Methods for reviewing and manipulating digital PCR data

Abstract

The present invention provides a method for visualizing, examining, and manipulating dPCR data. Furthermore, the present invention provides a system for visualizing, examining, and manipulating dPCR data, and a computer-readable storage medium with instructions for visualizing, examining, and manipulating dPCR data. In addition, the use of such a system and computer-readable storage medium for visualizing, examining, and manipulating dPCR data is provided. The present invention provides an improved method for the examination and manipulation of data obtained by biological assays such as dPCR, carried out in a biological analysis system, which allows reliable results as a basis for data interpretation.

Description

[Technical field]

[0001] The present invention provides improved methods for visualization, review, and manipulation of data acquired by biological analytical systems, such as digital polymerase chain reaction (dPCR). Additionally, the present invention provides systems and computer readable storage media for reviewing and manipulating data acquired by biological analytical systems. [Background technology]

[0002] Methods for visualization of data acquired by biological analytical systems capable of simultaneously processing a large number of samples are highly relevant because a huge number of data points need to be displayed to the user in a useful manner. Such biological analytical systems are commonly used in polymerase chain reaction (PCR), sequencing, genotyping, and other applications to provide quantitative data.

[0003] One technique that has gained increasing importance in recent years is digital PCR (dPCR). In dPCR, each sample containing a small number of target nucleic acids is divided into many subsamples, also called partitions. As a result, each partition either contains the target nucleic acid or does not. Then, end-point PCR according to standard protocols is performed and the signal per partition is measured at a certain position. For example, for partitions containing target nucleic acids, a positive detection signal can be measured due to an amplification reaction, while for partitions not containing target nucleic acids, no detection signal is generated because no amplification reaction has occurred. Thus, dPCR uses the standard procedure of end-time PCR, but splits the PCR into many single partitions in which the targets are randomly distributed across all available partitions. Because the targets are randomly distributed, the amount of target nucleic acid per positive partition can be calculated using Poisson statistics. Such dPCR systems combine high-throughput screening and multiplexing capabilities with high sensitivity, excellent precision, and very good reproducibility. Several dPCR systems are commercially available, such as the QIAcuity system (QIAGEN (Hilden)), that are designed for applications such as rare mutation detection, copy number variation, gene expression analysis, NGS (next generation sequencing) library quantification, (low level) pathogen detection, viral load detection, genotyping, miRNA studies, lncRNA analysis, and genetically modified organism (GMO) detection.

[0004] The processes of partitioning, thermal cycling, imaging (i.e., data collection), and analysis are typically implemented in a biological analysis system, such as a QIAcuity instrument (QIAGEN (Hilden)). These systems include at least a thermal cycler, an optical system, one or more processors, a memory, a storage device, and a display. In dPCR, the signal is usually a fluorescent signal emitted from a fluorophore and detected in different channels according to the wavelength of the emitted signal. Typically, one or more fluorophores are used. Based on all the signals detected in one channel, a threshold can be calculated to distinguish positive and negative partitions in that channel, corresponding to partitions where an amplification reaction has or has not occurred. As a result, these systems provide quality controls, such as the concentration in copies per microliter of the target nucleic acid and a positive sample or a no template control (NTC).

[0005] Due to the large number of amplification reactions carried out simultaneously in dPCR applications, a huge amount of data is obtained, which needs to be further processed and visualized to allow users to interpret the data.Generally, the processed data is displayed to the user through a graphical user interface (GUI) on a display.Different methods for the visualization of dPCR data have been disclosed by the prior art.

[0006] For example, EP 3014506 A1 teaches a method for visualization of dPCR data by displaying the relative location (X, Y coordinates) where the underlying signal was detected on the chip.

[0007] Furthermore, the data may be displayed in a histogram, visualizing the distribution of fluorescence intensity values ​​for a selected target channel, e.g., FAM. The X-axis represents the fluorescence intensity and the Y-axis represents the number of partitions with that fluorescence intensity. Typically, partitions where an amplification reaction has occurred will have a high fluorescence intensity, while partitions where no amplification reaction can be detected will have a low fluorescence intensity, resulting in two distinct peaks.

[0008] Data may also be visualized as a scatter plot. For example, a two-dimensional (2D) scatter plot shows the fluorescence intensity from two selected channels by plotting the fluorescence intensity of one channel against the fluorescence intensity of the other channel. Partitions without any amplification reaction (negative) occur as a cluster with low fluorescence intensity in both channels, while partitions with signals detected (positive) are clustered according to the individual dyes used for labeling the target nucleic acid. For example, partitions where signals can be detected in the FAM channel appear as one cluster, partitions where signals can be detected in the Cy5 channel appear as a second cluster, partitions where signals can be detected in both channels appear as a third cluster, and partitions where signals can not be detected in either the FAM or Cy5 channel (negative) appear as a fourth cluster. Such a representation of a two-dimensional scatter plot is described in US 2015 / 0269756 A1 and is provided by QIAcuity software (QIAGE (Hilden)).

[0009] The visualization of the data can be manipulated by the user by adjusting the signal intensity threshold for distinguishing between positive and negative partitions. Selection of a certain threshold changes the visualization of the data accordingly.

[0010] Furthermore, in some partitions, positive (false positive) signals different from the expected signals for successful amplification may be detected, or any other erroneous events may occur that lead to the invalidity of those partitions. In such cases, it is important to identify those events and correct the data accordingly by excluding all partitions that need to be corrected from the analysis, allowing reliable data interpretation. Such a method for data correction by excluding invalid signals is provided in the QIAcuity software (QIAGEN (Hilden)).

[0011] The unique characteristics of the signal from each partition lead to a lot of information that is available for the sample analyzed. This information needs to be visualized in a useful way so that the quality of the data can be evaluated and the results can be interpreted properly. The identification of partitions that need to be corrected is particularly important in situations where precise quantification of a minimum amount of target nucleic acid or target nucleic acid detection in complex samples is required, such as wastewater testing for Severe Acute Respiratory Syndrome Coronavirus Type 2 (SARS-CoV-2) or the identification of genetic disorders, including the rarest of cancer mutations. In this regard, the partitions that need to be corrected have a significant impact on the analysis and interpretation of the data and the subsequent measurements. However, the prior art methods only provide for the identification of erroneous events and subsequent data correction by the exclusion of partitions that need to be corrected, as explained above, but do not allow for the review or visualization of the reasons why a signal or partition was corrected due to it. Thus, to date, no method has been provided for the visualization of dPCR data that allows the visualization by distinguishing different categories of corrected signals or corrected partitions according to the reasons for the correction. Furthermore, prior art methods do not allow a user to manually exclude or include such categories from the visualization and reclassification of components of different categories. Summary of the Invention [Means for solving the problem]

[0012] The present invention overcomes the core drawbacks of the prior art. In particular, the present invention provides an improved method for the examination and manipulation of data obtained by biological assays, such as dPCR, carried out in a biological analysis system, which allows reliable results as a basis for data interpretation. Signal maps, i.e., signal intensities for each valid or correctly assigned partition, respectively, representing pre-processed or corrected data, respectively, are used as the basis for the method of the present invention.

[0013] In particular, a method is provided that is useful for processing data determined by a biological analysis system, such as dPCR data, that allows reliable interpretation of the presence or absence of a target molecule, such as a nucleic acid, in the sample being analyzed. The technique described herein is based on the classification of detected signals emitted from reaction sites, called partitions, in a certain region of a substrate containing the sample being analyzed. Furthermore, the method disclosed herein provides a preferred automatic identification of signals that need to be corrected according to the reason for correction, and a preferred automatic classification of those signals that need to be corrected. The review method disclosed herein provides an automatic visualization of data in a signal map that represents partitions located in a certain region of a substrate according to their spatial location in that region, and according to a distinct category or classification. In addition, the operation method provides manual adjustment of the representation of the categories or classifications in the signal map in response to the operation of a first interactive tool by a user. A second interactive tool allows the user to manually reclassify a single partition that has been automatically classified into a different category. Thereby, the method of the present invention in particular improves dPCR data processing and provides more reliable results based on the presence or absence of a target nucleic acid in a biological sample. The method described herein is particularly suitable for detecting target nucleic acid in complex biological samples and / or biological samples in which target nucleic acid is present only in minimal quantities, which leads to regular erroneous measurements.Therefore, the method provided herein combines dPCR with a higher accuracy compared to conventional real-time PCR, and also reliable data analysis of error-prone samples.The present invention therefore makes an important contribution to the art.

