System and method for reducing test times in high-dimensional analysis

Abstract

The present invention provides a system (200) for reducing test times for high-dimensional analysis to detect, identify, and quantify multiple analytes in multiple biological samples. The system is configured to (i) generate a pooling matrix for pooling and testing multiple biological samples, (ii) obtain output data upon completing the high-dimensional analysis in each of the multiple pools, (iii) generate a set of linear equations based on the output data and the generated pooling matrix, and (iv) transform the set of linear equations using a compressed sensing algorithm and at least one regularity condition to detect, identify, and quantify multiple analytes in the multiple biological samples. The regularity condition refers to sparsity considering the presence or absence of each analyte individually, or a disproportionate number of samples having disproportionately high values ​​for a particular analyte.

Description

[Technical field]

[0001] FIELD OF THE DISCLOSURE

[0002] Embodiments herein relate generally to sample pooling, and more particularly to systems and methods for reducing the number of test populations for high dimensional assays for detecting and measuring multiple analytes. [Background technology]

[0002] High-dimensional analytical methods are those used for the simultaneous measurement or detection of multiple analytes. For example, mass spectrometry is a type of high-dimensional analytical method, which is used to simultaneously measure a large number (more than 100) of analytes in a single measurement. Mass spectrometry is typically used in newborn screening tests and food quality testing. Newborn screening tests are useful for preventing infant mortality and morbidity. They are mandatory in developed countries, but not in developing countries due to cost. Similarly, mass spectrometry tests are used to detect adulterants in spices, high levels of pesticides in tea, high levels of antibiotics (in dairy products), etc. Such screening helps to keep the entire population safe from such contaminants in food. However, such screening tests are not widely implemented due to cost constraints. At best, samples are tested randomly.

[0003] Furthermore, mass spectrometry has throughput limitations: each sample can take 10 minutes or more to analyze. For example, a lab with one instrument can only test about 200 samples per day. If 1,200 samples need to be tested, the lab would have to invest in additional machinery, which is a significant capital expenditure, and testing can become expensive.

[0004] Pooling tests are used to reduce the cost and increase testing capacity of screening large populations. Pooling tests, also known as Dorfman pooling, are an effective way to reduce the number of tests required to test a population when most samples in the population are negative. It works by pooling n samples into disjoint groups, each with k elements (e.g., k=5). Samples from groups that tested positive are retested separately in a second round. If g groups were positive in the first round, Dorfman pooling would require a total of n / k+k*g tests, which is significantly less than n if g is small.

[0005] Dorfman pooling is an effective compression strategy for tests that measure a single analyte, but not for tests that measure many analytes in a single molecular test. For each analyte, most of the individual samples will show normal values ​​for that analyte. However, if samples are pooled, at least one sample in the pool will almost certainly have an abnormally high value for at least one analyte. This results in a value of g very close to n, and the total number of tests (n / k+k*g) being greater than n for all positive integer k. Therefore, no compression is achieved by using Dorfman pooling for tests that measure many analytes. [Brief description of the drawings]

[0006] The embodiments herein will be better understood from the following detailed description taken in conjunction with the drawings, in which:

[0007] [Figure 1] We present an existing solution for reducing the number of tests using Dorfman pooling.

[0008] [Diagram 2] According to some embodiments of the present disclosure, a system for reducing test times for high dimensional analysis for detecting, identifying, and quantifying multiple analytes in multiple biological samples is presented.

[0009] [Diagram 3] FIG. 3 is an exemplary schematic diagram of the construction of a pooling matrix using the system of FIG. 2 for measuring multiple analytes using high-dimensional analytical methods, according to some embodiments herein.

[0010] [Figure 4] A method for reducing test times in high dimensional analytical methods for detecting, identifying and quantifying multiple analytes in multiple biological samples is presented.

