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.
Upload data and choose method to assess stemness scores
By Model Type
By Input Type
By Model Type
The methods are categorized into 'Unsupervised' and 'Supervised' according to the calculation model type.
Unsupervised methods
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 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 assesses stem cells among all detectable cell types within a population by utilizing tree topology and transcriptome composition. Higher scores imply the higher stemness.
The score of single cell gene set enrichment analysis (ssGSEA) for our manually collected stemness-related signatures. Higher scores imply the higher stemness.
Supervised methods
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.
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.
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.
By Input Type
Users can choose method by the type of uploading data.
Bulk
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 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.
The score of single cell gene set enrichment analysis (ssGSEA) for our manually collected stemness-related signatures. Higher scores imply the higher stemness.
Single cell
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 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 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 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 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.
The score of single cell gene set enrichment analysis (ssGSEA) for our manually collected stemness-related signatures. Higher scores imply the higher stemness.