10x Genomics, Inc.

Neil Ira Weisenfeld

Kevin J Wu

Jeffrey Clark Mellen

Preyas Shah

Dongyao Li

Jasper Staab

Florian Baumgartner

Brynn Claypoole

Narek Dshkhunyan

Michael Schnall-Levin

Augusto Manuel Tentori

Patrick Marks

Denis Pristinski

Zachary Bent

Paul Ryvkin

Yifeng Yin

Ashkan Beyranvand Nejad

Pratomo Alimsijah

Xiaoyan Qian

Stephen R Williams

David Jaffe

Erik Leonard Henrik Borgstrom

Benjamin McCreath

William Nitsch

Reynaldo Farias Zorrilla

Ian Fiddes

Provided herein are methods for image segmentation. An image having a plurality of pixels arranged in a plurality of dimensions is obtained. The image is provided to a pretrained machine learning model and a plurality of amplitudes for each of the plurality of pixel is received from the model. Each of the plurality of amplitudes for each of the plurality of pixels corresponds to exactly one of the plurality of dimensions. At least one instance in the image is identified by: determining a first basin associated with the at least one instance, where the basin consists of a contiguous subset of the plurality of pixels having an amplitude with a magnitude not exceeding first threshold, determining a second subset of the plurality of pixels having an amplitude with a magnitude exceeding the first threshold, where the second subset of pixels bounds the first subset of pixels, and constructing the at least one instance by a flow of the second subset of pixels to the first subset of pixels according to the amplitudes thereof.

Systems and methods for image segmentation

In embodiments, a method includes reading a nuclear segmentation mask of an image including pixels arranged in two dimensions. The nuclear segmentation mask identifies cellular nuclei stained with a cellular nucleus stain. The method includes determining amplitudes for each pixel. Each amplitude corresponds to exactly one dimension of the two dimensions. The method includes constructing a graph having nodes and edges. Each node corresponds to a pixel. Nodes corresponding to neighboring pixels are connected by an edge. The method includes assigning a weight to each edge. Each edge's weight is based on amplitudes of pixels corresponding to nodes connected thereby. The method further includes, based on the graph, determining, for each cellular nuclei, a heat map corresponding to a predicted cell region associated with that cellular nucleus. The method further includes based on the heat maps of the cellular nuclei, determining a cellular segmentation mask comprising predicted cell regions.

Systems and methods for image segmentation using multiple stain indicators

Systems and methods for spatial analysis of analytes are provided. A data structure is obtained comprising an image, as an array of pixel values, of a sample on a substrate having a identifier, fiducial markers and a set of capture spots. The pixel values are used to identify derived fiducial spots. The substrate identifier identifies a template having reference positions for reference fiducial spots and a corresponding coordinate system. The derived fiducial spots are aligned with the reference fiducial spots using an alignment algorithm to obtain a transformation between the derived and reference fiducial spots. The transformation and the template corresponding coordinate system are used to register the image to the set of capture spots. The registered image is then analyzed in conjunction with spatial analyte data associated with each capture spot, thereby performing spatial analysis of analytes.

Systems and methods for spatial analysis of analytes using fiducial alignment

A method of self-testing an imaging system of a sample handling apparatus is provided. Systems and non-transitory computer readable mediums performing the method are also provided.

Self-test for imaging device

Systems and methods for tissue classification are provided. An image of tissue on a substrate is obtained as a plurality of pixels. Fiducial markers are on the substrate boundary. Pixels are assigned to a first class, indicating tissue sample, or a second class, indicating background. The assigning uses the fiducial markers to define a bounding box within the image and disregards pixels outside the box. Then, heuristic classifiers are applied to the pixels: for each respective pixel in the plurality of pixels, each heuristic classifier votes for the respective pixel between the first and second class, thereby forming an aggregated score for each pixel that in one of first class, likely first class, likely second class, and obvious second class. The aggregated score and intensity of each pixel is applied to a segmentation algorithm to assign a probability to each pixel of being tissue sample or background.

Systems and methods for binary tissue classification

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

Systems and methods for identifying morphological patterns in tissue samplers

Described are computer-implemented methods, systems, and media for de novo phased diploid assembly of nucleic acid sequence data generated from a nucleic acid sample of an individual utilizing nucleic acid tags to preserve long-range sequence context for the individual such that a subset of short-read sequence data derived from a common starting sequence shares a common tag. The phased diploid assembly is achieved without alignment to a reference sequence derived from organisms other than the individual. The methods, systems, and media described are computer-resource efficient, allowing scale-up.

Systems, methods, and media for de novo assembly of whole genome sequence data

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Lynnfield, MA, United States of America

Neil Ira Weisenfeld