[0014] According to a first aspect, there is provided a method for the examination and manipulation of data obtained from a signal determined by a biological analytical system, comprising: (A) receiving a plurality of detected signals from a plurality of partitions located in an area of ​​a substrate undergoing a biological assay; (B) selecting a value for a signal strength threshold; (C) classifying partitions detected with signal intensities above a signal intensity threshold as positive and / or partitions detected with signal intensities below a signal intensity threshold as negative; (D) identifying partitions that need to be corrected, and thus classifying some or all of the partitions that need to be corrected as negative and / or invalid, the partitions that need to be corrected thus becoming corrected partitions, and classifying all partitions classified as positive and negative as valid; (E) displaying a graphical user interface including a signal map of the detected signals, the signal map representing the valid partitions according to their signal strengths and the corrected negative partitions according to the signal strengths of other negative partitions; (F) categorizing the corrected partitions according to the reasons for correction; (G) displaying a signal map, where all positive partitions are represented by a first indicator; (H) displaying the signal map, the categories representing the reasons for the correction being represented by a second, third, fourth, or further indicator, respectively; and wherein the graphical user interface further comprises at least two interactive tools, and in response to a user's manipulation of at least one of the at least two interactive tools, the method further comprises the steps of: (I) adjusting the displayed signal map by excluding and / or including at least one category from being displayed; and / or (J) selecting a category for one or more partitions and / or for one or more categories of at least one partition, thereby reclassifying the partitions as being in the selected different category; displaying a signal map, where different selected categories are represented by indicators that are specific to the category; A method is provided that includes at least one of:

[0015] The method according to the first aspect is particularly advantageous for examining and manipulating data obtained by biological assays, such as dPCR data determined in a biological analysis system, which indicates the presence or absence of a target molecule, preferably a nucleic acid in the biological sample being analyzed. As disclosed herein, the method is particularly suitable for dPCR data processing, which reveals reliable results as a basis for data interpretation by a user.

[0016] According to a second aspect, there is provided a system for the examination and manipulation of data obtained from a signal determined by a biological analytical system, comprising: (a) at least one processor for carrying out the method according to the first aspect; (b) a memory encoded with instructions for performing the method according to the first aspect; and A system is provided comprising:

[0017] The system according to the second aspect further comprises: (c) a display; and (d) a thermal cycler; (e) an optical system; and (f) a storage device; and (g) optionally, an input device; The present invention may also include:

[0018] The system according to the second aspect is particularly suitable for detecting the presence or absence of a target molecule, such as a nucleic acid, in a biological sample by using a PCR-based approach, such as dPCR, and by implementing methods for examining and manipulating data obtained according to the first aspect.

[0019] According to a third aspect, there is provided a computer readable storage medium encoded with instructions executable by a processor for reviewing and manipulating data, the instructions including instructions for performing the method according to the first aspect.

[0020] According to a fourth aspect, the present disclosure relates to the use of a system according to the second aspect for carrying out the method according to the first aspect.

[0021] According to a fifth aspect, there is provided the use of a computer readable storage medium according to the third aspect for carrying out the method according to the first aspect.

[0022] Other objects, features, advantages, and aspects of the present application will become apparent to those skilled in the art from the following description and appended claims. It should be understood, however, that the following description, appended claims, and specific examples, while indicating preferred embodiments of the application, are given by way of illustration only. [Brief description of the drawings]

[0023] [Figure 1] Figure 1: (A) Raw images with fluorescent signal intensities according to their spatial location within one well of a multiwell plate as acquired by the camera. (B) Signal map depicting positive partitions according to the spatial location of the underlying raw signal.

[0024] [Diagram 2] Figure 2: (A) Raw images with the fluorescence signal intensity according to their spatial location in one well of a multi-well plate as acquired by the camera. The circled bright spots mark the bright signals caused by dust. (B) Signal map representing the positive partitions according to the spatial location of the underlying raw signal and corrected for false positive signals caused by dust (i.e. partitions or signals that need to be corrected). The signals emitted from dust are here excluded from the representation.

[0025] [Figure 3-1] Figure 3: Exemplary workflow of a method for reviewing and manipulating dPCR data according to the invention. A signal map is displayed in a display with a graphical user interface (GUI). (A) A signal map showing preprocessed (corrected) dPCR data according to their spatial location is used as a basis (upper box). Corrected partitions can be excluded from the representation, while valid partitions are represented according to their signal intensity. As an alternative, the corrected partitions may be represented by a first indicator, such as a color (black). The positive partitions may be represented by a second indicator, such as a color (color 1, represented by a circle with a thick diagonal line). Furthermore, the corrected signals are visualized according to the reason for correction, such as "dust" or "ratio" (middle box). Each category of corrected signals is shown by a different indicator, e.g. a color or pattern. The user can manually select the categories to be displayed by switching the individual categories off and on (lower box). This is achieved by a first interactive tool, such as a button for each category. The first interactive tool thus provides the selection of all partitions of a category. [Figure 3-2] (B) Single partitions or components from different categories can be manually selected by the user (upper box) and reclassified. Selection is accomplished by a second interactive tool, such as a box selection tool, that allows the user to select a portion or area of ​​the signal map partition independent of their categories, as illustrated by the white box. The selected area partition can be reclassified either to a different category already visualized or to a new category. For reclassification to a new category, this new category can be assigned, for example, "manually" by the user (lower box). The results are updated accordingly. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0026] Detailed Description of the Invention Different aspects and embodiments of the invention disclosed herein make important contributions to the art, as discussed below. Method according to the first aspect

[0027] According to a first aspect, there is provided a method for the examination and manipulation of data obtained from a signal determined by a biological analytical system, comprising: (A) receiving a plurality of detected signals from a plurality of partitions located in an area of ​​a substrate undergoing a biological assay; (B) selecting a value for a signal strength threshold; (C) classifying partitions detected with signal intensities above a signal intensity threshold as positive and / or partitions detected with signal intensities below a signal intensity threshold as negative; (D) identifying partitions that need to be corrected, and thus classifying some or all of the partitions that need to be corrected as negative and / or invalid, the partitions that need to be corrected thus becoming corrected partitions, and classifying all partitions classified as positive and negative as valid; (E) displaying a graphical user interface including a signal map of the detected signals, the signal map representing the valid partitions according to their signal strengths and the corrected negative partitions according to the signal strengths of other negative partitions; (F) categorizing the corrected partitions according to the reasons for correction; (G) displaying a signal map, where all positive partitions are represented by a first indicator; (H) displaying the signal map, the categories representing the reasons for the correction being represented by a second, third, fourth, or further indicator, respectively; and wherein the graphical user interface further comprises at least two interactive tools, and in response to a user's manipulation of at least one of the at least two interactive tools, the method further comprises the steps of: (I) adjusting the displayed signal map by excluding and / or including at least one category from being displayed; and / or (J) selecting a category for one or more partitions and / or for one or more categories of at least one partition, thereby reclassifying the partitions as being in the selected different category; displaying a signal map, where different selected categories are represented by indicators that are specific to the category; A method is provided that includes at least one of:

[0028] According to one embodiment, the negative partition is represented in the signal map by an additional indicator that is different from the indicators of steps (G), (H) and (J).

[0029] According to one embodiment, the indicator is selected from colors, geometric shapes, patterns, symbols, icons, numbers, letters, signs and / or any other means suitable for distinguishing the categories. According to an advantageous embodiment, the indicator is a color.

[0030] The term "category" or "classification" may be used for the following categories or classifications: "positive partition", "negative partition", "valid partition", "invalid partition", "false negative partition", and "false positive partition". Additionally, the term "category" or "classification" may be used for categories or classifications regarding reasons for correction.

[0031] The individual steps and preferred embodiments of the method according to the first aspect will now be described in detail.

