Basic description and terms
Unlike many areas of mathematics, the origins of group testing can be traced back to a single report written by a single person:Classification of group-testing problems
There are two independent classifications for group-testing problems; every group-testing problem is either adaptive or non-adaptive, and either probabilistic or combinatorial. In probabilistic models, the defective items are assumed to follow someVariations and extensions
There are many ways to extend the problem of group testing. One of the most important is called ''noisy'' group testing, and deals with a big assumption of the original problem: that testing is error-free. A group-testing problem is called noisy when there is some chance that the result of a group test is erroneous (e.g. comes out positive when the test contained no defectives). The ''Bernoulli noise model'' assumes this probability is some constant, , but in general it can depend on the true number of defectives in the test and the number of items tested. For example, the effect of dilution can be modelled by saying a positive result is more likely when there are more defectives (or more defectives as a fraction of the number tested), present in the test. A noisy algorithm will always have a non-zero probability of making an error (that is, mislabeling an item). Group testing can be extended by considering scenarios in which there are more than two possible outcomes of a test. For example, a test may have the outcomes and , corresponding to there being no defectives, a single defective, or an unknown number of defectives larger than one. More generally, it is possible to consider the outcome-set of a test to be for some . Another extension is to consider geometric restrictions on which sets can be tested. The above lightbulb problem is an example of this kind of restriction: only bulbs that appear consecutively can be tested. Similarly, the items may be arranged in a circle, or in general, a net, where the tests are available paths on the graph. Another kind of geometric restriction would be on the maximum number of items that can be tested in a group, or the group sizes might have to be even and so on. In a similar way, it may be useful to consider the restriction that any given item can only appear in a certain number of tests. There are endless ways to continue remixing the basic formula of group testing. The following elaborations will give an idea of some of the more exotic variants. In the 'good–mediocre–bad' model, each item is one of 'good', 'mediocre' or 'bad', and the result of a test is the type of the 'worst' item in the group. In threshold group testing, the result of a test is positive if the number of defective items in the group is greater than some threshold value or proportion. Group testing with inhibitors is a variant with applications in molecular biology. Here, there is a third class of items called inhibitors, and the result of a test is positive if it contains at least one defective and no inhibitors.History and development
Invention and initial progress
The concept of group testing was first introduced by Robert Dorfman in 1943 in a short report published in the Notes section of ''Combinatorial group testing
Group testing was first studied in the combinatorial context by Li in 1962, with the introduction of ''Li’s -stage algorithm''. Li proposed an extension of Dorfman's '2-stage algorithm' to an arbitrary number of stages that required no more than tests to be guaranteed to find or fewer defectives among items. The idea was to remove all the items in negative tests, and divide the remaining items into groups as was done with the initial pool. This was to be done times before performing individual testing. Combinatorial group testing in general was later studied more fully by Katona in 1973. Katona introduced theNon-adaptive and probabilistic testing
One of the key insights in non-adaptive group testing is that significant gains can be made by eliminating the requirement that the group-testing procedure be certain to succeed (the "combinatorial" problem), but rather permit it to have some low but non-zero probability of mis-labelling each item (the "probabilistic" problem). It is known that as the number of defective items approaches the total number of items, exact combinatorial solutions require significantly more tests than probabilistic solutions — even probabilistic solutions permitting only an asymptotically small probability of error. In this vein, Chan ''et al.'' (2011) introducedFormalisation of combinatorial group testing
This section formally defines the notions and terms relating to group testing. *The ''input vector'', , is defined to be a binary vector of length (that is, ), with the ''j''-th item being called ''defective'' if and only if . Further, any non-defective item is called a 'good' item. is intended to describe the (unknown) set of defective items. The key property of is that it is an ''implicit input''. That is to say, there is no direct knowledge of what the entries of are, other than that which can be inferred via some series of 'tests'. This leads on to the next definition. *Let be an input vector. A set, is called a ''test''. When testing is ''noiseless'', the result of a test is ''positive'' when there exists such that , and the result is ''negative'' otherwise. Therefore, the goal of group testing is to come up with a method for choosing a 'short' series of tests that allow to be determined, either exactly or with a high degree of certainty. *A group-testing algorithm is said to make an ''error'' if it incorrectly labels an item (that is, labels any defective item as non-defective or vice versa). This is ''not'' the same thing as the result of a group test being incorrect. An algorithm is called ''zero-error'' if the probability that it makes an error is zero. * denotes the minimum number of tests required to always find defectives among items with zero probability of error by any group-testing algorithm. For the same quantity but with the restriction that the algorithm is non-adaptive, the notation is used.General bounds
