scCertify 0.99.7
scCertify provides an explainable framework for evaluating confidence in
single-cell RNA-sequencing cell-type annotations.
The package combines several complementary sources of evidence:
scCertify supports both SingleCellExperiment and Seurat objects.
library(scCertify)
library(SingleCellExperiment)## Loading required package: SummarizedExperiment## Loading required package: MatrixGenerics## Loading required package: matrixStats##
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## Vignettes contain introductory material; view with
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## anyMissing, rowMedianslibrary(SummarizedExperiment)
library(Seurat)## Loading required package: SeuratObject## Loading required package: sp##
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## Assaysset.seed(42)The following example creates a small simulated
SingleCellExperiment containing count data and predicted cell-type labels.
n_genes <- 50
n_cells <- 40
counts <- matrix(
rpois(n_genes * n_cells, lambda = 5),
nrow = n_genes,
ncol = n_cells,
dimnames = list(
paste0("Gene", seq_len(n_genes)),
paste0("Cell", seq_len(n_cells))
)
)
sce <- SingleCellExperiment(
assays = list(
counts = counts
)
)
colData(sce)$predicted_label <- rep(
c("T cell", "B cell"),
each = n_cells / 2
)
sce## class: SingleCellExperiment
## dim: 50 40
## metadata(0):
## assays(1): counts
## rownames(50): Gene1 Gene2 ... Gene49 Gene50
## rowData names(0):
## colnames(40): Cell1 Cell2 ... Cell39 Cell40
## colData names(1): predicted_label
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):The predicted cell labels are stored in colData().
head(colData(sce))## DataFrame with 6 rows and 1 column
## predicted_label
## <character>
## Cell1 T cell
## Cell2 T cell
## Cell3 T cell
## Cell4 T cell
## Cell5 T cell
## Cell6 T cellA marker database is represented as a named list, with one element for each predicted cell type.
markers <- list(
"T cell" = c("Gene1", "Gene2", "Gene3"),
"B cell" = c("Gene4", "Gene5", "Gene6")
)
markers## $`T cell`
## [1] "Gene1" "Gene2" "Gene3"
##
## $`B cell`
## [1] "Gene4" "Gene5" "Gene6"neighbor_score() evaluates agreement among nearby cells. For a
SingleCellExperiment, a suitable low-dimensional representation is required.
For this simulated example, a logcounts assay is created from a
log-transformed count matrix. marker_score() requires this assay to be
present, and the same log-transformed matrix is also used to calculate PCA.
log_counts <- log1p(
assay(sce, "counts")
)
assay(sce, "logcounts") <- log_counts
pca <- stats::prcomp(
t(log_counts),
center = TRUE,
scale. = TRUE
)$x[, seq_len(5), drop = FALSE]
reducedDim(sce, "PCA") <- pca
dim(
reducedDim(sce, "PCA")
)## [1] 40 5Marker consistency evaluates whether cells express genes expected for their predicted identity.
scCertify uses rank-based enrichment scoring through UCell.
marker_scores <- marker_score(
sce,
markers,
label_column = "predicted_label"
)
head(marker_scores)## [1] 0.6423611 0.4270833 0.3055556 0.6701389 0.7951389 0.3784722The resulting scores provide a cell-level measure of marker consistency.
The neighborhood score measures whether nearby cells tend to share the same predicted annotation.
neighbor_scores <- neighbor_score(
sce,
label_column = "predicted_label"
)
head(neighbor_scores)## [1] 0.4 0.7 0.7 0.2 0.5 0.5entropy_score() provides an uncertainty measure based on the distribution of
expression values.
entropy_values <- entropy_score(
t(assay(sce, "counts"))
)
head(entropy_values)## Cell1 Cell2 Cell3 Cell4 Cell5 Cell6
## 3.799701 3.783424 3.811471 3.831461 3.809691 3.796189The values can be normalized and stored in the cell metadata.
entropy_norm <- entropy_values / max(
entropy_values,
na.rm = TRUE
)
colData(sce)$entropy_norm <- entropy_norm
head(colData(sce)$entropy_norm)## Cell1 Cell2 Cell3 Cell4 Cell5 Cell6
## 0.9849849 0.9807655 0.9880360 0.9932180 0.9875746 0.9840745For this simulated example, doublet scores are generated as demonstration metadata.
