A Gene Regulatory Program in Human Breast Cancer

Supporting Information for Li, Campos, and Iida, 2015

Supporting Information

• Figure S1 - Distributions of age at diagnosis in the three cohorts of breast cancer populations. (.pdf, 14 KB)
• Figure S10 - Validation of regulator-regulon interaction patterns in the Guedj cohort. (.pdf, 95 KB)
• Figure S11 - The relationships between MR16 and the PAM50. (.pdf, 114 KB)
• Table S1 - Mutation types in the top three genes. (.pdf, 11 KB)
• Figure S2 - A schematic framework for machine learning. (.pdf, 26 KB)
• Figure S3 - Gene expression patterns of the MR16 genes selected by machine learning. (.pdf, 37 KB)
• Figure S4 - Classification accuracy as a function of number of genes selected. (.pdf, 81 KB)
• Figure S5 - Gene selection frequency across different partitions of the TCGA samples. (.pdf, 18 KB)
• Figure S6 - Heatmapsof MR16 gene expression in the Curtis and Guedjcohorts. (.pdf, 48 KB)
• Figure S7 - Correlation analyses between representative master regulator genes by subtype. (.pdf, 290 KB)
• Figure S8 - Subtype-specific gene regulatory networks in the TCGA cohort. (.pdf, 72 KB)
• FigureS9 - Validation of regulator-regulon interaction patterns in the Curtis cohort. (.pdf, 144 KB)