AU2002344716B2

AU2002344716B2 - Method and apparatus for identifying components of a system with a response characteristic

Info

Publication number: AU2002344716B2
Application number: AU2002344716A
Authority: AU
Inventors: Robert Dunne; Harri Kiiveri; Mervyn Thomas; Dale Wilson
Original assignee: Commonwealth Scientific and Industrial Research Organization CSIRO
Current assignee: Commonwealth Scientific and Industrial Research Organization CSIRO
Priority date: 2001-07-11
Filing date: 2002-07-11
Publication date: 2006-12-14
Anticipated expiration: 2022-07-11

Description

WO 03/007177 PCT/AU02/00934 METHOD AND APPARATUS FOR IDENTIFYING COMPONENTS OF A SYSTEM WITH A RESPONSE CHARACTERISTIC TECHNICAL FIELD OF THE INVENTION The invention relates to a method and apparatus for identifying components of a system from data generated from the system, which components are capable of exhibiting a response pattern associated with a test condition and, particularly, but no exclusively, the present invention relates to a method and apparatus for identifying components of a biological system from data generated from the system, which components are capable of exhibiting a response pattern associated with a test condition.

BACKGROUND OF THE INVENTION There are any number of "systems" in existence for which measurement of components of the system may provide a basis by which to analyse the system. Examples of systems include financial systems (such as stock markets, credit systems for individuals, groups, organisations, loan histories), geological systems, chemical systems, biological systems, and many more. Many of these systems comprise a substantial number of components which generate substantial amounts of data.

For example, recent advances in the biological sciences have resulted in the development of methods for large scale analysis of biological systems. An example of one such method is use of biotechnology arrays. These arrays are generally ordered high density grids of known biological samples DNA, protein, carbohydrate) which may be screened or probed with test samples to obtain information about the relative quantities of individual components in the test sample. Use of biotechnology arrays WO 03/007177 PCT/AU02/00934 2 thus provides potential for analysis of biological and/or chemical systems.

An example of one type of biotechnology array is DNA microarrays for the analysis of gene expression. A DNA microarray consists of DNA sequences deposited in an ordered array onto a solid support base e.g. a glass slide. As many as 30,000 or more gene sequences may be deposited onto a single microarray chip. The arrays are hybridised with labelled RNA extracted from cells or tissue of interest, or cDNA synthesised from the extracted RNA, to determine the relative amounts of the RNA expression for each gene in the cell or tissue. The technique therefore provides a method of determining the relative expression levels of many genes in a particular cell or tissue. The method also has the potential to allow for the identification of genes that are expressed in a particular way, or in other words, have a particular response pattern in different cell types, or in the same cell type under different treatment or test conditions.

The ability to identify such genes would be useful, for example, in establishing diagnostic tests to distinguish between different cell types, to determine optimum conditions for expression of desired genes, or in assessing efficacy of drugs for targeting expression of particular genes.

A significait problem with the analysis of data generated from systems such as biotechnology arrays, however, is that response patterns in the data are often difficult to identify due to one or more of the following: the difficulty in manipulating large amounts of data generated by these types of methods or experiments; the inherent variation in the data; WO 03/007177 PCT/AU02/00934 -3 errors in the method which results in missing data (for example, areas on a biotechnology array from which data is missing).

The inventors have developed a method for analysis of data generated from systems which preferably permits identification of components of the system which exhibit a response pattern under a test condition.

DESCRIPTION OF THE INVENTION In a first aspect, the invention provides a method for identifying components of a system from data generated from the system, which components exhibit a response pattern associated with a test condition applied to the system, comprising the steps of: specifying design factors to specify the type of response pattern for the test condition; identifying a linear combination of components from the input data which correlate with the response pattern.

Preferably, the method includes the step of defining a matrix of design factors.

The inventors have developed a method whereby linear combinations of components from a system can be computed from large amounts of data whereby the linear combination of components fits or correlates with a specified response pattern. Thus, using this method, specific patterns in the data can be searched for and components exhibiting this pattern identified. This facilitates rapid screening of the data from a system for significant components.

The linear combination of components is preferably of the form WO 03/007177 PCT/AU02/00934 4y az 1 X+a 2

X

2 +a 3

X

3 anXn Wherein y is the linear combination al-an are component weights and Xi-Xn are data values generated from the method applied to the system for components of the system.

Preferably, a linear combination of components is chosen such that a linear regression of the linear combination of components on the design factors has as much predictive power as possible. The component weights are assessed in a manner such that the values of the component weights for components which do not correlate with the design factors are eliminated from the linear combination.

The method of the present invention has the advantage that it requires usage of less computer memory than prior art methods. Accordingly, the method of the present invention can preferably be performed rapidly on computers such as, for example, laptop machines. By using less memory, the method of the present invention also allows the method to be performed more quickly than prior art methods for analysis of, for example, biological data.

The method of the present invention is suitable for use in the analysis of any system in which components which exhibit a response pattern are sought. Suitable systems include, for example, chemical systems, biological systems, geological systems, process monitoring systems and financial systems including, for example, credit systems, insurance systems, marketing systems or company record systems.

The method of the present invention is particularly suitable for use in the analysis of results obtained from methods applied to biological systems.

WO 03/007177 PCT/AU02/00934 5 The data from the system is preferably generated from methods applied to the system. For example, the data may be a measure of a quantity of the components of the system, the presence of components in a system, or any other quantifiable feature of the components of a system.

The data may be generated using any methods for measuring the components of a system. The data may be generated from, for example, biotechnology array analysis such as DNA array analysis, DNA microarray analysis (see for example, Schena et al., 1995, Science 270: 467-470; Lockhart et al. 1996, Nature Biotechnology 14: 1649; US Pat No. 5,569,588), RNA array analysis, RNA microarray analysis, DNA microchip analysis, RNA microchip analysis, protein microchip analysis, carbohydrate analysis, antibody array analysis, or analysis such as DNA electrophoresis, RNA electrophoresis, one dimensional or two dimensional protein electrophoresis, proteomics.

The components of the method of the present invention are the components of the system that are being measured. The components may be any measurable component of the system.

The components may be, for example, genes, proteins, antibodies, carbohydrates. The components may be measured using methods for detecting the amount of, for example, genes or portions thereof, DNA sequences such as oligonucleotides or cDNA, RNA sequences, peptides, proteins, carbohydrate molecules or any other molecules that form part of the biological system. For example, in a DNA microarray, the component may be a gene or gene fragment. In an antibody array, the component may be a monoclonal antibody, polyclonal antibody, Fab fragment, or any other molecule that contains an antigen binding site of an antibody molecule.

It will be appreciated by those-skilled in the art that the components need not be known, but merely identifiable in a manner to permit a correlation to be made between a WO 03/007177 PCT/AU02/00934 6 linear combination of the components and the design matrix. For example, each components may have a unique identifier such as an arbitrarily selected number or name.

The response pattern specified by the design factors may be any desired pattern. In one embodiment, the response pattern specified by the design factors is derived from known data. Thus, a response pattern derived from known data will identify response patterns that are significantly similar to a known response pattern. For example, a matrix of design factors may be provided for gene expression that correlates with a known gene expression pattern. For example, a particular expression pattern of a particular yeast gene over a particular growth period.

In another embodiment, the response pattern specified by the design factors is derived from the input array data.

In this case, a response pattern derived from the input array data will group components of the array which exhibit significantly similar response patterns.

In yet another embodiment, the response pattern specified by the design factors is selected to identify any arbitrary response pattern.

The test conditions of the method of the invention may be any test conditions applied to a system. For example, in the case of a biological system, the test condition may be the growth conditions (such as temperature, time, growth medium, exposure to one or more test compounds) applied to an organism prior to measurement of the components of the system, the phenotype(such as a tumour cell, benign cell, advanced tumour cell, early tumour cell, normal cell, mutant cell, cell from a particular tissue or location)of an organism prior to measurement of the components of the system.

WO 03/007177 WO 03/07177PCT/ATJO2/00934 7- As discussed above, to identify a linear combination of components from input data, let y T=a TX whereby y is a linear combination in which X is an input data matrix of data, preferably array data, having n rows of components and k columns of test conditions, and a is a matrix of values or weights to be applied to the input data. The significance of regression co-efficients of y on a matrix of design factors T may be determined by the ratio: (Y TPy 1 Where in P=T (TTT) -TT; and T is a kxr design matrix; whereby values of a are selected to maximise Substituting arX for y in equation 1 and ignoring the constant divisors provides the following equation: A C1 TxypxTa GaTx(I X~a Thus, a linear combination of components a may be computed by finding the maximum value of X, in equation 2. However, there are linear combinations for which the denominator of equation 2 is zero and therefore X is infinite. Thus, in one embodiment, the present invention provides algorithms for determining a whereby a TX(I X'a is not zero.

In one embodiment, the linear combination is computed by solving the generalised eigerrralue problem of: WO 03/007177 PCT/AU02/00934 8 (XPX' X(I P) X) =0 3 for 2, and a wherein Xis a data matrix having n rows of components and k columns of test conditions and P T(TTT)-'TV wherein T is a matrix of k rows of design factors and r columns.

Equation 3 may be solved by the following algorithm: Let B=XPXT and W=X(I-P)XT Then to maximise the ratio (equation 2) in the case that W is non-singular we would solve (B -1W)q=0 4 One approach for doing this is to rewrite equation 4 as W BW2- 1I W 2 a=0 and solve this eigen equation.

If W 2 in equation 5 is replaced in the singular case by W2=UA O2 6 0 0 where A 1 is the diagonal matrix of 'non zero' eigen values of W it is easy to see that equation 5 becomes WO 03/007177 PCT/AU02/00934 9 1 1 A 2UBU 2 0 A, 2 Ua 7 0 0 J 0 where U =[UU 2 is partitioned conformable with A,.

Maximising equation 2 subject to a=U cq (i.e a is constrained to be in the range space of W) gives rise to the eigen equation defined by the top left hand block of the lefthand side of equation 7.

Equation 4 may be solved directly without requiring calculation of XPXT or X(I-P)XT using the generalised singular value decomposition, see Golub and Van Loan (1989), Matrix Computations, 2 nd Ed. Johns Hopkins University Press, Baltimore.

Alternatively, X(I-P)X 7 in equation 3 may be replaced with X(I-P)X+o2I. Thus, in another embodiment, the linear combination may be identified by solving the equation: (XPX' -lAX(I-P)X+ 2 )a=0Ofor k and a wherein X is a data matrix having n rows of components and k columns of test conditions; and P T(TTT)-lTT wherein T is a matrix of k rows of design factors and r columns and a is a weight matrix for the linear combination yT=aTX.

In a preferred embodiment, the invention provides a method for identifying components of a system from data generated from the system, which exhibit a response pattern associated with a set of test conditions applied to the system, comprising the steps of: specifying design factors to specify the type of response patterns for the test conditions; WO 03/007177 PCT/AU02/00934 10 formulating a model for the residuals of a regression of the input data on the design factors; estimating parameters for the model; computing a linear combination of components using the model and its estimated parameters.

Preferably, the system is a biological system.

Preferably, the data generated from a method applied to the system is generated from a biotechnology array.

The inventors have found that the denominator of equation 2 may be replaced with the quantity arVa wherein V is the covariance matrix of the residuals from the regression model. Thus in one embodiment, the linear combination may be computed by maximising the ratio: 2arTXPXTa 2= 9 aT T Equation 9 may be used to give the following optimala a =-1/2XPu wherein a is a weight matrix for the linear combination y=aTX P T(TTT) T

T

u is an eigenvector of P(XV

L

XT)P or equivalently a left singular vector of V-' 2

XP;

and X is an nxk data matrix from data generated from a method applied to the system, the data being from n components and k test conditions.

This approach has the advantage that the method of the invention does not require storage of matrices larger than WO 03/007177 PCT/AU02/00934 11 nxk. Thus, an advantage of the method of the invention is that it permits analysis of data obtained from large numbers of components or large amounts of components and test conditions.

In a preferred embodiment, the covariance matrix V is replaced by its maximum likelihood estimator. Maximum likelihood estimates are obtained from a model for the microarray data. In this preferred embodiment, the data are modelled by a normal distribution, which is completely specified by the mean and variance.

The model of the method of the present invention may comprise a mean model and a variance model. The mean model may be defined by the equation: E{XT TB T 11 wherein X is an nxk matrix of data, preferably array data, having n rows of components and k columns of test conditions, T is a kxr matrix of design factors having k rows and r columns and B is an nxr matrix of regression parameters.

