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Description
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Damaged cells are an artifact of single-cell RNA sequencing (scRNA-seq) formed when cells succumb to stress before being sequenced. As a result, the gene expression data captured does not reflect biologically viable cells and introduces technical variability that is indistinguishable from functionally relevant variability. Filtering these cells is a standard task in scRNA-seq quality control (QC), though lacks standardisation in practice.
The majority of approaches filter damaged cells according to deviations in cell-level QC metrics. This outlier-based detection implicitly assumes viable cells follow unimodal distributions across QC metrics, where deviation is synonymous with damage. This assumption falters in the context of heterogeneous data and risks introducing filtering bias related to cell type abundance. Recent methods address this by defining damage within distinct distributions, representing cell populations, independently. This, however, assumes all distinct distributions are associated with viable cell populations and risks leaving abundant damage undetected and ultimately misclassified.
DamageDetective takes a different approach, rather than detecting damage by measuring the extent to which cells deviate from one another, it measures the extent to which cells deviate from artificially damaged profiles of themselves, created through simulating cytoplasmic RNA escape–a characteristic of damage resulting from the loss of plasma membrane integrity. This is inspired by the approach of DoubletFinder—a high-performing tool of another prominent scRNA-seq artifact.
Like DoubletFinder, DamageDetective uses principal component analysis to compute the proximity of true cells to artificial cells. This is calculated as a proportion (pANN) of a cell’s nearest neighbours that are of artificial origin, reflecting the likelihood that the cell has experienced the same cytoplasmic RNA loss as its artificial neighbours, i.e., is damaged. This score, ranging from 0 to 1, provides an intuitive scale for filtering that is standardised across cell types, sample origin, and experimental design.
Installation
Install DamageDetective from CRAN (R >= 4.4.0),
install.packages('DamageDetective')Or the latest development version on GitHub (R >= 3.5.0),
library(devtools)
devtools::install_github("AlicenJoyHenning/DamageDetective", build_vignettes = TRUE)To verify installation, run the following to see if you can view the package vignette and the function help pages,
library(DamageDetective)
help(package = "DamageDetective")Quick start
This demonstration can be followed immediately after loading the package using the internal dummy dataset. For examples with true data and more detailed explanations, please refer to the package articles website.
Prepare input
Damage detection is carried out by the detect_damage function that accepts count matrices, Seurat or SingleCellExperiment objects, or alignment files (package tutorials) as input. We will demonstrate using a dummy count matrix, test_counts, a subset of the (kotliarov-pbmc-2020) PBMC dataset provided in the scRNAseq package.
library(DamageDetective)
library(Matrix)
data("test_counts", package = "DamageDetective")
dim(test_counts)Expected outcome,
Select parameters for damage detection
ribosome_penalty
While detect_damage requires only a count matrix as input, additional parameters control aspects of the function’s computations. Of these, we recommend ribosome_penalty be adjusted for each dataset using the select_penalty function as shown below,
penalty <- select_penalty(count_matrix = test_counts)
penaltyExpected outcome,
filter_threshold
DamageDetective performs filtering using the proximity scores according to a threshold. By default, DamageDetective offers the threshold of 0.5 where values greater than 0.5 reflect more permissive filtering and values closer to 0 reflect more stringent filtering. We recommend the default, but suggest that if adjustments are made, they are informed by the output detect_damage plots, generate_plot = TRUE.
Run damage detection
Damage detection is run as shown below, using the count matrix and ribosomal penalty as inputs. Below, we have additionally specified for filter_counts parameter to be TRUE. This will use the default filter_threshold to detect damaged cells for removal and return the filtered count matrix that can be used immediately afterwards for the remainder of pre-processing. Though implemented in R, DamageDetective provides output that is platform-agnostic and can be integrated into any existing single-cell analysis workflow.
# Perform damage detection
detection_results <- detect_damage(
count_matrix = test_counts,
ribosome_penalty = penalty,
filter_counts = TRUE
)
# View the resulting count matrix
dim(detection_results$output)Expected outcome,
Alternatively, if filter_counts is set to FALSE, a data frame will be given as output containing the damage scores for each cell. This is provided for the user if they wish to interact with the DamageDetective results directly. From here, a user can filter their data manually, as is done by filter_counts=TRUE automatically.
# Perform damage detection
detection_results <- detect_damage(
count_matrix = test_counts,
ribosome_penalty = penalty,
filter_counts = FALSE,
seed = 7
)
# View output
print(head(detection_results$output), row.names = FALSE)
# Filter matrix
undamaged_cells <- subset(detection_results$output, DamageDetective < 0.7)
filtered_matrix <- test_counts[, undamaged_cells$Cells]
dim(filtered_matrix)Expected outcome,
Contribute
We are committed to the improvement of DamageDetective and encourage users to report any bugs or difficulties they encounter. Contributions that refine or challenge the assumptions and heuristics used to detect damaged cells are also welcome. Please reach out via the maintainer’s email listed in the DESCRIPTION file or start a public discussion 
License
DamageDetective is made available for public use through the GNU AGPL-3.0
Authors
Alicen Henning
Stellenbosch University, Cape Town, South Africa
Bioinformatics and Computational Biology
This work was done under the supervision of Prof Marlo Möller, Prof Gian van der Spuy, and Prof André Loxton.
References
McGinnis, C. S., Murrow, L. M., & Gartner, Z. J. (2019). DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors. Cell Systems, 8(4), 329-337.e4. https://doi.org/10.1016/j.cels.2019.03.003
Risso D, Cole M (2024). scRNAseq: Collection of Public Single-Cell RNA-Seq Datasets. doi:10.18129/B9.bioc.scRNAseq https://doi.org/10.18129/B9.bioc.scRNAseq, R package version 2.20.0, https://bioconductor.org/packages/scRNAseq.