Since it is always possible to resort to individual testing by setting for each , it must be that that . Also, since any non-adaptive testing procedure can be written as an adaptive algorithm by simply performing all the tests without regard to their outcome, . Finally, when , there is at least one item whose defectiveness must be determined (by at least one test), and so . In summary (when assuming ), .Information lower bound
A lower bound on the number of tests needed can be described using the notion of ''sample space'', denoted , which is simply the set of possible placements of defectives. For any group testing problem with sample space and any group-testing algorithm, it can be shown that , where is the minimum number of tests required to identify all defectives with a zero probability of error. This is called the ''information lower bound''. This bound is derived from the fact that after each test, is split into two disjoint subsets, each corresponding to one of the two possible outcomes of the test. However, the information lower bound itself is usually unachievable, even for small problems. This is because the splitting of is not arbitrary, since it must be realisable by some test. In fact, the information lower bound can be generalised to the case where there is a non-zero probability that the algorithm makes an error. In this form, the theorem gives us an upper bound on the probability of success based on the number of tests. For any group-testing algorithm that performs tests, the probability of success, , satisfies . This can be strengthened to: .Representation of non-adaptive algorithms
Algorithms for non-adaptive group testing consist of two distinct phases. First, it is decided how many tests to perform and which items to include in each test. In the second phase, often called the decoding step, the results of each group test are analysed to determine which items are likely to be defective. The first phase is usually encoded in a matrix as follows. *Suppose a non-adaptive group testing procedure for items consists of the tests for some . The ''testing matrix'' for this scheme is the binary matrix, , where if and only if (and is zero otherwise). Thus each column of represents an item and each row represents a test, with a in the entry indicating that the test included the item and a indicating otherwise. As well as the vector (of length ) that describes the unknown defective set, it is common to introduce the result vector, which describes the results of each test. *Let be the number of tests performed by a non-adaptive algorithm. The ''result vector'', , is a binary vector of length (that is, ) such that if and only if the result of the test was positive (i.e. contained at least one defective). With these definitions, the non-adaptive problem can be reframed as follows: first a testing matrix is chosen, , after which the vector is returned. Then the problem is to analyse to find some estimate for . In the simplest noisy case, where there is a constant probability, , that a group test will have an erroneous result, one considers a random binary vector, , where each entry has a probability of being , and is otherwise. The vector that is returned is then , with the usual addition on (equivalently this is the element-wise XOR operation). A noisy algorithm must estimate using (that is, without direct knowledge of ).Bounds for non-adaptive algorithms
The matrix representation makes it possible to prove some bounds on non-adaptive group testing. The approach mirrors that of many deterministic designs, where -separable matrices are considered, as defined below. *A binary matrix, , is called ''-separable'' if every Boolean sum (logical OR) of any of its columns is distinct. Additionally, the notation ''-separable'' indicates that every sum of any of ''up to'' of 's columns is distinct. (This is not the same as being -separable for every .) When is a testing matrix, the property of being -separable (-separable) is equivalent to being able to distinguish between (up to) defectives. However, it does not guarantee that this will be straightforward. A stronger property, called ''-disjunctness'' does. *A binary matrix, is called ''-disjunct'' if the Boolean sum of any columns does not contain any other column. (In this context, a column A is said to contain a column B if for every index where B has a 1, A also has a 1.) A useful property of -disjunct testing matrices is that, with up to defectives, every non-defective item will appear in at least one test whose outcome is negative. This means there is a simple procedure for finding the defectives: just remove every item that appears in a negative test. Using the properties of -separable and -disjunct matrices the following can be shown for the problem of identifying defectives among total items. #The number of tests needed for an asymptotically small ''average'' probability of error scales as . #The number of tests needed for an asymptotically small ''maximum'' probability of error scales as . #The number of tests needed for a ''zero'' probability of error scales as .Generalised binary-splitting algorithm
The generalised binary-splitting algorithm is an essentially-optimal adaptive group-testing algorithm that finds or fewer defectives among items as follows: # If , test the items individually. Otherwise, set and . # Test a group of size . If the outcome is negative, every item in the group is declared to be non-defective; set and go to step 1. Otherwise, use aNon-adaptive algorithms
