Cancer Stemness Online

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Methods select & source

Methods applicable to Count (raw count)

Method	Description
CytoTRACE	CytoTRACE predicts the differentiation and developmental potential of each cell by assessing the number of detectably expressed genes per cell or gene counts, and eventually calculate a score which is higher in stem cell
SLICE	SLICE quantitatively measures cellular differentiation states based on single cell entropy by assuming that entropy is negatively correlated with cell differentiation state. Higher scores imply the higher stemness
SCENT	SCENT estimates the differentiation potential of a single cell by calculating the signal promiscuity or entropy of the cell transcriptome in the PPI interaction network. Higher scores imply the higher stemness
StemID	StemID assesses stem cells among all detectable cell types within a population by utilizing tree topology and transcriptome composition. Higher scores imply the higher stemness
StemSC	StemSC represents the percentage of gene pairs with the same relative expression orderings as the reference of embryonic stem cell samples. Higher scores imply the higher stemness
GSVA	The score of single cell gene set enrichment analysis (ssGSEA) for our manually collected stemness-related signatures. Higher scores imply the higher stemness
mRNAsi	mRNAsi is a transcriptome stemness index to evaluate the stemness based on the one-class logistic regression machine learning algorithm to extract transcriptomic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Higher scores imply the higher stemness
StemnessIndex	StemnessIndex provides an absolute index to evaluate stemness by comparing the relative expression orderings of the stem cell samples and the normal adult samples from different tissues. Higher scores imply the higher stemness

Methods applicable to TPM(Transcript per Kilobase per Million mapped reads)

Method	Description
CytoTRACE	CytoTRACE predicts the differentiation and developmental potential of each cell by assessing the number of detectably expressed genes per cell or gene counts, and eventually calculate a score which is higher in stem cell
SLICE	SLICE quantitatively measures cellular differentiation states based on single cell entropy by assuming that entropy is negatively correlated with cell differentiation state. Higher scores imply the higher stemness
SCENT	SCENT estimates the differentiation potential of a single cell by calculating the signal promiscuity or entropy of the cell transcriptome in the PPI interaction network. Higher scores imply the higher stemness
StemSC	StemSC represents the percentage of gene pairs with the same relative expression orderings as the reference of embryonic stem cell samples. Higher scores imply the higher stemness
GSVA	The score of single cell gene set enrichment analysis (ssGSEA) for our manually collected stemness-related signatures. Higher scores imply the higher stemness
mRNAsi	mRNAsi is a transcriptome stemness index to evaluate the stemness based on the one-class logistic regression machine learning algorithm to extract transcriptomic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Higher scores imply the higher stemness
StemnessIndex	StemnessIndex provides an absolute index to evaluate stemness by comparing the relative expression orderings of the stem cell samples and the normal adult samples from different tissues. Higher scores imply the higher stemness

Methods applicable to FPKM (Fragments Per Kilobase of exon model per Million mapped fragments)

Method	Description
CytoTRACE	CytoTRACE predicts the differentiation and developmental potential of each cell by assessing the number of detectably expressed genes per cell or gene counts, and eventually calculate a score which is higher in stem cell
SLICE	SLICE quantitatively measures cellular differentiation states based on single cell entropy by assuming that entropy is negatively correlated with cell differentiation state. Higher scores imply the higher stemness
SCENT	SCENT estimates the differentiation potential of a single cell by calculating the signal promiscuity or entropy of the cell transcriptome in the PPI interaction network. Higher scores imply the higher stemness
StemSC	StemSC represents the percentage of gene pairs with the same relative expression orderings as the reference of embryonic stem cell samples. Higher scores imply the higher stemness
GSVA	The score of single cell gene set enrichment analysis (ssGSEA) for our manually collected stemness-related signatures. Higher scores imply the higher stemness
mRNAsi	mRNAsi is a transcriptome stemness index to evaluate the stemness based on the one-class logistic regression machine learning algorithm to extract transcriptomic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Higher scores imply the higher stemness
StemnessIndex	StemnessIndex provides an absolute index to evaluate stemness by comparing the relative expression orderings of the stem cell samples and the normal adult samples from different tissues. Higher scores imply the higher stemness

Methods source

Method	Source	Speed
CytoTRACE	Single-cell transcriptional diversity is a hallmark of developmental potential	Fast
SLICE	SLICE: determining cell differentiation and lineage based on single cell entropy	Mediun
SCENT	Single-cell entropy for accurate estimation of differentiation potency from a cell's transcriptome	Slow
StemID	De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data	Slow
mRNAsi	Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation	Fast
StemSC	StemSC: a cross-dataset human stemness index for single-cell samples	Fast
StemnessIndex	An absolute human stemness index associated with oncogenic dedifferentiation	Fast

