Jitter corrected image analysis

Abstract

Systems, methods, and apparatuses are described herein. For example, a detection device may include a memory and at least one processor. The detection device may be configured to obtain an image including at least one feature and a plurality of fiducials. The plurality of fiducials may be arranged in a pattern. The detection device may be configured to determine a plurality of sub-regions of the image. Each sub-region includes a subset of references included in the image. The detection device may be configured to perform a geometric transformation on each sub-region to generate a respective local transformation associated with each sub-region. The detection device may be configured to register a respective position of a fiducial included in the image based on the respective local transformation associated with each sub-region. Each sub-region may be sized such that each sub-region is substantially unchanged for sample stage jitter.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 441,606, filed on January 27, 2023, which is incorporated herein by reference in its entirety. Background Art

[0003] In recent years, biotechnology companies and research institutions have improved hardware and software platforms to determine the sequence of nucleotide bases (or whole genomes) and identify variant calls of nucleotide bases that are different from the reference bases of the reference genome. Many of these platforms utilize image detection systems to capture and resolve sequencing images of nucleotide bases by using, for example, line scanning and confocal control of imaging optics. However, high-frequency movement (i.e., jitter) of the sample stage on the line scanning system typically causes nonlinear perturbations of cluster positions within the sequencing image, thereby negatively affecting data quality. Sample stage jitter may disturb the position of spots corresponding to deoxyribonucleic acid (DNA) molecule clusters within the circulation pool relative to the reference of the sequencing image. Additionally, sample stage jitter may also disturb the reference. Therefore, the spot position may be inaccurate. For example, Fig. 9 is an example heat map illustrating sample stage jitter present in both the X and Y directions of tile 900 during a conventional imaging cycle. Top curve 904a illustrates movement of fiducial 902 in the X direction 908, where region 906 of the heat map illustrates movement of fiducial 902 caused by sample stage jitter. Bottom curve 904b illustrates movement of fiducial 902 in the Y direction 912, where region 910 of the heat map illustrates movement of fiducial 902 caused by sample stage jitter. Summary of the invention

[0004] Systems and methods for image recognition and / or image processing are described that take into account and correct errors introduced by high-frequency jitter in an image capture device. For example, an image detection device may correct for high-frequency motion (i.e., jitter) of a sample stage that causes inconsistent movement of the position of a spot within an image. The spot may correspond to a cluster of DNA molecules within a hole of a circulation cell. The image detection device may obtain an image of an object (e.g., a circulation cell) having a plurality of fiducials arranged in a pattern. An example of a fiducial pattern may include another set of four fiducials arranged linearly adjacent to a set of four fiducials. Another exemplary fiducial pattern may include another set of nine fiducials arranged linearly adjacent to a set of nine fiducials, wherein a set of two fiducials is centrally disposed between a first set of nine fiducials and a second set of nine fiducials. The image detection device may generate the positions of these fiducials and decompose the image into sub-regions such that each sub-region includes a subset of the fiducials. The image detection device may construct the size of each sub-region such that each sub-region is substantially invariant to sample stage jitter. The image detection device may perform a transformation (i.e., an affine transformation) on each sub-region to generate an arrangement of the fiducials within each sub-region (e.g., a matrix of the affine transformation performed on each sub-region). Based on the generated arrangement of the fiducials, the image detection device may determine the position of the spot of the circulation pool. That is, the image detection device may use each determined matrix to register the corresponding positions of the fiducials, while taking into account jitter and determining the location of the position of the spot within the image.

[0005] In another example, a sequencing system may obtain an image including at least one feature and a plurality of fiducials. The plurality of fiducials may be arranged in a pattern. The sequencing system may determine a plurality of sub-regions of the image. Each sub-region may include a subset of the fiducials included in the image. The sequencing system may perform a geometric transformation on each sub-region to generate a corresponding local transformation associated with each sub-region. The sequencing system may register the corresponding positions of the fiducials included in the image based on the corresponding local transformation associated with each sub-region. In an example, the size of each sub-region is selected so that each sub-region is substantially invariant to sample stage jitter. In one or more cases, the sequencing system may determine the corresponding position of each of the fiducials in the image based on the determined position of the reference fiducial. In addition, the sequencing system may generate a sub-image based on the obtained image. In an example, the sub-image includes a pixel fill of the reference fiducial with a larger area included in the image. After generating the sub-image based on the obtained image, the sequencing system may determine a correlation between the obtained image and the sub-image. In addition, the sequencing system may determine whether to adjust the position of the reference fiducial based on the determined correlation. In one or more cases, each subset of fiducials included in the sub-regions may include at least three fiducials. In one or more cases, the sub-regions may be arranged linearly with respect to each other. In one or more cases, the geometric transformation includes an affine transformation.

[0006] In another example, a sequencing method may include obtaining an image including at least one feature and a plurality of fiducials. The plurality of fiducials may be arranged in a pattern. The sequencing method may include determining a plurality of sub-regions of the image. Each sub-region includes a subset of the fiducials included in the image. The sequencing method may include performing a geometric transformation on each sub-region to generate a corresponding local transformation associated with each sub-region. The sequencing method may include aligning the corresponding positions of the fiducials included in the image based on the corresponding local transformation associated with each sub-region. In an example, the size of each sub-region is selected so that each sub-region is substantially invariant to sample stage jitter. In one or more cases, the sub-regions are arranged linearly with respect to each other. In one or more cases, each subset of the fiducials included in the sub-regions includes at least three fiducials. In one or more cases, a reference fiducial is located within the image, and the sequencing method includes determining the corresponding position of each of the fiducials in the image based on the determined position of the reference fiducial. In one or more cases, the sequencing method includes generating a sub-image based on the obtained image. In an example, the sub-image includes a pixel fill of the reference fiducial with a larger area included in the image. In one or more cases, the sequencing method includes determining a correlation between the acquired image and the sub-image. In one or more cases, the sequencing method may determine whether to adjust the position of the reference datum based on the correlation determined. In an example, the size of each sub-region is selected so that each sub-region is substantially invariant to sample stage jitter of approximately 200 Hz or less. In an example, at least one datum is associated with two adjacent sub-regions. In an example, the pattern of the multiple datums includes a first group of linearly arranged datums adjacent to a second group of linearly arranged datums. In addition, the first group of linearly arranged datums and the second group of linearly arranged datums extend substantially parallel to each other. In an example, the image includes a sequencing image. In an example, the position of the feature is associated with a hole position of a circulation pool. In an example, each sub-region includes a corresponding feature. In addition, the corresponding local transformation associated with the sub-region including the corresponding feature is used to determine the position of the corresponding feature.

[0007] In another example, a computer-readable medium may include computer-readable instructions that, when executed by a processor, cause the processor to implement a sequencing method. In one or more cases, the sequencing method may include obtaining an image including at least one feature and a plurality of fiducials. In an example, the plurality of fiducials may be arranged in a pattern. In one or more cases, the sequencing method includes determining a plurality of sub-regions of the image. In an example, each sub-region includes a subset of the fiducials included in the image. In one or more cases, the sequencing method includes performing a geometric transformation on each sub-region to generate a corresponding local transformation associated with each sub-region. In one or more cases, the sequencing method includes aligning the corresponding positions of the fiducials included in the image based on the corresponding local transformation associated with each sub-region. In an example, the size of each sub-region is selected so that each sub-region is substantially invariant to sample stage jitter. In an example, the sub-regions are arranged linearly with respect to each other. In an example, each subset of the fiducials included in the sub-region includes at least three fiducials. In an example, a reference fiducial is located within the image, and the position of the reference fiducial is used to determine the corresponding position of each of the fiducials in the image. In one or more cases, the sequencing method includes generating a sub-image based on the obtained image. In an example, the sub-image includes a pixel fill of the reference datum having a larger area included in the image. In addition, in one or more cases, the sequencing method includes determining a correlation between the obtained image and the sub-image. Additionally, in one or more cases, the sequencing method may determine whether to adjust the position of the reference datum based on the correlation determined. In an example, the size of each sub-region is selected so that each sub-region is substantially unchanged for sample stage jitter of about 200 Hz or less. In an example, at least one datum is associated with two adjacent sub-regions. In an example, the pattern of the multiple datums includes a first group of linearly arranged datums adjacent to a second group of linearly arranged datums. In addition, the first group of linearly arranged datums and the second group of linearly arranged datums extend substantially parallel to each other. In an example, the image includes a sequencing image. In an example, the position of the feature is associated with a hole position of a circulation pool. In an example, each sub-region includes a corresponding feature, and the corresponding local transformation associated with the sub-region including the corresponding feature is used to determine the position of the corresponding feature. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figures 1A to 1C An example fiducial arrangement of an example sequencing image is illustrated.

[0009] Figure 2A A schematic diagram illustrating an example system environment is shown.

[0010] Figure 2B An example image scanning system is illustrated.

[0011] Figure 2C An example line scan imaging system is illustrated.

[0012] Figure 3 is a flow chart illustrating an example image calibration process

[0013] Figure 4 is a flow chart illustrating execution of an example imaging process to correct for shake.

[0014] Figure 5A and Figure 5B Example transformations of a number of example sub-regions of an example image are illustrated.

[0015] Fig. 6A and Figure 6B An example imaging data subset is illustrated.

[0016] 7A to 7D Execution metrics of an example imaging process that corrects for jitter are illustrated.

[0017] Figure 8 is a block diagram of an example computing device.

[0018] Fig. 9 is an example heat map illustrating the jitter present in both the X and Y directions of a tile during a conventional imaging cycle. DETAILED DESCRIPTION

[0019] The present disclosure relates to processing image data and correcting image distortion during imaging of a patterned array having a plurality of repeating spots or fiducials. Processing of the patterned array produces image data for analyzing the array (or any other form of detection output of sites on the array), such as image data for biological sample analysis. Biological sample analysis may include, but is not limited to, for example, nucleic acid analysis, protein analysis, cell analysis, etc. The array may contain a repeating pattern of features to be resolved in the low micron or submicron resolution range. Although the systems, devices, and methods described herein may be described with respect to regular patterns of analytical features, it should be understood that they may also be used for random distributions of features.

[0020] It should be noted that, as used herein, "patterned arrays" may include, but are not limited to, sequencing arrays formed as patterned flow cells. Such arrays include holes in which analytes can be positioned for processing and analysis. The holes can be arranged on one or more surfaces of a substrate (e.g., a flow cell) in a repeating pattern, a non-repeating pattern, or a random arrangement. For simplicity, all such devices are referred to as the terms "patterned arrays" or "arrays" and should be understood to be included in the terms "patterned arrays" or "arrays".

[0021] The systems, devices and methods described herein are robust to changes in the characteristics of features in the pattern or layout of the hole. These changes can be manifested as different signal attributes detected for one or more features in different images. For example, in nucleic acid sequencing technology, nucleic acid arrays are subjected to several cycles of biochemical treatment and imaging (e.g., sequencing runs). In some examples, each cycle can cause a marker in four different markers to be detected at each feature based on the nucleotide bases that are biochemically treated in the cycle. In such examples, multiple (e.g., four) different images are obtained in a given cycle, and each feature can be detected in the image. In the example, the image alignment of a given cycle presents a unique challenge because the features detected in one image may appear darker in other images. In addition, sequencing includes multiple cycles, and the alignment of the features represented in the image data from the continuous cycles is used to determine the sequence of the nucleotides at each hole based on the sequence of the markers detected at the corresponding hole. Incorrect registration of images within a cycle or incorrect registration of images between different cycles may have an adverse effect on sequencing analysis. For example, a method using a regular pattern may be susceptible to the influence of walk-off errors during image analysis. In one example, walk-off errors occur when two overlaying images offset one or more repeating units of a pattern so that the patterns appear to overlap but features that are neighbors in different patterns do not correctly correlate in the overlay.

[0022] The terms "spot" or "feature" used interchangeably herein may refer to a point or area in a pattern that can be distinguished from other points or areas according to relative position. A single feature may include one or more specific types of molecules. For example, a feature may include a single target nucleic acid molecule with a specific sequence. In another example, a feature may include several nucleic acid molecules with the same sequence (and / or its complementary sequence). Different molecules located at different features of a pattern can be distinguished from each other according to the position of the feature in the pattern. Features may include, but are not limited to, for example, a hole in a substrate (e.g., a flow cell), a cluster in a hole (e.g., a flow cell) of a substrate, a protrusion of a substrate, a ridge on a substrate, a gel material pad on a substrate, a channel in a substrate, etc.

