Table 2.

Selected tools and resources for the identification of malignant cells in scRNA-seq data

Resource	Type/readout	Comments	Availability and references
InferCNV	Copy number alterations	Arguably the most widely used method for CNA detection in scRNA-seq	https://github.com/broadinstitute/infercnv23
CopyKAT		Among top performers in recent benchmarks, especially when using only gene expression matrix	https://github.com/navinlabcode/copykat24
Numbat		Exploits allelic imbalance to improve CNA prediction; requires sequencing reads	https://github.com/kharchenkolab/numbat27
LISI	Inter-patient heterogeneity	A simple metric of patient mixing	https://github.com/immunogenomics/LISI45
scIntegrationMetrics		Implements per-cell-type LISI and additional metrics	https://github.com/carmonalab/scIntegrationMetrics129
scAllele	Single nucleotide alterations	SNA detection tailored for scRNA-seq	https://github.com/gxiaolab/scAllele50
Monopogen		SNA calling (germline + somatic) leveraging linkage disequilibrium from reference panels	https://github.com/KChen-lab/Monopogen51
STAR-fusion	Fusion transcripts	Primarily designed for bulk RNA-seq, but can be adapted for single-cell data	https://github.com/STAR-Fusion/STAR-Fusion62
scFusion		Specific for gene fusion detection at single-cell resolution	https://github.com/XiDsLab/scFusion65
UCell	Gene signature scoring	Simple and robust rank-based gene set scoring	https://github.com/carmonalab/UCell130
GSVA		Implements methods for gene set enrichment analysis	https://github.com/rcastelo/GSVA131
scATOMIC	Automated classifier	Integrated pipeline for cell type classification, including malignant vs. normal cells	https://github.com/copykat-lab/scATOMIC82
Ikarus		Relies on DEG signatures between normal and malignant cells	https://github.com/BIMSBbioinfo/ikarus122
scMalignantFinder		Uses logistic regression trained on curated pan‑cancer gene signatures and DEGs	https://github.com/Jonyyqn/scMalignantFinder123
OncoDB	Database	Collates expression profiles for cancer vs. normal tissues	https://oncodb.org/81
3CA		Provides robust transcriptional meta-programs for several cancer types	https://www.weizmann.ac.il/sites/3CA/114
HPA		Includes scRNA-seq expression profiles for many tissues and cell types	https://www.proteinatlas.org/132