Read Image Data for Visium

### Description
Read images and scale factors from 10x Genomics Visium spatial directories into a valid `imgData` `DataFrame`.

### Method
Not specified (function call in R)

### Endpoint
Not applicable (R function)

### Parameters
#### Path Parameters
- **path** (string) - Required - Path to the Visium spatial directory.
- **sample_id** (string) - Required - Identifier for the sample.
- **load** (boolean) - Optional - Whether to load image data into memory (default is TRUE).

### Request Example
```r
# Read from Visium spatial directory
dir <- system.file(file.path("extdata", "10xVisium", "section1", "outs", "spatial"), package = "SpatialExperiment")

# Read image data
img_data <- readImgData(path = dir, sample_id = "sample1", load = TRUE)
```

### Response
#### Success Response (200)
- **img_data** (DataFrame) - A DataFrame containing image data, including sample ID, image ID, data (image or path), and scale factor.

#### Response Example
```r
# When load = TRUE
#   sample_id image_id                   data scaleFactor
# 1   sample1   lowres LoadedSpatialImage   0.03462326

# When load = FALSE
# class(img_data$data[[1]])
# [1] "StoredSpatialImage"
```
```

--------------------------------

### Subsetting SpatialExperiment Objects

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

Methods for subsetting SpatialExperiment objects while maintaining attribute synchronization.

```APIDOC
## Subsetting

Subset `SpatialExperiment` objects while automatically synchronizing all attributes including `spatialCoords` and `imgData`.

### Subset by sample_id

```r
spe_section1 <- spe[, spe$sample_id == "section1"]
ncol(spe_section1)
# [1] 99
```

### Subset to spots mapped to tissue

```r
spe_tissue <- spe[, colData(spe)$in_tissue]
ncol(spe_tissue)
# Number of in-tissue spots
```

### Subset by genes

```r
genes_of_interest <- c("gene1", "gene2", "gene3")
spe_subset <- spe[genes_of_interest, ]
```

### Combined subsetting

```r
spe_filtered <- spe[
    rownames(spe)[1:100],
    spe$sample_id == "section1" & spe$in_tissue]
```
```

--------------------------------

### Image Retrieval, Addition, and Removal

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

Functions to retrieve, add, and remove images from the SpatialExperiment object's imgData.

```APIDOC
## getImg, addImg, rmvImg - Image Management

Functions to retrieve, add, and remove images from the `SpatialExperiment` object's `imgData`.

### Get a SpatialImage object
```r
spi <- getImg(spe)
```

### Get specific image by sample and image ID
```r
spi <- getImg(spe, sample_id = "section1", image_id = "lowres")
```

### Add an image from URL
```r
url <- "https://i.redd.it/3pw5uah7xo041.jpg"
spe <- addImg(spe,
    imageSource = url,
    scaleFactor = NA_real_,
    sample_id = "section1",
    image_id = "new_image",
    load = TRUE)
```

### Remove an image
```r
spe <- rmvImg(spe,
    sample_id = "section1",
    image_id = "new_image")
```

### Remove all images for a sample
```r
spe <- rmvImg(spe, sample_id = "section1", image_id = TRUE)
```
```

--------------------------------

### Apply Image Transformations

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

Rotate or mirror images stored within the SpatialExperiment object.

```r
# Rotate image 90 degrees clockwise (positive) or counter-clockwise (negative)
spe_rotated <- rotateImg(spe,
    sample_id = "section1",
    image_id = "lowres",
    degrees = 90)

# Rotate all images
spe_rotated <- rotateImg(spe,
    sample_id = TRUE,
    image_id = TRUE,
    degrees = -90)  # Counter-clockwise

# Mirror image horizontally
spe_mirrored <- mirrorImg(spe,
    sample_id = "section1",
    image_id = "lowres",
    axis = "h")
```

--------------------------------

### Retrieve, Add, and Remove Images

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

Functions for managing the collection of images stored within the imgData slot.