[0032] Steps (A)-(C) In step (A), a plurality of detected signals are received from a plurality of partitions located in an area of ​​a substrate undergoing a biological assay.

[0033] According to one embodiment, the detected signal is a signal detected by an optical system. According to one embodiment, the signal is a fluorescent signal emitted by at least one fluorophore. According to an advantageous embodiment, the detected signal is a fluorescent signal emitted by at least one fluorophore and detected by an optical system. The optical system may be a camera suitable for detecting the wavelengths emitted by the at least one fluorophore used.

[0034] According to one embodiment, the wavelength of the fluorescent signal emitted by at least one fluorophore is within the range of 500-700 nm, preferably within a range selected from the group consisting of 510-550 nm, 550-570 nm, 580-610 nm, 610-655 nm and 655-700 nm. Each signal emitted by a fluorophore can be detected according to a channel of the optical system. According to an advantageous embodiment, two or more, three or more, four or more or five or more fluorophores are used to detect signals in two or more, three or more, four or more or five or more different channels of the optical system. Several suitable fluorophores for the method according to the invention are commercially available, some of which are exemplarily presented in the table below. [Table 1-1] [Table 1-2]

[0035] The substrate comprises hundreds, thousands, tens of thousands, or hundreds of thousands of reaction sites, also referred to as partitions. Reaction sites or partitions as disclosed herein may include, but are not limited to, through-holes, wells, indentations, spots, cavities, sample retention areas, and reaction chambers. According to one embodiment, the substrate comprises at least 20,000, at least 50,000 partitions, preferably at least 200,000 partitions, at least 600,000 partitions, at least 800,000 partitions. According to one embodiment, the substrate is a multi-well plate, such as an 8-well plate, a 24-well plate, or a 96-well plate, comprising multiple partitions per well. According to an advantageous embodiment, the multi-well plate is selected from the group consisting of a 24-well plate with 26,000 partitions per well, a 24-well plate with 8,500 partitions per well, and a 96-well plate with 8,500 partitions per well. According to one embodiment, the substrate is a microfluidic dPCR plate. According to one embodiment, a region of the substrate is represented by one or more wells of a multi-well plate. Those skilled in the art can select a suitable substrate for any biological assay, in particular for dPCR. Such dPCR substrates are commercially available, for example, QIAcuity nanoplates (QIAGEN (Hilden)).

[0036] According to one embodiment, the biological assay carried out in the biological analysis system is an amplification reaction. According to one embodiment, the amplification reaction has one or more of the following characteristics: (i) it is a polymerase chain reaction (PCR); (ii) it is a digital polymerase chain reaction (dPCR); (iii) it is a reverse transcription amplification reaction; (iv) it is a reverse transcription PCR (RT-PCR); (v) it is an isothermal amplification reaction; (vi) it is a quantitative PCR; (vii) it is a quantitative reverse transcription PCR; (viii) it is an allele-specific PCR; (ix) it is an asymmetric PCR; (x) it is a ligation-mediated PCR; (xi) it is a multiplex PCR; (xii) it is a nested PCR; (xiii) it is a bridge PCR. According to an advantageous embodiment, the biological assay is a digital PCR or a digital reverse transcription PCR. The term "dPCR" as used herein refers to both digital PCR and digital RT-PCR. DPCR systems split the sample to be analyzed into hundreds, thousands, tens of thousands, or hundreds of thousands of subsamples. For example, this is accomplished in a microfluidic nanoplate with hundreds, thousands, tens of thousands, or hundreds of thousands of reaction sites, in a microfluidic chip with hundreds, thousands, tens of thousands, or hundreds of thousands of reaction sites (chip-based dPCR), or by splitting the sample into hundreds, thousands, tens of thousands, or hundreds of thousands of droplets (droplet-based dPCR). Several digital (RT-)PCR systems are commercially available, such as the QIAcuity system (QIAGEN (Hilden)).

[0037] In embodiments, a biological sample may contain one or more types of biological targets, including DNA sequences (including cell-free DNA), RNA sequences, genes, oligonucleotides, molecules, proteins, biomarkers, cells (e.g., circulating tumor cells), or any other suitable target biomolecules.

[0038] The biological sample may be obtained from any biological source. According to one embodiment, the biological sample is characterized by the following characteristics: (i) that it is a body sample; (ii) it is a human sample; (iii) it is a pathogen sample, such as a bacterial or viral sample; (iv) that it is an animal sample; (v) that it is a cell culture sample; (vi) that it is a tissue sample; (vii) that it is an environmental sample; (viii) that it is a wastewater sample; and / or (ix) that it is a plant sample; has one or more of:

[0039] The body sample may be a liquid body sample, such as blood, serum, plasma, or saliva, or a solid body sample, such as a biopsy tissue from any tissue. Additionally, the biological sample may be a swab, including nasal, buccal, nasopharyngeal, and oropharyngeal swabs. The sample may be obtained from a human subject suspected of being infected with a pathogen or having a genetic disorder. Additionally, the sample may include human material, such as human cells or cell components, e.g., human proteins or human nucleic acids.

[0040] According to one embodiment, the biological sample comprises nucleic acid. The nucleic acid may comprise or consist essentially of DNA, including genomic DNA, human DNA, pathogen DNA, viral DNA, environmental DNA (eDNA). Alternatively, the nucleic acid may comprise or consist essentially of RNA, including mRNA, miRNA, siRNA, lncRNA, human RNA, pathogen RNA, viral RNA.

[0041] According to one embodiment, the signal detected for the effective partition is based on the presence or absence of at least one target nucleic acid in a biological sample that is analyzed in the biological analytical system.

[0042] According to one embodiment, in each partition, PCR is carried out, leading to the amplification of at least one target nucleic acid if present in the biological sample. If an amplification reaction occurs, a signal, such as a fluorescent signal, can be detected by the optical system in the respective partition, indicating the presence of at least one target nucleic acid in the respective partition. If no amplification reaction occurs, no signal is detected by the optical system, indicating that at least one target nucleic acid is not present in the respective partition.

[0043] According to one embodiment, at least one target nucleic acid has the following properties: (i) it is selected from RNA and / or DNA; (ii) it is of human origin; (iii) it is derived from a pathogen, the pathogen being selected from the group consisting of a virus, a bacterium, a protozoan, a viroid, and a fungus; and / or (iv) it is selected from viral RNA and / or viral DNA; has one or more of:

[0044] According to one embodiment, the target nucleic acid is derived from an RNA virus. According to one embodiment, the target nucleic acid is derived from a severe acute respiratory syndrome-related coronavirus, preferably from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), severe acute respiratory syndrome coronavirus (SARS-CoV or SARS-CoV-1), or Middle East respiratory syndrome (MERS), optionally the target nucleic acid is derived from SARS-CoV-2, and optionally the one or more target nucleic acid sequences are selected from SARS-CoV-2 genes N, N1, N2, RdRP, E, and Orf1a / b.

[0045] According to one embodiment, the biological sample comprises at least one target nucleic acid associated with genetic disorder.Any genetic disorder can be detected due to the presence or absence of the target nucleic acid associated with genetic disorder, including cancer mutation, metabolic disorder, single gene disease, X-linked disorder, Y-linked disorder, and chromosomal disorder.

[0046] According to one embodiment, the biological sample comprises at least one target nucleic acid that is associated with genome editing.Gene editing can be caused by the insertion, deletion, modification or replacement of DNA in genome.Several genetic engineering techniques are available, such as gene editing using zinc finger nuclease or CRISPR (clustered regularly interspaced short palindromic repeats) approach.

[0047] In step (B), a value for the signal intensity threshold is selected. The signal intensity may be given in real fluorescence units (RFU). According to one embodiment, the signal intensity threshold is generated according to the distribution of signal intensities from all signals detected from multiple partitions located in a certain area of ​​the substrate undergoing the biological assay.

[0048] In step (C), partitions whose signal strength is detected above the signal strength threshold are classified as positive and / or partitions whose signal strength is detected below the signal strength threshold are classified as negative. The signal strength threshold may be manually adjusted by the user in response to manipulation of the interactive tool, thereby changing the number of partitions classified as positive and negative accordingly.