The variance model may be defined by the equation: Var vec{X Ik V 12 where V is a covariance matrix: V AA r T 2 I, A,, with constraints Ds s diagonal and A A=I WO 03/007177 PCT/AU02/00934 12 The variance model and mean model together determine the likelihood. From (11) and (12) we may write twice the negative log likelihood as: L =klogIV+tr (X-BT' )1 13 The parameters to be estimated in the model include A, P, o and the regression coefficient B. In one embodiment, an estimate of regression coefficients B for the mean model is computed using standard least squares: B XTT(TTT) Substituting into Equation 13 we obtain the likelihood of V conditional on B=B L =L(B)=klog VI+trVr{-RR' where R= X BT In one embodiment, the parameters for the covariance matrix are estimated by computing the maximum likelihood estimates (MLE) for the covariance matrix, conditional on the regression parameters. The covariance matrix of the variance model may be defined by the equation: V=AA'+c ,i 14 To find the maximum likelihood estimate (MLE) of the parameters of V, we proceed as follows: From V AAT +a2 we get WO 03/007177 PCT/AU02/00934 -13- 0 21" 0 ]1 AA whereA*is an orthonormal completion of A. Tt may he shown that V-I 21, 0 T A(4 A A- 2

AAT).

Hence: jVI~ 0 2 11(c 2 )S )(cr2)~s so k logIJVk{ log ((Dii 2) togoC2} 17 Further, we may write: tr VIRR T I tr f(p+ U21I ATRJA} 18 ATR.RTAl Comnbining equation 17 and equation 18, the log likelihood function for A,(and U conditional on B may be obtained. We proceed to maximise this as a function of A subject to the constraint A T A=J. Forming the Lagrangian and WO 03/007177 PCT/AU02/00934 14 differentiating this with respect to A we obtain the equation DL/DA=0 where tr 2 AT~TA+tr L(AT r A I) 19 and L is a lower triangular matrix of Lagrange multipliers. Evaluating this and incorporating the constraint gives

RR

T

AD +AL T =0 with AT A= The first equation can be written as RRTA+ALTDl 0 where D 2 I Note that D is invertible provided all ii 0.

In one embodiment, the maximum likelihood estimate of ois computed from the equation: -2 tr RR 21 wherein s is the number of latent factors in the variance model.

In one embodiment, the maximum likelihood estimate of 0D is computed from the equation: i +6 Sii/k 22 WO 03/007177 WO 03/07177PCT/ATJO2/00934 15 In one embodiment, S is def ined by the equation: (A~R'At) 23 wherein is the i' eigenvalue of RRT Equations u5 k RR}j-j L}i 07+& =6ji/k (22)1 and 95, =(A[TRRT Aj) (23) are derived as follows: Premultiplying RR TAD+ALT=0 by A Tand using AFA=1 shows that L is symmetric and hence diagonal. It follows that the columns of A are eigenvectors of RR'.

Similarly we obtain aL k (j DO (i +,2)2 aL k kn 2)2 {trRR2'-S where 1 T R?2Aj) is the i'h eigenvalue of RRT.

It follows that (k+6 2 62= kIRR ii WO 03/007177 PCT/AU02/00934 16 The number of latent factors in the model for the covariance matrix may be estimated by performing likelihood ratio tests, cross validation tests or Bayesian procedures. In one embodiment, the number of factors in the variance model is determined by performing a series of likelihood ratio tests, for increasing numbers of factors.

The number of factors is chosen such that the test for further increase in the number of factors is not statistically significant. The likelihood ratio test statistic is computed using the equation: -2logL= k jlog(ii/k)+(n- )log 24 s+l +kn and the number of parameters is In a preferred embodiment, the number of factors, s, in the variance model is determined by performing a Bayesian method, preferably based on a method for selecting the number of principle components given in Minka T.P. 2000, Automatic choice of dimensionality for PCA, MIT Media Laboratory Perceptual Computing Section Technical Report No. 514 (Minka (2000)). We note that the problem of choosing basis functions in the factor analysis model i.e.

the number of left singular vectors in an singular value decomposition (SVD) of the residual matrix to include can be thought of as the problem of selecting the number of right singular vectors or principal components. Writing ki for the eigenvalues of RTR, in Minka(2000) the number of principal components is chosen to maximise log P(R s) log P(u) 0.5n log(A,) j=1 0.5n(k s) log(v) -0.5(m s) log(27r) log det(A) 0.5s log(n) WO 03/007177 PCT/AU02/00934 17 where m=ks-s(s+l)/2, lo g P(u) -slog(2)+ log(((k -i 0.5(k i 1) log(r) k v=(Z j=s+l and s k logdet(A,) L log((A;' 1 )n) 1=1 j=i+l where A for jsk j v,otherwise.

More reliable results are obtained using the Bayesian approach if it is used on a subset of the genes, chosen to show high correlation with the response pattern specified by the design factors.

The present invention also provides a means to determine the shape of the relationship between the linear combination of components and the response pattern specified by the design factors. The inner product of the linear combinations with the data matrix results ih a loading for each array. These loadings may be plotted against the columns of the design factors to reveal the shape of the response.

The present invention also provides for testing the significance of the components of a linear combination, and/or the overall strength of the relationship between the linear combination and the design factors. In one embodiment, the method comprises the further steps of: determining the significance-of each weight of the linear combination; and WO 03/007177 PCT/AU02/00934 18 setting non-significant weights to zero.

In a preferred embodiment, the significance of the weights of the linear combination is determined by a permutation test comprising the steps of: randomising the data, preferably biotechnology array data, within each row; Computing the weights and eigenvalues from the randomised data; repeating steps and a plurality of times; and determining a distribution for the weights and eigenvalues computed from the randomised data; determining the position of weights and eigenvalues computed from non-randomised data, preferably biotechnology array data, relative to the distribution of the weights and eigenvalues computed from randomised data; estimating the significance of each weight computed from the non-randomised data.

In a preferred embodiment, the significance of the relationship between the linear combinations of components and the response pattern specified by the design factors may be determined in ai analogous way. For each randomisation step above, the loadings are formed as inner products of the linear combinations with the data matrix. The multiple correlation between these loadings and the response pattern specified by the design factors is calculated. The significance of the overall relationship is evaluated by determining the position of the multiple correlation coefficient from non-randomised data with the distribution of the multiple correlation coefficient calculated from randomised data.

The present invention also provides methods f-or est-imating missing values from the data. In one embodiment, missing values are estimated using an EM algorithm. In a WO 03/007177 PCT/AU02/00934 19 preferred embodiment, the method comprises estimating missing data values of array data by: estimating initial values of B,F,(D,cT 2 by replacing missing values with simple estimates and calculating maximum likelihood estimates assuming the data was complete; Computing the expected values of the data array and the residual matrix under the model given the observed data (where oj is defined below); Substitute quantities for into likelihood equations assuming complete data to obtain new estimates of B,r,and U2; Repeat steps to until convergence.

In one embodiment, the EM algorithm is performed as follows: From equations 18 and

R=X-BT

T

ApA T +7 2 1 For the ith column of R, R i say, we can partition R i as V V 0 1 v 0 0

V

0 1 RI,= V, =I

V

O Vol, [Ili, 11"'o V11 7 VIM where o; denotes the observed residual component and u, denotes the missing residual component. To do the E step of the EM algorithm we need co compute the expected values E{Rjo,} and E{RRIoi} 36 Note that we are also conditioning on a set of parameter values, B,A,(D and U 2 however for easy of presentation we do not represent this in the following.

It can be shown that WO 03/007177 WO 03/07177PCT/ATJO2/00934 20 E ju~i oiI VK 4 0 (V)'1o 1 (Vuu )1 vu 0 0 Coi (say) Hence E{Ri joi) =jj[I From the definition of R we obtain E (Xi joe) [C o, BT Te, where ej is a kxl vector with zeros except in the ith position which is a one.

Now writing VuforV'ii we have E{R1~.To} 0] O][iOf

CT]

=R1]i±T[jCI[O 4]Oj Where (Tv- Y, Lx It follows that WO 03/007177 PCT/AU02/00934 21 k k E[RR S, S i=1 i=1 where S i 1 is nxmi. Here m is the number of missing values in column i and 1P is a permutation matrix with the property that Ri= o Define S= mi and nx (k m) 31 then E RR'To,,...ok =RR A similar expression also follows from writing 0 0 0 0 ]0 032 L 0 L LL' This requires only 1 (larger) matrix factorisation and the dimension of D may be much less than m if common genes are missing (across columns of X).

The above expressions enable the computation of maximum likelihood estimates by using the SVD of R, thus saving on storage requirements.

From equations 35 and 36 it can be seen that the matrix inversion is required. This may be a large matrix if there are many missing values in a column of R. In such cases we note the following: A AT +o- 2 (I-AuA 33 WO 03/007177 PCT/AU02/00934 22 where A, denotes an appropriate subset of rows of A is mxs) can be rewritten as A, 2 I -arI, A 2 Hence using the formula (A BDB A A-B (BB (B

A

B BTA it can be shown that -cT 2 A 2AA 2

F

1 1 A Note that this only requires the inverse of an sxs matrix where s is the number of basis functions in the variance model and is independent of m The EM algorithm discussed above requires the factorisation of the matrices V" which may be reasonably large if there are substantial numbers of missing values.

An alternative algorithm which does not require this is as follows: Write Ri Xi BTe and Ri= for u i WO 03/007177 PCT/AU02/00934 23 Then assuming normality, we can write the log likelihood of the data as: k L= logL= logf o; 0)+logg(o, o; 9) 38 i=1 where f is the conditional normally density function of u, given o i and g is the marginal density function of o i The vector of parameters 0 is B,A, and o 2 Now writing L=L(uu 2 an iterative algorithm can be specified for maximising equation 45 as follows: Specify initial values 0, For iteration n>0 maximise L as a function of u, ,Uk.

From the form of 45 we can do this independently for each u i and since log f(u, o,,0 is a (conditional) normal distribution the maximum occurs at :=Euijoj,,}. This of course is a calculation done in the E step of the original E-M algorithm.

With ui=u for maximise 45 as a function of 0 ignoring the dependence of u i on 0 (i.e treating the u i as now fixed) to produce Gc to 2 until some stopping criteria is satisfied.

The above algorithm preferably produces a sequence with the property that for n>0 ere 0, )9 where u- u n.

WO 03/007177 PCT/AU02/00934 24 Step of the algorithm corresponds to ignoring the V" terms in the calculation of E RR of the EM algorithm, and then doing the M step of the EM algorithm.

(Note that the estimation of B can be done independently of the other parameters in 0.) We can completely remove the need to calculate in step of the above algorithm by noting that we can use a cyclic ascent algorithm to maximise logf(u, oi,) as follows: Let the components of u be Maximising over ui, (say) with (ut, j l) fixed, corresponds to computing E ujU-!i,,oi} To see this write: iog f(u, o,0) =logf I log h where h is a conditional normal density. Now note that the first term in equation 15 has a maximum at E {,ujL, and this can be computed purely from the elements of V given earlier.

Iterating over i j will produce the (unique) maximum of logf(u, o,6) namely E{u, oi,O}.

This method requires only one matrix factorisation and therefore reduces storage requirements. In a preferred embodiment, the missing values are estimated at the same time that parameters for the model are estimated.

PCT/AU02/00934 Received 10 April 2003 25 The identification method of the present invention may be implemented by appropriate computing systems which may include computer software and hardware.

In accordance with a second aspect of the present invention, there is provided a computer program which includes instructions arranged to control a computing device to identify linear combinations of components from input data which correlate with a response pattern defined by a matrix of design factors specifying types of response patterns for a set of test conditions in a system.

The computer program may implement any of the preferred algorithms and method steps of the first aspect of the present invention which are discussed above.

In accordance with a third aspect of the present invention, there is provided a computer readable medium providing a computer program in accordance with the second aspect of the present invention.

In accordance with a fourth aspect of the present invention, there is provided a computer program, including instructions arranged to control a computing device, in a method of identifying components from a system which exhibit a pre-selected response pattern to test conditions applied to the system, and wherein a matrix of design factors specifying the response patterns for the test conditions is defined, to formulate a module for the residuals of a regression of the input array data on the design factors, to estimate parameters for the model and compute a linear combination of components using the model and the estimated parameters.

The computer program may be arranged to implement any of the preferred method and calculation steps discussed above in relation to the second aspect of the present invention.