```r
# Get a SpatialImage object
spi <- getImg(spe)
spi
# LoadedSpatialImage object

# Get specific image by sample and image ID
spi <- getImg(spe, sample_id = "section1", image_id = "lowres")

# Add an image from URL
url <- "https://i.redd.it/3pw5uah7xo041.jpg"
spe <- addImg(spe,
    imageSource = url,
    scaleFactor = NA_real_,
    sample_id = "section1",
    image_id = "new_image",
    load = TRUE)

imgData(spe)
#   sample_id  image_id                   data scaleFactor
# 1   section1    lowres LoadedSpatialImage   0.03462326
# 2   section2    lowres LoadedSpatialImage   0.03462326
# 3   section1 new_image LoadedSpatialImage           NA

# Remove an image
spe <- rmvImg(spe,
    sample_id = "section1",
    image_id = "new_image")

# Remove all images for a sample
spe <- rmvImg(spe, sample_id = "section1", image_id = TRUE)
```

--------------------------------

### SpatialImage Class Operations

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

Create and manipulate SpatialImage objects from files or URLs, including dimension retrieval and coercion.

```r
# Create SpatialImage from file path
path <- system.file(
    "extdata", "10xVisium", "section1", "outs", "spatial",
    "tissue_lowres_image.png",
    package = "SpatialExperiment")

spi <- SpatialImage(path)
class(spi)
# [1] "StoredSpatialImage"

# Get image dimensions
dim(spi)
# [1] 600 600

nrow(spi)
# [1] 600

ncol(spi)
# [1] 600

# Convert to raster and plot
plot(imgRaster(spi))

# Or use as.raster method
plot(as.raster(spi))

# Coerce to LoadedSpatialImage (in-memory)
spi_loaded <- as(spi, "LoadedSpatialImage")
class(spi_loaded)
# [1] "LoadedSpatialImage"

# Create from URL (creates RemoteSpatialImage)
url <- "https://example.com/image.png"
spi_remote <- SpatialImage(url)
class(spi_remote)
# [1] "RemoteSpatialImage"
```

--------------------------------

### Image Content Access

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

Retrieve the raster representation or source path/URL of images stored in the SpatialExperiment.

```APIDOC
## imgRaster, imgSource - Access Image Content

Retrieve the raster representation or source path/URL of images stored in the `SpatialExperiment`.

### Get raster object for plotting
```r
img <- imgRaster(spe, sample_id = "section1", image_id = "lowres")
plot(img)
```

### Get raster for first available image
```r
img <- imgRaster(spe)
class(img)
```

### Get image source path/URL
```r
imgSource(spe, sample_id = "section1", image_id = "lowres")
```

### For remote images, get cached file path
```r
imgSource(spe, sample_id = "section1", image_id = "lowres", path = TRUE)
```
```

--------------------------------

### Read Visium Image Data

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

Reads image and scale factor data from a 10x Genomics Visium spatial directory. Set `load = TRUE` to load images into memory, or `load = FALSE` to store only file paths.

```r
# Read from Visium spatial directory
dir <- system.file(
    file.path("extdata", "10xVisium", "section1", "outs", "spatial"),
    package = "SpatialExperiment")

# List directory contents
list.files(dir)
# [1] "scalefactors_json.json"   "tissue_lowres_image.png"
# [3] "tissue_positions_list.csv"

# Read image data
img_data <- readImgData(
    path = dir,
    sample_id = "sample1",
    load = TRUE)  # Load into memory

img_data
#   sample_id image_id                   data scaleFactor
# 1   sample1   lowres LoadedSpatialImage   0.03462326

# Read without loading (store path only)
img_data <- readImgData(
    path = dir,
    sample_id = "sample1",
    load = FALSE)

class(img_data$data[[1]])
# [1] "StoredSpatialImage"
```

--------------------------------

### Transform Images and Coordinates

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

Perform vertical or horizontal mirroring and rotation on images and spatial coordinates.