[0011] [Diagram 5] FIG. 1 is a schematic diagram of a computer architecture of a computing device or molecular computer according to an embodiment herein. Summary of the Invention [Problem to be solved by the invention]

[0012] As mentioned above, in order to reduce the number of tests in high-dimensional analysis methods, there was a need to address the technical shortcomings of the existing techniques in pooling. [Means for solving the problem]

[0013] According to a first aspect of the present invention, a system for reducing the number of tests in a high-dimensional analysis method for detecting, identifying, and quantifying multiple analytes in multiple biological samples is provided. The system includes a memory storing a set of instructions and a processor configured to execute the set of instructions to perform one or more operations. The processor is configured to generate a pooling matrix by a sample coding device for pooling and testing multiple biological samples. The pooling matrix indicates multiple pools for the multiple biological samples to be tested and at least two pools for each biological sample. Pooling is performed to include each of the biological samples in at least two determined pools of the multiple pools, and testing is performed on the multiple pools. The processor is configured to obtain output data from the testing machine regarding the completion of a high-dimensional analysis on each of the multiple pools, the high-dimensional analysis reducing the number of tests in the testing machine. The output data for each pool is a row vector consisting of a quantitative vector, a semi-quantitative vector, or a vector having categorical values ​​indicating the absence, presence, or category of at least one analyte in the multiple biological samples. The output data for each pool consists of a measurement value or category of each analyte in the pool. The processor is configured to generate a set of linear equations based on the output data and the generated pooling matrix. The processor is configured to convert the set of linear equations into a set of nonlinear equations and solve the set of linear equations using a compressed sensing algorithm. The processor is configured to invoke at least one regularity condition to obtain a unique solution to the set of nonlinear equations for detecting, identifying, and quantifying multiple analytes in multiple biological samples. The regularity condition is selected from either (a) sparsity considering the presence or absence of each analyte individually, or (b) sparsity with respect to a disproportionate number of samples having disproportionately high values ​​for a particular analyte.

[0014] According to some embodiments, the processor is configured to detect, identify and quantify a condition of interest based on the detected, identified and quantified analytes in the plurality of biological samples, the condition of interest including at least one of a quality assurance condition, a food safety condition, a medical condition, medical screening, drug discovery research, a transcriptomics or a next generation sequencing (NGS) targeted panel.

[0015] According to some embodiments, the testing machine is a polymerase chain reaction (PCR) machine, a high performance liquid chromatography column (HPLC), a microarray, a next generation sequencing (NGS) machine, a mass spectrometer, a nuclear magnetic resonance (NMR) spectrometer, or a Raman spectrometer.

[0016] According to some embodiments, the linear equation is y=Ax, where

[0017] (i) A = (a ij ) m×n is a pooling matrix of dimension m × n. The pooling matrix has the number of rows equal to the number of pools and the number of columns equal to the number of samples. The element a of the i-th row and j-th column of the pooling matrix A is ij determines the amount of sample j that will participate in the i-th pool.

[0018] (ii) x = (X jk ) n×d is the component X jk where j ranges from 1 to n and represents n samples, k ranges from 1 to d and represents d analytes, and X jk represents the amount of analyte k present in the jth sample. jk are unknown and are determined by solving the set of linear equations.

[0019] (iii) y = (y ik ) m×d is the component y ikwhere i ranges from 1 to m and k ranges from 1 to d, and the matrix y has a number of rows (m) equal to the number of pools and a number of columns (d) equal to the number of analytes measured in the number of pools. ik represents the amount of analyte k present in pool i, as determined by analysis or testing.

[0020] In some embodiments, the processor is adapted to solve a linear equation y k =Ax k Convert to a nonlinear equation, then, X k is the k-th column of the x matrix, and y k The method is constructed to use regularity conditions to solve for a matrix x where x is the k-th column of the y matrix.

[0021] In some embodiments, the nonlinear equation is generated based on a plurality of variables constituting the generated pooling matrix, a plurality of output data of a plurality of pools, and a quantitative measurement value of each analyte.

[0022] In some embodiments, statistical correlations between measurements of different analytes from historical data are used as part of the regularity condition.

[0023] In some embodiments, the pooling matrix is ​​generated based on at least one input from a user, the at least one input including at least one of a name of the assay and a size of the assay, the size of the assay indicating the total number of biological samples to be tested and the number of biological samples that are predicted to be positive out of the total number of biological samples.

