receptLoss - Unsupervised Identification of Genes with Expression Loss in Subsets of Tumors
receptLoss identifies genes whose expression is lost in subsets of tumors relative to normal tissue. It is particularly well-suited in cases where the number of normal tissue samples is small, as the distribution of gene expression in normal tissue samples is approximated by a Gaussian. Originally designed for identifying nuclear hormone receptor expression loss but can be applied transcriptome wide as well.
Last updated 5 months ago
geneexpressionstatisticalmethod
4.00 score 61 dependenciesoncomix - Identifying Genes Overexpressed in Subsets of Tumors from Tumor-Normal mRNA Expression Data
This package helps identify mRNAs that are overexpressed in subsets of tumors relative to normal tissue. Ideal inputs would be paired tumor-normal data from the same tissue from many patients (>15 pairs). This unsupervised approach relies on the observation that oncogenes are characteristically overexpressed in only a subset of tumors in the population, and may help identify oncogene candidates purely based on differences in mRNA expression between previously unknown subtypes.
Last updated 5 months ago
geneexpressionsequencing
3.78 score 56 dependencies