```r
# Mirror image vertically
spe_mirrored <- mirrorImg(spe,
    sample_id = "section1",
    image_id = "lowres",
    axis = "v")

# Visual comparison
par(mfrow = c(1, 3))
plot(imgRaster(spe))
plot(imgRaster(rotateImg(spe, degrees = 90)))
plot(imgRaster(mirrorImg(spe, axis = "h")))
```

```r
# Rotate coordinates 90 degrees clockwise
# Subsets to specified sample
spe_rotated <- rotateCoords(spe,
    sample_id = "section1",
    degrees = 90,
    warn = TRUE)  # Warns about potential image mismatch

# Mirror coordinates across vertical axis
spe_mirrored <- mirrorCoords(spe,
    sample_id = "section1",
    axis = "v",
    warn = TRUE)

# Check transformed coordinates
head(spatialCoords(spe_rotated))
```

```r
# Rotate both coordinates and image by 90 degrees
spe_rotated <- rotateObject(spe,
    sample_id = "section1",
    image_id = "lowres",
    degrees = 90)

# Rotate 180 degrees
spe_rotated <- rotateObject(spe,
    sample_id = "section1",
    degrees = 180)

# Mirror both horizontally
spe_mirrored <- mirrorObject(spe,
    sample_id = "section1",
    image_id = "lowres",
    axis = "h")

# Apply multiple transformations
spe_transformed <- rotateObject(spe, sample_id = "section2", degrees = 180)
spe_transformed <- mirrorObject(spe_transformed, axis = "v")

# Visual verification
par(mfrow = c(1, 2))
plot(imgRaster(spe, sample_id = "section2", image_id = "lowres"))
plot(imgRaster(spe_transformed, sample_id = "section2", image_id = "lowres"))
```

--------------------------------

### Combined Transformations (Object Level)

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

Wrapper functions to transform both spatial coordinates and images together, ensuring consistency.

```APIDOC
## rotateObject, mirrorObject - Combined Transformations

Transform both spatial coordinates and images together to maintain consistency. These are wrapper functions around the individual transformation methods.

### Rotate both coordinates and image by 90 degrees

```r
spe_rotated <- rotateObject(spe,
    sample_id = "section1",
    image_id = "lowres",
    degrees = 90)
```

### Rotate 180 degrees

```r
spe_rotated <- rotateObject(spe,
    sample_id = "section1",
    degrees = 180)
```

### Mirror both horizontally

```r
spe_mirrored <- mirrorObject(spe,
    sample_id = "section1",
    image_id = "lowres",
    axis = "h")
```

### Apply multiple transformations

```r
spe_transformed <- rotateObject(spe, sample_id = "section2", degrees = 180)
spe_transformed <- mirrorObject(spe_transformed, axis = "v")
```

### Visual verification

```r
par(mfrow = c(1, 2))
plot(imgRaster(spe, sample_id = "section2", image_id = "lowres")),
plot(imgRaster(spe_transformed, sample_id = "section2", image_id = "lowres"))
```
```

--------------------------------

### Subset SpatialExperiment Objects

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

Filter SpatialExperiment objects by sample, tissue status, or genes while maintaining attribute synchronization.

```r
# Subset by sample_id
spe_section1 <- spe[, spe$sample_id == "section1"]
ncol(spe_section1)
# [1] 99

# Subset to spots mapped to tissue
spe_tissue <- spe[, colData(spe)$in_tissue]
ncol(spe_tissue)
# Number of in-tissue spots

# Subset by genes
genes_of_interest <- c("gene1", "gene2", "gene3")
spe_subset <- spe[genes_of_interest, ]

# Combined subsetting
spe_filtered <- spe[
    rownames(spe)[1:100],
    spe$sample_id == "section1" & spe$in_tissue]
```

--------------------------------

### Manage Molecule-Based Spatial Data

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

Store and access molecule-level spatial data using the BumpyMatrix assay within a SpatialExperiment object.

