Quality Control for Spatial Data

Source: R/add_qc_metrics.R

This function identify spots in a SpatialExperiment-class (SPE) with outlier quality control values: low sum_umi or sum_gene, or high expr_chrM_ratio, utilizing scuttle::isOutlier. Also identifies in-tissue edge spots and distance to the edge for each spot.

add_qc_metrics(spe, overwrite = FALSE)

Arguments

spe

a SpatialExperiment object that has sum_umi, sum_gene, expr_chrM_ratio, and in_tissue variables in the colData(spe). Note that these are automatically created when you build your spe object with spatialLIBD::read10xVisiumWrapper().

overwrite

a logical(1) specifying whether to overwrite the 7 colData(spe) columns that this function creates. If set to FALSE and any of them are present, the function will return an error.

Value

A SpatialExperiment with added quality control information added to the colData().

scran_low_lib_size

shows spots that have a low library size.

scran_low_n_features

spots with a low number of expressed genes.

scran_high_Mito_percent

spots with a high percent of mitochondrial gene expression.

scran_discard

spots belonging to either scran_low_lib_size, scran_low_n_feature, or scran_high_Mito_percent.

edge_spot

spots that are automatically detected as the edge spots of the in_tissue section.

edge_distance

closest distance in number of spots to either the vertical or horizontal edge.

scran_low_lib_size_edge

spots that have a low library size and are an edge spot.

Author

Louise A. Huuki-Myers

Examples

## Obtain the necessary data
spe_pre_qc <- fetch_data("spatialDLPFC_Visium_example_subset")
#> 2024-12-16 21:49:54.569267 loading file /github/home/.cache/R/BiocFileCache/5db7d271a6_spatialDLPFC_spe_subset_example.rds%3Fdl%3D1

## For now, we fake out tissue spots in example data
spe_qc <- spe_pre_qc
spe_qc$in_tissue[spe_qc$array_col < 10] <- FALSE

## adds QC metrics to colData of the spe
spe_qc <- add_qc_metrics(spe_qc, overwrite = TRUE)
vars <- colnames(colData(spe_qc))
vars[grep("^(scran|edge)", vars)]
#> [1] "scran_discard"                   "scran_high_subsets_Mito_percent"
#> [3] "scran_low_lib_size"              "scran_low_n_features"           
#> [5] "scran_quick_cluster"             "scran_high_Mito_percent"        
#> [7] "edge_spot"                       "edge_distance"                  
#> [9] "scran_low_lib_size_edge"        

## visualize edge spots
vis_clus(spe_qc, sampleid = "Br6432_ant", clustervar = "edge_spot")


## specify your own colors
vis_clus(
    spe_qc,
    sampleid = "Br6432_ant",
    clustervar = "edge_spot",
    colors = c(
        "TRUE" = "lightgreen",
        "FALSE" = "pink",
        "NA" = "red"
    )
)

vis_gene(spe_qc, sampleid = "Br6432_ant", geneid = "edge_distance", minCount = -1)


## Visualize scran QC flags

## Check the spots with low library size as detected by scran::isOutlier()
vis_clus(spe_qc, sample_id = "Br6432_ant", clustervar = "scran_low_lib_size")


## Violin plot of library size with low library size highlighted in a
## different color.
scater::plotColData(spe_qc[, spe_qc$in_tissue], x = "sample_id", y = "sum_umi", colour_by = "scran_low_lib_size")


## Check any spots that scran::isOutlier() flagged
vis_clus(spe_qc, sampleid = "Br6432_ant", clustervar = "scran_discard")


## Low library spots that are on the edge of the tissue
vis_clus(spe_qc, sampleid = "Br6432_ant", clustervar = "scran_low_lib_size_edge")


## Use `low_library_size` (or other variables) and `edge_distance` as you
## please.
spe_qc$our_low_lib_edge <- spe_qc$scran_low_lib_size & spe_qc$edge_distance < 5

vis_clus(spe_qc, sample_id = "Br6432_ant", clustervar = "our_low_lib_edge")


## Clean up
rm(spe_qc, spe_pre_qc, vars)