Fig. 6

Speed and memory usage comparison among imputation methods, as well as the effect of subsampling training data on DeepImpute accuracy. a, b Speed and memory comparisons on the Mouse1M dataset. This dataset is chosen for its largest cell numbers. Color labels different imputation methods. a Speed average over 3 runs. The x-axis is the number of cells, and the y-axis is the running time in minutes (log scale) of the imputation process. b RAM memory usage. The x-axis is the number of cells, and the y-axis is the maximum RAM used by the imputation process. Because of the limited amount of memory or time, scImpute, SAVER, and MAGIC exceeded the memory limit respectively at 10k, 30k, and 50k cells, thus no measurements at these and higher cell counts. VIPER and DrImpute each exceeded 24 h on 1k and 10k cells; therefore, they too do not have measurements at these and higher cell counts. c The effect of subsampling training data on DeepImpute accuracy. Neuron9k dataset is masked and measured for performance as in Fig. 2. x-axis is the fraction of cells in the training data set, and y-axis labels are values for mean squared error (left) and Pearson’s correlation coefficient (right). Color labels are as indicted in the graph. Error bars represent the standard deviations over the 10 repetitions