[0023] The term "reference" may refer to a distinguishable reference point in or on an object. The reference point may be, for example, but is not limited to, a mark, a second object, a shape, an edge, a region, an irregularity, a channel, a pit, a column, etc. The reference point may be present in an image of the object or in another data set obtained from detecting the object. The reference point may be specified by an X coordinate and / or a Y coordinate in the plane of the object. Alternatively or additionally, the reference point may be specified by a Z coordinate orthogonal to the XY plane. For example, the reference point may be specified by a Z coordinate defined by the relative position of the object and the detector. In one or more cases, one or more coordinates of the reference point may be specified relative to one or more other features of the object or an image or other data set obtained from the object.

[0024] The term "cluster" may refer to a collection of DNA molecules. In a patterned flow cell, a cluster may be located within a well of the flow cell.

[0025] The term "footprint" may refer to the perimeter of an object, fiducial, feature, or other thing in a plane. For example, the footprint may be defined by coordinates in an XY plane orthogonal to the detector viewing the plane. The footprint may be characterized by a shape (e.g., a circle, square, rectangle, triangle, polyhedron, ellipse, etc.) and / or by an area (e.g., at least 1 μm 2 , 5μm 2 , 10μm 2 , 100μm 2 , 1000μm 2 , 1mm 2 wait).

[0026] The term "image" may refer to a representation of all or part of an object (e.g., a sample). The representation may be a reproduction of an optical detection. For example, an image may be obtained from fluorescence, cold light, a scattered signal, an absorption signal, etc. The image may include any optical data set that can be used to obtain information about an object (e.g., a feature and / or a reference) within a defined area. The portion of the object present in the image may be the surface of the object or other XY plane. An image is a two-dimensional (2D) representation, but in some cases, the information in the image may be derived from three dimensions (3D). An image does not need to include a signal of an optical detection. Alternatively, a non-optical signal may be present. An image may be provided in a computer-readable format or medium, such as one or more of those described herein.

[0027] The term "tile" may refer to one or more images of the same area of ​​a sample. For example, each of the one or more images may represent a respective color channel. A tile may form a subset of imaging data of an imaging data set for one imaging cycle.

[0028] The term "optical signal" may refer to, for example, a fluorescent signal, a luminescent signal, a scattered signal, or an absorbed signal. The optical signal may be detected in the ultraviolet (UV) range (about 200 nm to 390 nm), the visible (VIS) range (about 391 nm to 770 nm), the infrared (IR) range (about 0.771 micrometers to 25 micrometers), or other ranges of the electromagnetic spectrum. The optical signal may be detected in a manner that excludes all or part of one or more of these ranges, for example, depending on the features of interest.

[0029] The term "position data" may refer to information about the relative position of two or more things. For example, the information may relate to the relative position of at least one fiducial and the object on which it appears, the relative position of at least one fiducial on the object and the relative position of at least one feature on the object, the relative position of two or more features on the object, the relative position of a detector and the object, the relative position of two or more parts of a fiducial, etc. The information may be included in any of a variety of formats indicating relative position, including but not limited to digital coordinates, pixel identifiers, images, etc. The position data may be provided in a computer-readable format or medium, such as one or more of those described herein.

[0030] The term "repeating pattern" may refer to a relative position of a subset of features in one area of ​​an object being the same as the relative position of the subset of features in at least one other area of ​​the object. In one or more cases, one area of ​​the pattern may be adjacent to another area. The relative position of features in one area of ​​the repeating pattern may be predicted based on the relative position of features in another area of ​​the repeating pattern. The subset of features used for measurement may include at least 2 features, but may include at least 3, 4, 5, 6, 10 or more features. Alternatively or additionally, the subset features used for measurement may include no more than 2, 3, 4, 5, 6 or 10 features. In some cases, a repeating pattern may include multiple repetitions of a sub-pattern.

[0031] The term "signal level" may refer to the amount or quantity of detected energy or encoded information having desired or predefined characteristics. For example, an optical signal may be quantified by one or more of intensity, wavelength, energy, frequency, power, brightness, etc. Other signals may be quantified according to characteristics such as voltage, current, electric field strength, magnetic field strength, frequency, power, temperature, etc. The absence of a signal may refer to a zero signal level or a signal level that is meaninglessly distinguished from noise.

[0032] The term "virtual fiducial" may refer to a reference point applied to an object within an image, and from a source other than the object or image, respectively. For example, a virtual fiducial may be derived from a first object (e.g., a template object or a standard object) and applied to an image of a second object. Additionally, the virtual fiducial may be derived from a design, drawing, or plan used to create the object. In one or more cases, the virtual fiducial may be represented or specified as described herein for the fiducial. In one or more cases, the virtual fiducial may be provided in a computer-readable format or medium, such as those described herein.

[0033] The term "XY coordinates" may refer to information that specifies a position, size, shape, and / or orientation in an XY plane. The information may be, for example, numerical coordinates in a Cartesian system. The coordinates may be provided relative to one or both of the X-axis and the X-axis, or may be provided relative to another position in the XY plane. For example, the coordinates of a feature of an object (e.g., a cluster of holes corresponding to a flow cell) may specify the location of the feature relative to a reference or other feature of the object. The term "XY plane" may refer to a 2D region defined by linear axes X and Y. When used to refer to a detector (e.g., Figure 2B When the detector subsystem 215 is used and the object is viewed by the detector, the region can be further specified as being orthogonal to the viewing direction between the detector and the object being detected. The term "Z coordinate" can refer to information that specifies the position of a point, line, or region along an axis orthogonal to the XY plane. In one or more cases, the Z axis is orthogonal to the object region viewed by the detector. For example, the focus direction of the optical system can be specified along the Z axis.

[0034] More and more applications have been developed for arrays with features of biomolecules (such as nucleic acids and polypeptides). Such arrays typically include deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) probes, but the technology can also be applied to other proteins or chemical analyses. For example, DNA and / or RNA probes can be used to identify nucleotide sequencing present in humans and other organisms. In one or more applications, for example, a single DNA or RNA probe can be attached to a single feature of an array. Samples such as from known people or organisms can be exposed to the array so that target nucleic acids (e.g., gene fragments, mRNA, its amplicon, etc.) hybridize with complementary probes at corresponding features in the array. The probe can be labeled in a target-specific process (e.g., due to a label present on the target nucleic acid or due to an enzyme label of a probe or target present in a hybridized form in the feature). The array can then be checked by scanning the light of a specific frequency on the feature to identify which target nucleic acids are present in the sample.

[0035] Bioarrays can be used for genetic sequencing and similar applications.Gene sequencing can include determining the nucleotide sequence in the length of a target nucleic acid (such as a fragment of DNA or RNA).Relatively short sequences can be sequenced at each feature, and the resulting sequence information can be used in various bioinformatics methods to logically fit sequence fragments together, thereby reliably determining a wider sequence of the length range of the genetic material from which the fragment is obtained.Automated, computer-based systems for characteristic fragments can be used for genome mapping, identification of genes and their functions, etc.Arrays can be used to characterize genome content, because there are a large number of variants in the array, which replaces the selection scheme of many experiments on single probes and targets.

[0036] Any of the variants in the multiple analyte array can be used in the process described herein. The array can include features, wherein each feature has a separate probe or probe group. In the latter case, the probe group at each feature can be homogeneous and can correspond to a single type of probe. For example, with respect to nucleic acid arrays, each feature can have a plurality of nucleic acid molecules, each nucleic acid molecule having a common sequence. In other cases, the probe group at each feature in the array can be heterogeneous.

[0037] Similarly, protein arrays may have features containing a single protein or protein group, and a single protein or protein group has the same amino acid sequence. The probe may be attached to the surface of the array, for example, by covalent bonding of the probe to the surface or by non-covalent interaction of the probe with the surface. In some examples, probes such as nucleic acid molecules may be attached to the surface by a gel layer, such as described in U.S. Patent No. 9,012,022 authorized on April 21, 2015 and / or U.S. Patent Publication No. 2011 / 0059865 filed on January 7, 2005, which are incorporated herein by reference. In other examples, the array may include an array for nucleic acid sequencing applications. In some examples, the array for nucleic acid sequencing generally has a random spatial pattern of nucleic acid features. For example, HiSeq or MiSeq sequencing platforms from Illumina (San Diego, Calif.) utilize flow cells, on which nucleic acid arrays are formed by random inoculation and then bridge amplification. In addition, patterned arrays may also be used for nucleic acid sequencing or other analytical applications. Features of such patterned arrays can be used to capture single nucleic acid template molecules for seeding and subsequent formation of homogeneous colonies, for example, via bridge amplification.

[0038] In one or more cases, the size of the features on the array (or other objects used in the processes described herein) is selected to be suitable for a particular application. For example, in some cases, the features of the array may have a size to accommodate a single nucleic acid molecule. A surface having multiple features within this size range can be used to construct a molecular array for detection at single molecule resolution. Features within this size range can be used in an array having features, each of which contains a colony of nucleic acid molecules. In one or more examples, features such as holes in a circulation pool may have a diameter in the range of about 200nm to about 1μm and a spacing of about 400nm. In one or more examples, the height of the tile may be about 600μm. In one or more examples, the width of the tile may be about 1000μm. In one or more examples, the features of the array may each have a diameter of no more than about 1mm. 2 , not more than about 500μm 2 , not more than about 100μm 2 , not more than about 10μm 2 , not more than about 1 μm 2 , not more than about 500nm 2 , not more than about 100nm 2 , not more than about 10nm 2 , not more than about 5nm 2 or not greater than about 1 nm 2 Alternatively or additionally, the features of the array may be no less than about 1 mm 2 , not less than about 500μm 2 , not less than about 100μm 2 , not less than about 10μm 2 , not less than about 1μm 2 , not less than about 500nm 2 , not less than about 100nm 2 , not less than about 10nm 2 , not less than about 5nm 2 or not less than about 1nm 2 Features may have a size in a range selected from those upper and lower limits exemplified above. Although several size ranges for features on a surface have been exemplified with respect to nucleic acids and nucleic acid scales, it should be understood that features within these size ranges can be used for applications that do not include nucleic acids. It should also be understood that the size of a feature need not be limited to scales for nucleic acid applications.

[0039] For the case of an object comprising a plurality of features, such as an array of features, the features may be discrete and spaced apart from one another. Thus, the array may have features separated by an edge-to-edge distance of at most 100 μm, 50 μm, 10 μm, 5 μm, 1 μm, or 0.5 μm. Alternatively or additionally, the array may have features separated by an edge-to-edge distance of at least 0.5 μm, 1 μm, 5 μm, 10 μm, 50 μm, or 100 μm. These ranges may apply to average edge-to-edge spacing of features as well as minimum or maximum spacing.

[0040] In one or more cases, the features of the array may not be discrete, but adjacent features may be adjacent to each other. Whether the features are discrete or not, the size and / or sub-feature spacing of the features may vary so that the array may have a desired density. For example, the average feature spacing in the regular pattern may be at most 100 μm, 50 μm, 10 μm, 5 μm, 1 μm or 0.5 μm. Alternatively or additionally, the average feature spacing in the regular pattern may be at least 0.5 μm, 1 μm, 5 μm, 10 μm, 50 μm or 100 μm. In some cases, these ranges may apply to the maximum or minimum spacing of the regular pattern. For example, the maximum feature spacing in the regular pattern may be at most 100 μm, 50 μm, 10 μm, 5 μm, 1 μm or 0.5 μm; and / or the minimum feature spacing in the regular pattern may be at least 0.5 μm, 1 μm, 5 μm, 10 μm, 50 μm or 100 μm.

[0041] The density of features in an array can also be understood in terms of the number of features present per unit area. For example, the average density of features in an array can be at least 1×10 3 Features / mm 2 , 1×10 4 Features / mm 2 , 1×105 features / mm 2 , 1×10 6 Features / mm 2 , 1×10 7 Features / mm 2 , 1×10 8 Features / mm 2 or 1×10 9 Features / mm 2 Alternatively or additionally, the average density of features of the array may be at most about 1×10 9 Features / mm 2 , about 1×10 8 Features / mm 2 , about 1×10 7 Features / mm 2 , about 1×10 6Features / mm 2 , about 1×10 5 Features / mm 2 , about 1×10 4 Features / mm 2 or about 1×10 3 Features / mm 2 or lower.

