Description Usage Arguments Details Author(s) Examples
This function generates a Venn diagram using RNA-seq data from the The Cancer Genome Atlas (TCGA) database. Users specify which cancer types to include (varselectVenn currently supports 2- and 3-set Venn diagrams), as well as the target variable to predict. The user-specified data is processed by a random forest classifier, and the variables (genes) are ranked by their influence on the model’s predictive power. Users specify how many of the high-importance genes to retain, and a Venn diagram is generated that shows which genes are of high-importance among the different cancer types. The function also returns a list object that specifies which genes were retained for each cancer type, as well as which genes were at the intersection of all specified cancers types.
1 | varselectVenn(types, num_var, target)
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types |
A vector of TCGA-supported acronyms that designate the type of cancer. varselectVenn currently supports ACC, BLCA, KIRC, KIRP, LIHC, and THCA. varselectVenn currently supports 2- and 3-set diagrams |
num_var |
a numeric that specifies how many of the high-importance variables to retain. The random forest classifier will rank each of the 20501 genes features in the order of their impact on the model. Setting num_var to 100 for example, will retain the 100 most important variables for each cancer type to include in the Venn diagram. |
target |
the variable to be predicted. varselectVenn currently supports tumor “patholigicstage” (which attempts to distinguish stage I tumors from stage II, III, and IV tumors), the patient’s “vitalststus” (a binary for whether the patient is alive or not), and the patient’s “gender”. |
Cancer type acronyms: ACC Adrenocortical Carcinoma, BLCA Bladder Urothelial Carcinoma, KIRC Kidney Renal Clear Cell Carcinoma, KIRP Kidney Renal Papillary Cell Carcinoma, LIHC Liver Heptocellular Carcinoma, THCA Thyroid Carcinoma
Jacob Blamer, jwilliamblamer@gmail.com
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