Non-adaptive group-testing algorithms tend to assume that the number of defectives, or at least a good upper bound on them, is known. This quantity is denoted in this section. If no bounds are known, there are non-adaptive algorithms with low query complexity that can help estimate .Combinatorial orthogonal matching pursuit (COMP)
Combinatorial Orthogonal Matching Pursuit, or COMP, is a simple non-adaptive group-testing algorithm that forms the basis for the more complicated algorithms that follow in this section. First, each entry of the testing matrix is chosen i.i.d. to be with probability and otherwise. The decoding step proceeds column-wise (i.e. by item). If every test in which an item appears is positive, then the item is declared defective; otherwise the item is assumed to be non-defective. Or equivalently, if an item appears in any test whose outcome is negative, the item is declared non-defective; otherwise the item is assumed to be defective. An important property of this algorithm is that it never creates false negatives, though a false positive occurs when all locations with ones in the ''j''-th column of (corresponding to a non-defective item ''j'') are "hidden" by the ones of other columns corresponding to defective items. The COMP algorithm requires no more than tests to have an error probability less than or equal to . This is within a constant factor of the lower bound for the average probability of error above. In the noisy case, one relaxes the requirement in the original COMP algorithm that the set of locations of ones in any column of corresponding to a positive item be entirely contained in the set of locations of ones in the result vector. Instead, one allows for a certain number of “mismatches” – this number of mismatches depends on both the number of ones in each column, and also the noise parameter, . This noisy COMP algorithm requires no more than tests to achieve an error probability at most .Definite defectives (DD)
The definite defectives method (DD) is an extension of the COMP algorithm that attempts to remove any false positives. Performance guarantees for DD have been shown to strictly exceed those of COMP. The decoding step uses a useful property of the COMP algorithm: that every item that COMP declares non-defective is certainly non-defective (that is, there are no false negatives). It proceeds as follows. #First the COMP algorithm is run, and any non-defectives that it detects are removed. All remaining items are now "possibly defective". #Next the algorithm looks at all the positive tests. If an item appears as the only "possible defective" in a test, then it must be defective, so the algorithm declares it to be defective. #All other items are assumed to be non-defective. The justification for this last step comes from the assumption that the number of defectives is much smaller than the total number of items. Note that steps 1 and 2 never make a mistake, so the algorithm can only make a mistake if it declares a defective item to be non-defective. Thus the DD algorithm can only create false negatives.Sequential COMP (SCOMP)
SCOMP (Sequential COMP) is an algorithm that makes use of the fact that DD makes no mistakes until the last step, where it is assumed that the remaining items are non-defective. Let the set of declared defectives be . A positive test is called ''explained'' by if it contains at least one item in . The key observation with SCOMP is that the set of defectives found by DD may not explain every positive test, and that every unexplained test must contain a hidden defective. The algorithm proceeds as follows. # Carry out steps 1 and 2 of the DD algorithm to obtain , an initial estimate for the set of defectives. # If explains every positive test, terminate the algorithm: is the final estimate for the set of defectives. # If there are any unexplained tests, find the "possible defective" that appears in the largest number of unexplained tests, and declare it to be defective (that is, add it to the set ). Go to step 2. In simulations, SCOMP has been shown to perform close to optimally.Example applications
The generality of the theory of group testing lends it to many diverse applications, including clone screening, locating electrical shorts; high speed computer networks; medical examination, quantity searching, statistics; machine learning, DNA sequencing; cryptography; and data forensics. This section provides a brief overview of a small selection of these applications.Multiaccess channels
A multiaccess channel is a communication channel that connects many users at once. Every user can listen and transmit on the channel, but if more than one user transmits at the same time, the signals collide, and are reduced to unintelligible noise. Multiaccess channels are important for various real-world applications, notably wireless computer networks and phone networks.Chlebus, B. S. (2001). "Randomized communication in radio networks". In: Pardalos, P. M.; Rajasekaran, S.; Reif, J.; Rolim, J. D. P. (Eds.), ''Handbook of Randomized Computing'', Vol. I, p.401–456. Kluwer Academic Publishers, Dordrecht. A prominent problem with multiaccess channels is how to assign transmission times to the users so that their messages do not collide. A simple method is to give each user their own time slot in which to transmit, requiring slots. (This is called ''time division multiplexing'', or TDM.) However, this is very inefficient, since it will assign transmission slots to users that may not have a message, and it is usually assumed that only a few users will want to transmit at any given time – otherwise a multiaccess channel is not practical