Figure 3From: Fast accurate missing SNP genotype local imputationAverage imputation accuracies of the eight methods, fastPHASE (fPH), NN, WNN, SVM, Npute, MC, NeuralNet (NeuN) and BaseLine (BL), on the three different density level mouse datasets: density-0.01, density-0.1 and density-1. Compared to the above missing genotype imputation, the general tendencies are different: 1) in all three density level datasets, our methods NN and WNN performed no differently from fastPHASE, and even slightly better on the density-0.01 datasets; all three of them performed statistically significantly better (by ∼6%, p-values <0.0001) than previously the best method Npute. 2) There are only three groups here, the first group consists of fastPHASE, NN and WNN; the second group includes SVM, Npute, MC and NeuralNet; and the last group contains only BaseLine. 3) Even on the low density datasets, all other methods performed better than BaseLine; on the median and high density datasets, the gaps became significantly larger. It should be pointed out that SVM performed very strangely on the high density datasets with missing rates 5% (and 10%, 20%). We have in fact separately tested multiple times, but the same pattern was always there.Back to article page