Supplementary Figures 1 - 5, Tables 1 - 9 from A Novel Algorithm for Simplification of Complex Gene Classifiers in Cancer
Year: 2023
Type: supplementary-materials
Abstract: PDF file - 714K, Supplementary Figure 1 Two-gene combinations from the 50-gene F1/F2 classifier with >98% classification efficiency in the Williamson data set. Supplementary Figure 2 Thirteen two-gene combinations with >99% classification efficiency, identified from exhaustive analysis of top 2000 genes. Supplementary Figure 3 Gene dyads with classification efficiency >95% identified through exhaustive search of all possible combinations. Supplementary Figure 4 Detection of gene expression is comparable for fresh-frozen and paraformaldehyde-fixed material with nCounter. Supplementary Figure 5 Gene expression detected in each case with the Williamson F1/F2 metagene nCounter Code set. Supplementary Table 1 Four-gene combinations with ≥ 99% accuracy. Supplementary Table 2 Dyad classifiers from T-test ranking of Williamson data set with ≥ 98% accuracy, applied to Davicioni data. Supplementary Table 3 T-test dyad classifier genes found in F1/F2 metagenes (L) and F1/F2 metagene probes excluded from dyad pairs (R). Supplementary Table 4 Top-scoring classifiers from exhaustive search of 1.5 billion gene pairs from Williamson data set, also applied to Davicioni and Exon data. Supplementary Table 5 Tumor sample characteristics. Supplementary Table 6 Genes in nCounter set. Supplementary Table 7 PCA Scores by tumor sample for nCounter Data. Supplementary Table 8 CA Loadings for nCounter Data. Supplementary Table 9 Top-scoring nCounter dyad classifiers. Supplementary Table 10 Top gene combinations - Lung cancer data
Institutions: Children's Medical Center, The University of Texas Southwestern Medical Center, University of Oklahoma Health Sciences Center, University of Southern California, Children's Hospital of Los Angeles, Nationwide Children's Hospital, The Ohio State University, National Cancer Institute, Seattle Children's Hospital, University of Washington, University of Nebraska Medical Center, University of Chicago, Research Center for Informatics
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
OpenAlex ID: W4361258652