Single-Cell Quality Control & Preprocessing

SCTK (singleCellTK)

Comprehensive R/Bioconductor pipeline integrating 7 doublet detection and 3 decontamination algorithms into a unified framework. Features automated parameter optimization, consensus-based filtering, and supports both interactive (Shiny GUI) and command-line workflows.

Reference Optimization

Systematic study showing that reference selection significantly impacts scRNA-seq analysis. Demonstrates how to optimize gene annotations and transcriptome models for improved quantification accuracy, especially for intron-containing reads.

DoubletFinder

Identifies doublets by creating artificial doublets (pK-NN approach) and measuring proximity in gene expression space. Features parametric parameter sweeping (pK/pN optimization) for dataset-specific calibration. Integrated into Seurat workflows.

Scrublet

Python-based doublet detection using simulated doublet enrichment analysis. Computes doublet scores based on k-nearest neighbor density in PCA space. Fast, scalable, and well-integrated with Scanpy ecosystem.

SoupX

Estimates and removes ambient RNA contamination using empty droplet profiles. Uses cluster-specific marker genes to estimate contamination fraction per cell. Works with 10x Genomics data and integrates with Seurat.

DecontX

Bayesian method for decontamination using variational inference. Models each cell's expression as a mixture of native and contaminating RNA. Provides uncertainty estimates and works without clustering pre-requisites.

scCLEAN

Molecular (wet-lab) method, not a computational tool. Single-cell CRISPRclean uses CRISPR/Cas9 to target and remove highly abundant, uninformative transcripts (both genomic and transcriptomic) from prepared cDNA before sequencing, redistributing roughly half of reads toward lower-abundance transcripts. Improves detection of rare/low-expression genes in a way that cannot be matched by deeper sequencing alone; complements — rather than replaces — computational decontamination.

Ambient RNA Analysis (snRNA-seq)

Comprehensive analysis of ambient RNA contamination specifically in single-nucleus RNA-seq (snRNA-seq). Provides guidelines for contamination assessment and benchmarks decontamination tools for nuclear preparations.

DELVE

Dynamic feature selection method that identifies genes with coherent expression patterns across local cell neighborhoods. Uses Laplacian scores to select features that capture continuous biological processes like differentiation trajectories.

SnapATAC2

Fast, scalable framework for single-cell ATAC-seq analysis with novel dimensionality reduction using spectral embedding. Handles millions of cells efficiently with out-of-core computation. Also applicable to RNA-seq data.

scPRINT

Large foundation model pre-trained on 50M+ cells for diverse single-cell tasks. Provides cell and gene embeddings enabling zero-shot annotation, denoising, and batch correction. Supports multi-task learning across preprocessing applications.

SIGnature

Applies gradient-based attribution methods (Integrated Gradients, Input×Gradient, DeepLIFT) to scRNA-seq foundation models to score per-gene importance at the single-cell level. Attribution scores are strikingly robust to the technical artifacts that QC tries to manage: they correlate far less with per-cell UMI counts and library complexity than raw expression, automatically demote ribosomal and mitochondrial genes, and remain 93% stable under 50% simulated dropout. This makes SIGnature's gene-importance signal substantially less confounded by sequencing depth, dropout events, and ambient contamination than log-normalized counts alone.

• Attributions promote cell-type marker genes and lineage TFs (GATA3, RORC, FOXP3) while demoting ribo/mito genes vs expression (Wilcoxon P<0.01)
• Top-ranked genes 93% stable under 50% dropout simulation — robust to the very missingness QC filters aim to reduce
• Attribution-based NMF gene programs less batch/study-specific than expression-based; FOXP3 a top Treg gene in 98% of attribution configs vs 4% for expression
• Attribution signature scoring tops F1 in 23/32 tasks, beating ANS, Scanpy, UCell, JASMINE, and mean expression
• Scales to ~22 million cells / 412 studies in minutes; enables large-scale QC-aware gene-program discovery

MetaQ

Metacell inference via deep single-cell quantization. Quantizes cells into a discrete codebook where each entry (a metacell) can reconstruct the similar cells it represents, reducing complexity from exponential to linear with constant memory. Scales to arbitrarily large datasets and supports both uni- and multi-omics data while preserving biological heterogeneity.

Memento

Method-of-moments framework for differential expression that accounts for technical noise and sampling variability. Provides rigorous statistical inference for mean and variance differences with proper false discovery control.

scBaseCount (formerly scBaseCamp)

AI agent-curated, continually expanding repository — now over 500M uniformly processed cells across 27 organisms and 75 tissues (the largest freely accessible single-cell collection). An AI agent (SRAgent) automates dataset discovery and metadata extraction from the SRA, and the scRecounter Nextflow pipeline performs standardized reprocessing. Released as part of the Arc Virtual Cell Atlas. Note: first circulated under the name "scBaseCamp" (230M cells); later renamed scBaseCount as it expanded.