[0042] The above ranges may apply to all or part of a regular pattern, including, for example, all or part of an array of features.

[0043] The features in the pattern can have any of a variety of shapes. For example, when viewed in a 2D plane, such as on the surface of the array, the features can appear circular, annular, elliptical, rectangular, square, symmetrical, asymmetrical, triangular, polygonal, etc. The features can be arranged in a regular repeating pattern, including, for example, a hexagonal or linear pattern. The pattern can be selected to achieve a desired fill level. For example, circular features are filled in a hexagonal arrangement. In another example, circular features can be filled in a linear pattern.

[0044] A pattern can be characterized by the number of features present in a subset of the smallest geometric units that form the pattern. The subset can include, for example, at least 2, 3, 4, 5, 6, 10 or more features. Depending on the size and density of the features, a geometric unit can occupy less than 1 mm. 2 , 500μm 2 , 100μm 2 , 50μm 2 , 10μm 2 , 1μm 2 , 500nm 2 , 100nm 2 , 50nm 2 , 10nm 2 Alternatively or additionally, the geometric unit may occupy an area greater than 10 nm. 2 , 50nm 2 , 100nm 2 , 500nm 2 , 1μm 2 , 10μm 2 , 50μm 2 , 100μm 2 , 500μm 2 , 1mm 2 The characteristics of the features in the geometric unit, such as shape, size, spacing, etc., can be selected from the characteristics for the array or pattern.

[0045] An array with a regular pattern of features may be ordered with respect to the relative positions of the features, but random with respect to one or more other characteristics of each feature. For example, with respect to a nucleic acid array, the nucleic acid features may be ordered with respect to their relative positions, but random with respect to one's knowledge of the sequence of the nucleic acid material present at any particular analyte. As a more specific example, a nucleic acid array formed by inoculating a repeating pattern of features with a template nucleic acid, and amplifying the template at each feature to form a copy of the template at the feature (e.g., by cluster amplification or bridge amplification) may have a regular pattern of nucleic acid features, but may be random with respect to the distribution of nucleic acid sequences across the array. Therefore, detecting the presence of nucleic acid material on an array may produce a repeating pattern of features, while sequence-specific detection may produce a non-repetitive distribution of signals across the array.

[0046] It should be noted that descriptions of patterns, sequences, randomness, etc. herein refer not only to features on an object, such as features on an array, but also to features in an image. Thus, patterns, sequences, randomness, etc. may exist in any of a variety of formats for storing, manipulating, or transmitting image data, including but not limited to computer readable media or computer components, such as a graphical user interface or other output device.

[0047] In one or more cases, the benchmark is included on the object (or in the image) to facilitate the identification and positioning of each feature on the object. The benchmark can be used to align the spatial ordered pattern of the feature because the benchmark provides a reference point for the relative position of other features. The benchmark can be used in the application of repeated detection arrays to follow the changes that occur over time on each feature. For example, the benchmark allows the sequential images obtained in multiple sequencing cycles to follow each nucleic acid cluster, so that the sequence of the nucleic acid material present in each cluster can be accurately determined. The benchmark can be included on or in the array (such as on one or more surfaces of a patterned array support or substrate (that is, whether in the array or in any random or other layout)) and in the image data of the hole and molecule to store the identification and positioning of each feature on the array, including the hole where the molecule is located.

[0048] The fiducials can be arranged in a variety of patterns, such as Figures 1A to 1C 101, 103 and 105. In some cases, such as Figure 1A and Figure 1B As illustrated in , the references may be arranged linearly in directions parallel or substantially parallel to each other. Figure 1AAs illustrated in the example tile 102 of FIG. 1 , a set of fiducials 108a may extend across the tile 102 in a direction parallel to another set of fiducials 108b. It should be noted that a set of fiducials may include any number of fiducials (e.g., but not limited to, two fiducials, three fiducials, four fiducials, eight fiducials, and nine fiducials). In addition, for simplicity, the example images 101, 103, and 105 are illustrated for one tile (e.g., tiles 102, 104, and 122), respectively. However, it should be understood that the image may be divided into multiple imaging data subsets (e.g., tiles) that are associated with corresponding areas of the patterned sample.

[0049] In some cases, a set of benchmarks and an adjacent set of benchmarks may include the same number of benchmarks. For example, benchmark group 108a may include four benchmarks, and benchmark group 108b may include four benchmarks, such as Figure 1A In other cases, one set of benchmarks may have a greater number of benchmarks than an adjacent set of benchmarks. For example, benchmark group 116a may have eight benchmarks, and an adjacent set of benchmarks may have six benchmarks. In one or more cases, one set of benchmarks may be centered between two adjacent sets of benchmarks. For example, Figure 1B As illustrated in example block 104 of FIG. 1 , reference group 110c may include two references disposed between reference groups 110a and 110b. In one or more cases, one set of references may be offset relative to another set of references. For example, Figure 1C As illustrated in example block 122 of , the fiducials 118 within fiducial group 116a may be evenly spaced from one another, while the fiducials of fiducial group 116b may be separated into several groups (e.g., groups 120a and 120b). In some cases, the groups may be spaced from one another such that the spacing between the groups is greater than the spacing between the fiducials within the respective groups. In one or more cases, the fiducials in one group may be aligned with the fiducials in another group. For example, Figure 1A , the datum 106a of the group 108a may be horizontally aligned with the datum 106b of the group 108b, and the datum 107a of the group 108a may be horizontally aligned with the datum 107b of the group 108b. Figures 1A to 1C The arrangements 101 , 103 , and 105 illustrate two and three groups of reference columns, but it should be understood that the arrangements may include any number of reference groups.

[0050] With respect to the arrangement of the fiducials within the image 103 (i.e., the 9x2x9 flow cell fiducial layout), the arrangement is configured to ensure that data quality is not affected by increased jitter. The arrangement includes two fiducials 112a and 112b, which are included at the center of the arrangement at a sufficient distance from all other fiducials (such as the fiducials in the fiducial groups 110a, 110b) to avoid finding the wrong fiducials due to drift. That is, when jitter causes disturbances within the image and the data quality becomes distorted, the fiducials 112a and 112b can be used as reference fiducials to correct the drift and locate the fiducials within the fiducial groups 110a, 110b. Furthermore, in order to reduce the computational cost of locating additional fiducials and significantly increase the speed of the image registration process, Figure 1A Compared with the size of the reference used in Figure 1B 1. For example, the fiducials of tile 102 may each have a diameter of 50 μm. In another example, the fiducials of tile 104 may each have a diameter of 25 μm.

[0051] The reference may include various shapes. For example, the reference may be a set of concentric circles (e.g., Figure 1A 106a, 106b in FIG. 106b). That is, the benchmark may form, for example, a bull's eye benchmark, examples of which are described in U.S. Pat. No. 9,512,422, issued Dec. 6, 2016, U.S. Pat. No. 11,262,307, issued Mar. 1, 2022, and U.S. Pat. No. 11,308,640, issued Apr. 19, 2022, each of which is incorporated herein by reference. In another example, the benchmark may be a circle (e.g., Figure 1B114a, 114b in the image). In one or more cases, the array of holes or other features may include a reference that forms a pattern of multiple rings. In one or more cases, the registration of the images may be performed by aligning (e.g., overlapping) the reference in the image (e.g., the virtual reference image) with the reference in the sub-image, as further described herein. The correlation of the fit may be determined by calculating a similarity measure, such as a two-dimensional cross-correlation, the sum of squares of intensity differences, etc. The best fit correlation may be identified as the position where the brightest pixel in the image overlaps the brightest pixel in the reference image. Alternatively or additionally, the best correlation may be identified as a measure of how strong the best correlation between the virtual reference image and the sub-image is relative to the sub-best correlation between the virtual reference image and the sub-image, as further described herein. It should be noted that circular symmetry is optional for the reference. Other symmetries may also be used instead. In addition, symmetry is generally optional and an asymmetric reference may be used instead. In addition, in one or more cases, the reference may have an occupied area larger than the area of ​​each individual feature of the object to be registered. In some cases, the reference may have an occupied area larger than the geometric unit of the feature repeated in a repeating pattern. The larger footprint of the fiducials can reduce the risk of "walk-off" or overall shift (e.g., such as vertical or horizontal translation), where registration may appear locally correct within a geometric unit of features in a repeating pattern, but each feature (or geometric unit of features) is mistaken for its neighbors.

[0052] In an example, a known fiducial at a well-defined position may be located in the image data using template matching techniques. For example, a theoretical grid or model of the fiducial positions may be generated based on the known positions of the fiducials in the flow cell. Then, an image may be captured that includes data corresponding to one or more fiducials within the image region. Although the image including the fiducials may be disturbed and / or distorted so that the relative positions between the captured fiducials include a certain amount of error, the template may be used to determine the approximate position of the fiducial within the image despite such error. Once the relative positions of the fiducials in the image have been determined using the template, image processing techniques (as described in more detail below) may be used to determine the positions of other features in the image based on their known positions relative to the fiducials. For example, one or more geometric transformations (e.g., affine transformations) may be performed on the image using the located fiducials to account for errors in the relative positions of objects within the image due to various environmental and / or optical disturbances. In order to account for errors introduced by high-frequency jitter, for example due to uneven movement in the sample stage, a given area may be divided into multiple sub-areas, and geometric transformations may be applied separately for each sub-area. Such techniques will be described in more detail below.

[0053] Figure 2AA schematic diagram of a system environment (or "environment") 200 as described herein is illustrated. As shown, the environment 200 includes one or more server devices 202 connected to one or more of a client device 208, a database 216, and a sequencing device 214 via a network 212.

[0054] like Figure 2A As shown in , server device 202, client device 208, database 216, and sequencing device 214 can communicate with each other via network 212. Network 212 may include any suitable network over which computing devices can communicate. Network 212 may include wired and / or wireless communication networks. Example wireless communication networks may include one or more types of radio frequency (RF) communication signals using one or more wireless communication protocols (such as a cellular communication protocol, a wireless local area network (WLAN) or WIFI communication protocol, and / or another wireless communication protocol). Although Figure 2A The components of environment 200 are illustrated as communicating via network 212, but it should be understood that the components of environment 200 may communicate directly with each other, for example, bypassing network 212. For example, client device 208 may communicate directly with sequencing device 214.

[0055] like Figure 2A As shown, the sequencing device 214 may include a device for sequencing a biological sample. In one or more cases, the biological sample may include, but is not limited to, for example, human and non-human DNA to determine each nucleotide base of the nucleic acid sequence (e.g., by synthetic sequencing). In one or more cases, the biological sample may include, for example, but not limited to, human and non-human RNA. The sequencing device 214 may analyze the nucleic acid fragments and / or oligonucleotides extracted from the sample to generate nucleotide reads and / or other data directly or indirectly on the sequencing device 214 using the computer-implemented methods and systems described herein. More specifically, the sequencing device 214 may receive and analyze the nucleic acid sequence extracted from the sample in a nucleotide sample slide (e.g., a flow cell). The sequencing device 214 may utilize synthetic sequencing (SBS) to sequence the nucleic acid fragments into nucleotide reads.

[0056] In one or more cases, the sequencing device 214 may correct for high-frequency motion (i.e., jitter) of the sample stage that causes inconsistent movement of the position of the spot within the image. That is, the sequencing device 214 may perform the processes described herein that reduce the introduction and / or amount of jitter on a per-subregion basis. The spots may correspond to clusters of DNA molecules and / or benchmarks within the holes of the circulation pool. The sequencing device 214 may obtain an image of an object (e.g., a circulation pool) having a plurality of benchmarks arranged in a pattern. An example of a benchmark pattern may include another set of four benchmarks arranged linearly adjacent to a set of four benchmarks. Another example benchmark pattern may include another set of nine benchmarks arranged linearly adjacent to a set of nine benchmarks, wherein a set of two benchmarks is centrally disposed between the first set of nine benchmarks and the second set of nine benchmarks. The sequencing device 214 may generate the positions of these benchmarks and decompose the image into subregions such that each subregion includes a subset of the benchmarks. The sequencing device 214 may perform processing (e.g., template matching processing) on ​​the benchmarks within the image to first locate the benchmarks. For example, the relative positions of the benchmarks may be known before the image is obtained. The known positions of the fiducials can be used to generate a pattern or reference data that is compared to an image that includes a captured or recorded version of the fiducial. For example, due to limitations of the optical system of the image capture device and / or other errors associated with obtaining the image, the fiducials captured within the image may not be located at the exact positions expected based on the known pattern relative to each other. However, by using a known reference pattern and a template matching process, after locating a first reference fiducial and comparing the image to the known pattern, the fiducials within the image can be located.