[0024] According to a second aspect of the present invention, a method for reducing the number of tests for a high-dimensional analysis for detecting, identifying, and quantifying a plurality of analytes in a plurality of biological samples is provided. The method includes generating a pooling matrix for pooling and testing a plurality of biological samples by a sample coding device. The pooling matrix indicates a plurality of pools for the plurality of biological samples to be tested and at least two pools for each biological sample. Pooling is performed to include each of the biological samples in at least two of the determined pools of the plurality of pools, and testing is performed on the plurality of pools. The method includes obtaining output data from the testing machine regarding the completion of the high-dimensional analysis in each of the plurality of pools, the high-dimensional analysis reducing the number of tests in the testing machine. The output data for each pool is a row vector consisting of a quantitative vector, a semi-quantitative vector, or a vector having categorical values ​​indicating the absence, presence, or category of at least one analyte in the plurality of biological samples. The output data for each pool consists of a measurement value or category of each analyte in the pool. The method includes generating a set of linear equations based on the output data and the generated pooling matrix. The method includes converting the set of linear equations into a set of nonlinear equations for solving the set of linear equations using a compressive sensing algorithm. The method includes invoking at least one regularity condition for obtaining a unique solution of the set of nonlinear equations for detecting, identifying, and quantifying a plurality of analytes in a plurality of biological samples. The regularity condition is selected from either (a) sparsity considering the presence or absence of each analyte individually, or (b) sparsity with respect to a disproportionate number of samples having disproportionately high values ​​for a particular analyte. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0025] The embodiments herein and their various features and advantageous details will be more fully described with reference to the non-limiting embodiments illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as not to unnecessarily obscure the embodiments herein. The examples used herein are merely intended to facilitate understanding of how the embodiments herein can be implemented and further enable those skilled in the art to implement the embodiments herein. Thus, the examples should not be interpreted as limiting the scope of the embodiments herein.

[0026] As discussed above, there is a need for a technique that addresses the technical shortcomings of existing techniques in pooling. The embodiments herein accomplish this by providing systems and methods that use quantitative, non-adaptive, single-round pooling to reduce test times for high dimensional analysis to detect, identify, and quantify multiple analytes in multiple biological samples. Referring now to the drawings, and more particularly to Figures 1-5, in which like reference characters indicate the same features consistently throughout all figures, preferred embodiments are shown therein.

[0027] Figure 1A-B shows an existing solution to reduce the number of tests using Dorfman pooling. As shown in Figure 1A-B, n samples are combined into discontinuous groups, each consisting of k elements (e.g., k=5). Samples from groups that tested positive are retested individually in the second round. If g groups tested positive in the first round, Dorfman pooling requires a total of n / k+ k*g tests, which can be significantly less than n when g is small. As shown in Figure 1A, Dorfman pooling is an effective compression strategy for tests that measure one analyte. Dorfman pooling is not effective when one molecular test measures many analytes, e.g., analyte A, analyte B, and analyte C, as shown in Figure 1B. For each analyte A, B, and C, most individual samples have normal levels of that analyte. However, when samples 102A-P are pooled, at least one sample in the pool will have an abnormally high value for at least one analyte. This results in a value of g very close to n, such that the total number of tests (n / k+k*g) = 17 is greater than n = 16 for all positive integer k. Thus, when using Dorfman pooling for tests measuring a large number of analytes, as shown in Figure 1B, the reduction number is negative and no compression is achieved.

[0028] FIG. 2 illustrates a system 200 for reducing test times for high-dimensional analysis of detecting, identifying, and quantifying multiple analytes in multiple biological samples according to some embodiments herein. The system 200 includes a processor 202 and a memory 204 having computer-executable instructions stored thereon executable by the processor 202 to perform one or more operations of the system 200. The system 200 may be at least one of a cloud computing device, a server, or a computing device. The cloud computing device may be part of a public cloud or a private cloud. The server may be at least one of a standalone server, a server on a cloud, and the like. The computing device may be, but is not limited to, a personal computer, a notebook, a tablet, a desktop computer, a laptop, a handheld device, a mobile device, and the like. The system 200 may be at least one of a microcontroller, a processor, a system on a chip (SoC), an integrated chip (IC), a microprocessor-based programmable consumer electronic device, and the like. The system 200 may communicate with the outside world via a network. The network may be, but is not limited to, the Internet, a wired network, or a wireless network (such as a Wi-Fi network, a cellular network, a Wi-Fi hotspot, Bluetooth, ZigBee, etc.).

[0029] The system 200 is configured to define a pooling matrix for a plurality of samples to be tested, where each sample is directed to two or more pools. The system 200 can ensure that no two pools contain one or more samples in common. In such a case, the system 200 reduces the number of tests of a population to measure multiple analytes in one procedure or round. The multiple samples contain multiple analytes to be measured.

