Heterogeneity in transcriptional data hampers the identification of differentially expressed genes (DEGs) and understanding of cancer, essentially because current methods rely on cross-sample normalization and/or distribution assumption—both sensitive to heterogeneous values. Here, we developed a new method, Cross-Value Association Analysis (CVAA), which overcomes the limitation and is more robust to heterogeneous data than the other methods.
devtools::install_github("liqg/CVAA")See R help page for how to use.
library(CVAA)
?CVAALi, Q. G., He, Y. H., Wu, H., Yang, C. P., Pu, S. Y., Fan, S. Q., ... & Kong, Q. P. (2017). A normalization-free and nonparametric method sharpens large-scale transcriptome analysis and reveals common gene alteration patterns in cancers. Theranostics, 7(11), 2888.