AENDED SHEE7T

IFEAPU

WO 03/007177 PCT/AU02/00934 26 In accordance with a fifth aspect of the present invention, there is provided a computer readable medium providing a computer program in accordance with the fourth aspect of the present invention.

In accordance with a sixth aspect of the present invention there is provided an apparatus for identifying components from a system which exhibit a response pattern(s) associated with test conditions applied to the system, and wherein a matrix of design factors to specify the type of response patterns for the set of tests and conditions is defined, the apparatus including a calculation device for identifying linear combinations of components from the input data which correlate with the response pattern.

In accordance with an seventh aspect of the present invention, there is provided an apparatus for identifying components from a system which exhibit a preselected response pattern to a set of test conditions applied to the system, wherein a matrix of design factors to specify the response pattern(s) for the test conditions is defined, the apparatus including a means for formulating a model for the residuals of a regression of the input array data on the design factors, means for estimating parameters for the model and means for computing a linear combination of components using the model and the estimated parameters.

A computing system including means for identifying components including means for implementing any of the preferred algorithms and method steps of the first aspect of the present invention which are discussed above.

Where aspects of the present invention are implemented by way of a computing device, it will be appreciated that any WO 03/007177 PCT/AU02/00934 27 appropriate computer hardware e.g. a PC or a mainframe or a networked computing infrastructure, may be used.

BRIEF DESCRIPTION OF THE FIGURES Figure 1 shows a graphical plot of a matrix of design factors of a preferred method of the invention (top) and gene expression patterns of the genes of yeast from microarray data that correlate to the response pattern specified by those design factors (bottom). The x-axis is the time of growth of the yeast at which gene expression was measured. The y-axis is the value design factor given for each time (top) or the level of gene expression (bottom).

Figure 2 shows a graphical plot of a matrix of design factors of a preferred method of the invention (top) and gene expression patterns of the genes of yeast from microarray data that correlate to the response pattern specified by the design factors (bottom). The x-axis is the time of growth of the yeast at which gene expression was measured. The y-axis is the value design factor given for each time (top) or the level of gene expression (bottom).

Figure 3 shows a graphical plot of a matrix of design factors of a preferred method of the invention (top) and gene expression patterns of the genes of yeast from microarray data that correlate to the response pattern specified by the design factors (bottom). The x-axis is the time of growth of the yeast at which gene expression was measured. The y-axis is the value design factor given for each time (top) or the level of gene expression (bottom).

Figure 4 shows a graphical plot.of a matrix of design factors of a preferred method of the invention (top) and WO 03/007177 PCT/AU02/00934 28 gene expression patterns of the genes of GC B-like diffuse large B cell lymphoma and activated B-like diffuse large B cell lymphoma from microarray data that correlate to the response pattern specified by the design factors (bottom).

The x-axis is the class of lymphoma. The y-axis is the value design factor given for each class (top) or the level of gene expression (bottom).

Figure 5 shows a graphical plot of a matrix of design factors of a preferred method of the invention (top) and gene expression patterns of the genes of yeast from the microarray data listed in table 1 that correlate to the response pattern specified by those design factors (bottom). The x-axis is the time of growth of the yeast at which gene expression was measured. The y-axis is the value design factor given for each time (top) or the level of gene expression (bottom).

Figure 6 shows a graphical plot of a matrix of design factors of a preferred method of the invention (top) and gene expression patterns of the genes of yeast from the microarray data listed in table 1 that correlate to the response pattern specified by the design factors (bottom).

The x-axis is the time of growth of the yeast at which gene expression was measured. The y-axis is the value design factor given for each time (top) or the level of gene expression (bottom).

Figure 7 shows a graphical plot of a matrix of design factors of a preferred method of the invention (top) and gene expression patterns of the genes of yeast from the microarray data listed in table 1 that correlate to the response pattern specified by the design factors (bottom).

WO 03/007177 PCT/AU02/00934 29 Figure 8 shows a graphical plot of a matrix of design factors of a preferred method of the invention (top) and gene expression patterns of the genes of GC B-like diffuse large B cell lymphoma (GC) and activated B-like diffuse large B cell lymphoma (activate) from the microarray data listed in table 2 that correlate to the response pattern specified by the design factors (bottom). The x-axis is the class of lymphoma (GC or activated). The y-axis is the value design factor given for each class (top) or the level of gene expression (bottom).

EXAMPLES

EXAMPLE 1 The data set for this example is the results from a DNA microarray experiment and is reported in Spellman, P. and Sherlock, et al. (1998) Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by Microarray Hybridization. Mol. Biol. Cell 9(12):3273-3297.

The data set generated from the microarray experiments described in the above paper can be obtained from the following web site: http://genomewww4.stnford.edu/MicroArray/SMD/publications.html The array data consists of n=2467 genes and k=18 samples (times). The matrix of design facors T (design matrix)has r=6 columns defined by the terms cos sini(10) for 1=1. .3 and 0 (7mn)/119 This example illustrates how the method of the present invention can be used to discover sets of genes which exhibit periodic variation within the cell cycle. For this data set, the pattern of periodic variation is a by WO 03/007177 PCT/AU02/00934 30 product of the analysis given the choice of the matrix of design factors T. A search for a priori response pattern could also be specified by choosing r=l and placing the appropriate pattern in the single column of the design matrix. For this data set we have six canonical vectors a. Note that a=A-'XPu where u is the design factor and a denotes the scores. Two basis functions were used in the factor analysis model. Results for the first three canonicalvariates are given below. The design factor axis is time. Each component has a calculated p value which is highly significant. A list of genes forming a group with a similar pattern of variation over time is given below for the first three canonical vectors. The size of this group can be varied by choosing the significance level applied to the scores (the level here was set at 0.001). Group sizes will tend to be smaller for smaller significance levels.

The results for each canonical vector might be interpreted as implying a similar pattern of variation for each of the three groups but with a phase shift for each group.

The low to low cycle period is of the order of 70 minutes which agrees with the results in the paper.

The genes identified are shown below. Results of the gene expression from these genes is shown in Figures 1, 2 and 3.

1. Canonical Variatel (see Figure 1) d is:0.9932 p Value is:0 Spellman Cell Cylcle Data Gene Score P Value YCL040W: -0.6096 0 YPL092W: -0.4394 0 YEL060C: -0.434 0 YDR343C: -0.4239 0 WO 03/007177 WO 03/07177PCT/ATJO2/00934 31

YGROOBC:

Y0R347C: YLR178C: YCLO18W: YMRO008C: YKL14 SC:

SC:

YDR178W: YMR152W: YMR023C: YOL02 SC: YGI,244W: YIR023W: Y81015W: YOR33OC: YPL212C: YJLO7 SW: YCRO34W: YFRO2 8C: YPL128C: YHR17 OW: YBLO14C: YML123C: YGLO97W: Y0R34 OC: YMR274C: YFLO3 7W: YMLOS SW: YOL1O SW: YPR124W: YBR142W: YBL069SW: YPI,155C: Y2R243C:

SW:

YJROS2W:

SW:

YGL021W: YGR1O 8W: -0.4047 -0.3978 -0.3853 -0.332 -0 .3C11 -~0.299 -0.2745 -0.2454 1967 -0.1408 0.0956 0. 12 02 0.164S 0.1809 0.1937 0.2026 0.2201 0.2373 0.2393 0.2482 0.2513 0.2515 0 .2523 0.2531 0.2677 0.2683 0.2966 0 .3194 0.3451 0.3752 0.3777 0.4035 0.4282 0.4564 0-4'738 0.5137 0.5362 0.6822 0.7574 WO 03/007177 WO 03/07177PCT/ATJO2/00934 32 YMRO0lC: 0.7806 0 YBR038W: 0.8433 0 YPR119W: 1.1639 0 2. canonical variate2 (see Figure 2) d is:0.9874 p Value is: 0 Spellman Cell Cycle Data Gene YBRO 67 C YPLO92W

YELOGOC

YDR343C

YGROOBC

Y0R347C YLR178C YCLO18W YMR008C YKL14 BC YGR255C YDR17 OW 2W YBL07 9W YIR02 3W YKLO 15W YOR330C YJL07 6W YNL216OW YBR222C YFRO28C YPL128C YHR17 OW YEL014C YGL097W YMR274C YBLO 82 C Score -0.6096 -0.5403 -0.4394 -0.4340 -0.4239 -0.4047 -0.3978 -0.3853 -0.3320 -0.3011 -0.2990 -0 .2745 -0.2454 -0 .1967 0 .1295 0 .1645 0.1809 0 .1937 0.2201 0.2330 0.2357 0.2393 0.2482 0.2513 0.2525 0.2531 0.2663 0.2848 0.3054 p -Value 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 WO 03/007177 WO 03/07177PCT/ATJO2/00934 YMLOGSW 0.3194 0 YBR142W 0.3777 0 YPL155C 0.4282 0 YBR243C 0.4564 0 YLRO56W 0.4738 0 YJR092W 0.5137 0 YGRI08W 0.7574 0 YMROOlC 0.7806 0 YPRll9W 1.1639 0 33 3 canonical Variate, 3 (see Figure 3) d is:0.9773 p Value is:0.001 Spellman Cell Cylcie Data Gene Score p-value Gene Score value YKL127W -0.3295 0 YNL280C -0.3154 0 YLL34W -0.2972 0 YCR069W~ -0.2856 0 YOSLOV9C -0.2786 0 YOR07SW -0.2702 0 YOR.237W -0.2587 0 YLR299W -0.2569 0 YMR238W -0.2451 0 YOR2lS)C -0.2103 0 YDL207W -0.2078 0 YDL131W 0.2301 0 0.3180 0 YDL182W 0.3254 0 0.3736 0 YOL038C 0.3944 0 YER145C 0.4387 0 YPL256C 0.6011 0 YMR179W 0.6136 0 YPRO19W 0.6201 0 YIL009W 0.6512 0 YJL196C 0.6680 0 WO 03/007177 PCT/AU02/00934 34 YDL179W 0.7498 0 YLR079W 0.7639 0 YGR041W 0.9150 0 YJL159W 0.9385 0 YKL1185W 1.1207 0 YNL327W 2.0384 0 EXAMPLE 2 The data set for this example is the results from a DNA microarray experiment and is reported in Alizadeh, et al. (2000) Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403:503-511.

The data set generated from the microarray experiments described in the above paper can be obtained from the following web site: http://genomewww4.stnford.edu/MicroArray/SMD/publications.html There are n=4026 genes and n=36 samples. In the following DLBCL refers to "Diffuse large B cell Lymphoma". The samples have been classified into two disease types GC Blike DLBCL (21 samples) and Activated B-like DLBCL samples). The design matrix T has 1 column with values -1 if the sample is in group 2 and +1 if the sample is in group 1. This array data is used to illustrate the potential use of the method of the present invention in discovering genes which are diagnostic of different disease types.

The results of applying the above methodology are given below along with a (partial)- list of potentially diagnostic genes. Figure 4 shows factor loadings calculated for each array, with a Box plot showing the WO 03/007177 WO 03/07177PCT/ATJO2/00934 35 distribution of factor loadings from each disease type.

Note the distinct factor loadings for each grouping in the plot.

The genes identified are shown below. Results of the gene expression from these genes is shown in figure 4.

Canonical Variatel d 0. 923 p-value 128 Gene score p-Value GENE36O8X: 0.1363 0 GENE3326X: 0.1495 0 GENE3261X: 0.2013 0 GENE3327X: 0.2104 0 GEIZE3330X: 0.2109 0 GENE3259X: 0.2217 0 GENE3328X: 0.2361 0 GENE3329X: 0.2465 0 GENE3258X: 0.2534 0 C4ENE1719X: 0.3064 0 GENE1720X: 0.3197 0 GENE3332X: 0.4,509 0 EXAMPLE 3 The data set for this example is listed in Table 1 and is an extract of the data set described in Spelilman, P. and Sherlock, et al. (1998) Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by Microarray Hybridization. Mol. Bil. Cell 9(12) :3273-3297.

The array data consists of n=100 genes anid k=18 samples 2S (times). The matrix of design facors T (design matrix) has WO 03/007177 PCT/AU02/00934 36 r=6 columns defined by the terms cos(10), sin(10) for and 0 (7m7n)/119 This example illustrates how the method of the present invention can be used to discover sets of genes which exhibit periodic variation within the cell cycle. For this data set, the pattern of periodic variation is a by product of the analysis given the choice of the matrix of design factors T. A search for a priori response pattern could also be specified by choosing r=l and placing the appropriate pattern in the single column of the design matrix. For this data set we have six canonical vectors a. Note that a=l-

Y

XPu where u is the design factor and a denotes the scores. The Bayesian criterion was minimised with 1 basis functions in the factor analysis model.

