Abstract: Systems and methods for evaluating one or more biological samples are provided. A dataset is obtained from nucleic acid sequencing of the biological samples. The dataset comprises a discrete attribute value for each of a plurality of reference sequences for each entity in a plurality of entities in the biological samples. A two-dimensional spatial arrangement of the plurality of entities is indexed, each entity independently assigned a unique two-dimensional position in a k-dimensional binary search tree, and the spatial arrangement is displayed. A user selection of a subset of the displayed arrangement is received. Each entity that is a member of the subset is determined using the k-dimensional binary search tree, thus identifying a subset of entities. Each entity in the subset of entities is assigned to a user-provided category, and the dataset is modified to store an association of each entity in the subset to the category.

Abstract: Systems and methods for visualizing patterns in discrete attribute value datasets are provided. A dataset comprises a discrete attribute value for each gene in a plurality of genes, for each cell in a plurality of cells. The dataset further comprises ATAC counts for each ATAC peak in a plurality of peaks, for each of the cells. Cells are assigned cluster groups in a first plurality of cluster groups based on a first clustering of discrete attribute values for the genes across the cells. Cell are also assigned cluster groups in a second plurality of cluster groups based on a second clustering of ATAC fragment count values for the ATAC peaks across the cells. A projection of the cells uses one of the first or second cluster group assignments. There is indicated, for each cell within the projection, membership in the other of the first or second cluster group assignments.