SYSTEMS AND METHODS FOR IDENTIFYING MORPHOLOGICAL PATTERNS IN TISSUE SAMPLES
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.
1 . A computer system comprising one or more processing cores, a memory, and a display, wherein the memory stores instructions for performing a method for identifying a morphological pattern, the method comprising:
A) obtaining a discrete attribute value dataset associated with a plurality of probe spots having a spatial arrangement, wherein each probe spot in the plurality of probe spots is assigned a unique barcode in a plurality of barcodes and the plurality of probe spots comprises at least 1000 probe spots, the discrete attribute value dataset comprising:
(i) a two-dimensional image taken of a tissue section, obtained from the biological sample, overlaid on a substrate having the plurality of probe spots arranged in the spatial arrangement, and
(ii) a corresponding plurality of discrete attribute values for each respective probe spot in the plurality of probe spots, wherein each respective discrete attribute value in the corresponding plurality of discrete attribute values is for a locus in a plurality of loci;
B) obtaining a corresponding cluster assignment in a plurality of clusters, of each respective probe spot in at least a subset of the plurality of probe spots of the discrete attribute value dataset, wherein the corresponding cluster assignment is based, at least in part, on the corresponding plurality of discrete attribute values of the respective probe spot, or a corresponding plurality of dimension reduction components derived, at least in part, from the corresponding plurality of discrete attribute values of the respective probe spot;
C) displaying, on the display, pixel values of all or portion of the two-dimensional image; and
D) overlaying on the two-dimensional image first indicia for one or more probe spots in the plurality of probe spots that have been assigned to a first cluster in the plurality of clusters, thereby identifying the morphological pattern.
2 - 8 . (canceled)
9 . The computer system of claim 1 , wherein:
the two-dimensional image is acquired using Haemotoxylin and Eosin, a Periodic acid-Schiff reaction stain, a Masson's trichrome stain, an Alcian blue stain, a van Gieson stain, a reticulin stain, an Azan stain, a Giemsa stain, a Toluidine blue stain, an isamin blue/eosin stain, a Nissl and methylene blue stain, a sudan black and/or osmium staining of the biological sample.
10 . The computer system of claim 1 , the method further comprising:
storing the two-dimensional image in a first schema, wherein the first schema comprises a first number of tiles; and
storing the two-dimensional image in a second schema, wherein the second schema comprises a second number of tiles, wherein the second number of tiles is less than the first number of tiles.
11 . The computer system of claim 10 , the method further comprising, responsive to receiving display instructions from a user;
switching from the first schema to the second schema in order to display all or a portion of the two-dimensional image, or
switching from the second schema to the first schema in order to display all or a portion of the two-dimensional image.
12 . The computer system of claim 10 , wherein:
at least a first tile in the first number of tiles comprises a first predetermined tile size,
at least a second tile in the first number of tiles comprises a second predetermined tile size, and
at least a first tile in the second number of tiles comprises of a third predetermined tile size.
13 . (canceled)
14 . The computer system of claim 1 , wherein each respective cluster in the plurality of clusters consists of a unique subset of the plurality of probe spots.
15 . The computer system of claim 1 , wherein;
each locus in the plurality of loci is a respective gene in a plurality of genes, and
each discrete attribute value in the corresponding plurality of discrete attribute values is a count of unique molecular identifiers that map to a corresponding probe spot and that also map to a respective gene in the plurality of genes.
16 . The computer system of claim 15 , wherein the discrete attribute value dataset represents a whole transcriptome sequencing experiment that quantifies gene expression in counts of transcript reads mapped to the plurality of genes or the discrete attribute value dataset represents a targeted transcriptome sequencing experiment that quantifies gene expression in unique molecular identifier counts mapped to probes in the plurality of probes.
17 . The computer system of claim 1 , wherein
the first indicia is a first graphic or a first color.
18 . The computer system of claim 1 , wherein;
each locus in the plurality of loci is a respective feature in a plurality of features,
each discrete attribute value in the corresponding plurality of discrete attribute values is a count of unique molecular identifiers that map to a corresponding probe spot and that also map to a respective feature in the plurality of features, and
each feature in the plurality of features is an open-reading frame, an intron, an exon, an entire gene, an RNA transcript, a predetermined non-coding part of a reference genome, an enhancer, a repressor, a predetermined sequence encoding a variant allele, or any combination thereof.
19 - 20 . (canceled)
21 . The computer system of claim 1 , wherein each probe spot in the plurality of probe spots is assigned a unique barcode in a plurality of barcodes.
22 . The computer system of claim 1 , wherein the plurality of probe spots comprises at least 1000 probe spots.
23 . The computer system of claim 1 , wherein the two-dimensional image comprises at least 100,000 pixel values.
24 . The computer system of claim 1 , wherein the overlaying D) (i) co-aligns the first indicia for the one or more probe spots in the plurality of probe spots that have been assigned to a first cluster in the plurality of clusters with the two-dimensional image, and (ii) further comprises overlaying, on the two-dimensional image, second indicia for each probe spot in the plurality of probe spots that have been assigned to a second cluster in the plurality of clusters.
25 . The computer system of claim 1 , wherein each respective probe spot in the plurality of probe spots has a center to center distance to a neighboring probe spot in the plurality of probe spots of 100 UM or less.
26 . The computer system of claim 1 , the method further comprising, for each respective probe spot overlayed on the two-dimensional image, displaying a corresponding discrete attribute value, associated with the respective probe spot, for a first locus in the plurality of loci.
27 . The method of claim 26 , wherein the corresponding discrete attribute value is a unique molecular identifier count indicating a number of copies of a product of the first locus that were detected in the respective probe spot.
28 . The method of claim 26 , wherein the corresponding discrete attribute value is displayed in color coded log-space in accordance with a log-space heat map scale.
29 . A non-transitory computer-readable medium storing one or more computer programs executable by a computer for identifying a morphological pattern, the computer comprising a memory, the one or more computer programs collectively encoding computer executable instructions for performing a method comprising:
A) obtaining a discrete attribute value dataset associated with a plurality of probe spots having a spatial arrangement, wherein each probe spot in the plurality of probe spots is assigned a unique barcode in a plurality of barcodes and the plurality of probe spots comprises at least 1000 probe spots, the discrete attribute value dataset comprising:
(i) a two-dimensional image taken of a tissue section, obtained from the biological sample, overlaid on a substrate having the plurality of probe spots arranged in the spatial arrangement, and
(ii) a corresponding plurality of discrete attribute values for each respective probe spot in the plurality of probe spots, wherein each respective discrete attribute value in the corresponding plurality of discrete attribute values is for a locus in a plurality of loci;
B) obtaining a corresponding cluster assignment in a plurality of clusters, of each respective probe spot in at least a subset of the plurality of probe spots of the discrete attribute value dataset, wherein the corresponding cluster assignment is based, at least in part, on the corresponding plurality of discrete attribute values of the respective probe spot, or a corresponding plurality of dimension reduction components derived, at least in part, from the corresponding plurality of discrete attribute values of the respective probe spot;
C) displaying, on the display, pixel values of all or portion of a first the two-dimensional image; and
D) overlaying on the two-dimensional image first indicia for one or more probe spots in the plurality of probe spots that have been assigned to a first cluster in the plurality of clusters, thereby identifying the morphological pattern.
30 . The non-transitory computer-readable medium of claim 29 , wherein each probe spot in the plurality of probe spots is assigned a unique barcode in a plurality of barcodes.