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Additional file 2. Figure S1: Tumor Mutation Burden compared between grade 1, 2 and 3 breast cancer patients. The redline indicates median TMB. Figure S2: Kaplan–Meier survival analyses stratified by CBFB, HERC2 and TP53 gene mutations. Figure S3: Tumor Mutation Burden compared between ER/PR+veHER2-ve, ER/PR+veHER2+ve, ER/PR-ve HER2+ve, and Triple-negative breast cancer patients. The redline indicates median TMB. Figure S4: Distribution of patient cohort across the hormonal subtypes. The upper panel of the figure provides the distribution of tumor mutation burden for each patient assigned to ER/PR+veHER2-ve, ER/PR+veHER2+ve, ER/PR-ve HER2+ve, or Triple-negative. Patient characteristics, including vital status, family history, subtype status, and age, are also included for each group. The bottom panel represents the top mutated genes across the three groups. The percentage of deleterious variants in each gene is also represented, along with the type of mutation detected. Figure S5: Distribution of patient cohort across the histological subtypes. The upper panel of the figure provides the distribution of tumor mutation burden for each patient assigned to Invasive lobular carcinoma (ILC), Invasive ductal carcinoma (IDC), and Other. Patient characteristics, including vital status, family history, subtype status, and age, are also included for each group. The bottom panel represents the top mutated genes across the three groups. The percentage of deleterious variants in each gene is also represented, along with the type of mutation detected. Figure S6: Tumor Mutation Burden compared across the histological subtypes. The redline indicates median TMB.

Because of the high operation costs involved in microarray experiments, the determination of the number of replicates required to detect a gene significantly differentially expressed in a given multiple-testing procedure is of considerable significance. Calculation of power/replicate numbers required in multiple-testing procedures provides design guidance for microarray experiments. Based on this model and by choice of a multiple-testing procedure, expression noises based on permutation resampling can be considerably minimized. The method for mixture distribution model is suitable to various microarray data types obtained from single noise sources, or from multiple noise sources. By using the biological replicate number required in microarray experiments for a given power or by determining the power required to detect a gene significantly differentially expressed, given the sample size, or the best multiple-testing method can be chosen. As an example, a single-distribution model of t-statistic was fitted to an observed microarray dataset of 3 000 genes responsive to stroke in rat, and then used to calculate powers of four popular multiple-testing procedures to detect a gene of an expression change D. The results show that the B-procedure had the lowest power to detect a gene of small change among the multiple-testing procedures, whereas the BH-procedure had the highest power. However, all multiple-testing procedures had the same power to identify a gene having the largest change. Similar to a single test, the power of the BH-procedure to detect a small change does not vary as the number of genes increases, but powers of the other three multiple-testing procedures decline as the number of genes increases.