Stemness marker gene set

Gene set	Publication	Data	Range
Ben_Eed_targets	An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors	2008/5/1	Pan-cancer
Ben_ES_exp1	An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors	2008/5/1	Pan-cancer
Ben_ES_exp2	An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors	2008/5/1	Pan-cancer
Ben_ES_TFs	An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors	2008/5/1	Pan-cancer
Ben_H3K27_bound	An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors	2008/5/1	Pan-cancer
Ben_Myc_targets1	An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors	2008/5/1	Pan-cancer
Ben_Myc_targets2	An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors	2008/5/1	Pan-cancer
Ben_Nanog_targets	An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors	2008/5/1	Pan-cancer
Ben_NOS_targets	An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors	2008/5/1	Pan-cancer
Ben_NOS_TFs	An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors	2008/5/1	Pan-cancer
Ben_Oct4_targets	An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors	2008/5/1	Pan-cancer
Ben_PRC2_targets	An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors	2008/5/1	Pan-cancer
Ben_Sox2_targets	An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors	2008/5/1	Pan-cancer
Ben_Suz12_targets	An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors	2008/5/1	Pan-cancer
Kim_ES_TFs_ref_m2h	Embryonic stem cell-specific signatures in cancer: insights into genomic regulatory networks and implications for medicine	2011/11/29	Pan-cancer
Kim_et_al_core_m2h	Embryonic stem cell-specific signatures in cancer: insights into genomic regulatory networks and implications for medicine	2011/11/29	Pan-cancer
Kim_et_al_Myc_m2h	Embryonic stem cell-specific signatures in cancer: insights into genomic regulatory networks and implications for medicine	2011/11/29	Pan-cancer
Kim_et_al_PRC_m2h	Embryonic stem cell-specific signatures in cancer: insights into genomic regulatory networks and implications for medicine	2011/11/29	Pan-cancer
Palme_SCGS	A gene expression profile of stem cell pluripotentiality and differentiation is conserved across diverse solid and hematopoietic cancers	2012/8/21	Pan-cancer
Zhang_Stem.Sig	Integrated analysis of single-cell and bulk RNA sequencing data reveals a pan-cancer stemness signature predicting immunotherapy response	2022/4/29	Pan-cancer
Yan_CD133_GBM_up	A CD133-related gene expression signature identifies an aggressive glioblastoma subtype with excessive mutations	2011/1/25	GBM
Mizuno_iPSC118	Inactivation of p53 in breast cancers correlates with stem cell transcriptional signatures	2010/12/28	BRCA
Mizuno_iPSC340	Inactivation of p53 in breast cancers correlates with stem cell transcriptional signatures	2010/12/28	BRCA
Shats_et_al_iPS	A gene expression profile of stem cell pluripotentiality and differentiation is conserved across diverse solid and hematopoietic cancers	2011/3/1	BRCA
Shats_et_al_CSR	A gene expression profile of stem cell pluripotentiality and differentiation is conserved across diverse solid and hematopoietic cancers	2011/3/1	BRCA
VeneziaHSC_cPsig_m2h	Molecular signatures of proliferation and quiescence in hematopoietic stem cells	2004/10/1	Hematopoietic stem cells
VeneziaHSC_ES_m2h	Molecular signatures of proliferation and quiescence in hematopoietic stem cells	2004/10/1	Hematopoietic stem cells

Methods comparison

Accuracy of methods using gold standard data

Jerby-Arnon L, Shah P, Cuoco MS, Rodman C, Su MJ, Melms JC, Leeson R, Kanodia A, Mei S, Lin JR, Wang S, Rabasha B, Liu D, Zhang G, Margolais C, Ashenberg O, Ott PA, Buchbinder EI, Haq R, Hodi FS, Boland GM, Sullivan RJ, Frederick DT, Miao B, Moll T, Flaherty KT, Herlyn M, Jenkins RW, Thummalapalli R, Kowalczyk MS, Cañadas I, Schilling B, Cartwright ANR, Luoma AM, Malu S, Hwu P, Bernatchez C, Forget MA, Barbie DA, Shalek AK, Tirosh I, Sorger PK, Wucherpfennig K, Van Allen EM, Schadendorf D, Johnson BE, Rotem A, Rozenblatt-Rosen O, Garraway LA, Yoon CH, Izar B, Regev A. A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade. Cell. 2018 Nov 1;175(4):984-997.e24. doi: 10.1016/j.cell.2018.09.006. PMID: 30388455; PMCID: PMC6410377.

Remove batch and identify tumor cells

Batch remove

Method	Source
FastMNN	Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Seurat v3 (CCA and RPCA)	Comprehensive Integration of Single-Cell Data
scVI	Deep generative modeling for single-cell transcriptomics
scANVI	Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models
Scanorama	Efficient integration of heterogeneous single-cell transcriptomes using Scanorama
BBKNN	BBKNN: fast batch alignment of single cell transcriptomes
LIGER	Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity
Conos	Joint analysis of heterogeneous single-cell RNA-seq dataset collections
SAUCIE	Exploring single-cell data with deep multitasking neural networks
ComBat	Adjusting batch effects in microarray expression data using empirical Bayes methods
DESC	Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis
trVAE	Conditional out-of-distribution generation for unpaired data using transfer VAE
scGen	scGen predicts single-cell perturbation responses
Harmony	Fast, sensitive and accurate integration of single-cell data with Harmony

Identify tumor cells

inferCNV	Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma
Copykat	Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes
Numbat	Haplotype-aware analysis of somatic copy number variations from single-cell transcriptomes

Contact

Please contact us when you have any questions in Cancer Stemness Online.

Juan Xu,	Email : xujuanbiocc@ems.hrbmu.edu.cn
Yongsheng li,	Email : liyongsheng@hainmc.edu.cn
Laboratory,	Email : bioinformatics2021@163.com