In a real analysis, these values would normally come from a dedicated doublet detection method.
colData(sce)$doublet_score <- runif(
ncol(sce),
min = 0,
max = 1
)
head(colData(sce)$doublet_score)## [1] 0.9899656 0.4384936 0.6999032 0.8890770 0.8341595 0.7344215cell_certify() combines marker consistency, neighborhood agreement,
entropy-based uncertainty, and doublet information into a calibrated
confidence score.
sce <- cell_certify(
sce,
markers,
label_column = "predicted_label"
)
head(
colData(sce)[,
c(
"predicted_label",
"marker_score",
"neighbor_score",
"entropy_norm",
"doublet_score",
"confidence_score",
"confidence_class"
)
]
)## DataFrame with 6 rows and 7 columns
## predicted_label marker_score neighbor_score entropy_norm doublet_score
## <character> <numeric> <numeric> <numeric> <numeric>
## Cell1 T cell 0.642361 0.4 0.984985 0.989966
## Cell2 T cell 0.427083 0.7 0.980766 0.438494
## Cell3 T cell 0.305556 0.7 0.988036 0.699903
## Cell4 T cell 0.670139 0.2 0.993218 0.889077
## Cell5 T cell 0.795139 0.5 0.987575 0.834159
## Cell6 T cell 0.378472 0.5 0.984074 0.734421
## confidence_score confidence_class
## <numeric> <character>
## Cell1 0.450 Low
## Cell2 0.850 High
## Cell3 0.575 Moderate
## Cell4 0.075 Low
## Cell5 0.725 Moderate
## Cell6 0.325 LowConfidence classes can be customized using the moderate_threshold and
high_threshold arguments.
sce_custom <- cell_certify(
sce,
markers,
label_column = "predicted_label",
moderate_threshold = 0.4,
high_threshold = 0.75
)
head(
colData(sce_custom)[,
c(
"confidence_score",
"confidence_class"
)
]
)## DataFrame with 6 rows and 2 columns
## confidence_score confidence_class
## <numeric> <character>
## Cell1 0.450 Moderate
## Cell2 0.850 High
## Cell3 0.575 Moderate
## Cell4 0.075 Low
## Cell5 0.725 Moderate
## Cell6 0.325 Lowexplain_confidence() identifies factors that contributed to lower confidence
for an individual cell.
explain_confidence(
sce,
cell_id = "Cell1"
)## [1] "Neighborhood disagreement" "High annotation uncertainty"
## [3] "Possible doublet contamination"explain_cell() provides a concise summary containing the predicted label,
confidence score, individual evidence components, and interpretation.
explain_cell(
sce,
cell_id = "Cell1"
)## Cell: Cell1## Predicted Label: T cell## Confidence Score: 0.45## Marker Score: 0.642## Neighbor Agreement: 0.4## Entropy: 0.985## Interpretation:## - Moderate confidence annotation## - High uncertainty detected## - Local neighborhood disagreement## [1] "Moderate confidence annotation" "High uncertainty detected"
## [3] "Local neighborhood disagreement"scCertify also supports Seurat objects. The following example demonstrates a
complete evaluated Seurat workflow.
seurat_counts <- matrix(
rpois(2000, lambda = 5),
nrow = 100,
ncol = 20,
dimnames = list(
paste0("Gene", seq_len(100)),
paste0("Cell", seq_len(20))
)
)
seurat_object <- Seurat::CreateSeuratObject(
counts = seurat_counts
)## Warning: Data is of class matrix. Coercing to dgCMatrix.seurat_object$predicted_label <- rep(
c("T cell", "B cell"),
each = 10
)
seurat_object$entropy_norm <- runif(
ncol(seurat_object)
)
seurat_object$doublet_score <- runif(
ncol(seurat_object)
)
seurat_object## An object of class Seurat
## 100 features across 20 samples within 1 assay
## Active assay: RNA (100 features, 0 variable features)
## 1 layer present: countsThe Seurat object is processed using its standard normalization and PCA workflow.
seurat_object <- Seurat::NormalizeData(
seurat_object,
verbose = FALSE
)
seurat_object <- Seurat::FindVariableFeatures(
seurat_object,
verbose = FALSE
)
seurat_object <- Seurat::ScaleData(
seurat_object,
verbose = FALSE
)
