Abstract: A discrete attribute value dataset is obtained that is associated with a plurality of probe spots each assigned a different probe spot barcode. The dataset comprises spatial projections, each comprising images of a biological sample. Each image includes a corresponding plurality of discrete attribute values for the probe spots. Each such value is associated with a probe spot in the plurality of probes spots based on the probe spot barcodes. The dataset is clustered using the discrete attribute values, or dimension reduction components thereof, for a plurality of loci at each respective probe spot across the images of the projections thereby assigning each probe spot to a cluster in a plurality of clusters. Morphological patterns are identified from the spatial arrangement of the probe spots in the various clusters.

Abstract: A dataset is obtained comprising data blocks, each representing a different characteristic, for a plurality of cells across a plurality of bins, each bin representing a different portion of a reference sequence. Cells are clustered on one such characteristic across the bins thereby forming a tree that includes root, intermediate, and terminal nodes, where the cells are terminal nodes and intermediate nodes have daughter nodes, themselves being intermediate nodes or a cell. A subset of the tree is displayed that includes the root and leaves, each leaf representing an intermediate node or a cell. A heat map of the characteristic is also displayed, the map including a segment for each leaf, across the bins. When a segment represents an intermediate node, it is an average of the characteristic across daughters of the node. Graphs of characteristics for the root across the bins are also displayed.

Abstract: A discrete attribute value dataset is obtained that is associated with a plurality of probe spots each assigned a different probe spot barcode. The dataset comprises spatial projections, each comprising images of a biological sample. Each image includes a corresponding plurality of discrete attribute values for the probe spots. Each such value is associated with a probe spot in the plurality of probes spots based on the probe spot barcodes. The dataset is clustered using the discrete attribute values, or dimension reduction components thereof, for a plurality of loci at each respective probe spot across the images of the projections thereby assigning each probe spot to a cluster in a plurality of clusters. Morphological patterns are identified from the spatial arrangement of the probe spots in the various clusters.

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: A discrete attribute value dataset is obtained that is associated with a plurality of probe spots each assigned a different probe spot barcode. The dataset comprises spatial projections, each comprising images of a biological sample. Each image includes a corresponding plurality of discrete attribute values for the probe spots. Each such value is associated with a probe spot in the plurality of probes spots based on the probe spot barcodes. The dataset is clustered using the discrete attribute values, or dimension reduction components thereof, for a plurality of loci at each respective probe spot across the images of the projections thereby assigning each probe spot to a cluster in a plurality of clusters. Morphological patterns are identified from the spatial arrangement of the probe spots in the various clusters.

Abstract: A dataset is obtained comprising data blocks, each representing a different characteristic, for a plurality of cells across a plurality of bins, each bin representing a different portion of a reference sequence. Cells are clustered on one such characteristic across the bins thereby forming a tree that includes root, intermediate, and terminal nodes, where the cells are terminal nodes and intermediate nodes have daughter nodes, themselves being intermediate nodes or a cell. A subset of the tree is displayed that includes the root and leaves, each leaf representing an intermediate node or a cell. A heat map of the characteristic is also displayed, the map including a segment for each leaf, across the bins. When a segment represents an intermediate node, it is an average of the characteristic across daughters of the node. Graphs of characteristics for the root across the bins are also displayed.

Abstract: A discrete attribute value dataset is obtained that is associated with a plurality of probe spots each assigned a different probe spot barcode. The dataset comprises spatial projections, each comprising images of a biological sample. Each image includes a corresponding plurality of discrete attribute values for the probe spots. Each such value is associated with a probe spot in the plurality of probes spots based on the probe spot barcodes. The dataset is clustered using the discrete attribute values, or dimension reduction components thereof, for a plurality of loci at each respective probe spot across the images of the projections thereby assigning each probe spot to a cluster in a plurality of clusters. Morphological patterns are identified from the spatial arrangement of the probe spots in the various clusters.

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.

Abstract: Systems and methods for spatial analysis of analytes include placing a sample on a substrate having fiducial markers and capture spots. Then, an image of the sample is acquired and sequence reads are obtained from the capture spots. Each capture probe plurality in a set of capture probe pluralities is (i) at a different capture spot, (ii) directly or indirectly associates with analytes from the sample and (iii) has a unique spatial barcode. The sequencing reads serve to detect the analytes. Sequencing reads include a spatial barcode of the corresponding capture probe plurality. Spatial barcodes localize reads to corresponding capture spots, thereby dividing them into subsets, each subset for a respective capture spot. Fiducial markers facilitate a composite representation comprising (i) the image aligned to the capture spots and (ii) a representation of each subset of sequence reads at respective positions within the image mapping to the corresponding capture spots.

Abstract: A discrete attribute value dataset is obtained that is associated with a plurality of probe spots each assigned a different probe spot barcode. The dataset comprises spatial projections, each comprising images of a biological sample. Each image includes a corresponding plurality of discrete attribute values for the probe spots. Each such value is associated with a probe spot in the plurality of probes spots based on the probe spot barcodes. The dataset is clustered using the discrete attribute values, or dimension reduction components thereof, for a plurality of loci at each respective probe spot across the images of the projections thereby assigning each probe spot to a cluster in a plurality of clusters. Morphological patterns are identified from the spatial arrangement of the probe spots in the various clusters.

Abstract: A dataset is obtained comprising data blocks, each representing a different characteristic, for a plurality of cells across a plurality of bins, each bin representing a different portion of a reference sequence. Cells are clustered on one such characteristic across the bins thereby forming a tree that includes root, intermediate, and terminal nodes, where the cells are terminal nodes and intermediate nodes have daughter nodes, themselves being intermediate nodes or a cell. A subset of the tree is displayed that includes the root and leaves, each leaf representing an intermediate node or a cell. A heat map of the characteristic is also displayed, the map including a segment for each leaf, across the bins. When a segment represents an intermediate node, it is an average of the characteristic across daughters of the node. Graphs of characteristics for the root across the bins are also displayed.