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 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 machine learning tissue classification are provided herein. Datasets for a plurality of biological samples are first generated. The dataset of each biological sample includes image data of the biological sample and molecular measurement data of the biological sample captured at a plurality of capture areas of the biological sample. The capture areas of the biological sample are registered to corresponding locations in the image data of the biological sample. Then, a machine learning module is trained with the datasets. Another dataset for another biological sample is generated (e.g., in the same or similar manner as the other datasets). And, the other dataset of the other biological sample is processed through the trained machine learning module to predict features in the other biological sample.

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 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 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.

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: 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.

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.

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.