```r
library(BumpyMatrix)

# Create simulated molecule-based data
n <- 1000   # number of molecules
ng <- 50    # number of genes
nc <- 20    # number of cells

# Sample xy-coordinates
x <- runif(n)
y <- runif(n)

# Assign molecules to gene-cell pairs
gs <- paste0("gene", seq(ng))
cs <- paste0("cell", seq(nc))
gene <- factor(sample(gs, n, TRUE), gs)
cell <- factor(sample(cs, n, TRUE), cs)

# Create data frame of molecule coordinates
df <- data.frame(gene, cell, x, y)

# Convert to BumpyMatrix
mol <- splitAsBumpyMatrix(
    df[, c("x", "y")],
    row = df$gene,
    col = df$cell)

# Create count matrix
y <- with(df, table(gene, cell))
y <- as.matrix(unclass(y))

# Construct SpatialExperiment with molecules assay
spe_mol <- SpatialExperiment(
    assays = list(
        counts = y,
        molecules = mol))

# Access molecules assay
molecules(spe_mol)
# 50 x 20 BumpyDataFrameMatrix
# ...

# Access molecule positions for specific gene-cell
molecules(spe_mol)["gene1", "cell1"]
# DataFrame with x and y columns
```

--------------------------------

### Manage Image Data Metadata

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

Access the DataFrame containing image identifiers and scale factors for samples.

```r
# View image data structure
imgData(spe)
#   sample_id image_id                   data scaleFactor
# 1   section1   lowres LoadedSpatialImage   0.03462326
# 2   section2   lowres LoadedSpatialImage   0.03462326

# Get scale factors for images
scaleFactors(spe)
# [1] 0.03462326 0.03462326

# Get scale factors for specific sample/image
scaleFactors(spe, sample_id = "section1", image_id = "lowres")
# [1] 0.03462326
```

--------------------------------

### Image Data Management

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

Access and manipulate the DataFrame storing image information including sample identifiers, image identifiers, image data, and scale factors.

```APIDOC
## imgData - Manage Image Data

Access and manipulate the `DataFrame` storing image information including sample identifiers, image identifiers, image data (as `SpatialImage` objects), and scale factors.

### View image data structure
```r
imgData(spe)
```

### Get scale factors for images
```r
scaleFactors(spe)
```

### Get scale factors for specific sample/image
```r
scaleFactors(spe, sample_id = "section1", image_id = "lowres")
```
```

--------------------------------

### SpatialImage Class

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

The SpatialImage class hierarchy provides representations for images from various sources.

```APIDOC
## SpatialImage Class - Image Storage

The `SpatialImage` class hierarchy provides representations for images from various sources: `LoadedSpatialImage` (in-memory raster), `StoredSpatialImage` (local file), and `RemoteSpatialImage` (URL).

### Create SpatialImage from file path
```r
path <- system.file(
    "extdata", "10xVisium", "section1", "outs", "spatial",
    "tissue_lowres_image.png",
    package = "SpatialExperiment")

spi <- SpatialImage(path)
class(spi)
```

### Get image dimensions
```r
dim(spi)
nrow(spi)
col(spi)
```

### Convert to raster and plot
```r
plot(imgRaster(spi))
plot(as.raster(spi))
```

### Coerce to LoadedSpatialImage (in-memory)
```r
spi_loaded <- as(spi, "LoadedSpatialImage")
class(spi_loaded)
```

### Create from URL (creates RemoteSpatialImage)
```r
url <- "https://example.com/image.png"
spi_remote <- SpatialImage(url)
class(spi_remote)
```
```

--------------------------------

### Spatial Coordinate Transformations

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

Functions to transform spatial coordinates independently of images, with options for rotation and mirroring.

```APIDOC
## rotateCoords, mirrorCoords - Spatial Coordinate Transformations

Transform spatial coordinates independently from images. Use with caution as this may cause mismatches with images.

