10x Genomics, Inc.

Jasper Staab

Zachary Bent

Yifeng Yin

Kevin J Wu

Jeffrey Clark Mellen

Neil Ira Weisenfeld

Vijay Kumar Sreenivasa Gopalan

Preyas Shah

Brynn Claypoole

Florian Baumgartner

Nicolaus Lance Hepler

Narek Dshkhunyan

Arundhati Shamoni Maheshwari

Stephen R Williams

Li Wang

Jessica Hamel

Benjamin McCreath

Ian Fiddes

Erik Leonard Henrik Borgstrom

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.

Systems and methods for joint interactive visualization of gene expression and DNA chromatin accessibility

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.

Systems and methods for identifying morphological patterns in tissue samples

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.

Systems and methods for visualization of single-cell resolution characteristics

Systems and methods for identifying morphological patterns in tissue samplers

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Honolulu, HI, United States of America

Jasper Staab