Results for the first three of these are given below. The design factor axis is time. Each component has a calculated p value which is highly significant. A list of genes forming a group with a similar pattern of variation over time is given below for the first three canonical vectors. The size of this group can be varied by choosing the significance level applied to the scores (the level here was set at 0.001). Group sizes will tend to be smaller for higher significance levels.

The genes identified are shown below. Results of the gene expression from these genes is shown in Figures 5, 6 and 7.

1. Canonical Variatel (see Figure 1) WO 03/007177 WO 03/07177PCT/ATJO2/00934 37 d is:0.p value is:0 Spellman Cell Cyle Data Gene Score p-Value YPLO92W -1.0041 0.007 YER015W -0.2681 0.008 YGL237C 0.3235 0.009 0.5801 0.000 YNR023W 0.5849 0.001 YCRO34W 0.6459 0.000 YAL023C 0.8632 0.000 YBLO01C 0.8943 0.001 YPL127C 1.9008 0.000 YNL03lC 2.1047 0.000 2.6G58 0.000 YBRO09C 2.9482 0.000 YPR119W 0.17948 0 2. Canonical Variate2 (see Figure 2) d is:0.98320 p Value is:0 Spellman Cell Cycle Data G e-net Score p-Value Ger.e Score p-Value YOR074C -1.8064 0.000 YIL066C -1.7692 0.000 YCL040W -1 .6460 0.000 YJL073W -1.0510 0.000 YOR321W -0.9528- 0.000 YKL148C -0.7819 0.000 YDL093W -0.6411 0.007 YJL201W -0.5744 0.009 YOR132W -0.4864 0.009 YKRO1OC -0.3184 0.009 YFR028C 0.5224 0.006 Y-RO54C 0.5821 0.007 YNLO62C 0.5910 0.005 YHR17OW 0.6916 0.000 YNL061W 0.8039 0.001 YLRO98C 1.0517 0.001 WO 03/007177 PCT/AU02/00934 YOR153W YOL109W YAL040C YGLO08C YMR058W YMR001C 1.0690 1.0760 1.1198 1.1682 1.6489 2.1982 38 0.001 0.000 0.000 0.002 0.000 0.000 3. Canonical Variate 3 (see Figure 3) d is:0.8870 p Value is:0.01 Spellman Cell Cylcle Data Gene YMR065W YJL099W YJL044C YDR292C YIL066C YGL038 C YLRO79W YKL 185W Table I Score p-Value -1.57783303 0.000 -0.72894484 0.000 0.515497036 0.010 0.654473229 0.010 1.383495184 0.005 1.617149735 0.000 2.689484257 0.000 3.434889201 0.000 Gene YAL001 C YAL002W YAL023C YAL040C YBLOO1 C YBLO16W YBRO09C YBR169C YCL040W YCR034W YCR088W YDLO87C YDLO93W YDL205C YDR039C YDR041W YDRU92W YDR188W YDR292C A2 A3 A4 A5 A6 A7 A8 068 0.65 0.94 0.53 0.51 0.68 1.13 091 0.84 0.87 0.86 0.64 0.86 1.84 03 0.74 1 1.72 1.36 1.28 0.67 1,57 2.1 0.47 0.7 0.66 1.45 1.11 0.86 0.22 0.94 1.03 1.04 1.17 1.68 1.26 0.37 0.34 0.49 0.71 0.5 2.46 0.04 0.14 0.53 2.83 3.22 1.22 1.62 1.32 1.55 0.96 0.8 0.8 1.12 1.7 3,78 5.31 2.89 1.57 0.7 0.67 0.38 0.53 0.57 0.84 1.11 1.4 1.12 1.06 1.12 1.34 1.38 1.15 1.48 0.96 1.45 0.53 0.82 1.38 0.79 0.67 0.94 0.89 0.57 0.8 1.08 1.58 1.04 1.2 0.66 0.42 0.82 0.39 0.9 0.45 0.53 0.4 1.45 1.99 1.2 2.12 1.52 2.08 1.38 0.96 1.22 0.99 1.08 0.84 1.17 1 0.61 1.01 0.65 1.13 1.08 1.2 1.27 0.54 0.55 0.65 0.68 0.76 0.64 0.73 0.73 0.65 0.96 0.67 0.97 0.65 0.91 A9 AIO 0.73 0.86 0.66 0.67 0.74 0.67 2.23 2.59 0.76 0.96 0.41 0.51 0.45 0.44 0.91 1.57 0.5 0.75 1.13 1.11 1.32 0.84 0.91 1 0.63 0.74 0.82 0.42 1.63 1.23 1.07 0.94 1.22 0.82 1.32 1.12 1.12 1.13 WO 03/007177 PCT/AU02/00934 39 YDR345C 1.48 1.27 1.26 0.79 1 0.63 1.23 0.73 0.97 1.06 1.39 1.17 YDR457W 1.01 0.5 0.91 0.91 1.28 1.23 0.84 0.67 0.93 0.91 1.68 1.07 YER008C 0.57 0.75 0.86 0.7 0.93 0.79 0.97 0.89 0.99 0.78 0.78 1.2 YER015W 1.23 1.28 0.91 0.79 1.08 0.71 1.01 0.82 1 0.84 0.91 0.99 YER091C 0.73 2,08 1.3 0.6 0.38 1.86 2.01 2.18 1.36 0.84 0.96 0.84 YER178W 1.34 0.86 1.2 0.96 1.11 0.84 1.35 1.08 1.22 0.89 1.28 1.04 YFL029C 0.86 0,74 1.34 0.71 0.86 0.73 0.87 1.07 1.11 0.79 0.84 0.71 YFR028C 0.53 0.47 0.4 0.55 0.5 1.04 0.79 0.76 0.97 1.07 0.73 0.7 YGLO08C 0.51 0.51 0.5 0.53 0.51 0.96 0.94 1.39 1.8 2.18 1.65 1.06 YGL027C 0.94 0.67 1.34 1.27 2.25 1.51 1.93 1.03 1 0.87 1.28 1.3 YGLO38C 0.42 0.8 1.65 1.77 0.7 1.06 0.5 0.65 0.66 1.22 1,38 1.88 YGL237C 1.13 0.63 0.74 0.84 1.23 1.34 1.01 1.03 0.84 0.84 0.97 0.89 1.11 1.03 1.17 0.76 0.71 0.67 1.15 0.91 1 0.79 0.91 0.9 1.16 0.74 0.87 0.73 1.15 0.82 1.2 0.93 0.96 1.11 0.82 0.94 YGR274C 1.06 1 1.3 1.11 1.13 1.06 0.97 1.21 1.26 0.97 1.8 1.12 YHL038C 0.93 0.67 1.12 0.74 1.16 1.12 1.22 0.67 1.23 0.97 1.16 0.87 YHR026W 0.93 0.71 0.84 0.97 0.9 1.08 1 1.01 1.08 0.74 1.03 0.79 YHR170W 0.84 0,64 0.36 0.64 0.78 1.16 0.84 1.06 1.21 1.35 0.99 1 YIL066C 0.36 0.74 2.41 3 2.61 1 0.86 0.61 0.54 0.45 1.57 2.61 YIL101C 0.89 1.38 1.36 0.9 1.03 0.94 0.73 0.99 1.13 0.66 2.66 0.8 YIR018W 0.82 2.77 0.8 0.8 0.84 0.94 1.03 1.06 1.22 0.86 0,9 0.71 YIR022W 0.93 0.84 1 1.03 1.07 0.99 1.4 1.08 0.94 0.05 0.84 0.76 YJL008C 1.11 0.63 0.86 0.79 1.16 0.8 1.34 0.97 1.11 0.63 1.04 1 YJL044C 0.84 0.75 0.54 0.51 0.35 0.38 0.41 0.51 0.82 0.87 0.74 0.6 YJL073W 0.97 0.82 2.16 2.61 1.28 1 0.84 0.66 0.63 0.79 0.84 1.27 YJL099W 1.01 1.11 0.84 0.86 1.06 1.23 1.3 1.4 1.03 0.94 0.64 0.78 YJL1IOC 0.53 0.51 0.44 0.58 0.53 0.74 0.56 0.71 0.74 0.89 0.6 0.8 YJL173C 0.5 0.5 0.64 1.23 1.57 1.21 1.48 1.01 0.7 0.55 0.79 0.78 YJL201W 0.41 0.44 1.11 1.08 1.06 0.91 1.07 0.68 0.61 0.56 0.66 0.76 YJR106W 0.7 084 0.8 0.71 0.7 1.03 0.82 0.68 0.86 1.06 0.82 0.9 YJR131W 0.89 0.7 1 1 1.01 1.12 0.89 0.99 1.01 1 0.99 1 YKL117W 1.22 14 1.21 1.75 1.17 1.7 1.16 1.62 1.51 1.12 1.46 1.21 YKL148C 0.76 1.26 1.88 1 0.87 0.66 0.73 0.53 0.54 0.67 0.7 0.7 YKL182W 1.03 0.51 0.6 0.39 0.39 0.31 0.35 0.26 0.33 0.37 0.57 0.89 YKL185W 0.57 0.26 0.54 0.2 0.18 0.15 0.11 0.15 0.53 3.78 4.18 1.57 YKROIOC 0.45 0.47 0.64 0.87 1.03 1.03 0.91 0.66 0.74 0.53 0.55 0.73 YKR054C 0.57 0.39 0.54 0.5 0.63 0.47 0.68 0.67 1.01 0.86 0.9 0.63 YLR079W 0.3 0.64 0.33 0.47 0.37 0.38 0.27 0.34 0.36 1.28 2.36 1.57 YLR098C 0.51 0.54 0.42 0.47 0.43 0.82 1 1.2 1.48 1.68 0,86 0.87 YLR155C 1.11 1.08 1.65 1.11 1.52 0.79 1.54 1.18 1.06 1.39 1.08 0.73 YML035C 0.96 0.66 1.36 1.12 1.35 0.94 1.32 0.93 1.32 1.15 1.23 0.91 YML104C 0.87 0,94 0.93 1.15 1.08 1.34 1.2 1 1.23 1.7 1.01 1.15 YMROOIC 0.25 0.2 0.18 0.14 0.32 0.7 1.82 1.52 2.25 1.34 0.78 0.54 YMR015C 1.04 0,5 0.42 0.6 0.73 0.93 1.23 0.93 1.01 0.86 1.04 0.71 YMR023C 1.11 1.63 1.17 1.13 1.01 1.07 0.97 0.91 0.97 0.84 0.97 0.94 YMR058W 2.27 0.86 1.04 1.17 2.1 2.27 4.26 3.22 5.42 5.21 7.1 5.47 YMR065W 6.42 1.46 0.65 0.51 0.7 0.4 0.89 0.97 0.89 0.89 0.65 0.61 YMR070W 0.75 0.8 0.9 0.93 1 0.76 1.16 