[0049] Steps (D)-(H) In step (D), the partitions that need to be corrected are identified, in particular automatically. According to one embodiment, step (D) further comprises a step of classifying some or all of the partitions that need to be corrected as negative and / or invalid. Furthermore, step (D) comprises a step of classifying all partitions classified as positive and negative as valid. In some embodiments, a "partition that needs to be corrected" refers to a partition in which a signal is detected that does not meet the characteristics of a signal that represents a successful amplification reaction. In further embodiments, a partition can be assigned as "to be corrected" when it is present in an area of ​​a region of the substrate that represents an unequal distribution of positive partitions compared to the entire area of ​​that region of the substrate. The identification of a partition that needs to be corrected may be based on several different reasons for correction. For example, possible reasons for determining a signal that needs to be corrected may be dust or other particles, scratches, or other interfering factors that emit a signal detected by the optical system, an unequal distribution of positive partitions, a statistically unlikely clustering of positive partitions, or any other erroneous event.

[0050] Thus, the term "partition that needs to be corrected" encompasses partitions that provide a reason for correction. The reason for correction may be caused by the intensity signal for the partition itself, or may be caused by the intensity signal of a partition that forms a line or area with adjacent partitions and / or the partition under consideration. If a partition has been identified as a partition that needs to be corrected, and the partition has been corrected, i.e., "(re)classified", the partition becomes a corrected partition.

[0051] In an embodiment, different signal characteristics are determined for the identification of partitions caused by dust or other particles, scratches, or any other interfering factors that need to be corrected. The identification of false positives or other partitions caused by these interfering factors that need to be corrected, respectively, is due to the following reasons: Shape: Inconsistencies between the shape of the dust / particle / scratch and the shape of the positive partition resulting from the successful amplification reaction, including the brightness distribution; Position: the misalignment between the X and Y coordinates in the image due to the partitions being regularly arranged in a grid; - Size: the number of bright pixels that do not follow the regular brightness distribution of the positive partitions received from successful amplification reactions; Brightness / clustering (only performed up to a certain threshold of the number of positives): Images with only a few positive partitions and some areas with many positive partitions due to diffuse dust. can be based on.

[0052] In a further embodiment, for the identification of partitions that need to be corrected, which are caused by uneven distribution of positive partitions, first, the overall number of positive partitions in the image is counted. If the number of positive partitions reaches a certain value, the following instructions are executed. The image is divided into smaller areas, such as square, rectangular, rhomboid, circular, elliptical, or any other means that are suitable for image division, and then the number of positive partitions per area is counted. If one of the areas shows too many or too few positive partitions, the partitions in this area are classified as partitions that need to be corrected.

[0053] In a further embodiment, the identification of statistically impossible clustering of positives (e.g. in rows or columns) is performed without violating the uneven distribution of positives. Based on the number of positive partitions against negative partitions, a probability calculation according to Poisson is performed. If impossible clusters exist, all positive partitions are classified as false positives, i.e. as partitions that need to be corrected, except for a certain number of partitions, such as 1, 2, 3 or more partitions, depending on the total number of partitions that are identified and need to be corrected.

[0054] Following identification of the partitions that need to be corrected, some or all of the partitions that need to be corrected are classified as negative and / or invalid, and thus the partitions that need to be corrected become corrected partitions. Furthermore, all partitions classified as positive or negative are further classified as valid. The negative and positive partitions, i.e., valid partitions, are included in the final target nucleic acid quantification, while the invalid partitions are excluded from the final target nucleic acid quantification. However, the invalid partitions are further included in the data review and manipulation process prior to the final target nucleic acid quantification.

[0055] Thus, in step (D), partitions can be identified as partitions that need to be corrected, and partitions that need to be corrected are separated as corrected negative and / or invalid, and thus partitions that need to be corrected become corrected partitions, and all partitions that are positive, negative, and corrected negative partitions are valid partitions. Thus, each partition in the region of the substrate is classified into at least one classification as positive, negative, corrected negative, and / or invalid, and partitions classified as positive, negative, and corrected negative (i.e., valid partitions) are included in the data, and partitions classified as invalid are excluded from the data. Thus, all valid partitions can be included in the data, and all invalid partitions can be excluded from the data as a basis for analysis such as nucleic acid quantification.

[0056] For example, partitions that are identified as partitions that need to be corrected and emit signals above a threshold intensity (i.e., false positives) can be classified as corrected negative in step (D). This correction can affect the results of quantification, since the total number of partitions (including corrected negative partitions) classified as negative in the analyzed substrate increases, which can lead to a reduction in the total amount of target analyte in individual wells compared to an analysis in which the partitions that need to be corrected would only be excluded, and therefore would not be available for quantification. Thus, the identification and classification in step (D) improves the accuracy of quantification of target analytes, e.g., target nucleic acids, leading to more reliable results of biological assays such as dPCR.

[0057] In an embodiment, the corrected partitions classified as invalid in step (D) are "corrected invalid partitions."

[0058] In step (E), a graphical user interface is displayed, including a signal map of the detected signals. A "signal map" as described herein refers to a visualization of the spatial distribution of the detected signals in the partitions and / or the categories of the partitions in the XY coordinates. In step (E), the signal map represents the effective partitions by their signal intensity. The effective partitions are all the positive and negative partitions, including the corrected negative partitions and the corrected positive partitions. The corrected negative partitions are represented in the signal map according to a value that depends on the signal intensity of the other negative partitions represented in the signal map. The corrected negative partitions can be represented by the average value of the other negative partitions represented in the signal map. The corrected negative partitions can also be represented by a value that correlates to a signal intensity threshold, which in turn depends on the value of the negative partition. The corrected positive partitions are represented according to their signal intensity. Such a signal map with the raw or preprocessed signals, i.e. the corrected signals, of each partition is shown in the examples.

[0059] In an embodiment, the signal map in step (E) may represent valid partitions classified as positive and negative according to their signal strength, and valid partitions classified as corrected negative may be represented according to a value that depends on the signal strength of the partitions classified as negative. Thus, the signal map may represent all partitions that are valid (i.e., positive, negative, corrected negative) and therefore included in the data analysis.

[0060] According to one embodiment, the corrected partitions are represented in the signal map of step (E) by a unique indicator that is different from the indicators of steps (G), (H) and (J). Thus, all corrected partitions may be represented by one indicator, and partitions that were not identified as partitions that need to be corrected (i.e., true positive and true negative partitions) may be represented according to their signal strength. According to one embodiment, the corrected partitions may be excluded from the representation in the signal map in step (E). This may be advantageous to visualize only partitions that were not identified as partitions that need to be corrected, i.e., true positive and true negative partitions, so that the user can evaluate the pattern of true positive and true negative partitions for decision making in step (J).

[0061] In step (F), the corrected partitions are classified into categories according to the reason for correction. As explained above, some reasons for correction may cause a partition to be considered as a partition that needs to be corrected, such as dust or other particles that emit signals, scratches, other interfering factors, uneven distribution of positive partitions, and statistically unlikely clustering of positive partitions. Those skilled in the art will recognize any erroneous events that may cause false positives or other signals that may need to be corrected.

[0062] In an embodiment, according to steps (C), (D) and / or (F) of the method of the present invention, each partition in the region of the substrate may be classified into at least one classification and / or into multiple categories as positive, negative, corrected negative, invalid according to the reason for correction. Furthermore, partitions classified as positive, negative and / or corrected negative (i.e. valid partitions) may be included in the data, and partitions classified as invalid may be excluded from the data.

[0063] In step (G), a signal map is displayed, in which all positive partitions are represented by a first indicator. As explained above, the indicator may be a color, a geometric shape, a pattern, a symbol, an icon, a number, a letter, a code, and / or any other means suitable for distinguishing categories. As demonstrated in the examples, it is particularly advantageous to use color as an indicator for visualizing and distinguishing different categories.

[0064] In step (H), a signal map is displayed, in which the categories representing the reasons for correction are represented by a second, third, fourth or further indicator, respectively, in particular according to the number of categories displayed. According to one embodiment, steps (G) and (H) are performed simultaneously. As demonstrated in the example, the signal map may represent positive partitions by a first color, corrected partitions caused by dust by a second color, and corrected partitions caused by uneven distribution by a third color.