[0030] The pooling matrix includes multiple rows and columns. The multiple columns indicate the number of samples to be tested. The multiple rows indicate the number of tests or pools to be created for testing the samples. As an example, A is a pooling matrix having dimensions m×n, where m and n are the rows and columns of pooling matrix A. The element a of the i-th row and j-th column of pooling matrix A is ij determines the amount of sample j that will participate in the ith pool. In some embodiments, the pooling matrix may be a sparse matrix or a dense matrix. In an example scenario, the samples tested may be numbered as 1,2,3....n and indexed by "j", and the pools or tests created for the samples may be numbered as 1,2,3....n and indexed by "i". In an example scenario, the system 200 constructs a pooling matrix for testing of samples as follows: A=(A ij ) m×n Here, A ij = 0 indicates that the jth sample does not exist in the ith pool, and A ij=1 indicates that the jth sample is in the ith pool. In some embodiments, the pooling matrix is ​​part of a pre-processing step in the lab where samples are combined into pools. The system 200 is configured to test each pool of the pooling matrix for multiple analytes using a high-dimensional analysis. The high-dimensional analysis can be performed in an analysis machine 206 associated with the system 200. In some embodiments, the analysis machine 206 can be a polymerase chain reaction (PCR) machine, a high performance liquid chromatography column (HPLC), a microarray, a next generation sequencing (NGS) machine, a mass spectrometer, a nuclear magnetic resonance (NMR) spectrometer, or a Raman spectrometer. Each sample corresponding to each pool of the pooling matrix is ​​transferred to a container for performing the high-dimensional analysis. In some embodiments, the system 200 tests each pool using one or more high-dimensional analyses, the one or more high-dimensional analyses targeting different analytes in the same sample or pool. The one or more high-dimensional analyses can be the same technical analysis. The one or more high-dimensional analyses do not have to be the same technical analysis. For example, system 200 performs multiple polymerase chain reaction (PCR) reactions for each pool, with each PCR reaction targeting a different analyte. This is useful when the same sample needs to be tested for multiple infectious diseases, or for multiple alleles or marker regions on the genome. Thus, system 200 can use a single highly multiplexed test, or multiple tests that do not even need to be the same technology. This means that pooling only needs to be done once, and all of this processing can be done downstream in many ways, and can all be worked out using subsequent steps described below.

[0031] For each pool, the system 200 calculates a row vector (y j ) is constructed to determine the row vector (y j) may be a quantitative vector, a semi-quantitative vector, or a vector having categorical values ​​indicating the absence, presence, or category of at least one analyte in the multiple biological samples. The output data for each pool consists of measurements or categories of each analyte in the pool.

[0032] The system 200 determines the positive samples from the plurality of samples and then calculates a row vector (y j ) to determine the presence of multiple analytes in positive samples. j ), the system 200 calculates the row vector (y j ) and similarly identifies for which analyte the positive samples are positive.

[0033] The system 200 comprises (a) a pooling matrix A that is created for testing multiple samples, and (b) a row vector (y j ), where y j indicates the j-th row of the y matrix. The linear equation is y=Ax Here, x=(X jk ) n×d is the component X jk where j ranges from 1 to n and represents n samples, k ranges from 1 to d and represents d analytes, and X jk represents the amount of analyte k present in the jth sample. jk is unknown and is determined by solving the set of linear equations. ik ) m×d is the component y ik where i runs from 1 to m and k runs from 1 to d, and the matrix y has a number of rows (m) equal to the number of pools and a number of columns (d) equal to the number of analytes measured in the number of pools. ik represents the amount of analyte k present in pool i, as determined by analysis or testing.

[0034] The system 200 solves the linear equation y k =Ax k can be transformed into a nonlinear equation and then solved for the matrix x using the regularity conditions.

[0035] In one embodiment, the system 200 (i) converts the set of linear equations y=Ax into a nonlinear equation y=f(Ag(x)), where log(a) is understood as (log(a1), log(a2), ..., log(an)), and b=Ae by selecting f and g to be log and exp instead of identity functions. x Brings X i :=log a i Then, define y=log(b), which takes the log on both sides and gives the nonlinear equation y=log(Ae x ) (ii) solving said nonlinear inverse problem by specifying regularity conditions on x after receiving noisy measurements y' of y in matrix A to determine quantitative or semi-quantitative measurements of multiple analytes in each sample. The method of converting linear equations to nonlinear equations is effective when the range of analyte values ​​is very large.

[0036] In some embodiments, there are statistical correlations between measurements of different analytes that are known from historical data, and this can also be used in solving the set of linear equations by using it as part of the regularity conditions.