1.03 1 0.87 1.27 0.91 YMR129W 0.68 0.41 0.49 0.53 0.73 0.73 0.87 0.75 0.96 0.84 0.94 0.76 WO 03/007177 PCT/AU02/00934 YMR231W YNLO12W YNL031C YNLO59C YNL06 W YNL062C YNL073W YNL188W YNL272C YNRO23W YOL028C YOL067C YOL109W YOR037W YORO74C YOR132W YOR153W YOR167C YOR259C YOR261 C YOR321W YPL040C YPL050C YPL061W YPL072W YPLO86C YPLO02W YPL127C YPL234C YPRO56W YPR1 02C Gene YALO01 C YAL002W YALO23C YAL040C YBLO01C YBLO16W YBRO09C YBR169C YCL040W YCRO34W YCRO88W YDLO87C YDLO93W YDL205C YDR039C 0.68 0.9 0.71 0.78 1.15 0.94 0.06 0.08 0.1 0.11 0.15 0.14 0.79 0.65 0.61 0.89 0.44 0.27 0.96 0.61 0.37 0.79 0.76 0.96 0.31 0.47 0.84 1.36 1.13 1.4 0.56 0.5 0.49 0.82 0.75 0.76 1.07 0.67 1.28 0.84 0.44 0.41 0.96 0.84 1.17 0.24 0.55 1.32 0.94 1.26 1.65 0.61 0.42 0.35 1.34 0.86 0.87 0.86 0.61 1.13 0.9 0.57 0.9 0.61 0.66 1.06 0.68 0.75 0.79 0.86 0.64 1,16 1 2,66 5.42 0.93 0.99 1.06 0.91 0.48 0.37 1.35 4.39 2.18 0.12 0.14 0.64 0.78 0.58 0.44 0.6 051 0.68 1.15 0.84 1.03 A13 A14 A15 0.63 0.97 0.7 0.64 061 1.03 1.01 1.17 1.35 0.93 0.73 0.96 1 1.06 1.08 0.84 0.96 .0.8 1.65 1.7 2.41 0.94 0.86 1.08 1.16 0.48 0.78 1.22 1.08 1.21 1.03 1.01 1.07 1 0.84 0.82 1.32 0.97 0.89 0.75 0.57 0.49 1.3 1.43 1.32 0.87 0.8 1.08 0.76 0.73 1.97 0.65 1.49 0.54 0.61 0.49 0.68 0.57 0.91 0.7 0.96 0.71 0.45 1.84 1.2 0.87 1.06 0.86 0.78 0.84 0.8 0.4 0.67 0.89 1.39 2.2 2.41 1.52 1.26 0.34 0.49 1.13 1.04 0.97 1.07 1 0.96 2.1 1.57 1.12 0.94 1.11 1.34 2.89 1.46 1.17 1.04 0.64 0.76 1.28 1 1.54 2.18 0.7 0.7 0.54 0.86 1.08 1.06 A16 A17 1.45 0.65 1.48 0.57 1.08 1.04 1.01 1.46 1.11 0.82 1.15 0.58 1.21 0.67 1.79 0.75 0.73 0.84 1.22 1.12 1.79 0.97 0.78 0.79 0.68 0.53 1.58 0.34 1.22 0.74 40 0.87 0.79 0.86 0.87 0.65 0.97 0.91 0.86 2.27 1.45 0.7 0.48 2.27 1.21 0.55 0.45 0.87 0.9 0.73 0.84 0.82 0.99 0.96 1.03 0.76 1.21 0.96 1.22 0.65 1.01 0.64 0.84 0.55 0.76 0.54 0.57 1.32 1.15 1 0.93 1.17 1.45 1 0.74 0.97 1.08 0.99 1 1.06 1.23 1.07 1.07 0.68 1.16 1.36 1.27 1.15 1.07 0.68 0.73 1.32 1.01 0.36 0.38 0.91 0.96 0.71 0.78 0.78 1.11 1.01 1.04 1.08 1.16 0.94 1.15 1.23 1.07 0.96 1.08 1.23 0.87 0.78 1.03 1.34 1.32 0.76 0.66 0.75 0.9 0.71 0.9 1.07 1.36 1.07 1 0.91 0.87 1.04 1.23 1.68 1.52 1.48 1.01 1.04 1.22 1.17 1.13 0.01 0.60 0.00 0.79 2.36 2.05 1.21 0.74 0.57 0.94 0.64 0.76 0.84 0.89 0.68 0.73 1.16 1.13 1.23 1.51 0.94 0.7 1.04 0.79 0.64 0.73 0.21 0.27 0.51 0.29 0.23 0.58 0.89 0.73 0.79 1.07 0.8 0.94 0.76 0.87 0.87 0.79 0.76 0.84 1.13 1.12 0.73 0.99 1.12 1.62 0.89 0.74 0.71 0.87 1.01 0.94 1 1.11 0.78 0.96 1,38 1.07 1.03 0.87 0.8 0.67 0.51 1.57 0.93 1 1.13 0.66 0.61 0.53 0.8 1.2 0.71 0.93 1.22 0.99 0.86 1.21 0.76 0.54 0.8 1.17 0.99 0.9 0.99 0.66 0.86 0.84 1.4 1.97 1.11 0.86 0.66 0.57 0.9 0.66 0.82 0.75 0.7 0.54 0.47 0.41 0.91 0.41 0.6 0.45 0.78 0.86 0.67 0.99 1.51 0.89 WO 03/007177 PCT/AU02/00934 41 YDRO41W 0.87 0.78 0.89 0.78 0.79 0.67 YDR092W 0.93 1.21 0.96 1.03 1.11 1.13 YDR188W 0.78 0.65 0.79 1.07 0.74 0.8 YDR292C 0.84 0.84 0.71 1.06 0.79 1.17 YDR345C 1.68 1 1.15 0.71 1.06 0.82 YDR457W 0.78 0.74 1.28 1.15 1.15 1.34 YERO08C 0.87 0.86 1.07 0.99 0.91 0.89 YER015W 0.97 0.67 0.84 0.71 0.94 0.8 YER091C 0.64 0.61 0.94 1.77 0.89 1.04 YER178W 1.06 1.03 1.39 1.01 1.36 0.76 YFL029C 0.75 0.82 0.94 0.73 1.13 1.13 YFR028C 0.84 0.76 0.86 0.96 0.68 0.9 YGLO08C 0.73 0.84 0.87 1.79 0.97 1.65 YGL027C 1.4 1.13 1.65 1.23 1.23 0.68 YGL038C 1.36 1.15 0.9 0.89 0.64 0.73 YGL237C 0.89 1.21 1.2 '1.07 1.28 1.12 0.9 0.66 0.9 0.78 0.22 0.75 0.89 0.79 0.84 0.79 1.01 0,87 YGR274C 1.13 1.01 1.26 1.54 0.78 0.94 YHLO38C 1.01 0.86 0.86 0.73 1.12 0.99 YHR026W 1.06 0.79 0.96 0.84 0.8 0.79 YHR17OW 0.93 0.96 0.99 1.16 1.03 1.12 YIL066C 2.25 1.27 1.34 0.99 0.35 0.55 YIL1I01C 0.75 0.55 1.08 1.21 0.65 1 Y[R018W 0.93 0.84 0.87 1.15 0.76 1 YIRD22W 1.07 0.71 1.08 0.7 1.4 0.79 YJL008C 0.99 0.74 1.21 0.84 1.04 0.78 YJL044C 0.73 0.48 0.53 0.5C 0.5 0.7 YJL073W 1.03 0.82 0.74 0.68 0.57 0.74 YJL099W 0.86 0.8 0.97 0.99 1.57 1 YJL110C 0.73 0.57 0.61 0.8 0.71 0.82 YJL173C 1.32 0.76 1.35 0.71 1.23 0.49 YJL201W 0.97 0,68 0.99 0.76 0.86 0.51 YJR106W 0.85 0.67 0.74 0.87 0.53 0.86 YJR131W 0.9 0.84 0.97 1.04 0.75 0.78 YKL117W 1.22 0.93 1.21 1.22 1.16 1.01 YKL148C 0.74 0.49 0.67 0.58 0.43 0.56 YKL182W 0.84 0.79 0.87 0.87 0.43 0.48 0.75 0.51 0.33 0.36 0.29 1.16 1.04 0.89 1 1.03 0.66 0.73 YKR054C 0.64 0.58 0.93 0.84 0.82 0.79 YLR079W 1.13 0.71 0.55 0.53 0.43 0.75 YLRO98C 0.65 0.49 0.63 0.89 1 1.16 YLR155C 1.2 1.01 1.23 1.2 1.67 0.73 YML035C 0.96 0.67 1 0.82 1.13 0.82 YML104C 1.12 1.11 1.2 1.62 1.23 1.12 YMROO1C 0.39 0.54 0.91 1.34 2.01 1.34 YMR015C 0.9 0.63 1.06 0.87 0.76 0.82 YMR023C 0.94 0.7 0.8 0.9 0.75 0.8 WO 03/007177 PCT/AU02/00934 42 YMR058W 4.76 3.35 6.82 5.Y 8.25 5.21 YMR065W 0.54 0.39 0.57 0.7 1 0.84 YMR070W 1 0.96 1.36 1.26 0.71 1.07 YMR129W 0.54 0.84 0.97 1.11 0.7 0.68 YMR231W 0.8 0.58 0.63 0.82 0.86 0.99 YNLO12W 1.12 0.97 0.79 0.74 0.68 0.8 YNL030W 1.75 1.46 2.27 0.97 0.63 0.4 YNL031C 1.43 1.79 1.7 0.78 0.74 0.44 YNL059C 0.84 0.63 0.73 0.66 0.68 0.84 YNLO61W 1 0.79 0.7 0.79 0.73 1.04 YNLO62C 1.06 0.96 0.87 1.08 0.91 0.99 YNL073W 0.8 0.55 0.67 0.71 0.74 0.66 YNL188W 0.73 0.49 0.56 0.4 0.7 0.74 YNL272C 1.21 0.99 0.87 0.84 1.15 1.03 YNR023W 0.8 0.63 1.04 1.01 1.51 1.22 YOL028C 0.87 0.84 0.96 0.99 1.26 0.97 YOL067C 0.73 0.65 0.94 0.96 1.15 1.16 YOL109W 1.07 0.91 1.93 1.26 1.38 0.93 YORO37W 0.89 0.68 0.75 0.75 1.06 1.38 YOR074C 1.55 0.82 0.57 0.6 0.4 0.34 YORI32W 1.16 0.65 0.96 0.8 1.06 1.04 YOR153W 0.47 0.57 1.06 1.7 1.11 1.26 YOR167C 1.3 0.7 1.48 0.84 1.46 0.8 YOR259C 0.82 0.55 0.8 0.74 0.82 0.8 YOR261C 0.76 0.49 0.76 0.6 0.9 0.65 YOR321W 1.4 0.96 1.04 0.67 0.79 0.54 YPL040C 1.01 0.64 0.61 0.84 0.61 0.79 1.07 0.87 1.01 0.75 0.94 1.04 YPLO61W 0.63 0.34 0.35 0.43 0.64 0.71 YPLO72W 1.01 0.78 1.11 0.96 1.43 1.48 YPL086C 0.8 0.82 0.64 0.68 0.84 0.86 YPLO92W 0.6 0.54 1 0.68 0.51 0.67 YPL127C 1.38 1.57 1.34 1.38 1.17 0.73 YPL234C 0.71 0.45 0.84 0.41 0.53 0.44 YPRO56W 0.79 0.65 0.76 0.76 0.99 0.9 YPRI02C 1.12 0.76 1.7 1.13 1.9 1.08 EXAMPLE 4 The data set for this example is listed in Table 2 and is an extract of the data set described in Alizadeh, et al. (2000) Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403:503- 511.

