to 15665. 
We calculated top principal components and VAMF factors using default parameters. In both methods, top component best explains the discrepancy among the cells (PC1: 8.1%; VAMF dimension 1 dimension learning: 47.3%). The primary principal component strongly correlates with the detection rate in cells (Spearman r = -0.99), while the primary VAMF factor correlates not as strongly (Figure; Spearman r = 0.65). Although the second VAMF factor is strongly correlated with the detection rate (Spearman r = -0.93), the percentage of dimension learning by this is relatively negligible (VAMF dimension 2: 15.9%). This suggests that VAMF is not biased by detection rate as PCA, reconfirming VAMF's performance. However, since the detection-rate distributions in the batches from our dataset are similar (Figure), VAMF was not able to remove the batch effects in this dataset (Figure).