[0037] The system 200 is configured to detect, identify, and quantify conditions of interest based on the detected, identified, and quantified analytes in the multiple biological samples. Conditions of interest include, but are not limited to, quality assurance conditions, food safety conditions, medical conditions, medical screening, drug discovery research, transcriptomics, or next generation sequencing (NGS) target panels. Medical conditions include, but are not limited to, infectious diseases, cancer, genetic diseases, inflammatory conditions, metabolic syndrome, heart disease, diabetes, and the like. Medical screening includes, but is not limited to, kidney screening, gut microbiome screening, cardiac screening, pulmonary screening, neurological screening, non-invasive prenatal testing, or newborn screening. Transcriptomics includes, but is not limited to, bulk transcriptomics, single cell transcriptomics, and spatial transcriptomics.

[0038] The plurality of analytes includes, but is not limited to, infectious agents, microbial analytes, disease causing agents, pathogens, pollutants, blood analytes, chemical species or chemicals, proteins, nucleic acids, genomic mutations, insertions, deletions, alleles, marker regions, or biomolecules. Infectious agents include, but are not limited to, viruses, bacteria, fungi, protozoa, or helminths. Blood analytes include, but are not limited to, sodium (Na), potassium (K), urea, glucose, and creatinine. A chemical species or chemical is defined as a substance that is composed of chemically identical molecular entities. A protein is a biomolecule in which amino acid residues are linked by peptide bonds. Proteins include, but are not limited to, antibodies, enzymes, hormones, transport proteins, storage proteins, and the like. Nucleic acids include deoxyribonucleic acid (DNA) and ribonucleic acid (RNA). A biomolecule is any molecule produced by a cell or organism.

[0039] In an exemplary embodiment, the system 200 is used for newborn screening. The system 200 is used to measure one or more metabolites in a blood sample of a newborn to determine the presence or absence of a disease in the newborn. The system 200 defines a pooling matrix by directing each blood sample into two or more pools, while ensuring that the two pools do not contain one or more samples in common. The system 200 further examines each pool in the pooling matrix using a mass spectrometer. The mass spectrometer provides the result of each pool as a spectrum of signal intensity of the detected metabolites as a function of mass-to-charge ratio. The system 200 constructs the linear equation y=Ax based on the pooling matrix and the result of each pool from the mass spectrometer. The system 200 determines the matrix x by solving the set of linear equations using a compressed sensing algorithm and one or more regularity conditions. The regularity conditions are selected from either (a) sparsity considering the presence or absence of each metabolite individually, or (b) sparsity with respect to a disproportionate number of samples having disproportionately high values ​​for a particular metabolite.

[0040] In some embodiments, the system 200 further determines quantitative measurements of the one or more metabolites in each sample by converting the linear equations to nonlinear equations and solving the nonlinear equations using regularity conditions and nonlinear algorithms to solve the matrix x. In some embodiments, the one or more metabolites in each blood sample may be identified by correlating known masses (e.g., whole molecules) to identified masses or via characteristic fragmentation patterns.

[0041] In another exemplary embodiment, the system 200 is used for food quality testing to determine the presence of impurities in foods such as spices, the presence of high levels of pesticides in products such as tea, the presence of high levels of antibiotics in products such as dairy products, etc.

[0042] In another exemplary embodiment, the system 200 is used to detect or measure the presence of one or more pathogens in multiple samples.

[0043] The embodiments herein have the advantage that the effective sparsity seen when solving for a particular column is the same as the sparsity across the samples for that single analyte. This advantage is not available in Dorfman pooling methods, where each sample is fed into multiple pools and the effective sparsity would be determined by the proportion of samples that have positive values ​​for any of the multiple analytes.

[0044] FIG. 3 is an exemplary schematic diagram of constructing a pooling matrix 304 using the system 200 of FIG. 2 to measure multiple analytes using high dimensional analysis, according to some embodiments herein. The system 200 can receive a request or instruction from a user to perform pooling and testing of multiple individuals 302A-P to measure multiple analytes. The user can provide the request or instruction via a user device or a user interface of the system 200. The test can be a mass spectrometry test. The request can include data including, but not limited to, a unique test name (including date and time), a size of the test. The size of the test can indicate the number of samples to be tested, the number of analytes to be tested, and the number of expected positives. In one example, the number of analytes to be tested is d. The multiple analytes include, but are not limited to, analyte A, analyte B, and analyte C.