[0065] The signal map representation in step (H) may also provide a basis for data interpretation and decision-making by a user, as further discussed with respect to step (J) below.

[0066] Steps (I) and (J) The method according to the first aspect further allows a user to manipulate the processed data. Accordingly, the graphical user interface further comprises at least two interactive tools.

[0067] According to one embodiment, in response to the user's operation of at least one of the at least two interactive tools, the method further comprises a step (I) in which the signal map is adjusted by excluding and / or including at least one category from being displayed. As demonstrated in the examples, such an interactive tool may be a button for each category, a drop-down menu showing all categories, a freehand selection tool whereby one component of a category on the signal map is selected and thereby all partitions of that category are selected, or any other suitable interactive tool may be used to select a category and thus all partitions of the selected category. According to one embodiment, in response to the operation of the interactive tools, an instruction is received by the system and an indicator for each partition of the selected category represented in the signal map is visualized accordingly. Alternatively, raw signals or pre-processed data are visualized, such as the exclusion of corrected partitions from the representation as explained above.

[0068] According to one embodiment, in response to the user's operation of at least the other of the at least two interactive tools, the method further comprises a step (J) in which a category is selected for one or more partitions and / or for one or more categories of at least one partition, thereby reclassifying the partitions as being of the selected different category, and the signal map is displayed, the selected different category being represented by an indicator specific to the category. As demonstrated in the example, such an interactive tool may be a box selection tool for selecting a part or area of ​​the signal map comprising one or more partitions, or a freehand selection tool by selecting a single partition. Any tool suitable for selecting a single partition or a group of partitions, independent of the individual categories, may be used. According to one embodiment, the selected partition may be reclassified into a different category already displayed, and the signal map is updated accordingly. According to an alternative embodiment, the selected partition may be reclassified into a new category assigned by the user and indicating a different reason for the correction. According to one embodiment, the graphical user interface may provide a box for manually typing in a notation of the new category. According to an advantageous embodiment, this new category is displayed by a different indicator, optionally selected by the user, and the signal map is updated accordingly. According to one embodiment, since the criteria for different categories may also be fulfilled, suggestions are also provided regarding partitions that may be classified into different categories. Such suggestions may be displayed by a pop-up message when the cursor is moved over a partition, or by any other suitable means for indicating that a partition may also be classified into different categories.

[0069] According to one embodiment, steps (I) and (J) are carried out in sequence.

[0070] Thus, the method of the present invention may assist the user to perform reclassification in step (J) due to the visualization of categories according to the reasons for correction for each corrected partition (step (H)), due to adjustment of the signal map (step (I)), and optionally due to a suggestion (e.g. in the form of a pop-up message) that individual partitions may also be classified into different categories.

[0071] In step (J), reclassification of partitions, particularly automatically classified partitions, including corrected partitions, may affect the quantification results. The signal map representation in step (H) and the adjusted signal map in step (I) may help the user decide on reclassification in step (J), since it visualizes the categories according to the reasons for correction to the user. For example, if the user reclassifies a partition identified in step (D) as a partition that needs to be corrected and classified as corrected and invalid, and thus excluded from the data, as positive or negative (i.e., valid), the data obtained from that partition, previously excluded from the data, is included in the data and thus in the quantification of the target analyte (e.g., target nucleic acid in dPCR). In an embodiment, the corrected partition may be reclassified by the user as positive. As a result, the final amount of the target analyte, such as the target nucleic acid, may be higher because the total number of positive partitions in the area of ​​the substrate is increased. In an alternative embodiment, the corrected partition may be reclassified by the user as negative. As a result, the final amount of target analyte, such as target nucleic acid, may be less due to an increased number of negative partitions in the area of ​​the substrate. In a further embodiment, partitions classified as positive, negative, or corrected negative (i.e., valid) may be reclassified by the user as invalid. As a result, the total number of partitions included in the quantification is reduced, affecting the results. Thus, in addition to the improved accuracy provided by step (D), the reclassification by the user in step (J) provides a further improvement in the accuracy of the quantification of the target analyte, e.g., target nucleic acid, leading to more reliable results of biological assays such as dPCR.

[0072] In an embodiment, according to any one of steps (C), (D), (F), and / or (J), each partition in the region of the substrate is classified into at least one category corresponding to classification as positive, corrected positive, negative, corrected negative, invalid, according to the reason for correction, and / or into multiple categories. In an embodiment, a "corrected positive partition" is a partition classified as negative, corrected negative, or invalid in step (C) or (D) and reclassified as positive by the user in step (J). In an embodiment, a "corrected positive partition" is a valid partition. In an embodiment, a "corrected valid partition" is a partition classified as invalid in step (D) and reclassified as positive or negative (i.e., valid) by the user in step (J).

[0073] According to one embodiment, partitions classified as positive, corrected positive, negative, and / or corrected negative (i.e., valid partitions) are included in the data, and partitions classified as invalid are excluded from the data.

[0074] Thus, the classification of the methods of the present invention provides for accurate selection of the data to be included in the analysis, thereby improving the accuracy of the data analysis.

[0075] Further embodiments of the method according to the first aspect According to one embodiment, the displaying step further comprises displaying, in addition to the signal map, a multi-dimensional scatter plot of the data points, which represents the signal emission intensities detected in different channels. The signal intensities may be given in RFUs. According to one embodiment, the displaying step further comprises displaying, in addition to the signal map, a one-dimensional scatter plot of the data points, where the analyzed partitions are plotted on the X-axis and the signal intensities are plotted on the Y-axis. According to one embodiment, the displaying step further comprises displaying, in addition to the signal map, at least one two-dimensional scatter plot of the data points, which represents the signal emission intensities detected in two or more different channels, where the signal intensities detected in the two different channels are plotted against each other. The degree of the dimensions of the multi-dimensional scatter plot may be according to the number of fluorophores with different emission wavelengths used. Suitable fluorophores and their emission wavelengths are described elsewhere herein. According to this embodiment, it is possible to obtain clusters of partitions that emit similar signal characteristics. For example, in a two-dimensional scatter plot, the signal intensities detected in two different channels, which are due to the use of two different fluorophores in the amplification reaction, are plotted against each other. Partitions where signals with high intensity are detected in both channels may represent one cluster, partitions where signals with high intensity are detected in one of the channels cluster together, and partitions where signals with low intensity are detected in both channels or no signal is detected there cluster together. This multidimensional scatter plot allows for further data processing. For example, cluster analysis and / or principal component analysis may be performed to identify clusters of partitions with similar signal characteristics.

[0076] According to one embodiment, the displaying step further comprises displaying a histogram representation in addition to the signal map, optionally a multi-dimensional scatter plot, in which the signal intensity is plotted on the X-axis and the number of partitions emitting the signal intensity is plotted on the Y-axis. The signal intensity can be given in RFU. The histogram representation allows visualization and inspection of the number and distribution of positive and negative partitions in a region of the substrate. According to one embodiment, the histogram representation comprises a representation of the signal intensity threshold, such as by a vertical line.

[0077] According to one embodiment, the signal strength threshold can be adjusted by the user in response to the manipulation of at least one graphical interactive tool. Such a tool may be included in the histogram representation, such as a slider or a box for inputting a value for the signal strength threshold. According to one embodiment, in response to the manipulation of at least one graphical interactive tool for adjusting the signal strength threshold, the classifying and displaying steps are adjusted accordingly. By adjusting the signal strength threshold, the classification of positive and negative partitions may be changed accordingly, as well as the identification of partitions that need to be corrected. Thus, the classification of corrected partitions into categories representing reasons for correction may also be changed, and as a result, the signal map representation may also be changed.

[0078] Thus, with the method of the present invention, it is possible to improve the accuracy of the data analysis / assessment, since automatic corrections can be provided, but the user is provided with possible exact types of corrections that the user can influence with the interactive tool of the present invention. Surprisingly, it has been found that although the automatic corrections provide support for the user, possible supported influences from the user show results that lead to improved accuracy of the analysis.