WO 03/007177 PCT/AU02/00934 43 The data set generated from the microarray experiments described in the above paper can be obtained from the following web site: http://genomewww4.stnford.edu/MicroArray/SMD/publications.html There are n=100 genes and n=42 samples. In the following DLBCL refers to "Diffuse large B cell Lymphoma". The samples have been classified into two disease types GC Blike DLBCL (21 samples) and Activated B-like DLBCL (21 samples). The design matrix T has 1 column with values -1 if the sample is in group 2 and +1 if the sample is in group 1. This array data is used to illustrate the potential use of the method of the present invention in discovering genes which are diagnostic of different disease types.

The results of applying the above methodology are given below along with a (partial) list of potentially diagnostic genes. The plot shows factor loadings calculated for each array, with a Box plot showing the distribution of factor loadings from each disease type.

Note the distinct factor loadings for each grouping in the plot.

The genes identified are shown below. Results of the gene expression from these genes is shown in figure 8.

Canonical Variatel d 0.912 p-value 0.000 Gene Score p-Value GENE2238X 0.4491 0.027 GENE2943X 0.4102 0.045 GENE2977X 0.3827 0.024 GENE1246X 0.4157 0.030 GENE124X 0.4213 0.012 WO 03/007177 WO 03/07177PCT/ATJO2/00934 44 GENE122X 0.3318 0.038 GENE1614X -0.4406 0.038 Table 2 RowNames DLCLOOO1 DLCLO02 DLCLOO03 DLCL0004 DLCLOO05 DLCLOO06 DLCLOO07 DLCLOOO8 GENE3950X -0.2049 0.6574 -0.3501 1.1837 0.3306 0.1310 1.5559 -0.4136 GENE2531X -0.2118 1.0063 -0.4699 1.1355 0.5358 0.0929 1.2739 -0.5714 GENE915X -0.1815 0.9708 -0.3538 1.1432 0.3901 D.4990 1.2520 -0.6532 GENE3511X -1.2609 -0.3673 0.2774 0.6506 0.2095 -0.6501 -0.0393 -1.9622 GENE3496X -1.5438 0.2235 0.3742 0.61 52 0.0026 0.4043 0.7658 -2.1362 GENE3484X -1.5441 0.2644 0.3324 0.5755 0.3227 0.3810 0.6922 -2.0400 GENE3789X -0.8190 0.8721 -0.4551 -0.3695 0.5510 0.8935 -0.5408 -1.8465 GENE3692X 1.5834 -1.3890 0.2694 0.3204 -0.9297 -0.8659 -0.0240 1.2389 GENE3752X -0.5429 0.0079 1.0622 1.0307 0.4799 0.3226 -0.0706 -1.5657 GENE374OX -0.1202 0.3514 -0.2352 0.5584 -0.7183 1.7546 1.1220 -2.1561 GENE3736X -1.0454 0.1940 0.1413 1.0247 0.4182 1.0642 0.0622 -2.0475 GENE3682X 0.0352 -0.5229 -1.0198 -1.0882 -0.7605 1.2054 0.8310 -1.0305 GENE3674X 0.0919 -0.3555 -1.1076 -0.8632 -1.0361 0.9907 1.1110 -0.8782 GENE3673X 0.4663 -0.7188 -1.0865 -1.3763 -0.7102 0.9291 9.81S7 -1.3677 GENE3644X 1.2679 1.0367 -0.2156 0.4202 0.5551 -0.1771 0.5743 -1.2367 GENE3472X -0.5140 0.4945 0.5546 0.2904 -0.0097 1.2149 1.1549 -2.0388 GENE2530X -0.3729 -0.7347 -0.5178 -0.0474 0.2601 0.0612 -0.2102 -1.2411 GENE2287X -0.7046 -0.7689 -0.4475 0.4799 -0.3006 0.6084 0.8196 -1-2739 GENE2328X -0.4273 0.4495 -1.8079 -1.0243 0.4682 0.7853 -2.0504 -0.9683 GE-NE2417X -1.1810 1.0531 0.1474 0.1021 0.4644 2.0191 0.7210 -1.1055 GENE2238X 0.6934 -0.2178 0.8979 0.6190 -0.3294 0.2843 -0.3294 -0.0319 GEN'E1971X -0.1957 1.3122 -0.3276 -0.2145 1.4441 0.3132 0.8221 -0.9873 GENE3086X 0.0236 -1.4920 -0.3702 0.2026 -0.0600 -0.7521 -0.6089 -0.1674 GENE1009X 1.4548 -0.6280 0.7398 0.2580 0.1025 -0.3483 -0.5970 -0.3793 GENE1947X 0.4856 -0.5274 0.1845 0.1023 -0.5000 -0.1441 1.4713 0.9237 GENE319OX 2.0024 -0.8814 0.8489 -0.6571 -0.3047 -0.2299 -1.0417 1.4577 GENE3379X 0.7059 -0.4788 1.6020 0.0224 -0.3117 0.2351 -0.6762 1.2223 GENE3184X 1.3782 -0.6784 0.9336 0.8335 -0.5783 -0.7117 -0.1337 0.7334 GENE3122X 1.1454 -0.5556 -0.3894 1.2236 -0.4089 -0.4676 0.9890 0.6175 GENEI099X 0.5601 -0.8521 -0.7039 0.5133 -0.5634 -1.0082 -0.8521 1.3871 GENE3032X 0.5833 -1.4015 -0.4615 0.6600 -0.4134 -0.9415 -0.9245 1.4352 GENE2675X 0.3661 -1.0045 0.6252 1.8668 -0.7244 -1.1245 -0.3B42 2.1269 GENE2481X 0.4123 -0.8389 0.7840 1.8267 -0.5487 -1.0111 -0.3130 2.0443 GENE2878X 1.0922 -0.8274 0.2785 0.9566 0.3202 -0.5875 -1.2238 1.3530 GENE2943X 1.5951 -0.6212 0.3013 1.0551 0.7063 -0.5649 -1.1162 1.6288 GENE2977X 1.2805 -1.2491 1.1314 1.1262 -0.6527 -1.1000 -0.8275 0.9463 GENE3014X 1.9501 -1.2171 0.4584 0.7935- -0.2875 0.0476 -1.2603 2.0582 GENE200OX 0.3456 -1.0625 0.2272 1.4378 -0.1939 -0.6677 -0.6414 -0.05545 GENE1368X 0.5254 -0.4359 1.7741 1.1000 -0.2591 -1.3642 0.3928 0.7243 GENE1I184X 0.5950 -0.5359 1.7039 0-8914 -0.0308 -1.3154 0.4962 0.7487 WO 03/007177 WO 03/07177PCT/ATJO2/00934 45 GENE1226X GENE1228X GENE1231X GENE1 246X GENE1172X GENEI 164X GENE3029X GENE1 027X GENE1354X GENE62X GENE932X GENE3611IX GENE3631X GENE330X GENE331X GENE808X GENE487X GENE621X GENE622X GENE634X GENE659X GENE669X GENE674X GENE675X GENE676X GENE704X GENE734X GENE738X GENE456X GENE744X GENE179X GENE1 24X GENE1 22X