seurat_object <- Seurat::RunPCA(
seurat_object,
npcs = 10,
verbose = FALSE
)## Warning in svd.function(A = t(x = object), nv = npcs, ...): You're computing
## too large a percentage of total singular values, use a standard svd instead.seurat_object## An object of class Seurat
## 100 features across 20 samples within 1 assay
## Active assay: RNA (100 features, 100 variable features)
## 3 layers present: counts, data, scale.data
## 1 dimensional reduction calculated: pcaThe same marker definitions can then be used with scCertify.
seurat_object <- cell_certify(
seurat_object,
markers,
label_column = "predicted_label"
)
head(
seurat_object[[
c(
"predicted_label",
"marker_score",
"neighbor_score",
"entropy_norm",
"doublet_score",
"confidence_score",
"confidence_class"
)
]]
)## predicted_label marker_score neighbor_score entropy_norm doublet_score
## Cell1 T cell 0.4812925 0.7 0.6756233 0.4636337464
## Cell2 T cell 0.7159864 0.5 0.3624238 0.9391432542
## Cell3 T cell 0.3622449 0.4 0.3981606 0.3398535226
## Cell4 T cell 0.6683673 0.5 0.2449258 0.0003825743
## Cell5 T cell 0.7993197 0.4 0.1579488 0.2173832541
## Cell6 T cell 0.3945578 0.5 0.5307416 0.0360659512
## confidence_score confidence_class
## Cell1 0.85 High
## Cell2 0.70 Moderate
## Cell3 0.15 Low
## Cell4 0.95 High
## Cell5 0.80 High
## Cell6 0.40 LowThe resulting confidence information is stored directly in the Seurat metadata.
explain_confidence(
seurat_object,
cell_id = "Cell1"
)## [1] "Strong annotation support"explain_cell(
seurat_object,
cell_id = "Cell1"
)## Cell: Cell1## Predicted Label: T cell## Confidence Score: 0.85## Marker Score: 0.481## Neighbor Agreement: 0.7## Entropy: 0.676## Interpretation:## - High confidence annotation## [1] "High confidence annotation"This vignette demonstrates the complete scCertify workflow for both
SingleCellExperiment and Seurat objects.
The workflow consists of:
Because all examples in this vignette are evaluated, the document also provides
a continuously tested demonstration that the documented workflow remains
functional with the package dependencies used by scCertify.
sessionInfo()## R version 4.6.1 (2026-06-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.4 LTS
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## Matrix products: default
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## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
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## other attached packages:
## [1] Seurat_5.5.1 SeuratObject_5.4.0
## [3] sp_2.2-3 SingleCellExperiment_1.35.2
## [5] SummarizedExperiment_1.43.0 Biobase_2.73.2
## [7] GenomicRanges_1.65.4 Seqinfo_1.3.2
## [9] IRanges_2.47.5 S4Vectors_0.51.9
## [11] BiocGenerics_0.59.12 generics_0.1.4
## [13] MatrixGenerics_1.25.0 matrixStats_1.5.0
## [15] scCertify_0.99.7 BiocStyle_2.41.0
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## [40] irlba_2.3.7 progressr_1.0.0 spatstat.sparse_3.2-0
## [43] httr_1.4.9 polyclip_1.10-7 abind_1.4-8
## [46] compiler_4.6.1 S7_0.2.2 BiocParallel_1.47.0
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## [64] reshape2_1.4.5 gtable_0.3.6 spatstat.data_3.1-9
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## [73] RANN_2.6.3 pillar_1.11.1 stringr_1.6.0
## [76] spam_2.11-4 RcppHNSW_0.7.0 later_1.4.8
## [79] splines_4.6.1 dplyr_1.2.1 lattice_0.23-1
## [82] FNN_1.1.4.1 survival_3.8-11 deldir_2.0-4
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## [91] scattermore_1.2 xfun_0.60 stringi_1.8.9
## [94] yaml_2.3.12 evaluate_1.0.5 codetools_0.2-20
## [97] entropy_1.3.2 tibble_3.3.1 BiocManager_1.30.27
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## [103] reticulate_1.47.0 jquerylib_0.1.4 dichromat_2.0-1
## [106] Rcpp_1.1.2 globals_0.19.1 spatstat.random_3.5-1
## [109] png_0.1-9 spatstat.univar_3.2-0 parallel_4.6.1
## [112] ggplot2_4.0.3 dotCall64_1.2 listenv_1.0.0
## [115] viridisLite_0.4.3 scales_1.4.0 ggridges_0.5.7
## [118] purrr_1.2.2 rlang_1.3.0 cowplot_1.2.0