[0079] In one embodiment, a method for reviewing and manipulating data obtained from a signal determined by a biological analytical system, in particular for improving the accuracy of the evaluation of the data, is provided, comprising the following steps: (A) receiving a plurality of detected signals from a plurality of partitions located in an area of ​​a substrate undergoing a biological assay; (B) selecting a value for a signal strength threshold; (C) classifying partitions detected with signal intensities above a signal intensity threshold as positive and / or partitions detected with signal intensities below a signal intensity threshold as negative; (D) identifying partitions as partitions that need to be corrected and classifying the partitions that need to be corrected as corrected negative and / or invalid, so that the partitions that need to be corrected become corrected partitions, where all partitions, which are positive, negative, and corrected negative partitions, are valid partitions; (E) displaying a graphical user interface including a signal map of the detected signals, the signal map representing valid partitions classified as positive and valid partitions classified as negative according to their signal strength, and valid partitions classified as corrected negative are represented according to a value that depends on the signal strength of the partitions classified as negative; (F) categorizing the corrected partitions according to the reasons for correction; (G) displaying a signal map, where all positive partitions are represented by a first indicator; (H) displaying the signal map, the categories representing the reasons for the correction being represented by a second, third, fourth, or further indicator, respectively; and wherein the graphical user interface further comprises at least two interactive tools, and in response to a user's manipulation of at least one of the at least two interactive tools, the method further comprises the steps of: (I) adjusting the displayed signal map by excluding and / or including at least one category from being displayed; and / or (J) selecting a category for one or more partitions and / or for one or more categories of at least one partition, thereby reclassifying the partitions as being in the selected different category; displaying a signal map, where different selected categories are represented by indicators that are specific to the category; A method is provided that includes at least one of:

[0080] Overall, the method according to the first aspect achieves greater accuracy of analysis and evaluation of data obtained from biological assays, in particular dPCR, due to the precise (automatic and / or manual) selection of partitions to be included in or excluded from the data. As a result, the method of the invention provides improved reliability of dPCR results compared to prior art methods, which only provide for the identification and complete exclusion of partitions that need to be corrected from further analysis.

[0081] The second aspect of the system According to a second aspect, there is provided a system for the examination and manipulation of data obtained from a signal determined by a biological analytical system, comprising: (a) at least one processor for carrying out the method according to the first aspect; (b) a memory encoded with instructions for performing the method according to the first aspect; and A system is provided comprising:

[0082] According to one embodiment, the memory is a dynamic memory, preferably a random access memory (RAM). Such a RAM may have 4GB, 8GB, 16GB, 32GB, or 64GB, and preferably the RAM has at least 16GB. According to one embodiment, the memory is a static storage device, such as a read-only memory (ROM).

[0083] According to one embodiment, the system further comprises: (c) a display; and (d) a thermal cycler; (e) an optical system; and (f) a storage device; and (g) optionally, an input device; Equipped with.

[0084] In one embodiment, the display includes an input device. In one embodiment, the display is a touch screen. The input device may be alphanumeric and other keys coupled to the bus for communicating information and command selections to the processor, a cursor control such as a mouse for communicating information and command selections to the processor and for controlling cursor movement on the display, a trackball, or cursor direction keys.

[0085] According to one embodiment, components (a)-(b) or (a)-(g) are coupled to communicate and process information. The system may include a bus or other communication mechanism for communicating information, and a processor coupled with the bus for processing information.

[0086] The system provides data processing, such as dPCR data processing. Data processing according to the method of the first aspect is provided by the system in response to the processor executing one or more sequences of one or more instructions for performing the method according to the first aspect, the instructions being contained in a memory. Such instructions may be read into the memory from another computer-readable medium, such as a storage device.

[0087] Computer-readable storage medium according to a third aspect In a third aspect, the present invention relates to a computer-readable storage medium encoded with instructions executable by a processor for reviewing and manipulating data, the instructions including instructions for performing the method according to the first aspect.

[0088] The term "computer-readable medium" as used herein generally refers to any medium involved in providing one or more sequences or one or more instructions for performing the method of the first aspect to a processor for execution that enables the system to perform the features or functionality of embodiments of the present invention.

[0089] According to one embodiment, the computer-readable storage medium is selected from the group consisting of a non-volatile medium, a volatile medium, and a transmission medium. According to one embodiment, the computer-readable storage medium is a USB (Universal Serial Bus) flash drive. According to one embodiment, the computer-readable storage medium is a compact disk read-only memory (CD-ROM). The computer-readable storage medium can be read by a device directly connected to the processor by hardware, or the computer-readable storage medium may be stored remotely to the device, in particular in a cloud to which the processor may have access.

[0090] Use according to the fourth and fifth aspects According to a fourth aspect, there is provided a use of a system according to the second aspect to perform a method for reviewing and manipulating data according to the method of the first aspect.

[0091] According to a fifth aspect, there is provided a use of the computer readable storage medium of the third aspect to implement a method for reviewing and manipulating data in accordance with the method of the first aspect. EXAMPLES

[0092] It should be understood that the following examples are for illustrative purposes only and are not to be construed as limiting this invention in any manner.

[0093] The following examples demonstrate the superior performance of the method for visualization of dPCR data according to the invention.

[0094] Abbreviation: dPCR Digital Polymerase Chain Reaction GUI Graphical User Interface PCR Polymerase Chain Reaction RFU Real Fluorescence Units

[0095] The following examples are preferably carried out using the QIAcuity system (QIAGEN (Hilden)) and the corresponding kits. For sample preparation, consumables, buffers and reagents, thermal cycling, imaging, and additional devices required, the QIAcuity User Manual (QIAGEN (Hilden), March 2021 and December 2020 (extended version)) is referred to. The QIAcuity software suite (QIAGEN (Hilden)) provides a signal map for each well as the basis of the method of the present invention.

[0096] To carry out the method of the present invention, the following system requirements are recommended according to the QIAcuity User Manual (QIAGEN (Hilden), March 2021): -Processor: x64-compatible processor with 4 physical cores and 2.5GHz, -Memory: 16GB RAM, -Storage device: at least 500GB, and -Graphics: at least 1,920 x 1,080 pixels.

[0097] Example 1 Automatic exclusion of invalid signals from dPCR data visualization In dPCR, each well is divided into many subsamples, i.e. partitions. For each of these subsamples, an end-point PCR is performed and the signal is measured within a certain physical location. FIG. 1A shows an image obtained by a camera, representing the raw signal of the partitions of one 9 mm×9 mm well. Each partition is represented according to its individual spatial location and its signal intensity (RFU, real fluorescence units). The signal intensity (RFU) depends on the amplification reaction, meaning that the partitions where the amplification reaction occurred are represented by bright signals (white spots in FIG. 1A), while for partitions where no amplification reaction occurred, no signal is detected. In partitions where the amplification reaction occurred but where there are a few target nucleic acid molecules, the signal can still be detected, but it may have a lower intensity. According to the inherent characteristics of the signal, the partitions are classified. The visualization of the location of the partitions, each together with its classification or category, in the signal map provides a global context for data interpretation.

[0098] Automatic classification and visualization of positive partitions Partitions are automatically classified as positive / negative for signal intensity (i.e., amplification of target nucleic acid) based on a signal intensity threshold. This threshold is calculated based on the distribution of all signal intensities detected in individual wells. The signal intensity threshold can also be manually set or adjusted by the user, for example, when a histogram of all detected signal intensities is presented on a graphical user interface (GUI), which may include interactive tools such as sliders or boxes for inputting values. Partitions exhibiting signal intensity values ​​(RFU) above the signal intensity threshold are classified as positive, while partitions exhibiting signal intensity values ​​below the threshold are classified as negative. The positive partitions can then be visualized in the signal map by a first indicator, such as a color, a geometric shape, a pattern, a symbol, an icon, a number, a letter, or any other suitable indicator (bright spot in FIG. 1B).

[0099] Automatic identification and correction of partitions that need to be corrected In addition, the signals detected for certain partitions or areas of the well may be considered to be inaccurate, meaning that the signals detected in these partitions do not meet the characteristics of a signal representative of a successful amplification reaction, or other erroneous events have occurred that lead to an incorrect allocation of the individual partitions. For example, possible reasons for correction may be dust or other particles that emit signals, uneven distribution of positive partitions, statistically unlikely clustering of positive partitions, or any other erroneous events.