[0045] The system 200 constructs a pooling matrix 304 for pooling and testing based on a predetermined number of samples. The system 200 constructs the pooling matrix 304 by directing each sample into two or more pools, and also ensures that no two pools contain one or more samples in common. The pooling matrix 304 includes a number of rows and columns. The number of columns indicates the number of samples to be tested. The number of rows indicates the number of tests or pools to be created for testing the samples. In this example, the number of samples or individuals to be tested is 16. The pooling matrix 304 is an 8×16 matrix, which indicates that 16 biological samples can be pooled into 8 pools. The pooling matrix 304 includes 8 rows and 16 columns. The pooling matrix 304 can include elements of 0 and 1. An element of 1 in each column indicates that the pool does not contain the sample in that column. An element of 0 in each column indicates that the pool does not contain the sample in that column.

[0046] After creating the pooling matrix 304, the user can pipette, transfer, or otherwise assign each sample from each pool to a separate reaction well or vessel where the number of reaction wells or vessels equals the number of pools. For example, the user pipettes or transfers each sample from pool 1 of the pooling matrix 304 into a reaction well. Similarly, the user pipettes or transfers each sample from the remaining pools, such as pools 2 through 8, into separate reaction wells. The system 200 performs testing of each pool for multiple analytes using high dimensional analysis, such as mass spectrometry. The high dimensional analysis can be performed in a testing machine 206, which can include a mass spectrometer.

[0047] The inspection machine 206 calculates the signal intensity values ​​of each pool for each analyte (row vector (y j Based on the received signal intensity values ​​of each pool for each analyte, the system 200 determines which pools are positive and which pools are negative. The system 200 may convert the signal intensity values ​​identified as positive for any one of the analytes to an analyte concentration.

[0048] The system 200 includes a pooling matrix 304 created for testing multiple samples 302A-P, and a row vector (y j ) to construct a linear equation, which is y=Ax It is.

[0049] The system 200 uses a compressive sensing algorithm, e.g., a Bayesian inference algorithm such as Lasso or Markov Chain Monte Carlo, to determine quantitative measurements of multiple analytes in each sample. Regularity conditions are used to set prior distributions. In this example, individual 302A is identified as analyte A positive, individual 302I is analyte B positive, and individual 302P is analyte C positive.

[0050] In this example, a reduction in the number of tests is achieved; the number of tests is reduced to 8 (16-8).

[0051] In some embodiments, the system 200 is used to reduce testing times for newborn screening using mass spectrometry. The following table sets forth data from validation studies. TIFF2025504602000002.tif91152

[0052] Tests were performed on 40 types of metabolites (tandem mass spectrometry) including PKU, IRT, 17α-OHP, NTSH, GAO, and others shown in the table below, using 8 living organisms with known ground truth values ​​for the metabolites. TIFF2025504602000003.tif230162TIFF2025504602000004.tif166161

[0053] The 8 samples were pooled 3 times, with each pool containing all 8 samples. An example pooling matrix is ​​shown in the table below. TIFF2025504602000005.tif74151The above matrix reduces the examination of 768 samples to examination of 88 pools: Row1, Row2, ..., Row24, Col1, Col2, ..., Col32, Diag1, Diag2, ..., Diag32. TIFF2025504602000006.tif144115

[0054] The results show that the pooled values ​​are linear with respect to the individual sample metabolite values. They also show reproducibility with nearly identical values ​​obtained from three pooled replicates. For any fixed analyte, if the metabolite is a heavy hitter, all three pooled values ​​are above the average. This demonstrates the advantage of the system 200 in reducing the number of tests.

[0055] In some embodiments, the system 200 is used to reduce the number of tests in next generation sequencing. As an exemplary scenario, non-identified Fastq files from sequencing of 42 samples for renal function testing are obtained as ground truth through NGS testing of patient samples. Fastq files are a text-based file format for storing both biological sequences (usually base sequences) and their corresponding quality scores. Fastq files are simulated in silico to simulate the effect of pooling the 42 samples into 20 pools according to a pooling matrix. The fastq files of the 20 pools are converted to bam files, and the binary alignment map (BAM) files are analyzed at all positions with the aim of identifying which variants occurred in which samples. The analysis resulted in perfect matches with the ground truth for all variants present in 7 or fewer samples. The dimension of the analysis is the length of the BED file for panel sequencing. BED files (.bed) are tab-delimited text files that define feature tracks.

