Additional file 1: of Comparison of different functional prediction scores using a gene-based permutation model for identifying cancer driver genes
Creators: name: Nono, Alice
name: Chen, Ken
name: Liu, Xiaoming
Publisher: name: figshare
Year: 2019
Subjects: subject: Medicine
subject: Microbiology
subject: FOS: Biological sciences
subject: Genetics
subject: Molecular Biology
subject: Pharmacology
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Mathematical Sciences not elsewhere classified
subject: Hematology
subject: Infectious Diseases
subject: FOS: Health sciences
subject: Computational Biology

description: Figure S1. UPGMA dendrogram comparing the seven prediction scores for breast cancer (Panel A) and lung cancer (Panel B). Figure S2. Percentage of genes with Sum of Most Deleterious Scores D gc o $$ {D}_{gc}^o $$ &gt; 0.0 covered by each of the 7 predictive models in for breast and lung cancer data. Figure S3. Null distribution of the permuted Sum of Most Deleterious Scores D gc m $$ {D}_{gc}^m $$ for the CADD score in TP53 (p-value = 0.000) well-known breast cancer gene; and SLC1A2 (p-value = 0.195) gene not associated with cancer. The red dots and lines indicate the observed values 6.7 for TP53 and 3.7 for SALL4. Figure S4. Quantile–quantile plot of the observed p-values for breast cancer genes (y - axis) against the expected P values of a null distribution (x - axis). The red line represents the expectation under the null hypothesis. The grey area depicts the 95% confidence interval. Figure S5. Quantile–quantile plot of the observed p-values for lung cancer genes (y - axis) against the expected P values of a null distribution (x - axis). The red line represents the expectation under the null hypothesis. The grey area depicts the 95% confidence interval. Figure S6. Proportion of breast candidate driver genes predicted by one, two to three, and more than three permutation models: Panel A- Agreement between CADD, DANN, Fathmm-MKL coding and Fathmm-MKL noncoding; Panel B- Agreement between MetaLR, SPIDEX and VEST3. Figure S7. Comparison of breast candidate genes driver predicted by seven independent permutation models. Panel A- Venn diagram of candidate driver genes predicted by CADD, DANN, Fathmm-MKL coding, and Fathmm-MKL noncoding. Panel B- Venn diagram of candidate driver genes predicted MetaLR, SPIDEX and VEST3. Figure S8. Proportion of lung candidate driver genes predicted by one, two to three, and more than three permutation models: Panel A- Agreement between CADD, DANN, Fathmm-MKL coding and Fathmm-MKL noncoding; Panel B- Agreement between MetaLR, SPIDEX and VEST3. Figure S9. Comparison of lung candidate driver genes predicted by seven independent permutation models. Panel A- Venn diagram of candidate driver genes predicted by CADD, DANN, Fathmm-MKL coding, and Fathmm-MKL noncoding. Panel B- Venn diagram of candidate driver genes predicted MetaLR, SPIDEX and VEST3. Table S1. Summary of methods for scoring somatic mutations (SNVs) deleteriousness. Table S2. Pearson’s correlation Coefficients between the seven predictive scores for breast cancer (Upper Triangle) and lung cancer (Lower Triangle). Table S5. Breast cancer candidate driver genes predicted by one or more permutation models. Table S6. P-values for each permutation model for the 32 breast cancer genes in Cancer Genes Census. Table S7. Lung cancer candidate driver genes predicted by one or more permutation models. Table S8. P-values for each permutation model for the 12 lung cancer genes in Cancer Genes Census. (PDF 1721 kb)