Examples: The Selective Expression Algorithm Applied to Synthetic, yet Realistic, Data
| Example | Baseline adjusted | λ[ii] | gap[iii] | τ[iv] | log10(sp)[v] | d [vi] | Comments |
| 1a | no | 0.55 | 0.67 | −6.26 | 0.33 | reference example | |
| 1b | yes | 1.00 | 0.55 | 0.67 | −6.27 | 0.33 | same as 1a; λ has no effect[vii] |
| 2a | no | 0.28 | 0.68 | −6.26 | 0.24 | d different from 1a due to gap only | |
| 2b | yes | 0.99 | 0.28 | 0.68 | −6.28 | 0.24 | d different from 1a due to gap; λ has no effect[vii] |
| 3a | no | 0.28 | 0.68 | −6.26 | 0.24 | d different from 1a due to gap only | |
| 3b | yes | 0.87 | 0.28 | 0.58 | −4.90 | 0.00 | d different from 1a due to λ−adjusted log10(sp) > −5, hence d = 0[viii] |
[i] The example identification (1, 2, or 3) corresponds to Fig. 9; whether a baseline compression adjustment was omitted (a) or used (b) in the discordancy computation (equation 6 or 11). The intensities and source quality weights are from Table 3.
[ii] Equation { label needed for disp-formula[@id='E9'] }.
[iii] Equation { label needed for disp-formula[@id='E3'] }.
[iv] Equation { label needed for disp-formula[@id='E4'] }.
[v] Equation { label needed for disp-formula[@id='E6'] } or 11.
[vi] Equation { label needed for disp-formula[@id='E15'] }. Equation { label needed for disp-formula[@id='E9'] } sigmoidal parameters areb = 10 and c = 0.8. The parameter values in the decision function d (equations 13–15) are α = β = γ = 1.5, δ = 0.3, g thresh = 0.25, log10(sp)thresh = −5, and log10(sp)∞ = −20.
[vii] λ has no effect, as the baseline (i.e., ∼0.3, Table 3) is distant from the maximum allowed intensity (i.e., 1).
[viii] λ has non-negligible effect, as the baseline is near (i.e., 0.67, Table 3) the maximum intensity.