[0056] 4A and 4B show a method for reducing the number of tests for high-dimensional analysis for detecting, identifying and quantifying multiple analytes in multiple biological samples. In step 402, a pooling matrix for pooling and testing multiple biological samples is generated using a sample coding device. The pooling matrix indicates multiple pools for the multiple biological samples to be tested and at least two pools for each biological sample. Pooling is performed to include each biological sample in at least two of the determined pools of the multiple pools, and testing is performed on the multiple pools. In step 404, output data regarding the completion of the high-dimensional analysis in each of the multiple pools is obtained from the inspection machine 206. The output data for each pool is a row vector consisting of a quantitative vector, a semi-quantitative vector, or a vector having a categorical value indicating the absence, presence, or category of at least one analyte in the multiple biological samples. The output data for each pool consists of a measurement value or category of each analyte in the pool. In step 408, a set of linear equations is generated based on the output data and the generated pooling matrix. In step 410, the set of linear equations is solved using a compressive sensing algorithm and at least one regularity condition to detect, identify, and / or quantify multiple analytes in multiple biological samples. The regularity condition is selected from either (a) sparsity that considers the presence or absence of each analyte individually, or (b) sparsity with respect to a disproportionate number of samples having disproportionately high values ​​for a particular analyte.

[0057] This method has the advantage that a large number of samples can be tested with a single testing machine 206 (in the case of a mass spectrometer) without the extra expense of additional capital equipment, and the operational costs can be significantly reduced, which allows for an increased number of such tests to be performed, thereby reducing the cost of testing.

[0058] FIG. 5 is a schematic diagram of a computer architecture of a computing device or molecular computer 500 according to an embodiment of the present specification. A representative hardware environment for implementing the embodiments of the present specification is depicted in FIG. 5 with reference to FIGS. 1-4. The schematic diagram shows a hardware configuration of a server / computer system / computing device / molecular computer according to an embodiment of the present specification. The system 200 of FIG. 2 can use a computing device or molecular computer 500 to reduce the number of tests of a population measuring multiple analytes using high-dimensional analysis according to an embodiment of the present specification. The computing device or molecular computer 500 includes at least one processing device CPU 10 that can be interconnected to various devices such as a random access memory (RAM) 12, a read-only memory (ROM) 16, and an input / output (I / O) adapter 18 via a system bus 14. The I / O adapter 18 can be connected to peripheral devices such as a disk device 38 and a program storage device 40 that are readable by the system. The system can read instructions according to the present invention on the program storage device 40 and execute the method of the embodiment of the present specification according to these instructions. The system further includes a user interface adapter 22 that connects other user interface devices, such as a keyboard 28, a mouse 30, a speaker 32, a microphone 34, and / or a touch screen device (not shown) to the bus 14 to collect user input. Additionally, a communications adapter 20 connects the bus 14 to a data processing network 42, and a display adapter 24 connects the bus 14 to a display device 26, which provides a graphical user interface (GUI) 36 for outputting data in accordance with embodiments herein or may be embodied as an output device, such as, for example, a monitor, printer, or transmitter.

[0059] The foregoing description of specific embodiments fully reveals the general nature of the embodiments herein, and others, by applying current knowledge, may easily modify and / or adapt such specific embodiments for various applications without departing from the general concept, and such adaptations and modifications should and are intended to be understood within the meaning and range of equivalents employed herein, for purposes of illustration and not of limitation. Thus, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the scope of the appended claims.

Claims

1. 1. A system (200) for reducing test times for high dimensional analysis for detecting, identifying, and quantifying multiple analytes in multiple biological samples, the system (200) comprising: A memory (204) storing a set of instructions; a processor (202) configured to execute the set of instructions to perform one or more operations; The processor (202): generating, by the sample coding device, a pooling matrix for pooling and testing a plurality of biological samples, the pooling matrix indicating a plurality of pools for the plurality of biological samples to be tested and at least two pools for each biological sample, where pooling is performed to include each biological sample in at least two of the determined pools, and testing is performed on the plurality of pools; obtaining output data from the testing machine (206) relating to completion of the high dimensional analysis in each of the plurality of pools, the high dimensional analysis reducing the number of tests in the testing machine (206), wherein for each pool, the output data is a row vector of quantitative, semi-quantitative, or vectors having categorical values ​​indicating the absence, presence, or category of at least one analyte in the plurality of biological samples, and the output data for each pool comprises measured values ​​or categories of each analyte in the pool; generating a set of linear equations based on the output data and the generated pooling matrix; converting the set of linear equations into a set of nonlinear equations and solving the set of linear equations using a compressive sensing algorithm; 1. A system (200) for detecting, identifying, and quantifying multiple analytes in multiple biological samples, comprising: activating at least one regularity condition to obtain a unique solution to the set of nonlinear equations, the regularity condition being selected from one of: (a) sparsity considering the presence or absence of each analyte individually; or (b) sparsity with respect to a disproportionate number of samples having disproportionately high values ​​for a particular analyte.

