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公开(公告)号：US20250045923A1
公开(公告)日：2025-02-06
申请号：US18920755
申请日：2024-10-18
Applicant: 10X Genomics, Inc.
Abstract: 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.
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公开(公告)号：US12154266B2
公开(公告)日：2024-11-26
申请号：US16951854
申请日：2020-11-18
Applicant: 10X Genomics, Inc.
Abstract: 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.
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公开(公告)号：US20210155982A1
公开(公告)日：2021-05-27
申请号：US16951864
申请日：2020-11-18
Applicant: 10X Genomics, Inc.
Inventor： Yifeng Yin , Zachary Bent , Stephen Williams , Ian Fiddes , Jeffrey Clark Mellen , Jasper Staab , Kevin J. Wu , Neil Ira Weisenfeld , Florian Baumgartner , Brynn Claypoole , Preyas Shah , Narek Dshkhunyan , Erik Leonard Henrik Borgstrom , Benjamin McCreath
IPC: C12Q1/6869 , C12Q1/6837 , G06T7/194 , G06T7/13 , G06T7/00 , G06N5/00
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.
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公开(公告)号：US20210150707A1
公开(公告)日：2021-05-20
申请号：US16951854
申请日：2020-11-18
Applicant: 10X Genomics, Inc.
Abstract: 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.
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