in the first place. In the context of group testing, this problem is usually tackled by dividing time into 'epochs' in the following way. A user is called 'active' if they have a message at the start of an epoch. (If a message is generated during an epoch, the user only becomes active at the start of the next one.) An epoch ends when every active user has successfully transmitted their message. The problem is then to find all the active users in a given epoch, and schedule a time for them to transmit (if they have not already done so successfully). Here, a test on a set of users corresponds to those users attempting a transmission. The results of the test are the number of users that attempted to transmit, and , corresponding respectively to no active users, exactly one active user (message successful) or more than one active user (message collision). Therefore, using an adaptive group testing algorithm with outcomes , it can be determined which users wish to transmit in the epoch. Then, any user that has not yet made a successful transmission can now be assigned a slot to transmit, without wastefully assigning times to inactive users.Machine learning and compressed sensing
Machine learning is a field of computer science that has many software applications such as DNA classification, fraud detection and targeted advertising. One of the main subfields of machine learning is the 'learning by examples' problem, where the task is to approximate some unknown function when given its value at a number of specific points. As outlined in this section, this function learning problem can be tackled with a group-testing approach. In a simple version of the problem, there is some unknown function, where , and (using logical arithmetic: addition is logical OR and multiplication is logical AND). Here is ' sparse', which means that at most of its entries are . The aim is to construct an approximation to using point evaluations, where is as small as possible. (Exactly recovering corresponds to zero-error algorithms, whereas is approximated by algorithms that have a non-zero probability of error.) In this problem, recovering is equivalent to finding . Moreover, if and only if there is some index, , where . Thus this problem is analogous to a group-testing problem with defectives and total items. The entries of are the items, which are defective if they are , specifies a test, and a test is positive if and only if . In reality, one will often be interested in functions that are more complicated, such as , again where .Multiplex assay design for COVID19 testing
During a pandemic such as the COVID-19 outbreak in 2020, virus detection assays are sometimes run using nonadaptive group testing designs. One example was provided by the Origami Assays project which released open source group testing designs to run on a laboratory standard 96 well plate. In a laboratory setting, one challenge of group testing is the construction of the mixtures can be time-consuming and difficult to do accurately by hand. Origami assays provided a workaround for this construction problem by providing paper templates to guide the technician on how to allocate patient samples across the test wells. Using the largest group testing designs (XL3) it was possible to test 1120 patient samples in 94 assay wells. If the true positive rate was low enough, then no additional testing was required.Data forensics
Data forensics is a field dedicated to finding methods for compiling digital evidence of a crime. Such crimes typically involve an adversary modifying the data, documents or databases of a victim, with examples including the altering of tax records, a virus hiding its presence, or an identity thief modifying personal data. A common tool in data forensics is the one-way cryptographic hash. This is a function that takes the data, and through a difficult-to-reverse procedure, produces a unique number called a hash. Hashes, which are often much shorter than the data, allow us to check if the data has been changed without having to wastefully store complete copies of the information: the hash for the current data can be compared with a past hash to determine if any changes have occurred. An unfortunate property of this method is that, although it is easy to tell if the data has been modified, there is no way of determining how: that is, it is impossible to recover which part of the data has changed. One way to get around this limitation is to store more hashes – now of subsets of the data structure – to narrow down where the attack has occurred. However, to find the exact location of the attack with a naive approach, a hash would need to be stored for every datum in the structure, which would defeat the point of the hashes in the first place. (One may as well store a regular copy of the data.) Group testing can be used to dramatically reduce the number of hashes that need to be stored. A test becomes a comparison between the stored and current hashes, which is positive when there is a mismatch. This indicates that at least one edited datum (which is taken as defectiveness in this model) is contained in the group that generated the current hash. In fact, the amount of hashes needed is so low that they, along with the testing matrix they refer to, can even be stored within the organisational structure of the data itself. This means that as far as memory is concerned the test can be performed 'for free'. (This is true with the exception of a master-key/password that is used to secretly determine the hashing function.)Notes
References
Citations
General references
* * Atri Rudra's course on Error Correcting Codes: Combinatorics, Algorithms, and Applications (Spring 2007), LectureSee also
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