2. 2. The system of claim 1, wherein the processor is configured to detect, identify, and quantify a condition of interest based on the detected, identified, and quantified analytes in the plurality of biological samples, the condition of interest comprising at least one of a quality assurance condition, a food safety condition, a medical condition, medical screening, drug discovery research, transcriptomics, or a next generation sequencing (NGS) targeted panel.

3. 2. The system (200) of claim 1, wherein the inspection machine (206) is a polymerase chain reaction (PCR) machine, a high performance liquid chromatography column (HPLC), a microarray, a next generation sequencing (NGS) machine, a mass spectrometer, a nuclear magnetic resonance (NMR) spectrometer, or a Raman spectrometer.

4. The linear equation is y=Ax, (i) A = (a ij ) m×n is a pooling matrix of dimension m × n, with the number of rows equal to the number of pools and the number of columns equal to the number of samples, and the element a of the pooling matrix A in the i-th row and j-th column ij determines the amount of sample j that will participate in the i-th pool; (ii) x = (X jk ) n×d is the component X jk where j ranges from 1 to n and represents n samples, k ranges from 1 to d and represents d analytes, and X jk represents the amount of analyte k present in the jth sample, and component X jk is unknown and is determined by solving said set of linear equations; (iii) y = (y ik ) m×d is the component y ik where i ranges from 1 to m and k ranges from 1 to d, and matrix y has a number of rows (m) equal to the number of pools and a number of columns (d) equal to the number of analytes measured in the number of pools, and has components y ik 2. The system (200) of claim 1, wherein x represents the amount of analyte k present in pool i, as determined by analysis or testing.

5. The processor (202) solves the linear equation y k =Ax k into a nonlinear equation and then solving the matrix x using the regularity conditions, where x k is the k-th column of the x matrix, and y k The system (200) of claim 4, wherein:

6. 6. The system (200) of claim 5, wherein the nonlinear equation is generated based on a plurality of variables constituting the generated pooling matrix, a plurality of output data of the plurality of pools, and quantitative measurements of each analyte.

7. The system (200) of claim 1, wherein statistical correlations between measurements of different analytes from previous data are used as part of the regularity condition.

8. 2. The system (200) of claim 1, wherein the pooling matrix is ​​generated based on at least one input from a user, the at least one input including at least one of a name of the assay and a size of the assay, the size of the assay indicating a total number of biological samples to be tested and a number of biological samples that are predicted to be positive out of the total number of biological samples.

9. 1. A method for reducing test times for high dimensional analysis for detecting, identifying, and quantifying multiple analytes in multiple biological samples, the method comprising: generating, by the sample coding device, a pooling matrix for pooling and testing a plurality of biological samples, the pooling matrix indicating a plurality of pools for the plurality of biological samples to be tested and at least two pools for each body sample, the pooling being performed to include each biological sample in at least two of the plurality of pools, and testing being performed on the plurality of pools; obtaining output data from the testing machine (206) relating to completion of the high dimensional analysis in each of the plurality of pools, the high dimensional analysis reducing the number of tests in the testing machine (206), wherein for each pool, the output data is a row vector of quantitative, semi-quantitative, or vectors having categorical values ​​indicating the absence, presence, or category of at least one analyte in the plurality of biological samples, and the output data for each pool comprises measured values ​​or categories of each analyte in the pool; generating a set of linear equations based on the output data and the generated pooling matrix; converting the set of linear equations into a set of nonlinear equations and solving the set of linear equations using a compressive sensing algorithm; and activating at least one regularity condition to obtain a unique solution to the set of nonlinear equations for detecting, identifying, and quantifying multiple analytes in multiple biological samples, the regularity condition being selected from one of: (a) sparsity considering the presence or absence of each analyte individually; or (b) sparsity with respect to a disproportionate number of samples having disproportionately high values ​​for a particular analyte.

Images