[0100] For identification of partitions that need to be corrected caused by dust or other particles and scratches, different signal characteristics are determined and then appropriately identified as partitions that need to be corrected (see circled dust in FIG. 2A). Identification of dust is done for the following reasons: - Shape: Inconsistency between the shape of the dust / particle / scratch and the shape of the positive partition, including the brightness distribution, Position: the misalignment between the X and Y coordinates in the image due to the partitions being regularly arranged in a grid; - Size: the number of bright pixels that do not follow the regular brightness distribution of the positive partition; Brightness / clustering (only performed up to a certain threshold of the number of positives): an image with only a few positive partitions and some areas with many positive partitions due to diffuse dust. can be based on.

[0101] For the identification of partitions that need to be corrected, which are caused by uneven distribution of positive partitions, first, the overall number of positive partitions in the image is counted. When the number of positive partitions reaches a certain value, the following instructions are executed. The whole image is divided into smaller areas, such as square, rectangular, rhomboid, circular, elliptical, or any other means that are suitable for image division, and then the number of positive partitions per area is counted. If one of the areas shows too many or too few positive partitions, it is automatically classified as a partition that needs to be corrected.

[0102] Identification of statistically impossible clustering of positives (e.g., in rows) is performed without violating the unequal distribution of positives. Based on the number of positive partitions relative to negative partitions, a Poisson-based probability calculation is performed. If impossible clusters exist, all positive partitions are classified as false positives, i.e., corrected partitions, except for a certain number of partitions, such as one, two, three or more partitions, depending on the total number of partitions identified that need to be corrected.

[0103] Depending on the reason for the correction, some or all of the corrected partitions are classified either as negative, for example in the case of impossible positive clustering, and / or as invalid.

[0104] The signal map representing the positive partition by a first indicator (eg, color) is then corrected (see FIG. 2B) such that the corrected signal is filtered out, i.e., is no longer displayed.

[0105] Example 2 Visualization of categories of corrected partitions due to reasons for correction and manipulation by the user Since the quantification of the target nucleic acid is based on positive and negative partitions (i.e., valid partitions) that indicate the presence or absence of the target nucleic acid, invalid partitions are excluded from the final target nucleic acid quantification. However, corrected partitions, including invalid partitions, are included in the data review and manipulation process to allow the user to include or exclude partitions in the analysis that are not provided by prior art methods.

[0106] The method according to the invention extends the automatic visualization to not only distinguish positive / negative and valid / invalid information, respectively, but also to include information about the reasons for correction per partition. Also, the user can manually select the information that will be displayed together. Still further, the user can manually modify the automatic validity judgment, for example, excluding certain partitions from the results or reclassifying the visualized data.

[0107] Fig. 3 illustrates exemplary workflows of the method according to the invention. These workflows can be advantageously implemented in the QIAcuity system (QIAGEN (Hilden)). As a basis, optionally with pre-processed or corrected data, a signal map per well showing partitions is visualized as described above. In an exemplary workflow, the signal map represents valid signals according to individual fluorescent signal intensity, while invalid / corrected signals or partitions can be automatically identified and excluded from visualization or represented by a first indicator as described above (upper box in Fig. 3A).

[0108] Automatic visualization of categories for corrected partitions According to the method of the present invention, several overlays, represented by different indicators suitable for differentiation, such as colors, geometric shapes, patterns, symbols, icons, numbers, letters, or similar expressions, visualize different categories of corrected partitions due to the reason for the correction.

[0109] Partitions that need to be corrected are automatically identified as explained above. However, according to the present invention, invalid / corrected partitions are not excluded from the visualization as in the prior art methods, but are visualized according to their individual reasons for correction (e.g., dust / particles / scratches, uneven distribution of positive partitions, statistically unlikely clustering of positive partitions, etc.). Each reason for correction is indicated by some indicator, such as color, geometric shape, pattern, symbol, icon, number, letter, or in a similar manner. Figure 3A shows a signal map (middle box, Figure 3A) that represents not only the positive partitions in the same color (color 1, circle with thick diagonal line), but also the category "dust" in color 2 (circle with wavy pattern) and "proportion" (with respect to uneven distribution) in color 3 (circle with thin diagonal line). Thus, the user receives information about the reason for correction of a partition or an area.

[0110] Manual selection of categories by the user According to the present invention, the user can select the categories to be displayed. This is accomplished by an interactive tool present on the GUI that allows user interaction. Such an interactive tool may be a button for each category, as illustrated by the buttons in FIG. 3. Alternatively or in addition, the user may select, directly or by a drop-down menu, one component of a category on the signal map, thereby switching the individual category off or on. However, any other suitable interactive tool may be used to select a category.

[0111] Commands responsive to the operation of the interactive tool are received by the system, and indicators (e.g., colors) presented in the signal map are updated accordingly. For example, a user can switch off the visualization of corrected partitions caused by "dust" (represented by color 2, center and bottom box in FIG. 3A). Additionally, the category "dust" can be switched on again.

[0112] Manual reclassification by the user The method of the present invention further offers the user to select a single partition or component from different categories by means of additional interactive tools. Such interactive tools may be a box selection tool (illustrated by a white box, see the top box in FIG. 3B) for selecting a part or area of ​​the signal map or a freehand selection tool by selecting a single partition. Any tool suitable for selecting a single partition or a group of partitions independent of a particular category may be used. The system may provide suggestions for partitions that may also meet the criteria of different categories. Such suggestions may be displayed to the user by a pop-up message or similar means.

[0113] The selected partition can then be reclassified into a different category already displayed, and the signal map is updated accordingly. Alternatively, the selected partition can be reclassified into a new category, assigned by the user, to indicate a different reason for the correction. The GUI may therefore provide a box for manually typing in a notation of the new category. This new category is optionally displayed by a different indicator / color, selected by the user, and the signal map is updated accordingly (category "manual", represented by the bottom box in FIG. 3B, color 4 (circle with checkerboard pattern)).

[0114] The methods of the present invention extend prior art methods for user visualization, review, and manipulation of dPCR data, thereby providing improved and reliable dPCR data analysis, particularly in testing for nucleic acids derived from viruses, bacteria, genetic disorders, or any other target nucleic acid, even in complex samples and samples containing minimal amounts of target nucleic acid.

[0115] Example 3 Accurate selection of partitions that need to be corrected for data analysis The method of the present invention provides accurate selection of partitions that need to be corrected for data analysis. By classifying partitions that need to be corrected as corrected negative (i.e., valid), those partitions are included in the data analysis. In contrast, classification of partitions that need to be corrected as invalid leads to the exclusion of those partitions from the data analysis. Users can also manually include or exclude data from the analysis by using the interactive tool of the present invention. As a result, a better accuracy can be achieved compared to the prior art methods.

[0116] According to the method of the present invention, partitions identified as partitions that need to be corrected may be classified as corrected negative. These corrected negative partitions are represented in the signal map according to a value that depends on the signal intensity of partitions that are classified as negative but were not identified as partitions that need to be corrected. Furthermore, corrected negative partitions are included in the data because they are valid partitions. Therefore, partitions that need to be corrected are not necessarily excluded from further analysis as in the prior art method, but are included in the quantification if the partitions identified are classified as corrected negative.

[0117] Similarly, one of the user interactive tools of the method of the present invention allows for reconsideration of partitions that have been identified as partitions that need to be corrected and have been classified as invalid and therefore excluded from the data. By utilizing this interactive tool, the user can manually reclassify those partitions that were automatically excluded due to classification as invalid as positive or negative. As a result, those corrected partitions become valid and are included in the target nucleic acid quantification. Also, those partitions that were automatically classified as valid, either positive, negative, or corrected negative, may be manually reclassified by the user as invalid and therefore excluded from the quantification.