[0057] Once the fiducials within the image have been located using the reference data, the image may be divided into a plurality of sub-regions, each sub-region including a plurality (e.g., at least three) of fiducials at given locations within the respective sub-region. In addition, the sequencing device 214 may perform a geometric transformation (i.e., an affine transformation) on each sub-region to generate a representation of how the image data is perturbed or distorted compared to the actual location of the object captured in the image (e.g., due to nonlinearities or other errors introduced during the image capture process). In this manner, since the arrangement of the fiducials within each sub-region and / or the location of one or more features within each sub-region relative to the location of the fiducials is known, a determined transformation of the sub-region may be used to locate the location of the feature within the image. Such techniques allow the location of the feature to be determined even due to errors introduced during the image capture process. In addition, by performing a geometric transformation on a per-sub-region basis, errors introduced by high frequency jitter due to uneven movement in the sample stage may be corrected.

[0058] By performing a geometric transformation on a per-subregion basis, a separate representation of the relative disturbance / error in the image data can be determined for a subregion of sufficiently small area so that high-frequency jitter has little effect and the error associated with such jitter is reduced. That is, the area of ​​the subregion can be selected so that the subregion is substantially invariant to high-frequency jitter and the error associated with such jitter that may affect the entire sequencing image. For example, the geometric transformation may correspond to an affine transformation, and a resulting affine matrix may be generated for each subregion. Based on the arrangement of the generated subregions and the transformation of the determined subregions, the sequencing device 214 may determine the location of the spots of the circulation pool within the subregion. That is, the sequencing device 214 may use each determined matrix to resolve the jitter and locate the location of the spot position within the image. Therefore, although most or all of the sequencing image may be affected by jitter, the sequencing device 214 may construct a sufficiently small local area within the image so that it is locally invariant to the same jitter that affects the entire sequencing image. For example, each subregion may be on the order of 200 Hz or lower so as to correct for jitter that is expected to have a frequency of 400 Hz and affect the entire sequencing image.

[0059] like Figure 2A As further illustrated in FIG. 1 , the server device 202 may generate, receive, analyze, store and / or transmit digital data, such as, but not limited to, data used to determine nucleotide base calls or sequence nucleic acid polymers. Figure 2A As shown in , sequencing device 214 can generate and send (and server device 202 can receive) image data, nucleotide reads, and / or other data that is analyzed by server device 202.

[0060] In one or more instances, server device 202 can communicate with client device 208. For example, server device 202 can send data, including image data, sequencing data, or other information, to client device 208, and server device 202 can receive input from a user via client device 208.

[0061] In some cases, server device 202 may include a distributed server collection, where server device 202 includes many server devices distributed across network 212. In some examples, distributed server devices 202 may be located at the same location or at different physical locations. In other cases, server device 202 may include a content server, an application server, a communication server, a web hosting server, or another type of server.

[0062] In one or more cases, the server device 202 and / or the sequencing device 214 may include a sequencing system 204 or a portion thereof. The sequencing system 204 may analyze nucleotide reads, image data and / or other data generated by, for example, the sequencing device 214, such as sequencing metrics, to correct image data distortion. In another example, the sequencing system 204 may analyze nucleotide reads, image data and / or other data generated by, for example, the sequencing device 214, such as sequencing metrics, to determine the nucleotide base sequence of the nucleic acid polymer. For example, the sequencing system 204 may receive raw data generated by the sequencing device 214. The sequencing system 204 may determine the nucleotide base sequence of the nucleic acid fragment based on the received raw data. In one or more cases, the raw data may be generated by the sequencing device 214 in a file format that can be identified for processing, such as, but not limited to, a FASTQ file. The FASTQ file may include a text file containing sequence data from a cluster that passes through a filter on a circulation pool. The FASTQ format is a text-based format for storing both biological sequences (e.g., such as nucleotide sequences) and corresponding quality scores of biological sequences. In one or more cases, the sequencing system 204 can process the sequencing data to determine the sequence of nucleotide bases in the DNA and / or RNA fragments or oligonucleotides. The sequencing system 204 or one or more portions thereof resident on the sequencing device 214 can allow for analysis of the sequencing data, image data, etc. on the device. The sequencing system 204 or one or more portions thereof resident on the sequencing device 214 can allow the sequencing device 214 to detect the status of one or more applications that operate to analyze the sequencing data.

[0063] The client device 208 may generate, store, receive and / or send digital data to implement the sequencing process and analysis described herein. For example, the client device 208 may receive sequencing metrics from the sequencing device 214. The client device 208 may communicate with the server device 202 and / or the sequencing device 214 to receive one or more files including nucleotide base calls and / or other metrics. In one or more cases, the client device 208 may present or display information related to nucleotide base calls to the user within a graphical user interface of the client device 208. The client device 208 may include various types of client devices. In some examples, the client device 208 may be a non-mobile device, such as a desktop computer, a server, etc. In other examples, the client device 208 may be a mobile device, such as a laptop computer, a tablet computer, a mobile phone, a smart phone, etc. The client device 208 may include a sequencing application 210. The sequencing application 210 may be, for example, a website application or a native application (e.g., a mobile application, a desktop application) stored and executed on the client device 208. In one or more instances, the sequencing application 210 may include instructions that, when executed, cause the client device 208 to receive data from the sequencing device 214 and present the data within a graphical user interface of the client device 208, such as, but not limited to, data from a variant call file.

[0064] The environment 200 may include a database 216. The database 216 may store information such as, but not limited to, variant call files, sample nucleotide sequences, nucleotide reads, nucleotide base calls, sequencing metrics, population data, imaging data, and / or other data as described herein. The server device 202, the client device 208, and / or the sequencing device 214 may communicate with the database 216 (e.g., via the network 212) to store and / or access information such as, but not limited to, variant call files, sample nucleotide sequences, nucleotide reads, nucleotide base calls, sequencing metrics, population data, imaging data, and / or other data as described herein.

[0065] Environment 200 can be included in a local network or local high performance computing (HPC) system. In one or more cases, environment 200 can be included in a cloud computing environment, which includes multiple server devices, such as server device 202, on which software and / or data are distributed. In one or more cases, sequencing system 204 can be implemented to operate one or more subsystems as described herein. Sequencing system 204 can be distributed across server devices 202, which can access database 216 via network 212 in a cloud-based computing system.

[0066] Figure 2BExamples of one or more sequencing subsystems are illustrated, which can be implemented by sequencing device 214 and can be monitored and / or controlled by a computing subsystem when performing analysis on one or more flow cells. Additionally or alternatively, the subsystems can be controlled in response to a schedule / workflow determined by the computing subsystem. In one or more cases, the detector subsystem 215 can be configured to perform analysis on one or more samples.

[0067] Example sequencing equipment 214 may include equipment for obtaining or generating an image of a sample. Figure 2B The system 215 (i.e., the detector subsystem) of exemplifies an example imaging configuration of a specific implementation of the backlight design. It should be noted that although systems and methods may be described from time to time herein in the context of the example system 215, these are merely examples of specific implementations that may implement the image distortion correction methods disclosed herein.

[0068] like Figure 2B As illustrated in FIG. 2 , the subject sample is located on a flow cell 225, which is positioned on a sample stage 270 below an objective lens 242. Note that Figure 2B One flow cell 225 is illustrated, but it should be understood that the system 215 can perform analysis on more than one flow cell 225. The light source 250 and associated optical devices direct a light beam (such as a laser) to a selected sample position on the flow cell 225. The sample emits fluorescence, and the resulting light is collected by the objective lens 242 and directed to the image sensor of the camera system 240 to detect the fluorescence. The sample stage 270 moves relative to the objective lens 242 to position the next sample position on the flow cell 225 at the focus of the objective lens 242. The movement of the sample stage 270 relative to the objective lens 242 can be achieved by moving the sample stage itself, the objective lens, some other components of the imaging system, or any combination of the foregoing. Additional specific implementations may also include moving the entire imaging system to a fixed flow cell.

[0069] The sequencing device 214 directs the flow of reagents (e.g., fluorescently labeled nucleotides, buffers, enzymes, cleavage reagents, etc.) to (and through) a circulation cell 225 and a waste valve 220. The circulation cell 225 can be a sample container, including one or more substrates on which a sample is provided. For example, in the case of a system for analyzing a large number of different nucleic acid sequences, the circulation cell 225 includes one or more substrates on which the nucleic acid to be sequenced is combined, attached or related. In one or more cases, the substrate may include any inert substrate or matrix to which nucleic acid may be attached, such as, for example, a glass surface, a plastic surface, latex, dextran, a polystyrene surface, a polypropylene surface, a polyacrylamide gel, a gold surface, and a silicon wafer. In one or more cases, the substrate is located in a channel or other region at a plurality of positions formed with a matrix or array on the circulation cell 225.

[0070] In one or more cases, the circulation pool 225 may include a biological sample imaged using one or more fluorescent dyes. For example, the circulation pool 225 may be implemented as a patterned circulation pool, including a translucent cover plate, a substrate and a liquid sandwiched therebetween, and the biological sample may be located at the inner surface of the translucent cover plate or the inner surface of the substrate. The circulation pool 225 may include a large number of (e.g., thousands, millions or billions) of holes or regions, which are patterned on the substrate to define an array (e.g., a hexagonal array, a rectangular array, etc.). Each region may form a cluster (e.g., a monoclonal cluster) of a biological sample (such as DNA, RNA or another genomic material) that can be sequenced, for example, using sequencing while synthesis. The circulation pool 225 may be further divided into a plurality of spaced-apart swimming lanes (e.g., eight swimming lanes), each of which includes a cluster of a hexagonal array. U.S. Patent No. 8,778,848 describes a circulation pool that can be used in a specific implementation disclosed herein, and the patent is incorporated herein by reference in its entirety.

[0071] The system 215 may include a temperature station actuator 230 and a heater / cooler 235 that optionally adjust the temperature of the fluid conditions within the circulation pool 225. A camera system 240 may be included to detect and track the sequencing of the circulation pool 225. The camera system 240 may be implemented, for example, as a charge coupled device (CCD) camera (e.g., a time delay integration (TDI) CCD camera), which may interact with various filters within the filter switching component 245, the objective lens 242, and the focused light source 250. The camera system 240 is not limited to a CCD camera, and other camera and image sensor technologies may be used. In some examples, the sensor of the camera system 240 may have a pixel size between about 5 μm and about 15 μm.

[0072] The output data from the sensors of the camera system 240 may be transmitted to a real-time analysis module (e.g., Figure 2C A real-time analysis module 291) can be implemented as a software application that analyzes image data (e.g., image quality scoring), reports or displays characteristics of the laser beam (e.g., focus, shape, intensity, power, brightness, position) to a graphical user interface (GUI), and, as further described below, dynamically corrects distortions in the image data.

[0073] A light source 250 (e.g., an excitation laser within an assembly that optionally includes multiple lasers) or other light source may be included to illuminate the fluorescent sequencing reaction within the sample via illumination through a fiber optic interface (which may optionally include one or more re-imaging lenses, fiber optic mounts, etc.). The system 215 may include a low-watt lamp 265, a focusing laser 261, and a reverse dichroic mirror. In some cases, the focusing laser 261 may be turned off during imaging. In other cases, an alternative focusing configuration may include a second focusing camera (not shown), which may be a quadrant detector, a position sensitive detector (PSD), or a similar detector to measure the position of a scattered light beam reflected from a surface while data is collected. Although illustrated as a backlight device, other examples may include light from a laser or other light source that is directed to a sample on the circulation pool 225 through an objective lens 242.

