Getting started with ‘SpotSweeper’
2024-12-02
Contents
0.1 Introduction
SpotSweeper is an R package for spatial transcriptomics data quality control
(QC). It provides functions for detecting and visualizing spot-level local
outliers and artifacts using spatially-aware methods. The package is designed
to work with SpatialExperiment
objects, and is compatible with data from 10X Genomics Visium and other spatial
transcriptomics platforms.
0.2 Installation
Currently, the only way to install SpotSweeper is by downloading the
development version which can be installed from
GitHub using the following:
if (!require("devtools")) install.packages("devtools")
remotes::install_github("MicTott/SpotSweeper")Once accepted in Bioconductor, SpotSweeper will be
installable using:
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("SpotSweeper")0.3 Spot-level local outlier detection
0.3.1 Loading example data
Here we’ll walk you through the standard workflow for using ‘SpotSweeper’ to
detect and visualize local outliers in spatial transcriptomics data. We’ll use
the Visium_humanDLPFC dataset from the STexampleData package, which is a
SpatialExperiment object.
Because local outliers will be saved in the colData of the SpatialExperiment
object, we’ll first view the colData and drop out-of-tissue spots before
calculating quality control (QC) metrics and running SpotSweeper.
library(SpotSweeper)
# load Maynard et al DLPFC daatset
spe <- STexampleData::Visium_humanDLPFC()## see ?STexampleData and browseVignettes('STexampleData') for documentation## loading from cache# show column data before SpotSweeper
colnames(colData(spe))## [1] "barcode_id" "sample_id" "in_tissue" "array_row" "array_col"
## [6] "ground_truth" "reference" "cell_count"# drop out-of-tissue spots
spe <- spe[, spe$in_tissue == 1]0.3.2 Calculating QC metrics using scuttle
We’ll use the scuttle package to calculate QC metrics. To do this, we’ll need
to first change the rownames from gene id to gene names. We’ll then get the
mitochondrial transcripts and calculate QC metrics for each spot using
scuttle::addPerCellQCMetrics.
# change from gene id to gene names
rownames(spe) <- rowData(spe)$gene_name
# identifying the mitochondrial transcripts
is.mito <- rownames(spe)[grepl("^MT-", rownames(spe))]
# calculating QC metrics for each spot using scuttle
spe <- scuttle::addPerCellQCMetrics(spe, subsets = list(Mito = is.mito))
colnames(colData(spe))## [1] "barcode_id" "sample_id" "in_tissue"
## [4] "array_row" "array_col" "ground_truth"
## [7] "reference" "cell_count" "sum"
## [10] "detected" "subsets_Mito_sum" "subsets_Mito_detected"
## [13] "subsets_Mito_percent" "total"0.3.3 Identifying local outliers using SpotSweeper
We can now use SpotSweeper to identify local outliers in the spatial
transcriptomics data. We’ll use the localOutliers function to detect local
outliers based on the unique detected genes, total library size, and percent of
the total reads that are mitochondrial. These methods assume a normal
distribution, so we’ll use the log-transformed sum of the counts and the
log-transformed number of detected genes. For mitochondrial percent, we’ll use
the raw mitochondrial percentage.
# library size
spe <- localOutliers(spe,
metric = "sum",
direction = "lower",
log = TRUE
)
# unique genes
spe <- localOutliers(spe,
metric = "detected",
direction = "lower",
log = TRUE
)
# mitochondrial percent
spe <- localOutliers(spe,
metric = "subsets_Mito_percent",
direction = "higher",
log = FALSE
)The localOutlier function automatically outputs the results to the colData
with the naming convention X_outliers, where X is the name of the input
colData. We can then combine all outliers into a single column called
local_outliers in the colData of the SpatialExperiment object.