[0118] The method of the present invention also allows for adjustment of the classifying and displaying steps in response to the operation of at least one further graphical interactive tool to adjust the signal intensity threshold. By adjusting the signal intensity threshold, the classification of partitions as positive and negative is adjusted accordingly. As a result, the automatic classification of partitions as corrected negative and / or invalid is adjusted. The signal map representation is also adjusted accordingly. For example, the signal intensity by which corrected negative partitions are represented in the signal map may change because the number of partitions classified as negative changes due to the adjustment of the signal intensity threshold. The user may also use the interactive tool to manually change the representation and / or reclassify the adjusted classification. As a result, the number of partitions that are valid and included in the data and the number of partitions that are invalid and excluded from the data are also adjusted, thus affecting the data analysis.

[0119] Thus, the accuracy of the data analysis can be further improved since the user is provided with the possible exact types of corrections that the user can affect due to the interactive tools provided by the method of the present invention. Such possible supported influences from the user show results that lead to an improvement in the accuracy of the analysis.

[0120] Overall, the method of the present invention provides accurate selection of data to be included or excluded in target nucleic acid quantification in dPCR.As a result, the method of the present invention provides better accuracy and improved reliability of dPCR results compared to prior art methods that only provide identification of partitions that need to be corrected and automatic exclusion from further analysis, but do not allow reconsideration of excluded partitions.

Claims

Claim 1 A method for scrutiny and manipulation of data obtained from signals determined by a biological analysis system, comprising: (A) receiving a plurality of detected signals from a plurality of partitions located in a region of a substrate that undergoes a biological assay; (B) selecting a value for a signal intensity threshold; (C) classifying the partitions as positive if the signal intensity is detected above the signal intensity threshold and / or as negative if the signal intensity is detected below the signal intensity threshold; (D) identifying partitions that need to be corrected, such that some or all of the partitions that need to be corrected are classified as negative and / or invalid, and thus the partitions that need to be corrected become corrected partitions, and classifying all partitions classified as positive and negative as valid; (E) displaying a graphical user interface including a signal map of the detected signals, wherein the signal map represents the valid partitions by their signal intensities, and the corrected negative partitions are represented according to a value depending on the signal intensities of the other negative partitions represented within the signal map; (F) classifying the corrected partitions into categories according to reasons for correction; (G) displaying the signal map, wherein all positive partitions are represented by a first indicator; (H) displaying the signal map, wherein each of the categories representing reasons for correction is represented by a second, third, fourth, or further indicator; and the graphical user interface further comprises at least two bidirectional tools, and in response to an operation by the user of at least one of the at least two bidirectional tools, the method further comprises the following steps, namely: (I) adjusting the displayed signal map by excluding and / or including at least one category from being displayed, and / or Step of selecting categories for one or more partitions and / or for one or more categories of at least one partition, thereby reclassifying the partitions as being of different selected categories, and displaying the signal map, wherein the different selected categories are represented by indicators specific to the categories A method comprising at least one of the above steps **Claim 2** The signal map in (E) has the following characteristics, namely (i) it represents the corrected partition by a unique indicator different from the indicators of steps (G), (H), and (J), or (ii) the corrected partition is excluded from the representation The method according to claim 1, having one of the above **Claim 3** The negative partition is represented in the signal map by a further indicator different from the indicators of steps (G), (H), and (J). The method according to claim 1 **Claim 4** Steps (G) and (H) are performed simultaneously. The method according to claim 1 **Claim 5** Steps (I) and (J) are performed sequentially. The method according to claim 1 **Claim 6** The indicator is selected from colors, geometric shapes, patterns, symbols, icons, numbers, letters, signs, and / or any other means suitable for distinguishing between the categories. The method according to claim 1 **Claim 7** The detected signal is a fluorescence signal emitted from one or more, two or more, three or more, four or more, or five or more fluorescent dye molecules detected by an optical system. The method according to claim 1 **Claim 8** The signal intensity threshold is generated according to the signal intensity distribution from all signals detected from a plurality of partitions located in a certain region of the substrate undergoing a biological assay. The method according to claim 1 **Claim 9** The signal intensity threshold can be adjusted by the user in response to the operation of at least one graphical bidirectional tool. The method according to claim 1 **Claim 10** In response to the operation of at least one graphical bidirectional tool for adjusting the signal intensity threshold, the classifying step and the displaying step are appropriately adjusted. The method according to claim 9 **Claim 11** The substrate has the following characteristics, namely, (i) it has at least 20,000, at least 50,000 partitions, preferably at least 200,000 partitions, at least 600,000 partitions, at least 800,000 partitions; (ii) it is a multi-well plate having a plurality of partitions per well, and / or (iii) it has at least one region, optionally, a region of the substrate being represented by one or more wells of a multi-well plate The method according to claim 1, having one or more of the above.

12. The method according to claim 1, wherein the biological sample analyzed in the biological assay contains nucleic acid.

13. The method according to claim 1, wherein the signal detected for the effective partition is based on the presence or absence of a target nucleic acid in the sample analyzed in the biological analysis system.

14. The method according to claim 1, wherein the biological assay performed in the biological analysis system is an amplification reaction.

15. The amplification reaction has the following characteristics, namely, (i) it is a polymerase chain reaction (PCR), (ii) it is a digital polymerase chain reaction (dPCR), (iii) it is a reverse transcription amplification reaction, (iv) it is a reverse transcription PCR (RT-PCR), (v) it is an isothermal amplification reaction, (vi) it is a quantitative PCR, (vii) it is a quantitative reverse transcription PCR, having one or more of the above. The method according to claim 14.

16. The method has the following characteristics, namely, (i) according to any one of steps (C), (D), (F), and / or (J), each partition in the region of the substrate is classified into at least one category, and / or a plurality of categories corresponding to the classification as positive, corrected positive, negative, corrected negative, invalid, according to the reasons for the correction; and / or (ii) partitions classified as positive, corrected positive, negative, and / or corrected negative are included in the data, and partitions classified as invalid are excluded from the data The method according to claim 1, comprising one or more of the above.

17. A system for scrutiny and manipulation of data obtained from signals determined by a biological analysis system, said system comprising: (a) at least one processor for implementing the method according to any one of claims 1 to 15 or 16; (b) a memory encoded with instructions for implementing the method according to any one of claims 1 to 15 or 16 A system comprising.

18. Said system further comprises (c) a display; (d) a thermal circulator; (e) an optical system; (f) a storage device; (g) optionally, an input device The system according to claim 17, comprising.

19. The system according to claim 17, wherein components (a) to (b) or (a) to (g) are connected for communicating and processing information.

20. A computer-readable storage medium encoded with instructions executable by a processor for scrutiny and manipulation of data, said instructions comprising instructions for implementing the method according to any one of claims 1 to 15 or 16.

21. A method for scrutiny and manipulation of data obtained from signals determined by a biological analysis system, comprising: (A) receiving a plurality of detected signals from a plurality of partitions located in a region of a substrate that undergoes a biological assay; (B) selecting a value for a signal intensity threshold; (C) classifying the partitions in which the signal intensity is detected as exceeding the signal intensity threshold as positive and / or the partitions in which the signal intensity is detected as below the signal intensity threshold as negative; (D) identifying partitions that need to be corrected, classifying the partitions that need to be corrected as corrected negative and / or invalid, such that the partitions that need to be corrected become corrected partitions, wherein all partitions that are positive, negative, and corrected negative partitions are valid partitions. (E) displaying a graphical user interface including a signal map of the detected signals, wherein the signal map represents the valid partitions classified as positive and the valid partitions classified as negative by their signal strengths, and the valid partitions classified as corrected negative are represented according to a value depending on the signal strength of the partitions classified as negative; (F) classifying the corrected partitions into categories according to reasons for correction; (G) displaying the signal map, wherein all positive partitions are represented by a first indicator; (H) displaying the signal map, wherein the categories representing reasons for correction are each represented by a second, third, fourth, or further indicator; comprising; the graphical user interface further comprises at least two bidirectional tools, and in response to an operation of at least one of the at least two bidirectional tools by a user, the method further comprises the following steps, namely, (I) adjusting the displayed signal map by excluding and / or including so that at least one category is not displayed; and / or (J) selecting categories for one or more partitions and / or for one or more categories of at least one partition, thereby reclassifying the partitions as belonging to the selected different categories, and displaying the signal map, wherein the selected different categories are represented by indicators specific to the categories; a method comprising at least one of the above.

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