[0074] The circulation cell 225 can be placed in a circulation cell holder (e.g., a sample container holder), which can be placed on a movable stage area 270. When sequencing is performed, the circulation cell holder can firmly hold, for example, the circulation cell in an appropriate position or orientation relative to the light source 250, a prism (not shown) that directs laser irradiation to the imaging surface, and the camera system 240. Therefore, the circulation cell 225 can be mounted on the sample stage 270 to provide movement and alignment of the circulation cell 225 relative to the objective lens 242. The sample stage 270 can have one or more actuators to allow the sample stage 270 to move in any one of the three dimensions. For example, with respect to a Cartesian coordinate system, an actuator can be provided to allow the sample stage 270 to move in the X, Y, and Z directions relative to the objective lens. This allows one or more sample positions on the circulation cell 225 to be positioned to be optically aligned with the objective lens 242. For example, depending on the device design and imaging technology used, the patterned array encapsulated in the flow cell 225 may initially be located in the XY plane and moved in that plane during imaging, or the imaging component may be moved parallel to that plane during imaging. Thus, the flow cell 225 may extend in the XY plane, wherein the X direction is the longer direction of the flow cell 225, and the Y direction is the shorter direction (the flow cell is rectangular). However, it should be understood that this orientation may be reversed.

[0075] The focusing (z-axis) component 217 can control the positioning of the optical components relative to the flow cell 225 in the focusing direction (commonly referred to as the z-axis or z-direction). The focusing component 217 may include one or more actuators physically coupled to the optical sample stage or the sample stage or both to move the flow cell 225 on the sample stage 270 relative to the optical components (e.g., the objective lens 242) to provide appropriate focus for the imaging operation. For example, the actuator may be physically coupled to the corresponding sample stage, such as by mechanical, magnetic, fluid or other attachment or direct or indirect contact with the sample stage or contact with it. The one or more actuators may be configured to move the sample stage 270 in the z-direction while maintaining the sample stage 270 in the same plane (e.g., maintaining a horizontal or horizontal attitude perpendicular to the optical axis). The one or more actuators may be configured to tilt the sample stage 270. For example, this operation can be performed so that the flow cell 225 can be dynamically leveled to account for any slope on its surface.

[0076] Focusing of the system 215 may refer to aligning the focal plane of the objective lens with the sample to be imaged at a selected sample position. However, focusing may also refer to adjusting the system 215 to obtain desired characteristics of the sample representation, such as, for example, a desired clarity or contrast level of the test sample image. Because the available depth of field of the focal plane of the objective lens may be small (sometimes on the order of 1 μm or less), the focusing component 217 closely follows the surface being imaged. Because the flow cell 225 may not be completely flat as if it were fixed in the instrument, the focusing component 217 may be arranged to follow the contours of the surface being imaged when moving along the scan direction (e.g., y-axis).

[0077] Light emitted from the test sample at the sample location being imaged can be directed to one or more detectors of the camera system 240. An aperture can be included and positioned to allow only light emitted from the focus area to pass to the detector. An aperture can be included to improve image quality by filtering out components of light emitted from areas outside the focus area. An emission filter can be included in the filter switching assembly 245, which can be selected to record a determined emission wavelength and cut off any stray laser light.

[0078] Although not shown, a controller may be provided to control the operation of the system 215. The controller may be implemented to control various aspects of the system operation, such as, for example, focusing, sample stage movement, and imaging operations. In one or more cases, the controller may be implemented using hardware, a process (e.g., a machine executable instruction), or a combination of the foregoing. For example, the controller may include one or more CPUs or processors with associated memory. In another example, the controller includes hardware or other circuit systems to control operations, such as a computer processor and a non-transitory computer-readable medium having machine-readable instructions stored therein. For example, the hardware or other circuit systems may include one or more of the following: a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a programmable logic device (PLD), a complex programmable logic device (CPLD), a programmable logic array (PLA), a programmable array logic (PAL), or other similar processing devices or circuit systems. For another example, the controller may include a combination of the circuit system and one or more processors.

[0079] In one or more cases, the flow cell 225 can be read from at least one side (i.e., the top side and / or the bottom side). Thus, multiple readers or imaging systems can be used to read the signals emitted from the channels of the flow cell 225. The flow cell 225 can include one or more complementary metal oxide semiconductor (CMOS) sensors, which can be implemented instead of or in addition to external cameras and / or optics in the camera system 240.

[0080] The sequencing device 214 may include an access subsystem configured to provide access to a cartridge holder by moving the cartridge (e.g., from a receiving position to an engaging position) and / or actuating a door (e.g., for opening and closing). The cartridge may include one or more flow cells, fluids, reagents, or other materials for loading into the sequencing device 214. The sequencing device 214 may include a status subsystem including a light bar that provides a visual indication of the status of one or more processes executed on the sequencing device 214 by a color change and / or an intensity change.

[0081] Each of the subsystems on the sequencing device can be powered by a power resource controlled by a power subsystem. The power subsystem can include a power source, such as an alternating current (AC) power source or a direct current (DC) power source. The power resource can be controlled by one or more applications for performing different tasks during the sequencing process. The power source can generate a supply voltage for powering the subsystems within the sequencing device 214.

[0082] In one or more cases, the system 215 (e.g., a detector subsystem) may obtain a target image of an object (e.g., a circulation pool 225), wherein the image includes a repeating pattern of features on the object and at least one reference on the object. In one or more cases, the system 215 may be configured to capture high-resolution imaging of the surface of the substrate (e.g., a circulation pool). The system 215 may have sufficient resolution to distinguish features by density, spacing, and / or feature size. The system 215 may be configured to keep the object and the detector in a static relationship when obtaining a zone image. In one or more cases, the camera system 240 of the system 215 may include a scanning device for obtaining an image. For example, a scanning device (e.g., a "step-and-shoot" detector) may obtain a continuous zone image. In one or more other cases, the scanning device may be configured to continuously scan points or lines on the surface of the object to accumulate data to construct an image of the surface. Such a scanning device (e.g., a point scanning detector) may be configured to scan points (i.e., a small detection zone) on the surface of the object via a raster motion in the XY plane of the surface. In one or more other cases, the scanning device (e.g., a line scan detector) may use confocal line scanning to generate progressive pixelated image data that can be analyzed to locate each feature / benchmark in the array. In some cases, the scanning device (e.g., a line scan detector) may be configured to scan a line along the Y dimension of the surface of the object, with the longest dimension of the line occurring along the X dimension. It should be noted that the system 215, the object, or both can be moved to achieve scanning detection. For example, the system 215 used in nucleic acid sequencing applications is described in U.S. patent applications. No. 2012 / 0270305A1; No. 2013 / 0023422A1; and No. 2013 / 0260372A1; and U.S. Patent Nos. 8,158,926 and 8,241,573, each of which is incorporated herein by reference. It should be noted that the process described herein can be used to analyze the collected image data of multiple bands or regions detected in the area of ​​the sample flow cell described herein. However, it should be understood that the processes described herein can also be used to analyze image data collected for other types of substrates containing molecular arrays or other detectable features.

[0083] In one or more cases, the system 215 generates image data representing individual features / benchmarks on the circulation cell, intervals between features / benchmarks, and representations of the benchmarks provided in or on the circulation cell. The sequencing system 204 can receive the image data and process the image data in accordance with the present disclosure to extract meaningful values ​​from the imaging data as described herein. In one or more cases, the sequencing system 204 can process the image data in real time or near real time while obtaining one or more sets of image data for the circulation cell. Such real-time analysis is useful for nucleic acid sequencing applications, in which nucleic acid arrays are subjected to repeated cycles of fluid and detection operations. Analysis of sequencing data can be computationally intensive, making it beneficial to perform the process in real time or near real time or in the background while performing other data acquisition or analysis. An example real-time analysis method that can be used with the present method is for MiSeq commercially available from Illumina. TM and HiSeq TM Methods for sequencing devices and / or described in U.S. Patent Application No. 2012 / 0020537A1, which is incorporated herein by reference in its entirety. When used in conjunction with the processing of samples and imaging thereof, the terms "real time" and "near real time" are intended to imply that processing occurs at least in part during the time that the sample is processed and imaged. In other examples, image data may be obtained and stored for subsequent analysis by similar processes. This may allow other equipment (e.g., a powerful processing system) to handle processing tasks at the same or different physical sites where imaging is performed. This may also allow reprocessing, quality verification, etc.

[0084] The sequencing system 204 can analyze the image data to determine the location of various features visible or encoded in the image data, as well as locations where no features are visible (i.e., no features are present at that location, or no meaningful radiation is detected from existing features at that location). The image data analysis can also be used to determine the location of fiducials that help locate features. Furthermore, the image data analysis can be used to locate patterned arrays in the system, to provide useful information for processing or reference purposes, etc.

[0085] In one or more cases, the sequencing system 204 may be configured to assign an intensity and / or digital value to each feature and / or benchmark based on the features of the image data represented by the pixels at the corresponding positions. That is, for example, the sequencing system 204 may be configured to identify a particular color (e.g., black, white, etc.) or a wavelength of light detected at a particular position, as indicated by a pixel group or cluster of pixels at that position. For example, in DNA imaging applications, four common nucleotides may be represented by separate and distinguishable colors (or more generally, wavelengths or wavelength ranges of light). The sequencing system 204 may assign an intensity and / or digital value corresponding to the nucleotide to each color. The sequencing system 204 assigns corresponding values ​​to the features, thereby alleviating the need to further process the image data itself, which may be more massive (e.g., many pixels may correspond to each feature) and have significantly larger values ​​(i.e., a larger number of bits for encoding each pixel). In one or more cases, sequencing system 204 can associate each of the assigned intensities and / or digital values ​​with a position in an image index or map, which can be done by reference to a known or detected position of a fiducial or by reference to any data encoded by such a fiducial. The map can correspond to a known or determined position of each feature within the array.

[0086] As discussed herein, sequencing device 214 can be used for SBS. In SBS, fluorescently labeled modified nucleotides can be used to sequence dense clusters (i.e., potentially millions of clusters) of amplified DNA present on the surface of a substrate (e.g., a flow cell). The flow cell containing the nucleic acid sample for sequencing can take the form of an array of discrete, separately detectable single molecules or an array of features (or clusters) containing a homogeneous population of a particular molecular species (such as amplified nucleic acids having a common sequence).

[0087] Figure 2C An example line scan imaging system 219 is illustrated. For example, the system 219 can be a dual-channel, line scan modular optical imaging system. It should be noted that although the system 219 is described as a dual-channel, line scan modular optical imaging system, it should be understood that the system 219 can be configured to utilize any number of channels (e.g., one channel, three channels, four channels, etc.).

[0088] In one or more cases, the system 219 can be used for nucleic acid sequencing. Applicable techniques include those in which nucleic acids are attached to fixed positions in an array (e.g., holes in a flow cell) and the array is repeatedly imaged. The system 219 can obtain images in two different color channels that can be used to distinguish a specific nucleotide base type from another base type. For example, the system 219 can implement a process known as "base calling," which generally refers to the process of determining a base call (e.g., adenine (A), cytosine (C), guanine (G), or thymine (T)) for a given spot position in an image during an imaging cycle. During dual-channel base calling, image data extracted from two images can be used to determine the presence of one of four base types by encoding the base identity as a combination of two image intensities. For a given spot or position in each of the two images, the base identity can be determined based on whether the combination of signal identifications is [on, on], [on, off], [off, on], or [off, off].

[0089] The system 219 may include a line generation module (LGM) 272 having at least one light source, such as, but not limited to, light sources 274 and 276. The light sources, such as, but not limited to, light sources 274 and 276, may be coherent light sources, such as, but not limited to, laser diodes that output laser beams. The light source 274 may emit light of a first wavelength (e.g., a red wavelength), and the light source 276 may emit light of a second wavelength (e.g., a green wavelength). The light beams output from the laser sources 274 and 276 may be directed through one or more beam shaping lenses 286. In some cases, a single light shaping lens may be used to shape the light beams output from the two light sources. In other cases, a separate beam shaping lens may be used for each light beam. In some examples, the beam shaping lens is, for example, but not limited to, a Powell lens, so that the light beam is shaped into a line pattern. The beam shaping lens of the LGM 272 or other optical component imaging system 219 may be configured to shape the light emitted by the light sources 274 and 276 into a line pattern (e.g., by using one or more Powell lenses or other beam shaping lenses, diffraction or scattering components).

