Visualizing low-dimensional representations with scatterplots is a crucial step in analyzing single-cell genomic data. However, this visualization has significant biases. The first bias arises when visualizing the gene expression levels or cell identities. The scatterplot only shows a subset of cells plotted last, and the cells plotted earlier are masked and unseen. The second bias arises when comparing the cell type compositions across samples. The scatterplot is biased by the unbalanced total numbers of cells across samples. We developed SCUBI, an unbiased method that visualizes the aggregated information of cells within non-overlapping squares to address the first bias, and visualizes the differences of cell proportions across samples to address the second bias. We show that SCUBI more faithfully visualizes the information in a real single-cell RNA-seq dataset and has the potential to change how low-dimensional representations are visualized in single-cell genomic data.