# combine all outliers into "local_outliers" column
spe$local_outliers <- as.logical(spe$sum_outliers) |
as.logical(spe$detected_outliers) |
as.logical(spe$subsets_Mito_percent_outliers)0.3.4 Visualizing local outliers
We can visualize the local outliers using the plotQCmetrics function. This
function creates a scatter plot of the specified metric and highlights the
local outliers in red using the escheR package. Here, we’ll visualize local
outliers of library size, unique genes, mitochondrial percent, and finally, all
local outliers. We’ll then arrange these plots in a grid using
ggpubr::arrange.
library(escheR)## Loading required package: ggplot2# all local outliers
plotQCmetrics(spe, metric = "sum_log", outliers = "local_outliers", point_size = 1.1,
stroke = 0.75) +
ggtitle("All Local Outliers")
0.4 Removing technical artifacts using SpotSweeper
0.4.1 Loading example data
# load in DLPFC sample with hangnail artifact
data(DLPFC_artifact)
spe <- DLPFC_artifact
# inspect colData before artifact detection
colnames(colData(spe))## [1] "sample_id" "in_tissue" "array_row"
## [4] "array_col" "key" "sum_umi"
## [7] "sum_gene" "expr_chrM" "expr_chrM_ratio"
## [10] "ManualAnnotation" "subject" "region"
## [13] "sex" "age" "diagnosis"
## [16] "sample_id_complete" "count" "sizeFactor"0.4.2 Visualizing technical artifacts
Technical artifacts can commonly be visualized by standard QC metrics, including
library size, unique genes, or mitochondrial percentage. We can first visualize
the technical artifacts using the plotQCmetrics function. In this sample, we can
clearly see a hangnail artifact on the right side of the tissue section
in the mitochondrial ratio plot.
plotQCmetrics(spe,
metric = "expr_chrM_ratio",
outliers = NULL, point_size = 1.1
) +
ggtitle("Mitochondrial Percent")
0.4.3 Identifying artifacts using SpotSweeper
We can then use the findArtifacts function to identify artifacts in the
spatial transcriptomics (data. This function identifies technical artifacts
based on the first principle component of the local variance of the specified QC
metric (mito_percent) at numerous neighorhood sizes (n_order=5). Currently,
kmeans clustering is used to cluster the technical artifact vs high-quality
Visium spots. Similar to localOutliers, the findArtifacts function then
outputs the results to the colData.
# find artifacts using SpotSweeper
spe <- findArtifacts(spe,
mito_percent = "expr_chrM_ratio",
mito_sum = "expr_chrM",
n_order = 5,
name = "artifact"
)
# check that "artifact" is now in colData
colnames(colData(spe))## [1] "sample_id" "in_tissue" "array_row"
## [4] "array_col" "key" "sum_umi"
## [7] "sum_gene" "expr_chrM" "expr_chrM_ratio"
## [10] "ManualAnnotation" "subject" "region"
## [13] "sex" "age" "diagnosis"
## [16] "sample_id_complete" "count" "sizeFactor"
## [19] "expr_chrM_ratio_log" "coords" "k6"
## [22] "k18" "k36" "k60"
## [25] "k90" "artifact"0.4.4 Visualizing artifacts
We can visualize the artifacts using the escheR package. Here, we’ll visualize
the artifacts using the plotQCmetrics function and arrange these plots using
ggpubr::arrange.
plotQCmetrics(spe,
metric = "expr_chrM_ratio",
outliers = "artifact", point_size = 1.1
) +
ggtitle("Hangnail artifact")
# Session information
utils::sessionInfo()## R Under development (unstable) (2024-10-21 r87258)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
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## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
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## other attached packages:
## [1] escheR_1.7.0 ggplot2_3.5.1
## [3] STexampleData_1.15.0 SpatialExperiment_1.17.0
## [5] SingleCellExperiment_1.29.1 SummarizedExperiment_1.37.0
## [7] Biobase_2.67.0 GenomicRanges_1.59.1
## [9] GenomeInfoDb_1.43.2 IRanges_2.41.1
## [11] S4Vectors_0.45.2 MatrixGenerics_1.19.0
## [13] matrixStats_1.4.1 ExperimentHub_2.15.0
## [15] AnnotationHub_3.15.0 BiocFileCache_2.15.0
## [17] dbplyr_2.5.0 BiocGenerics_0.53.3
## [19] generics_0.1.3 SpotSweeper_1.3.2
## [21] BiocStyle_2.35.0
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## [4] spatialEco_2.0-2 compiler_4.5.0 RSQLite_2.3.8
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## [58] BiocNeighbors_2.1.1 grid_4.5.0 AnnotationDbi_1.69.0
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