SpotClean is a computational method to adjust for spot swapping in spatial transcriptomics data. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind mRNA. Ideally, unique molecular identifiers at a spot measure spot-specific expression, but this is often not the case due to bleed from nearby spots, an artifact we refer to as spot swapping. SpotClean is able to estimate the contamination rate in observed data and decontamin
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Load raw 10x Visium Space Ranger output, perform decontamination, visualize results, and convert to a Seurat object.
library(SpotClean)
library(S4Vectors)
# 1. Load raw data and slide information
data(mbrain_raw)
spatial_dir <- system.file(file.path("extdata", "V1_Adult_Mouse_Brain_spatial"), package = "SpotClean")
mbrain_slide_info <- read10xSlide(
tissue_csv_file = file.path(spatial_dir, "tissue_positions_list.csv"),
tissue_img_file = file.path(spatial_dir, "tissue_lowres_image.png"),
scale_factor_file = file.path(spatial_dir, "scalefactors_json.json")
)
# 2. Create the slide object
slide_obj <- createSlide(count_mat = mbrain_raw, slide_info = mbrain_slide_info)
# 3. Visualize raw data
visualizeSlide(slide_obj = slide_obj)
visualizeHeatmap(slide_obj, "Mbp")
# 4. Decontaminate raw data
decont_obj <- spotclean(slide_obj, maxit = 10, candidate_radius = 20)
# 5. Visualize decontaminated gene and contamination rate
visualizeHeatmap(decont_obj, "Mbp")
visualizeHeatmap(
decont_obj,
metadata(decont_obj)$contamination_rate,
logged = FALSE,
legend_title = "contamination rate",
legend_range = c(0, 1)
)
# 6. Convert to Seurat object
seurat_obj <- convertToSeurat(decont_obj, image_dir = spatial_dir)
Input: Raw count matrix and 10x Visium slide metadata. Output: A decontaminated Seurat object ready for downstream analysis.
Decontaminate spatial transcriptomics data stored in a SpatialExperiment object.
library(SpotClean)
library(SpatialExperiment)
# Assuming slide_obj is a SpatialExperiment loaded with read10xVisium(..., data = "raw")
# slide_obj <- read10xVisium(samples = "/path/to/spaceranger/output/", data = "raw")
# decont_obj <- spotclean(slide_obj)
Input: A SpatialExperiment object containing raw spatial transcriptomics data. Output: A decontaminated SpatialExperiment object.
spotclean().arcScore().visualizeLabel() and visualizeHeatmap().convertToSeurat().spotclean() relies on background spots to estimate contamination.spotclean() reassigns UMIs and changes estimated sequencing depth, which may conflict with standard depth normalization.read10xRaw()).read10xSlide()).SpatialExperiment object constructed with data = "raw".createSlide().createSlide().30): Maximum number of iterations for the decontamination optimization in spotclean().spotclean().convertToSeurat().TRUE): Logical flag in visualizeHeatmap() to apply log-scaling to the heatmap values.spotclean(), specifying data = "raw" if loading via read10xVisium().visualizeHeatmap() before and after running spotclean() to verify the reduction of background noise.spotclean() on slides with nearly 100% tissue coverage leads to unreliable contamination estimates; ensure at least 25% of spots are background.read10xRaw() or read10xVisium(..., data = "raw").Seurat: For general spatial transcriptomics downstream analysis and clustering.scater: For single-cell and spatial QC and visualization.scran: For variance modeling and normalization of single-cell/spatial data.No reviews yet. Tell others what you thought.
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