[0090] The LGM 272 may also include a reflective mirror 280 and a semi-reflective mirror 282 configured to direct the light beam through a single interface port to an emission optics module (EOM) 275. The light beam may pass through a shutter element 284. The EOM 275 may include an objective lens 298 and a z-sample stage 296 that moves the objective lens 298 longitudinally closer to or farther from a target 292. For example, the target 292 may include a liquid layer 290 and a translucent cover 288, and the biological sample may be located at the inner surface of the translucent cover 288 and at the inner surface of a substrate layer located below the liquid layer. The z-sample stage may then move the objective lens 298 to focus the light beam onto any inner surface of the flow cell (e.g., onto the biological sample). The biological sample may be, for example, but not limited to, DNA, RNA, protein, or other biological material that responds to optical sequencing.

[0091] The EOM 275 may include a semi-reflective mirror 278 to reflect a focus tracking beam emitted from a focus tracking module (FTM) 281 onto a target 292, and to reflect light returning from the target 292 back into the FTM 281. The FTM 281 may include a focus tracking optical sensor to detect characteristics of the returning focus tracking beam and generate a feedback signal to optimize the focus of the objective lens 298 on the target 292.

[0092] The EOM 275 may also include a semi-reflective mirror 298 to direct light through the objective lens 298 while allowing light returning from the target 292 to pass through. In some cases, the EOM 275 may include a tube lens 273. Light transmitted through the tube lens 273 may pass through the filter element 271 and enter the camera module (CAM) 285. The CAM 285 may include one or more optical sensors 283 to detect light emitted from the biological sample in response to an incident light beam (e.g., fluorescence in response to red and green light received from the light sources 274 and 276).

[0093] The output data from the sensor of CAM 285 can be transmitted to real-time analysis module 291. Real-time analysis module 291 can execute computer-readable instructions to analyze image data (e.g., quality scores, base calls, etc.). These operations can be performed in real time during the imaging cycle to minimize downstream analysis time and provide real-time feedback and troubleshooting during the imaging run. In one or more cases, real-time analysis module 291 can be a computing device (e.g., computing device 800) that is communicatively coupled to imaging system 219 and controls the imaging system. In one or more cases, real-time analysis module 291 can execute computer-readable instructions to correct for distortions in image data.

[0094] As described herein, image distortion can be particularly detrimental to multi-cycle imaging of patterned arrays (e.g., patterned flow cells) because image distortion can cause the actual position of features in the scanned image to deviate from the expected position of the features. This distortion effect can become particularly pronounced along the edges of the field of view, potentially rendering imaging data from these features unusable. This can result in decreased data throughput and increased error rates during multi-cycle imaging runs. In addition, high-frequency motion (i.e., jitter) of the sample stage during the cycles of the imaging run exacerbates this distortion effect. Specific implementations described herein involve dynamically correcting image distortion during an imaging run (e.g., a sequencing run), thereby increasing data throughput and reducing error rates during the imaging run.

[0095] Figure 3 3 is a flow chart illustrating a procedure 300 for performing an example image calibration process. In one or more cases, the sequencing device 214 may implement the following: Figure 3 , to determine the position of the reference datum. One or more portions of the procedure 300 may be executed by one or more computing devices. For example, one or more portions of the procedure 300 may be executed by one or more sequencing devices. One or more portions of the procedure 300 may be stored in a memory as computer-readable or machine-readable instructions that may be executed by a processor of one or more computing devices. Although portions of the procedure 300 may be described herein as being executed by a sequencing device, the procedure 300 or portions thereof (e.g., the detector subsystem 215 or the imaging system 219) may be executed by another computing device or distributed across multiple computing devices (such as one or more sequencing devices, one or more client devices, and / or one or more server devices).

[0096] Procedure 300 may begin at 302. Figure 3 As shown in , at 302, the sequencing device 214 may generate a virtual reference image. To generate the virtual reference image, the sequencing device 214 may capture an image of a sample (e.g., a sample located on a flow cell as described herein) in an initial imaging cycle. In one or more cases, the initial imaging cycle may be a calibration cycle and the first cycle of a multi-cycle imaging run (e.g., a DNA sequencing run). The captured image may include image data corresponding to at least one feature and a plurality of references.

[0097] In some cases, a virtual reference image may include multiple references arranged in a pattern. For example, a virtual reference image (e.g., Figure 1AThe sequencing image 102 of the present invention may be a sequencing image that includes two sets of fiducials (e.g., set 108a and set 108b) that are linearly arranged in directions parallel or substantially parallel to each other. In an example, one set of fiducials (e.g., set 108a) may include four fiducials, and another set of fiducials (e.g., set 108b) may include four fiducials. In some examples, fiducials from one set of fiducials (e.g., fiducials 106a) may be aligned (e.g., horizontally aligned) with fiducials from another set of fiducials (e.g., 106b). As described herein, fiducials are included in an object (e.g., a flow cell) or an image (e.g., Figure 1A The procedure 300 described herein may be implemented using the example arrangement 101 of the sequencing image 102, but it should be noted that the procedure 300 may be similarly implemented using the example arrangements 103, 105, and other arrangements of the references.

[0098] The sequencing device 214 can obtain a virtual fiducial image by, for example, but not limited to, a system 215 or an imaging system 219 as described herein. The virtual fiducial image includes a repeating pattern of features on the object and at least one fiducial also located on the object. In one or more cases, the sequencing system 204 can process the image data in real time or near real time while obtaining one or more sets of image data of the flow cell.

[0099] The sequencing device 214 may generate a virtual fiducial image based on fiducial configuration parameters. The fiducial configuration parameters may be parameters describing the shape and size of the fiducial. For example, for a bullseye fiducial, such as Figure 1A The configuration parameters of the datum 106a may include the diameters of the outer circle and the inner circle forming the datum 106a.

[0100] In one or more cases, based on the image data and / or the fiducial configuration parameters, the sequencing device 214 may associate one of the fiducials (e.g., fiducial 106a) as a reference fiducial (e.g., Figure 4 A). The sequencing device 214 can use the reference fiducial to locate one or more fiducials associated with the virtual fiducial image. In one or more cases, the reference fiducial is a global fiducial that the sequencing device 214 uses to locate and / or register other fiducials within the image.

[0101] At 304, sequencing device 214 generates a sub-image of the virtual reference image with a larger padding (e.g., but not limited to, a padding that is twice the total size / area of ​​the reference). To ensure that the reference is located during the search (i.e., the reference does not move more than a certain number of pixels, such as but not limited to 20 pixels), sequencing device 214 may generate a sub-image with a padding of, for example, 20 pixels, the diameter of which is larger than the diameter of the reference within the virtual reference image. Based on the reference configuration parameters, sequencing device 214 generates the sub-image so that the sub-image is centered or substantially centered on the reference (i.e., the reference reference) of the virtual reference image. In an example, the reference reference may be the upper left reference within the virtual reference image. In some cases, the reference reference may be a reference reference for each tile within the virtual reference image. In other cases, the reference reference may be a reference reference for a corresponding tile within the virtual reference image.

[0102] After obtaining the virtual reference image and the sub-image, at 306, the sequencing device 214 determines the correlation between the virtual reference image and the sub-image to locate the reference within each region of the image. For example, the sequencing device 214 may use the image data and the reference reference as a point (e.g., a unique key point) within the image to implement a template matching process and / or an image matching process to locate the reference within each region of the image. For example, during the initial imaging cycle, the sequencing device 214 may implement a matching process to search for the reference reference within the image. When implementing the template matching process, the sequencing device 214 may assume the shape of the object being searched, and may incorporate the assumed shape into the search for the reference reference. In some cases, the sequencing device 214 may implement a template matching process, such as a cross-correlation process. The cross-correlation process may be, for example, but not limited to, a cross-correlation process based on a fast Fourier transform (FFT) to locate the object within a pixel. The sequencing device 214 may fit a two-dimensional Gaussian function to the result correlation peak of the FFT-based cross-correlation process to fit the object to sub-pixel accuracy. Therefore, the sequencing device 214 can locate each reference in the image to sub-pixel accuracy. That is, the sequencing device 214 may apply an FFT to the location of the reference fiducial within the virtual fiducial image, a portion of the sub-image containing, for example, the upper left fiducial. In some other cases, the sequencing device 214 may use one or more of a keypoint detection process, a Harris detector, a scale-invariant feature transform (SIFT), or other similar processes to locate the reference fiducial and other fiducials within the image. The template matching process may utilize a template that includes information indicating the relative location of the fiducial. The template may represent the known actual location of the fiducial. By using the template, the fiducial in the image may be located relative to the located reference fiducial even if some error is introduced in the image for one or more fiducial locations.

[0103] As described herein, sequencing device 214 may be configured to assign a digital value to each fiducial based on features of the image data represented by the pixel at the corresponding position. Sequencing device 214 assigns the corresponding value to the fiducial, thereby alleviating the need to further process the image data itself. In one or more cases, sequencing device 214 may associate each of the assigned values ​​with a position in an image index or map, which may be done by reference to a known or detected position of the fiducial or by reference to any data encoded by such a fiducial.

[0104] In one or more cases, the sequencing device 214 may determine a correlation score at 308. The correlation score may be a measure of how strong the best correlation between the virtual reference image and the sub-image is relative to the second best correlation between the virtual reference image and the sub-image. The correlation score may be an output result of a cross-correlation process. For example, in some cases, the output of the cross-correlation process may be graphically shown to indicate whether the reference is located within the image and / or the location of the reference. The output of the cross-correlation process of the reference associated with two images (e.g., a virtual reference image and a sub-image) may be shown as a single peak. The location of the peak may indicate the location of the reference in the real image. In addition, by locating the peak, it can be assumed that the reference in the virtual image is located at the center of the virtual image.

[0105] The sequencing device 214 may use the correlation score to determine whether the peak of the correlation process is the maximum peak. For example, the sequencing device 214 may locate the highest peak in the output of the cross-correlation process. The position of the highest peak may be recorded as the maximum peak. In addition, the sequencing device 214 locates the second highest peak in the output of the cross-correlation process, and records the corresponding position as the second highest peak. In some cases, the sequencing device 114 may include a partition around the highest peak (i.e., the maximum peak) position, wherein the pixels in the partition are excluded from the area searched to locate the second high peak value. The sequencing device 114 may search for the second highest peak at a location outside the region. In one or more cases, the sequencing device 114 may determine whether the position of the second highest peak is the same as the position of the highest peak. For the situation where the positions of the second highest peak and the maximum peak are the same, the sequencing device 114 determines that there is no peak, and outputs a correlation score of 0. For the situation where the positions of the second highest peak and the maximum peak are different, the sequencing device 114 generates a correlation score indicating the correlation between the maximum peak and the second highest peak.

[0106] In one or more cases, the sequencing device 214 determines whether the correlation score is above a threshold at 310. The threshold can be, for example, but not limited to, any numerical value that can be associated with the correlation score (e.g., a threshold score of 0.3) to indicate the confidence that the fiducial (e.g., the image data of the fiducial) is located within the virtual fiducial image and / or sub-image. For the case where the correlation score is not greater than the threshold score (310: No), the sequencing device 214 can determine and maintain the position of the reference fiducial at 316. Therefore, the sequencing device 214 does not change the position of the reference fiducial indicated by the configuration parameters.

[0107] refer to Figure 3 312, at 306, the sequencing device 214 may also determine the position of the maximum correlation based on the determination of the correlation between the virtual reference image and the sub-image. The position (i.e., translation) of the maximum correlation between the virtual reference image and the sub-image may be based on the output result of the cross-correlation process. For the case where the correlation score is greater than the threshold score (310: yes), the sequencing device 214 adjusts the position of the reference reference at 314. In one or more cases, the sequencing device 214 adjusts the position of the reference reference to match the position of the maximum correlation. In addition, the sequencing device 214 may adjust the position of the reference reference by adjusting the configuration parameters indicating the position of the reference reference to parameters corresponding to the position of the maximum correlation.

[0108] Based on the determined position of the reference fiducial (e.g., the reference fiducial position adjusted at 314 or the reference fiducial position maintained at 316), the sequencing device 214 determines and records the corresponding position of the fiducial included in the image and / or tile. For example, the sequencing device 214 locates one or more other peaks in the output of the cross-correlation process. The sequencing device 214 searches for one or more peaks at positions outside the exclusion partition of the maximum peak. The sequencing device 214 compares the located peak with the maximum peak and generates a registration score to indicate whether the fiducial is located within the virtual fiducial image and / or sub-image. In addition, based on the located peak and the determined position of the reference fiducial, the sequencing device 214 associates the located peak with the fiducial position within the virtual fiducial image and / or sub-image. The positions of the respective fiducials and the associated registration scores (i.e., fiducial configuration parameters) may be stored in a database (such as database 216).

