Research map / Probabilistic and scientific modeling / Molecules and proteins
Finding significant combinations of features in the presence of categorical covariates
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| condition on ancestry covariate (FACS vs LAMP-chi2) on LY GWAS | FACS · A. thaliana LY | significant combinations: 100,883 -> 433 | Helped |
| condition on ancestry covariate (FACS vs LAMP-chi2) on LY GWAS | FACS · A. thaliana LY | genomic inflation factor: 3.18 -> 1.17 | Helped |
| condition on ancestry covariate (FACS vs LAMP-chi2) on avrB | FACS · A. thaliana avrB | significant combinations: 546 -> 43 | Helped |
| condition on ancestry covariate on avrB | FACS · A. thaliana avrB | genomic inflation factor: 2.38 -> 1.21 | Helped |
| use Tarone testability instead of Bonferroni with CMH test (ablation) | FACS vs BONF-CMH · A. thaliana LY | significant combinations: 19 -> 433 | Helped |
| use Tarone testability instead of Bonferroni with CMH test (ablation) | FACS vs BONF-CMH · A. thaliana avrB | significant combinations: 1 -> 43 | Helped |
| condition on 8 and 16 category covariate grouping genes by motifs | FACS · Breast cancer motif data | significant motif combinations: 1, 214 -> 26 | Helped |
| FACS O(k log k) pruning vs 2k-FACS and mk-FACS as covariate cardinality k grows (ablation) | FACS · Simulated data, runtime vs k | runtime | Helped |
| add covariate correction (FACS vs LAMP-chi2) as number of features p grows | FACS · Simulated data, runtime vs p | runtime | No clear effect |
| Tarone correction instead of Bonferroni (FACS vs Bonf-CMH) as p grows (ablation) | FACS · Simulated data, runtime vs p | runtime | Helped |
| FACS vs LAMP-chi2 across signal strength | FACS · Simulated data, precision | precision | Mixed |
| condition on categorical covariate (FACS vs LAMP-chi2) under confounding | FACS · Simulated data, false positive detection | false detection proportion | Helped |
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