KeyGenes (Roost et al., 2015) is an algorithm that uses the transcriptional profile of human fetal organs of different gestational ages as well as adult tissues (training set) and can predict the identity of your differentiated samples (test set), provided you input their transciptional data. Keygenes will determines the identity scores of your queried samples (test set) using the transcriptional profiles of the queried data (test set) and matches them to sets of transcriptional profiles of the human organs or cell types (training set). KeyGenes uses a 10-fold cross validation on the basis of a LASSO (Least Absolute Shrinkage and Selection Operator) regression available in the R package “glmnet” (Friedman et al., 2010).
The keygenes algorithm can be run online through the keygenes website: http://keygenes.nl/tool.
The package can be installed using the following commands in R:
library(devtools)
install_github("chuvalab/KeyGenes")Please see the vignette.
Roost, M. S., van Iperen, L., Ariyurek, Y., Buermans, H. P., Arindrarto, W., Devalla, H. D., Passier, R., Mummery, C. L., Carlotti, F., de Koning, E. J. P., van Zwet, E. W., Goeman, J. J., & Chuva de Sousa Lopes, S. M. (2015). KeyGenes, a Tool to Probe Tissue Differentiation Using a Human Fetal Transcriptional Atlas. In Stem Cell Reports (Vol. 4, Issue 6, pp. 1112–1124). Elsevier BV. https://doi.org/10.1016/j.stemcr.2015.05.002
Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization Paths for Generalized Linear Models via Coordinate Descent. In Journal of Statistical Software (Vol. 33, Issue 1). Foundation for Open Access Statistic. https://doi.org/10.18637/jss.v033.i01