[0109] In one or more cases, the sequencing device 214 may divide the image data obtained in the initial imaging cycle into a plurality of imaging data subsets (e.g., tiles) corresponding to respective regions of the patterned sample. That is, the imaging data subsets may include a subset of pixels of the imaging data set of one imaging cycle. As indicated herein, for simplicity, in Figures 1A to 1CExample images 101, 103, and 105 are illustrated for one tile (e.g., tiles 102, 104, and 122, respectively). Dividing the image data into multiple tiles allows for parallelization of image processing operations. In one or more cases, the placement of the fiducials in the field of view of the imaging system, in the sample, or on the sample can be used to determine the size of the imaging data subset. The imaging data subset can be divided so that the pixels of each imaging data subset or tile have a predetermined number of fiducials (e.g., at least three fiducials, four fiducials, six fiducials, eight fiducials, etc.). For example, the total number of pixels of the imaging data subset can be predetermined based on a predetermined pixel distance between the boundary of the imaging data subset and the fiducial point. In another example, for an initial imaging period, the imaging data subset can be divided so that the size of the imaging data subset is no greater than half the distance between two fiducials.

[0110] Figure 4 is a flow chart illustrating a procedure 400 for performing an example image distortion correction process. One or more portions of the procedure 400 may be performed by one or more computing devices. For example, one or more portions of the procedure 400 may be performed by one or more sequencing devices. One or more portions of the procedure 400 may be stored in a memory as computer-readable or machine-readable instructions that may be executed by a processor of one or more computing devices. Although portions of the procedure 400 may be described herein as being performed by a sequencing device, the procedure 400 or portions thereof (e.g., the system 215 or the imaging system 219) may be performed by another computing device or distributed across multiple computing devices (such as one or more sequencing devices, one or more client devices, and / or one or more server devices).

[0111] Procedure 400 may begin at 402. Figure 4 As shown in , at 402, the sequencing device 214 can obtain image data associated with a calibration process. In one or more cases, the image data can include fiducial configuration parameters (e.g., the positions of fiducials determined during a calibration process such as the process described in procedure 300). Additionally or alternatively, the image data can include sub-region parameters that define one or more subsets of fiducials included in the image and / or tile.

[0112] At 404, the sequencing device 214 may determine a plurality of sub-regions. In one or more cases, each sub-region may include a subset of the fiducials included in the image and / or tile. For example, Figure 5A As illustrated in FIG. 1 , the tile 102 includes sub-regions 502a, 502b, and 502c. In one or more cases, the sequencing device 214 determines the plurality of sub-regions based on the baseline configuration parameters and the sub-region parameters.

[0113] The sub-region parameters may be based on one or both of a baseline number of rows per sub-region and a baseline number of rows overlapped between consecutive sub-regions. Figure 5A As illustrated in FIG. 1 , a tile 101 of an image 102 may include two rows per sub-region and one row overlapping. Fig. 6A As illustrated in FIG. 1 , the tile 104 of the image 103 may include eight sub-regions (sub-regions 602a, 602b, 602c, 602d, 602e, 602f, 602g, and 602h), wherein the number of rows in each region is two and the number of overlapping rows is one. In another example, Figure 6B As shown in , the tile 104 of the image 103 may include four sub-regions (sub-regions 606a, 606b, 606c and 606d), wherein the number of rows in each region is four and the number of overlapping rows is two. The sequencing device 214 may determine multiple groups of local benchmarks based on the benchmark configuration parameters and the sub-region parameters and associate the multiple groups of local benchmarks with the corresponding sub-regions. In one or more cases, the sequencing device 214 partitions the position of the hole of the circulation pool. For example, the sequencing device 214 partitions the position of the hole by associating the position of the hole with the adjacent benchmark. Based on the association with the adjacent benchmark, the sequencing device 214 further associates the position of the hole to the sub-region associated with the corresponding benchmark. In other words, the sequencing device 214 partitions the hole position and assigns the hole position to the local sub-region corresponding to them. For example, the sequencing device 214 can utilize the equal horizontal partitioning of the image above the sub-region. The sequencing device 214 can assign an equal number of hole rows to each sub-region. In some cases, the sequencing device 214 can assign top sub-regions and bottom sub-regions to include the hole rows near the top edge and bottom edge of the tile. In one or more cases, sequencing device 214 may define horizontal boundaries of the sub-regions to extend between the left and right sides of the tile, and may define vertical boundaries of the sub-regions by dividing the height of the tile by the number of sub-regions to consider.

[0114] In one or more cases, the sequencing device 214 may construct the sub-regions so that the sub-regions are arranged linearly on the image. In some cases, the sequencing device 214 constructs the sub-regions so that the sub-regions are constructed adjacently, that is, one sub-region is adjacent to another sub-region (e.g., sub-region 502c is adjacent to sub-region 502b). In other cases, the sequencing device 214 constructs the sub-regions so that some sub-regions are constructed adjacently and so that one or more sub-regions overlap each other.

[0115] In one or more cases, the sequencing device 214 may construct the sub-regions so that a reference may be associated with two or more sub-regions. For example, the sequencing device 214 may construct the sub-region 502c associated with the reference 107a and the reference 107b, and may construct the sub-region 502b associated with the reference 107a and the reference 107b. In one or more other cases, the sequencing device 214 may construct the sub-regions so that adjacent sub-regions do not share references.

[0116] The sequencing device 214 may initialize the transformation of each sub-region. The sequencing device 214 may estimate a local transformation matrix based on a plurality of parameters, including, for example, but not limited to, translation parameters, scale parameters, and shear parameters in the X and Y directions. For example, the sequencing device 214 may initialize a default affine transformation matrix, wherein the scaling and shear coefficients of the default affine transformation matrix may be initialized to zero, and the translation coefficients may be initialized to one or both of the X and Y directions. The sequencing device 214 may construct a transformation matrix (e.g., an affine transformation matrix) based on the position of the reference. Figure 5A , the sequencing device 214 constructs, for example, an affine transformation matrix using the positions of the references located at the corresponding sub-regions, such as the sub-regions 502a, 502b, and 502c of the tile 102. That is, the sequencing device 214 constructs the affine transformation matrix for each sub-region using the positions of the references associated with the corresponding sub-regions.

[0117] In one or more cases, the sequencing device 214 uses at least three fiducials to construct an affine transformation matrix. For example, the sequencing device 214 constructs the sub-region 502c based on the positions of the fiducials 106a, 106b, 107a, and 107b. In this example sub-region, the sequencing device 214 can determine that the fiducials 106a, 106b, 107a, and 107b are arranged in a two-by-two pattern within the sub-region 502c. It should be noted that Figure 5A An image and a reference decomposed into three sub-regions are illustrated. It should also be understood that the image and the reference can be decomposed into any number of sub-regions. In addition, it should be noted that any of a variety of geometric transformation models can be used, such as but not limited to linear transformations or affine transformations. The transformation may include, for example, one or more of rotation, translation, scaling, shearing, etc. After initializing the affine transformation for each sub-region, the sequencing device 214 continues to iterate the procedure 400 for each cycle of each tile of each sub-region.

[0118] Reference again Figure 4In the procedure 400 of the present invention, the sequencing device 214 obtains the registration score for each reference position associated with the sub-region at 405. For example, the sequencing device 214 obtains the registration score from a database, such as the database 216. The sequencing device 214 may obtain the registration score determined from an image calibration process, such as the process described in the procedure 300. At 406, the sequencing device 214 may determine whether at least three registration scores are above a threshold score.

[0119] For the case where the three registration scores for the corresponding reference positions are not greater than the threshold score (406: No), at 408, the sequencing device 214 provides an indication of registration failure. The sequencing device 214 provides an indication of registration failure by associating the corresponding sub-region with an indication of image registration failure for the corresponding sub-region. For example, the sequencing device 214 may set a local failure flag for the corresponding tile and provide a -9999 conversion. Registration failure may occur when fewer than three references are accurately found. For example, when the three scores for the corresponding reference positions are not greater than the threshold score (i.e., the sequencing device 214 may be unable to locate or accurately locate at least three references within the corresponding sub-region), the sequencing device 214 may determine that registration has failed. Therefore, the sequencing device 214 may determine that the associated tile cannot be base-checked in the corresponding cycle. Registration failure may also occur when the three references are collinear (i.e., the three references are aligned in a single column or row). For example, in some cases, the sequencing device 214 may determine that three scores of corresponding reference positions are greater than a threshold score, however, the sequencing device 214 may further determine that the reference positions are collinear. In this case, at 408, the sequencing device 214 provides an indication of a failed registration.

[0120] For the case where each of the three alignment scores is greater than the threshold score (406: yes), the sequencing device 214 performs a transformation process at 410. The sequencing device 214 may apply the transformation process to transform the position of the reference associated with the corresponding sub-region. In one or more cases, the sequencing device 214 may perform the transformation process on each sub-region and the reference having a position associated with the corresponding sub-region. The sequencing device 214 performs the transformation process to generate a corresponding local transformation associated with each sub-region (i.e., a transformed position of the reference associated with the corresponding sub-region). For example, the sequencing device 214 may apply a transformation process (e.g., an affine transformation) to the position of the sub-region 502c and the references 106a, 106b, 107a, and 107b to generate a local transformed position of the references 106a, 106b, 107a, and 107b associated with the sub-region 502c, as shown in FIG. Figure 5B exemplified in .

[0121] At 412, the sequencing device 214 may correct for optical distortion of the image. For example, to correct for optical distortion of the image, the sequencing device 214 applies a nonlinear distortion process to each reference position within the image. At 414, the sequencing device 214 may determine an intensity value of the image, such as a pixel intensity value. In one or more cases, the sequencing device 214 may determine the intensity value of the image by sharpening the image using, for example, an adaptive equalizer. The sequencing device 214 may perform equalization by convolving the image with a channel-specific mask to sharpen the image. The sequencing device 214 may apply the same mask to different sub-regions of the image. The sequencing device 214 may apply different masks to different sub-regions of the image.

[0122] In order to extract the intensity value (e.g., pixel intensity value) of the sharpened image at each position, the sequencing device 214 may interpolate the balanced pixel intensity in a square centered at the position. The sequencing device 214 may use an interpolation method on a number of pixels, such as, but not limited to, a bilinear method, a Hamming method, etc. The sequencing device 214 may perform spatial normalization on the tiles of each sub-region. For example, the sequencing device 214 may perform spatial normalization on the tiles of the image of each sub-region so that the 90th percentile of the extracted intensity of the normalized tiles is equal to the 10th percentile. The sequencing device 214 may compress the intensity so that the 1st percentile and the 99th percentile of the extracted pixel intensity have pixel intensity values ​​of 1 and 255, respectively. Lossy compression of the intensity may reduce memory load. In one or more cases, the sequencing device 214 may store a clustered array of pixel intensity values ​​in a memory.

[0123] In one or more cases, the sequencing device 214 is configured to repeat the procedure 400 for each tile within the image. When the cycle associated with the image is completed, the sequencing device 214 can initiate a subsequent imaging cycle of the patterned sample and generate new image data of the patterned sample. Therefore, the sequencing device 214 is configured to repeat the procedure 400 for the new image data as described herein.

[0124] The sequencing device 214 may be configured to implement base calling to determine a base call (e.g., adenine (A), cytosine (C), guanine (G), or thymine (T)) for a given spot position in an image during an imaging cycle. For example, during dual-channel base calling, the sequencing device 214 may use image data extracted from two images to determine the presence of one of four base types by encoding the base identity as a combination of the two image intensities. For example, for a given spot or position in each of the two images, the sequencing device 214 may be configured to determine the base identity based on whether the combination of signal identifications is [on, on], [on, off], [off, on], or [off, off].