### Rotate coordinates 90 degrees clockwise

```r
# Subsets to specified sample
spe_rotated <- rotateCoords(spe,
    sample_id = "section1",
    degrees = 90,
    warn = TRUE)  # Warns about potential image mismatch
```

### Mirror coordinates across vertical axis

```r
spe_mirrored <- mirrorCoords(spe,
    sample_id = "section1",
    axis = "v",
    warn = TRUE)
```

### Check transformed coordinates

```r
head(spatialCoords(spe_rotated))
```
```

--------------------------------

### Spatial Coordinates Management

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

Access and modify the numeric matrix of spatial coordinates (typically x and y positions) for each spot or cell.

```APIDOC
## spatialCoords - Access and Set Spatial Coordinates

Access or modify the numeric matrix of spatial coordinates (typically x and y positions) for each spot or cell.

### Get spatial coordinates
```r
head(spatialCoords(spe))
```

### Get coordinate column names
```r
spatialCoordsNames(spe)
```

### Rename coordinate columns
```r
spatialCoordsNames(spe) <- c("x", "y")
head(spatialCoords(spe))
```

### Replace spatial coordinates
```r
new_coords <- spatialCoords(spe)
new_coords[, 1] <- new_coords[, 1] * 2  # Scale x coordinates
spatialCoords(spe) <- new_coords
```
```

--------------------------------

### Molecule-Based ST Data Access

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

Accessing and creating molecule-based spatial transcriptomics data stored as a BumpyMatrix.

```APIDOC
## molecules - Molecule-Based ST Data

Access the `molecules` assay for molecule-based spatial transcriptomics data stored as a `BumpyMatrix`, where each entry contains coordinates of individual molecules.

### Create simulated molecule-based data

```r
library(BumpyMatrix)

# Create simulated molecule-based data
n <- 1000   # number of molecules
ng <- 50    # number of genes
nc <- 20    # number of cells

# Sample xy-coordinates
x <- runif(n)
y <- runif(n)

# Assign molecules to gene-cell pairs
gs <- paste0("gene", seq(ng))
cs <- paste0("cell", seq(nc))
gene <- factor(sample(gs, n, TRUE), gs)
cell <- factor(sample(cs, n, TRUE), cs)

# Create data frame of molecule coordinates
df <- data.frame(gene, cell, x, y)

# Convert to BumpyMatrix
mol <- splitAsBumpyMatrix(
    df[, c("x", "y")],
    row = df$gene,
    col = df$cell)

# Create count matrix
y <- with(df, table(gene, cell))
y <- as.matrix(unclass(y))

# Construct SpatialExperiment with molecules assay
spe_mol <- SpatialExperiment(
    assays = list(
        counts = y,
        molecules = mol))
```

### Access molecules assay

```r
molecules(spe_mol)
# 50 x 20 BumpyDataFrameMatrix
# ...

# Access molecule positions for specific gene-cell
molecules(spe_mol)["gene1", "cell1"]
# DataFrame with x and y columns
```
```

--------------------------------

### Access and Modify Spatial Coordinates

Source: https://context7.com/drighelli/spatialexperiment/llms.txt

Use these functions to retrieve, rename, or update the spatial coordinate matrix associated with spots or cells.

```r
# Get spatial coordinates
head(spatialCoords(spe))
#                         pxl_col_in_fullres pxl_row_in_fullres
# AAACAACGAATAGTTC-1                    2312               1252
# AAACAAGTATCTCCCA-1                    5765               2107
# AAACAATCTACTAGCA-1                    2744               1577

# Get coordinate column names
spatialCoordsNames(spe)
# [1] "pxl_col_in_fullres" "pxl_row_in_fullres"

# Rename coordinate columns
spatialCoordsNames(spe) <- c("x", "y")
head(spatialCoords(spe))
#                            x    y
# AAACAACGAATAGTTC-1      2312 1252
# AAACAAGTATCTCCCA-1      5765 2107
# AAACAATCTACTAGCA-1      2744 1577

# Replace spatial coordinates
new_coords <- spatialCoords(spe)
new_coords[, 1] <- new_coords[, 1] * 2  # Scale x coordinates
spatialCoords(spe) <- new_coords
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.