Methods and systems for computational decoding of biological, chemical, and physical entities
The present disclosure provides systems and methods for detecting components of an array of biological, chemical, or physical entities. In an aspect, the present disclosure provides a method for detecting an array of biological, chemical, or physical entities, comprising: (a) using one or more light sensing devices, acquiring pixel information from sites in an array, wherein the sites comprise biological, chemical, or physical entities that produce light; (b) processing the pixel information to identify a set of regions of interest (ROIs) corresponding to the sites in the array that produce the light; (c) classifying the pixel information for the ROIs into a categorical classification from among a plurality of distinct categorical classifications, thereby producing a plurality of pixel classifications; and (d) identifying one or more components of the array of biological, chemical, or physical entities based at least in part on the plurality of pixel classifications.
1. A method for detecting an array of biological, chemical, or physical entities, comprising:
(a) using a set of light sensing devices, acquiring a first set of pixel information from sites in an array, and (ii) acquiring a second set of pixel information from the sites in the array, wherein the sites comprise biological, chemical, or physical entities that produce light;
(b) processing the pixel information to (i) register the first set of pixel information and the second set of pixel information to a common coordinate system, and (ii) identify a set of regions of interest (ROIs) corresponding to the sites in the array that produce the light in the first set of pixel information and the second set of pixel information;
(c) classifying the pixel information for the set of ROIs into a categorical classification from among a plurality of distinct categorical classifications, thereby producing a plurality of pixel classifications; and
(d) identifying a component of the array of biological, chemical, or physical entities based at least in part on the plurality of pixel classifications.
2. The method of claim 1 , wherein individual sites of the array comprise a biological, chemical, or physical entity selected from the group consisting of: (i) a structured nucleic acid particle; (ii) a structured nucleic acid particle comprising a fluorescent label; (iii) a protein; (iv) a protein attached to a structured nucleic acid particle; (v) an affinity agent bound to a protein; and (vi) an affinity agent bound to a protein, wherein the protein is attached to a structured nucleic acid particle.
3. The method of claim 1 , wherein individual sites of the array each comprise a single biological, chemical, or physical entity that produces the light.
4. The method of claim 3 , wherein the single biological, chemical, or physical entity is selected from the group consisting of (i) a single structured nucleic acid particle; (ii) a single structured nucleic acid particle comprising a fluorescent label; (iii) a single-protein; (iv) a single-protein attached to a single structured nucleic acid particle; (v) an affinity agent bound to a single-protein; and (vi) an affinity agent bound to a single-protein, wherein the single-protein is attached to a single structured nucleic acid particle.
5. The method of claim 1 , wherein the set of light sensing devices is configured to use four-beam interference to create a two-dimensional sine wave pattern.
6. The method of claim 1 , wherein the set of light sensing devices comprises a material compatible with complementary metal-oxide semiconductor (CMOS) processing, and wherein the set of light sensing devices is configured to be functionalized.
7. The method of claim 1 , wherein the set of light sensing devices comprises an array of light sensing devices, wherein a pitch of individual devices in the array of light sensing devices is matched to a pitch of sites in the array of biological, chemical, or physical entities.
8. The method of claim 1 , wherein each ROI of the set of ROIs comprises pixel information corresponding to a single cluster of pixels.
9. The method of claim 8 , wherein (d) further comprises applying a classifier to the set of ROIs to classify each of the pixel information into the categorical classification.
10. The method of claim 9 , wherein the classifier comprises a trained machine learning classifier.
11. The method of claim 10 , wherein the trained machine learning classifier comprises a supervised machine learning algorithm.
12. The method of claim 11 , wherein the supervised machine learning algorithm comprises a support vector machine (SVM), a linear regression, a logistic regression, a nonlinear regression, a neural network, a Random Forest, a deep learning algorithm, a naïve Bayes classifier, or a combination thereof.
13. The method of claim 10 , wherein the trained machine learning classifier comprises an unsupervised machine learning algorithm.
14. The method of claim 13 , wherein the unsupervised machine learning algorithm comprises clustering analysis, principal component analysis, independent component analysis, non-negative matrix factorization, singular value decomposition, anomaly detection, neural network, expectation-maximization algorithm, method of moments, or a combination thereof.
15. The method of claim 1 , wherein the plurality of distinct categorical classifications comprises a first categorical classification associated with a light signal from a site in the array indicative of a presence of a biological, chemical, or physical entity, and a second categorical classification associated with an absence of a light signal from the array indicative of an absence of a biological, chemical, or physical entity.
16. The method of claim 15 , wherein the first categorical classification is indicative of presence of light produced from an affinity agent bound to a structured nucleic acid particle-protein complex.
17. The method of claim 15 , wherein the second categorical classification is indicative of an absence of an affinity agent bound to a structured nucleic acid particle-protein complex.
18. The method of claim 1 , wherein the common coordinate system is determined by deconvolving the first set of pixel information with an edge kernel, the edge kernel representing a signal from a set of sites at an edge of the array.
19. The method of claim 18 , wherein the common coordinate system is determined by deconvolving the first set of pixel information with a site kernel, the site kernel representing a signal from a single site in the array.
20. The method of claim 1 , wherein the individual sites of the array each comprise a biological entity.
21. The method of claim 20 , wherein the biological entity comprises a protein.
22. The method of claim 20 , wherein the biological entity comprise a protein attached to a structured nucleic acid particle.
23. The method of claim 20 , wherein the biological entity comprises an affinity agent bound to a protein, wherein the protein is attached to a structured nucleic acid particle.
24. The method of claim 1 , wherein the sites are arranged in a repeating pattern in the array.
25. The method of claim 1 , wherein the first set of pixel information is acquired from a first detection channel and the second set of pixel information is acquired from a second detection channel, wherein the first detection channel differs from the second detection channel.
26. The method of claim 1 , wherein the first set of pixel information and the second set of pixel information comprise different patterns of sites in the array that produce the light.