[0125] Base calling can be performed by fitting a mathematical model relative to the intensity data. The mathematical model can be, for example, but not limited to, a k-means clustering algorithm, a k-like clustering algorithm, an expected maximization clustering algorithm, a histogram-based method, etc. In an embodiment, the sequencing device 214 can fit four Gaussian distributions to a dual-channel intensity data set so that one distribution is applied to each of the four nucleotides represented in the data set. In one example, the sequencing device 214 can apply an expectation maximization (EM) algorithm to the intensity data. As a result of the EM algorithm, for each X, Y value (i.e., each channel intensity of the two channel intensities, respectively), a value representing the possibility that a certain X, Y intensity value belongs to one of the four Gaussian distributions fitted by the data can be generated. For the case where four independent distributions are provided for four bases, the X, Y intensity values ​​can also have four associated probability values, and each base in the four bases corresponds to a probability value. The maximum value of the four probability values ​​indicates a base call. For example, if the cluster is "off" in two channels, the base call is G. If the cluster is "off" in one channel and "on" in the other channel, the base call is either C or T (i.e., depending on which channel is "on"). If the cluster is "on" in both channels, the base call is A.

[0126] Fig. 7A A simulation comparing the effects of jitter on image registration of a global affine transformation process 702 and image registration using a process 704 (e.g., procedure 200) as described herein is illustrated. A "0" on the simulated jitter axis represents no jitter. A "1" on the simulated jitter axis represents true jitter. A "2" on the simulated jitter axis represents twice the jitter. A "3" on the simulated jitter axis represents three times the jitter. As shown, image registration processes 704a, 704b, and 704c (e.g., procedure 200) using a 12x2x12 reference arrangement, a 9x2x9 reference arrangement, and a 4x4 reference arrangement, respectively, recover most of the performance loss exhibited in the image registration process using global affine transformation processes 702a, 702b, and 702c with the same reference arrangement. That is, the image registration processes 704a, 704b, and 704c (eg, the procedure 200) can tolerate higher levels of jitter than the global affine transformation processes 702a, 702b, and 702c.

[0127] Figure 7B Results from an actual run using a 4x4 benchmark arrangement are illustrated. Figure 7B The improvement over six runs is summarized, showing an improvement in error rate ranging from 2.7% to 17%, with a mean improvement of 8%. Thus, the results show that the 4x4 reference arrangement provides an improvement consistent with real sample stage jitter.

[0128] Figure 7C and Fig.7DIllustrated is a spatial representation of the error rate across an example sequencing image. Figure 7C The spatial representation of the error rate of the global affine transformation process is illustrated. Fig.7D The spatial representation of the error rate of image registration using procedure 200 is illustrated. By using a global affine transformation process, Figure 7C The example shows a periodic pattern of error rate bands 708 caused by sample stage jitter. Fig.7D The same error bands are corrected for image registration using procedure 200. Additionally, due to dithering, not every tile may be affected by the error rate bands. Some effects on specific cycles are more subtle, causing the distortion correction to break, resulting in bands 710 shown in the two global affine transforms on the left, but in Fig.7D does not exist in the spatial representation of .

[0129] Figure 8 800. One or more computing devices, such as one or more computing devices 800, may implement one or more features for generating and / or processing sequencing tasks, as described herein. For example, computing device 800 may include Figure 2A One or more of the sequencing device 214, the client device 208, and / or the server device 202 shown in FIG. Figure 8 As shown, computing device 800 may include a processor 802, a memory 804, a storage device 806, an I / O interface 808, and a communication interface 810 that may be communicatively coupled via a communication infrastructure 812. It should be understood that computing device 800 may include more than one embodiment of the present invention. Figure 8 Fewer or more components than those shown.

[0130] The processor 802 may include hardware for executing instructions, such as those constituting a computer program. In an example, in order to execute instructions for dynamically modifying a workflow, the processor 802 may retrieve (or extract) instructions from an internal register, an internal cache, a memory 804, or a storage device 806, and decode and execute the instructions. The memory 804 may be a volatile or non-volatile memory for storing data, metadata, computer-readable or machine-readable instructions and / or programs for execution by the processor to operate as described herein. The storage device 806 may include a storage device for storing data or instructions for executing the methods described herein, such as a hard disk, a flash drive, or other digital storage device.

[0131] The I / O interface 808 may allow a user to provide input to the computing device 800, receive output from the computing device, and / or otherwise transmit data to and receive data from the computing device. The I / O interface 808 may include a mouse, a keypad or keyboard, a touch screen, a camera, an optical scanner, a network interface, a modem, other known I / O devices, or a combination of such I / O interfaces. The I / O interface 808 may include one or more devices for presenting output to a user, including but not limited to a graphics engine, a display (e.g., a display screen), one or more output drivers (e.g., a display driver), one or more audio speakers, and one or more audio drivers. The I / O interface 808 may be configured to provide graphics data to the display for presentation to the user. The graphics data may represent one or more graphical user interfaces and / or any other graphical content.

[0132] The communication interface 810 may include hardware, software, or both. In any case, the communication interface 810 may provide one or more interfaces for communication (such as, for example, packet-based communication) between the computing device 800 and one or more other computing devices or networks. The communication may be wired communication or wireless communication. As an example and not by way of limitation, the communication interface 810 may include a network interface controller (NIC) or a network adapter for communicating with an Ethernet or other wired-based network, or a wireless NIC (WNIC) or a wireless adapter for communicating with a wireless network such as WI-FI.

[0133] Additionally, the communication interface 810 can facilitate communication with various types of wired or wireless networks. The communication interface 810 can also facilitate communication using various communication protocols. The communication infrastructure 812 can also include hardware, software, or both that couple the components of the computing device 800 to each other. For example, the communication interface 810 can use one or more networks and / or protocols to enable multiple computing devices connected through a specific infrastructure to communicate with each other to perform one or more aspects of the processes described herein. For illustration, a sequencing method can allow multiple devices (e.g., a client device, a sequencing device, and a server device) to exchange information such as sequencing data and error notifications.

[0134] In addition to what has been described herein, the methods and systems may also be implemented in a computer program, software, or firmware incorporated into one or more computer-readable media for, for example, a computer or processor to execute. Examples of computer-readable media include electronic signals (transmitted via a wired or wireless connection) and tangible / non-transient computer-readable storage media. Examples of tangible / non-transient computer-readable storage media include, but are not limited to, read-only memory (ROM), random access memory (RAM), removable disks, and optical media such as CD-ROM disks and digital versatile disks (DVDs).

[0135] As used herein, the term "about" with respect to a numerical value refers to the numerical value itself, or the numerical value itself plus or minus 10% of the numerical value with which it is used.

[0136] Although the present disclosure has been described according to certain embodiments and generally associated methods, the changes and permutations of these embodiments and methods will be apparent to those skilled in the art. Therefore, the above description of the exemplary embodiments does not limit the present disclosure. Other changes, substitutions and variations are also possible without departing from the spirit and scope of the present disclosure.

Claims

1. A sequencing system, comprising: an image capture device configured to obtain an image including at least one feature and a plurality of fiducials, the plurality of fiducials being arranged in a pattern; A computing device comprising a processor and a memory, wherein the processor and the memory are configured to: determining a plurality of sub-regions of the image, wherein each sub-region includes a subset of the fiducials included in the image; performing a geometric transformation on each sub-region to generate a corresponding local transformation associated with each sub-region; and Respective positions of the fiducials included in the image are registered based on the respective local transformations associated with each sub-region, wherein a size of each sub-region is selected such that each sub-region is substantially invariant to sample stage jitter.

2. The sequencing system according to claim 1, wherein the processor and the memory are configured to: A respective position of each of the fiducials in the image is determined based on the determined position of the reference fiducial.

3. The sequencing system according to claim 2, wherein the processor and the memory are configured to: generating a sub-image based on the acquired image, wherein the sub-image includes a pixel filling of the reference fiducial having a larger area included in the image; determining a correlation between the acquired image and the sub-image; and A determination is made whether to adjust the position of the reference datum based on the determined correlation.

4. The sequencing system of claim 1, wherein each subset of the references included in the subregions comprises at least three references.

5. The sequencing system according to claim 1, wherein the sub-regions are arranged linearly with respect to each other.

6. A sequencing system according to claim 1, wherein the geometric transformation comprises an affine transformation.

7. A computer-implemented method, the computer-implemented method comprising: obtaining an image including at least one feature and a plurality of fiducials, the plurality of fiducials being arranged in a pattern; determining a plurality of sub-regions of the image, wherein each sub-region includes a subset of the fiducials included in the image; performing a geometric transformation on each sub-region to generate a corresponding local transformation associated with each sub-region; and Respective positions of the fiducials included in the image are registered based on the respective local transformations associated with each sub-region, wherein a size of each sub-region is selected such that each sub-region is substantially invariant to sample stage jitter. The computer-implemented method of claim 7 , wherein the sub-regions are arranged linearly with respect to each other.

9. The computer-implemented method of claim 7, wherein each subset of fiducials included in a sub-region includes at least three fiducials.

10. The computer-implemented method of claim 7, wherein a reference fiducial is located within the image, and the method of claim 9 further comprises determining a respective position of each of the fiducials in the image based on the determined position of the reference fiducial.

11. The computer-implemented method of claim 10, further comprising: generating a sub-image based on the acquired image, wherein the sub-image includes a pixel filling of the reference fiducial having a larger area included in the image; determining a correlation between the acquired image and the sub-image; as well as A determination is made based on the determined correlation whether to adjust the position of the reference datum.

12. The computer-implemented method of claim 7, wherein a size of each sub-region is selected such that each sub-region is substantially invariant to sample stage jitter of approximately 200 Hz or less.

13. The computer-implemented method of claim 7, wherein at least one reference is associated with two adjacent sub-regions.

14. The computer-implemented method of claim 7, wherein the pattern of the plurality of fiducials comprises a first set of linearly arranged fiducials adjacent to a second set of linearly arranged fiducials, the first set of linearly arranged fiducials and the second set of linearly arranged fiducials extending substantially parallel to each other.

15. The computer-implemented method of claim 7, wherein the image comprises a sequencing image.

16. The computer-implemented method of claim 7, wherein the location of the feature is associated with a hole location of a circulation pool.

17. The computer-implemented method of claim 7, wherein each sub-region includes a respective feature, and the respective local transformation associated with the sub-region including the respective feature is used to determine a position of the respective feature.

18. A non-transitory computer-readable medium comprising computer-readable instructions, which when executed by a processor causes the processor to implement a method comprising: obtaining an image including at least one feature and a plurality of fiducials, the plurality of fiducials being arranged in a pattern; determining a plurality of sub-regions of the image, wherein each sub-region includes a subset of the fiducials included in the image; performing a geometric transformation on each sub-region to generate a corresponding local transformation associated with each sub-region; and Respective positions of the fiducials included in the image are registered based on the respective local transformations associated with each sub-region, wherein a size of each sub-region is selected such that each sub-region is substantially invariant to sample stage jitter. The non-transitory computer readable medium of claim 8 , wherein the sub-regions are arranged linearly with respect to each other.

20. The non-transitory computer readable medium of claim 18, wherein each subset of fiducials included in a sub-region includes at least three fiducials.

21. The non-transitory computer readable medium of claim 18, wherein a reference fiducial is located within the image, and a position of the reference fiducial is used to determine a corresponding position of each of the fiducials in the image.

22. The non-transitory computer readable medium of claim 21, further comprising: generating a sub-image based on the acquired image, wherein the sub-image includes a pixel filling of the reference fiducial having a larger area included in the image; determining a correlation between the acquired image and the sub-image; and A determination is made based on the determined correlation whether to adjust the position of the reference datum.

23. The non-transitory computer readable medium of claim 18, wherein a size of each sub-region is selected such that each sub-region is substantially invariant to sample stage jitter of approximately 200 Hz or less.

24. The non-transitory computer readable medium of claim 18, wherein at least one reference is associated with two adjacent sub-regions.

25. The non-transitory computer readable medium of claim 18, wherein the pattern of the plurality of fiducials comprises a first set of linearly arranged fiducials adjacent to a second set of linearly arranged fiducials, the first set of linearly arranged fiducials and the second set of linearly arranged fiducials extending substantially parallel to each other.

26. The non-transitory computer readable medium of claim 18, wherein the image comprises a sequencing image.

27. The non-transitory computer readable medium of claim 18, wherein the position of the feature is associated with a well position of a circulation pool.

28. The non-transitory computer-readable medium of claim 18, wherein each sub-region includes a respective feature, and the respective local transformation associated with the sub-region including the respective feature is used to determine the position of the respective feature.

Images