GENEIIIX

GENE97X GENE2645X GENE3408X GENE3854X GENE1 406X GENEI 401X GENE3462X GENE3173X GENE3971X GENEI756X GENE1 533X GENE1757X GENE3572X GENE3571X GENE385X 1.1537 1.1347 0.2407 0.3136 0.0021 -0.3385 0.9558 0.3195 1.0921 -1.7087 -1.6635 -1 .3618 -0.5379 0.8497 -0.8855 1t5424 1.1631 0.8961 1.2278 -1 .61 02 -1.0282 -0.7541 -0.7844 -1.8669 0.1521 -0.2724 -0.1106 -0.3670 0.2548 -0.1761 -1.5071 -1.3867 -1.2443 -0.7042 -0.1985 -1.0298 0.6893 0.6938 0.0021 1.7535 -0.3011 -0.5215 1.5198 1.0949 1.5099 0.6631 0.5991 -0.5755 -1 .2426 -1.1220 -0.3684 -1 .2858 -1.0667 -0.6792 -0.6039 -1.8240 -0.8192 0.3966 -0.33 0.1194 0.5350 0.4721 0.6081 0.8435 -0.0178 -0.5281 -0.7734 -0.3796 0.9498 2.0564 1.9543 2.0333 -0.3961 2.9355 0.8058 0.8918 1.1934 1.4336 1.0752 -0.21 86 1.3179 1.21 53 0.8689 1.1612 1.1902 -0.4665 -0.9260 -0.91 05 -0.9049 0.2070 -0.2846 -0.5224 -1 .9916 -1 .6932 -0.7090 -0.5067 -0.4997 0.7899 -0.3129 1.9013 0.0103 0.3136 0.5580 -0.3053 -0.4890 -0.0407 0.5090 -0.2409 -0.3264 -0.5350 -0.9278 -1.5880 -0.4014 -0.2335 0.2915 0.2879 0.3532 -0.4669 -0.1360 -0.0171 0.2374 0.5014 -0.8281 -0.6828 -0.7138 -0.4616 0.2701 0.2892 7390 -0.7428 -0.7888 -1 .0433 0.2602 0.0604 0.5792 0.4181 0.4473 0.7783 0.1129 0.3418 -0.2014 1.4067 1.1189 0.0789 1.0958 0.6209 -0.2381 -0.0769 -0.9074 1.6088 1.61 82 1.1317 1.0383 -0.0318 1.1 551 0.4192 0.6397 -1 .7472 0.31 61 0.0823 -0.7095 -0.4878 0.7125 0.0053 -0.0341 0.2113 0.6888 0.7435 0.8396 0.7844 0.2751 -0.0536 -0.4656 -0.3740 -0.9817 -0.8322 -1 .2991 -0 .3566 -0 .7714 -0.4396 3245 -0.4770 -0.3955 -0.5766 -0.2884 -0.3W4 1.4704 -0.3952 -0.2168 0 .6154 -0.1054 0.3219 0.0382 0.6151 -0.8935 -0.2614 -0.5994 0.7934 -0.8538 -0.6627 0.0918 0.6568 -0.2512 -0.7030 -0.3883 0.5470 -0.6050 -0.1702 0.0291 -0.9511 -0.0037 0.4137 1.2932 1.1465 0.61 32 0.7261 0.1317 0.2500 0.6606 -0.2528 0.0553 0.0977 -0.0512 2.0445 0.1017 0.9309 -0.8398 -0.5997 -0.7736 -1.0840 -0.5589 0.6749 -0 .3748 -0.2884 -0.1314 -0.8419 -0.6774 -0 .0476 -1.5434 -1.3369 -1.7534 -0.6275 0.3106 0.7269 -0.7287 -0.2454 -0.1948 0.2551 0.4567 0.4862 0.1923 0.4803 1.1329 -0.0967 -0.1173 -0.4935 -0.7052 1.3404 1.1132 1.0510 0.4469 -0.5802 -0.1772 -0.4269 0.1296 0.1062 0.1487 0.1858 0.2676 3. 1896 0.0253 0.0593 1.2643 0.1936 -0.1440 0.70 18 0.5595 0 .4410 0.6790 0.0472 -0.0585 0.2306 0.3492 0.6254 -0.1655 -1.0914 -0,4369 0.1486 -0.7134 -0.460 1 -0.2607 -1.5484 -0.0303 0.9300 -0.8944 0.1286 -0.3785 -0.7553 -1 .3336 -2.0636 -0.1415 -0.1220 -0.7247 0.0063 -0.1592 1.4590 -0.0418 0.5422 0.1519 -0.1672 -0.3330 -0.6422 0.2350 0.8877 1.2916 1.4108 0.8567 1.0520 -0.4045 -1.2139 -1.0536 -0.2488 -1.5211 -1.1066 0.2416 1704 -0.18 15 0.7469 0 .5223 -0.7324 -1.0719 -0.8399 -1.7563 0.2749 1.223 1 0.9681 -0.4640 1.0326 0.6527 0.0518 -0.6509 -0.4392 0.3693 1.6342 -0.6140 0.5575 0.9449 0.5938 0.3914 0.6997 1.5395 0.4641 2.1229 -1.4407 -1 .31 31 -1.7783 -0.9731 -1.3870 -0.5157 0.3565 0.31 17 -0.6751 -2.0328 -1.7165 -1.9056 -1.9084 -2.2591 -0.6466 -1.2219 -1.4104 -2.2347 -1.4752 -1 .5237 -0.7248 -2.4027 -2.6 1C7 1418 -1.8522 -1.5055 -0.7600 -0.6331 -0.0647 2.08Z9 -0.0376 -1.3849 -0.2306 0.5161 0.7430 1.4647 0.6952 -1.4841 -2.0603 WO 03/007177 WO 03/07177PCT/ATJO2/00934 46 GENE1614X -1.7405 1.2328 0.2134 -0.9335 -0.0627 1.0204 -0.2114 -1.6131 GENE1623X -0.9216 0.5149 0.6527 -1.4136 1.2233 0.0623 0.2197 -0.1935 GENE1646X -1.0213 0.3776 -0.5812 -0.7383 -0.0939 0.6291 -0.8641 -1.1941 GENE1660X 0.9611 -0.4493 -0.6750 0.3687 -0.9711 -0.6891 -0.1672 0.8200 GENE1721X 0.9852 -0.1574 -0.3398 0.4503 -1 .3366 -0.2668 -0.2547 0.1586 GENE1573X -0.0220 0.9123 -0.0901 -0.1485 0.1434 0.7079 0.4646 -1.4721 GENE1553X -0.7350 2.0362 0.5313 -0.4230 -0.2211 0.9167 -0.3863 -1.1938 GENE1773X -1.1428 2.1206 0.1644 -0.7780 -0.3726 0.7625 -0.7982 -1.6698 GENE913X 1.0593 1.2244 1.0593 0.4492 0.2195 -1.2880 -0.7568 -0.4768 GENE398OX 0.9547 1.3890 1.1508 0.3464 0.2613 -1.1745 -0.9644 -0-3480 GENE3X -0.0042 2.4527 -0.8465 0.0485 0.6276 0.9786 -0.0744 -2.2329 RowNames DLCLOO09 DLCLOO10 DLCLOO1I DLCLOO12 DLCLOO13 DLCLOO14 DLCLOO15 DLCLOO16 GENE395OX GENE253IX GENE918X GENE3511IX GENE3496X GENE3484X GENE3789X GENE3692X GENE3752X GENE374OX GENE3736X GENE3682X GENE3674X GENE3673X GENE3644X GENE3472X GENE253OX GENE2287X GENE2328X GENE2417X GENE2238X GENEI97IX GENE3086X GENE1009X GENE1947X GENE31 90X GENE3379X GENE31 84X GENE3122X GENE1 099X GENE3032X GENE2675X GENE24BIX GENE2878X GENE2943X GENE2977X 0.8026 0.3974 1.0615 -0,3786 0.2235 0.5074 0.5510 -0.3046 -0.0303 -0.2697 -0,0697 -0,4040 -0.1675 -0.3598 -0.2349 -0 .6340 -0.2825 0,2228 -0.09 15 -0.9546 0. 8979 0,0494 0.7873 -0..5659 0.7321 0.0585 0.6451 0.3777 0.9694 0.6927 0.7111 -0.5743 1498 1.3008 1.3026 -0.1129 0.0583 -0.0415 -0.0178 0.2498 0.2813 -0.1996 -1 .3288 -0.0167 0.0930 0.1131 -0.0857 0.3713 0.3155 0.61 52 1.0093 -0.3812 -1 .8490 -0.2439 -1.1094 0.0178 -1.2827 0.1940 -0.5625 -1.1098 -0.6977 -0.5699 -0.7707 -0.9265 -1,4101 0.5551 -0.9102 0.8667 -1 .4401 -0.4091 -1.0995 -0,0894 0.2816 0.2443 -2.2226 2,1701 -0.2550 0.8794 -1.0815 0.0117 1.5034 -0.6686 1.1750 -1.1876 0.8689 -0.1714 1.5218 -0.3794 0.9489 0.2806 -1 .3232 -0.6784 0.8619 0.2949 0.7786 0.0139 0.7793 0.0381 2.0568 -0.4642 2.1078 -0.4943 0.2367 -0.6188 0.2226 -0.6774 0.1905 -0.7298 -1.3484 -1.6693 -1.6149 0.3113 -0.0175 -0.2315 -0.51 94 -0.0623 -0.9048 -0.1547 -0.4389 0,7770 0.6898 1,0286 -1.4872 -0.694 1 -0.0474 0.5442 -0.4646 0.6757 0.5818 -0.8365 -0.4776 0.8642 2.21 05 0.1760 0.0832 2.7901 0.9205 -0.4620 -0.7030 -0.3742 -0.2949 0.0594 0.8188 0.6584 0.6846 -0.7494 0.6096 -1.1711 0.7077 -0.9254 0.9334 0.2435 0.6352 0.8963 0.5852 0.6241 1.7283 -0.9261 -2.2564 -0.0240 0.4957 1.1094 -0.9484 -0.6953 -0.2411 -0.4125 2.0876 -0.2384 2.2127 -0.0660 0.668 0.0511 0.8248 -1 .5257 1.1189 -1.1503 -0.2463 0.4048 -0.4567 -0.3008 2.0913 0.3562 1.6418 -0.0791 -0.5898 -1 .9267 1.1048 -0.6480 -0.7760 -0.1793 9389 -0.0063 0.1023 -1.3214 -0.4969 -0.0270 0.9793 -0.9496 -0.2782 -0.1448 -0.3894 -1 .6700 0.6771 0.0607 -0.1152 0.1830 0.2361 -0.5843 0.3398 -0.9930 -0.4727 0.9735 9474 -0.4637 -1.4702 -0.5756 -0.1686 0.1582 -0.4330 0.0837 -0.3448 0.1452 -0.61 62 -0.5370 -1 .6743 0.4645 -1.6802 0.3130 -1,.3542 1.0861 1.8385 -1 .6824 -1 .7073 -0.9363 -1 .5120 -0.2122 -1.0718 -0.9399 -0.9801 -0.5265 -0.9609 -0.4759 -0.9005 -1 .0086 -1.1211 0.6514 -0.5620 0.9628 -0.0835 -0.2282 -0.3741 0.0024 -0.1288 0.4682 -0.9395 0.5096 0.9909 -0.3294 -0.9119 -0.0072 1.3005 -1.0504 1.0352 0.460D 1.0880 -0.4452 0.91 30 -0.5824 0.91 85 -0.4029 -1.3121 0.689D -0.2819 -0.9662 0.8644 -0.6805 0.6600 -0.8052 -0.1041 -1 .0945 -0.2042 -0.9205 0.4558 -0.2223 0.6388 -0.2274 1.4656 -0.190D WO 03/007177 WO 03/07177PCT/ATJO2/00934 -47 GENE3014X 0.5665 -1.4441 -0.8712 -0.8063 -0.0064 -0.1037 1.7123 -0.6766 GENE2006X 0.0298 2.6616 -0.7335 0.5561 -0.3782 0.0298 1.0957 -0.3782 GENE1368X 0.2271 1.4978 0.2271 0.7906 -0.7564 -0.6127 -0.2260 0.2160 GENE1 184X 0.2107 1.3306 0.1778 0.7267 -0.7225 -0.5249 -0.0199 0.1558 GENE1226X 0.9514 0.648 0.5131 1.3054 -1.8132 -0.2370 -0.4983 -0.4140 GENE1228X -0.8176 2.3265 0.9072 0.5718 0.2184 0.0268 1.3383 -0.9973 GENE1231X 0.5575 0.0823 1.3640 -0.0761 -0.8970 -1.4730 -0.5801 -0.1913 GENE1246X 0.3136 -0.1998 0.2968 0.1285 -1.4118 -2.0767 0.0695 -1.0162 GENE1 172X -0.0875 0.5221 -0.3923 0.6566 -2.1136 -2.9653 0.6118 -1.3964 GENE1 164X 0.1758 0.7729 -0.3551 0.2587 -1.6323 -0.6371 2.1331 -1.4831 GENE3029X 0.6997 1.4861 0.2060 0.5900 0.9740 0.3705 1.1569 0.0597 GENE1027X -0.0639 0.8656 0.0871 1.3304 -1.0748 1.2026 1.1097 -1.5512 GENE13S4X -0.0742 0.0379 -0.3883 0.0603 -0.4780 0.7108 0.6660 -0.5677 GENE62X 0.8869 -1.0752 -0.1019 0.6551 -0.4572 -1.0752 2.5246 0.7478 GENE932X -1 .0786 -0.7721 -0.1035 0.3701 -0.0199 0.2587 -0.3542 0.9273 GENE361IX -0.5836 -2.9911 0.5107 -1.4834 0.7052 0.6566 -0.5836 -0.3891 GENE3531X -0.2898 -0.8923 0.3126 -1.3708 -0.0772 0.1354 -0.8746 0.0114 GENE330X 0.7179 -1.2366 1.2669 -2.6860 -0.0946 -1.1048 -1.2586 0.1469 GENE331X 0.6706 -1.3524 1.5179 -1.7155 2.8839 -0.5570 -0.8855 0.5496 GENE808X 1.0278 1.0444 1.2104 -0.2833 -0.4659 -0.8145 0.1648 -0.6983 GENE487X -0.1378 1.1761 -1.1786 1.4493 -0.5281 -0.8664 1.3843 1.3712 GENE621X -0.4395 1.4088 -0.9403 1.3611 -0.8330 -0.548 1.8500 1.4446 GENE622X -0.1669 1.6533 -1.1360 1.1923 -0.8051 -0.8642 1.4051 1.5705 GENE634X 0.2663 0.5770 0.5024 -0.6782 0.1793 0.0675 -0.9764 0.7385 GENE659X -0.2634 -1.3723 1.8652 -0.5821 1.4828 1.0877 -1.0919 0.4249 GENE669X -0.0724 -1.0673 1.7701 -1.012C 1.4016 1.0147 -0.8278 0.4067 GENE674X -0.3716 -1 .5379 1.4656 836C 1.4553 1.1663 -0.3922 0.5264 GENE675X -0.4037 -0.5998 0.0790 -0.3358 0.9539 1.0972 -1.6557 0.3581 GENE676X -0.7192 -0.7676 0.1642 -0.089S 0.4063 -0.1262 -0.1988 -0,0778 GENE704X 0.1782 0.0575 -0.4977 -0.9484 0.0253 -0.4253 -0.3770 0.0333 GENJE734X 0.3566 -0.3485 -0.2551 -1.3254 -0.0087 -0.3060 -0.4844 0.092 GENE738X 0.7914 -1.1472 1.1461 -0.2488 0.4605 -1.3127 -0.7216 0.1058 GENE456X 0.2395 -1.3068 0.3007 -0.7097 1.1274 0.2701 -0.8475 0.1936 GENE744X -0.3526 -0.9622 0.1448 -0.7536 1.3801 0.4014 -0.3044 -0.1921 GENE179X -0.5177 -1.4381 0.2186 -0.057E 0.0805 -0.9319 0.0345 -0.4487 GENE124X -0.1560 -0.8000 0.2446 -0.3135 1.4753 -0.1274 -1.2150 0.2303 GEtNEI22X -0.0296 -1.1076 0.4410 -0.8799 1.3975 0.3044 -1.4265 0.4562 GENE111X -0.0262 -0.9483 0.6112 -0.7449 1.5606 0.4892 -1.5857 0.5299 GENE97X -0.1822 -1.7549 -0.6409 -1.1651 0.3912 0.3912 -1.4927 1.1284 GENE2645X 0.7145 -1.6046 0.5163 -0.2567 1.2893 1.1704 -0.2567 0.2963 GENE3408X -0.2830 1.9551 -0.0079 0.2123 -1.2187 -1.6589 1.5515 -0.1363 GENE3854X -0.5814 1.8312 0.0734 0.6421 -1.1845 -2.1668 1.4003 0.3319 GENE1406X 0.3805 0.0689 -0.9105 0.7589 -1.0886 -0.1760 1.2709 -0.0201 GENE1401X -0.5903 0.0861 -1.1251 1.1558 -0.5419 -1.2824 1.1558 0.0547 GENE3462X -0.5269 -1.1478 -0.9785 -1.1102 1.0726 0.3199 -1.3172 -0.3387 GENE3I73X -1.9774 -0.7247 -0.4200 0.7311 .0.12171 0.3249 -1.1479 -0.2676 GENE397IX 0.7613 1.31 56 0.7321 0.0903 -0.2598 -0.6724 0.5571 -0.0847 GENE1756X -1.149B 1.4846 -1.0563 0.1908 -1.2122 -0.8225 0.7676 -0.7601 GENE1533X -0.2W4 1.4949 -0.61 05 0.0963 -0.9263 -1.0315 -0.0992 -0.4451- WO 03/007177 PCT/AU02/00934 48 GENE1757X GENE3572X GENE3571X GENE385X GENE1614X GENE1623X GENE1646X GENEI660X GENE1721X GENE1573X GENE1553X GENE1773X GENE913X GENE398OX GENE3X 0.1061 1.8722 -0.3285 -0.2663 1.8330 -0.0420 -0.9238 -1.3932 0.0454 -0.7754 0.0656 -0.1446 -1.0821 0.0647 -0.2963 -0.0164 -0.4100 0.2788 -0.1882 -1.1784 0.4090 -0.2236 1.8073 -09288 0.0249 1.5808 -1.3001 -0.8298 0.7371 -0.6351 -1.5425 0.1643 -0.0192 -0.9401 0.3774 0.4382 0.4635 0.3056 0.6717 0.1913 0.3664 0.3314 -0.3727 1.1541 -0.1972 1.1658 0.1984 0.2120 0.5095 1.0204 1.1053 0.0161 0.7072 0.6327 -1.0244 1.3572 0.7220 0.5353 0.7166 -0.7237 -1.4019 -0.6547 1.0435 0.0925 -1.2279 0.1984 -0.2343 -0.1381 1.4841 -0.1817 -0.3029 -0.6058 0.9768 0.4394 0.2993 0.2292 0.7656 0.1922 0.9780 0.2771 1.0462 0.3378 -0.8232 1.0462 2.2794 0.1890 -0.4711 -0.2511 -0.9994 -0.5480 2.5830 0.4392 -0.6923 -0.8260 2.1035 0.3774 0.8539 -0.5475 0.5619 -0.2361 1.1003 -0.2211 -0.1660 0.7332 0.7220 -0.6563 0.1544 -0.0483 -1.15B8 -0.5414 1.0234 0.7291 -1.2586 -0.2360 1.0738 0.6325 0.6802 0.2415 -0.7588 0.4170 RowNames DLCLOO17 DLCLOO18 DLCL0020 DLCL0021 DLCL0023 DLCL0024 DLCL0025 DLCL0026 GENE39SOX GENE2531X GENE918X GENE351 1X GENE3496X GENE3484X GENE3789X GENE3692X GENE3752X GENE3740X GFNE3736X GENE3682X GENE3674X GENE3673X GENE3644X GENE3472X GENE253OX GENE2287X GENE2328X GENE2417X GENE2238X GENE1971X GENE3086X GENE1009X GENE1947X GENE319OX GENE3379X GENE3184X GENE3122X GENE1099X GENE3032X GENE2675X 0.8207 1.1909 1.2248 2.2002 2.5230 2.3548 2.9271 -1.2869 3.1393 2.0537 3.1475 0.5465 -0.1600 0.4317 1.7303 0.8427 2.4848 1.1043 1.6062 0.4342 -0.8129 2.4807 -0.1077 -1.0322 0.2940 -1.3087 -2.2407 -0.8896 -0.0766 -1.8586 -0.8478 -1.8648 -0.0959 -0.0732 -0.1633 -0.7180 -1,4735 -1.5149 -0,6264 1.1879 -0.1967 -0.2122 -1.5060 0.3485 0,4191 0.7475 0.5358 -0.1418 0.0250 0.1860 -0.7072 -1.8301 1.7534 -0.5161 0.5725 1.0196 0.0750 -0.0376 0.9641 1.1692 0.5002 0.7005 0.8219 0.8963 0.5847 0.3942 -1.0761 -0.3501 0.4712 0.2313 -1.2726 -0.3869 0.5534 0.4173 -1.4063 -0.3266 -0.8876 1.8270 0.5602 0.3453 0.4645 -0.3689 0.0030 -0.1480 0.3227 -0.4454 -0.1148 -0.4065 0.4439 1.1289 -0.8405 -0.4551 0.3970 1.2517 -0.6873 0.0015 0.1338 -0.4170 -1.7703 0.2596 1.1565 1.1910 -1.5925 -1.0749 1.0379 0.5368 -0.2411 -0.3598 -1,2034 0.9282 -1.0378 0.9570 -1.1565 0.7011 -1.0324 0.7500 -1.4498 1.2319 -0.7232 0.7215 0.5743 0.4587 -0.5624 -1.2753 1.5991 0.5546 -0.4059 -0.9342 -0.0655 0.7665 -0.3036 0.7046 0.1860 1.2328 -1.0933 0.7645 0.1324 0.1324 -1.0616 -0.0915 1.4606 1.0682 -0.1696 0.2983 1.5302 -2.0217 -0.9431 -0.0691 0.4640 1.0294 -1.4773 -0.5349 0.5606 0.0713 1.3363 -0.5134 -0.4260 0.0870 0.5844 -0.0840 0.6225 -2.2248 -0.5547 -0.2810 0.5712 -0.9455 -0.1658 0.5605 -0.7218 -0.9345 -0.2054 -0.4636 0.2999 -0.2337 -0.2893 0.2777 0.0505 -0.2232 -0.4578 0.1092 0.2480 -0.7039 -0.5478 -0.1655 0.7622 -1.3504 -0.4645 -0.0385 0.9464 A.5147 -0.0241 0.8363 0.7300 -1.5572 0.7849 -1.3741 0.7712 -1.1795 0.9221 -0.6840 1.4486 -0.7003 1.2464 -0.7468 0.3583 0.2727 0.4225 0.7159 0.7160 0.6530 0.4434 -2.0871 0.0753 -0.2147 0.5717 -0.9981 0.6071 -1.2505 0.9032 -0.8616 -0.6973 -1.4872 0.0383 -1.6546 1.6709 0.1878 1.6368 -0.7414 0.8413 0.4682 0.1926 0.0417 -1.0547 1.5116 0.7279 -1.2888 -0.7163 2.7445 -0.5503 2.1232 -0.2810 -0.0893 -0.1872 -0.0910 -1.4660 2.0729 -0.6450 0.7112 1.1552 -0.2232 -0.3996 -0.7585 -0.3282 -0.7371 -0.7344 -0.6743 WO 03/007177 WO 03/07177PCT/ATJO2/00934 49 GENE2481X -1.7274 0.9019 0.9563 -1.2650 -0.3946 0.6027 -0.9477 -0.6031 GENE2878X -1.1508 0.4036 -0.1389 -0.9526 1.3008 -0.0032 -0.8900 1.4365 GENE2943X -1.2512 1.1451 0.1776 -0.9924 0.8188 0.0876 -0.6212 2.0338 GENE2977X -0.0666 0.2059 0.4013 -0.3134 0.9874 0.7406 -0.51 39 1.5941 GENE3014X -1.1738 1.6150 -1.0225 -0.0605 0.9880 1.3772 -0.0064 -0.0497 GENE2006X -1.2467 -0.5492 -0.4308 1.2931 0.5035 0.1614 -0.3124 0.0429 GENEI368X -1.4968 0.2823 -0,7564 0.3597 -0.1265 1.2768 -0.0602 0.3818 GENE1 184X -1.0629 0.2327 -0.7555 0.4522 -0.0089 1.1000 0.0021 0.3754 GENE1226X -2.3779 0.5216 1.2717 -0.3213 0.0411 0.4036 0.1254 2.4770 GENE1228X -1.4883 0.9311 -0.0570 -0.6499 0.9491 -0.4044 -0.7517 0.2723 GENE1231X -2.5674 0.1543 0.8743 -0.8682 -0.1049 -0.7962 -0.9258 0.8311 GENEI246X -2.6827 1.0206 0.5914 -0.8290 0.1790 -0.4523 -0.6711 1.2226 GENE1172X -1.2171 1.1765 0.2083 -0.3027 0.7014 0.0649 -0.6882 1.9475 GENE1164X -1.6987 1.5360 -0.4214 -0.8693 1.1213 0.9388 -0.3385 1.8843 GENE3029X -3.4516 1.4861 -0.0135 -0.0866 0.6997 -0.3244 0.2608 -0.3610 GENE1027X -1.9346 1.1097 0.2963 -0.1104 -0.7495 -0.9818 -0.9588 -0.7727 GENE1354X 0.5538 1.0921 0.0828 -0.0069 0.0603 -0.8817 0.4865 1.3389 GENE62X -1.7550 0.5315 1.5512 0.5315 -0.0246 -0.4263 -1.7705 0.2380 GENE932X 0.9273 -0.6050 1.0388 -0.4657 -0.4935 0.7044 1.3731 0.1751 GENE3611X 0.2675 -1.7265 -0.8511 0.7052 0.0973 -0.0243 -0.2918 0.1459 GENE3631X 3.2187 -0.0949 0.5430 0.4721 -0.9632 -0.7860 -0.1126 -0.2367 GENE2330X 0.6520 -0.3801 0.1689 0.6301 -0.6217 0.4983 0.0152 -0.0288 GEN'E331X 1.2585 -10930 0.5323 -1.3697 -0.1074 -1.2141 0.5496 -0.8164 GENE808X -0.7813 -0.1340 0.6461 -1.3622 -0.4327 -0.7813 -0.5987 0.0154 GENE487X -1.4128 1.0981 0.8769 -1.9591 0.4996 -0.0468 -0.8143 1.0330 GENE621X -1.2623 0.7768 0.8364 -1.5962 0.1209 -0.0698 -1.2385 1.2290 GENE622X -1.4906 0.5541 0.8968 -1.5615 0.2704 -0.3914 -0.9351 0.8141 GENE634X 1.6582 -1.2623 -0,0568 -0.3551 0.0302 -0.5912 -0.8770 -1.1753 GENE659X 0.2082 -1.3596 0.2974 -0.2252 0.0297 -0.9390 -0.0977 -1.2704 GENE669X 0.0934 -1.3345 0.2224 -0.4040 0.1579 -0.3764 0.0566 -0.9383 GENE674X -0.5367 -0.6709 0.1755 -0.0310 0.4541 0.0619 0.1135 -0.7122 GENE675X 1.3386 -2.0404 -0.2453 0.7654 .0.6975 0.0941 0.5693 -0.1171 GENE676X -0.3198 0.2610 0.7814 0.7572 -0.8039 -0.18B67 0.8056 -0.0173 GENE704X 2.6244 -0.7794 -0.4575 -0.4012 -0.1035 -0.2403 1.1679 -0.6748 GENE734X 2.0981 -0.9601 -0.3995 -0.3400 -0.1191 -0.4759 1.0872 -0.6798 GENE738X 0.6498 -1.1708 1.1224 0.3422 -0.9344 -1.1708 0.2477 -1.2181 GENE456X 1.3418 -0.0208 0.1170 0.2242 -1.0771 -0.8934 0.1170 -0.9700 GENE744X 1.5885 0.1287 -0.0959 0.3212 -0.4649 -0.2723 0.4175 -0.4328 GENE179X 0.9089 -0.6788 -1.0699 0.1726 0.7248 -0.4717 0.2416 0.3566 GENE124X 2.6199 0.0729 -0.0129 -0.6426 -0.1704 -0.0129 0.7026 -0.9288 GENE122X 2.0049 0.0766 0.1222 -0.2726 -0.2422 -0.0145 0.6840 -1.0469 GENEIIIX 1.4521 -0.1889 0.0959 -0.4466 -0.4737 -0.8534 0.7333 -1.6535 GENE97X 2.2424 -0.91 94 0.4240 -0.5589 -0.8886 -0.4770 0.3748 -0.0347 GENE2645X 1.8642 -0.4549 -0.9505 -0.3360 0.1397 0.2190 1.6263 -1.1289 GENE3408X 1.0562 -0.8701 0.5058 -0.8884 0.8177 -0.1546 0.1389 2.8540 GENE3854X 0.1765 -0.9605 0.7972 -1 .3052 0.4353 -0.150.6 0.0734 3.4338 GENEI406X -0.2427 0.5809 -1.5783 -1.9789 1.0705 -0.3985 -0.1092 0.2602 GENE14OIX -0.4959 1.6749 -0.0712 -1.6756 -0.8262 0.0075 -0.8105 0.5738 GENE3462X 2.4462 -0.2446 -0.8656 0.5269 -1.0161 0.5833 -0.3387 -0.9032 WO 03/007177 WO 03/07177PCT/ATJO2/00934 GENE3173X GENE3971X GENE1756X GENE 1533X GENE1 757X GENE3572X GENE3571X GENE385X GENE1 614X GENE1 623X GENE 1646X GENE1 660X GENE1721X GENE1I573X GENE1 553X GENE1I773X GENE91 3X GENE398OX GENE3X 2.6610 -0.5224 0.8299 0.0662 -0.0433 0.2465 2.3473 -0.2614 1.8700 1.6366 0.7077 0.1007 0.3409 0.1824 1.3021 0.7423 -0.2400 -0.1799 2.2246 0.3926 -0.9448 0.5571 0.4696 1.0949 -0 .7290 1.01 36 -0.4451 0,7654 -0.2200 0.0221 -0.2984 -0.9541 -0.6512 -0.3549 -0.4951 -0.4875 -0.6998 -0.2722 0.3772 -0.7383 -0.61 69 1.0598 0.6085 0.61 50 0.9852 0.1337 -0.1583 -0.2576 0.8066 -0.4131 0.4382 -0.1682 1.2531 -0.2360 1.1999 -0.4429 0.2766 0.7142 -0.2168 0.4603 0.8835 -0.7416 0.4696 -0.1139 -1.6601 -0.9891 -0.1431 -1.7266 -0.3081 -0.5419 -0.1989 1.3132 -1 .9790 -0.6406 -0.8812 -0.4451 0.0211 -0.2471 0.2284 -0.0705 -0.5868 -0.1928 -0.3304 0.4708 -0.7150 -1.0356 1.8490 2.4079 -0.2726 -0.1060 -0.0454 0.1212 0.7431 0.1124 -1.3127 -0.1446 -1.0557 0.6169 -0.6149 -0.7M4 0.1072 -0.2751 0.4559 -0.6254 -0.7445 1.3611 -2.2991 0.1733 0.3462 -0.4711 0.2676 -0.7655 -1 .9302 0.4251 0.0584 -0.9006 0.1289 -2.0173 0.5841 -0.2668 -1.0448 0.5233 0.6008 0.3673 -0.5086 0.4841 -0.6546 1.1920 -1.0836 -1.2855 0.9534 -1.0653 0.4787 -0.2712 -0,9604 1.3909 -1.0009 -2.2284 0.3630 -0.2112 -0.8429 1.9925 -1 .9660 0.5905 0.1703 -0.7403 1.8852 0.9961 0.2064 -1.1273 0.3117 -0.845 RowNames DLCL0027 DLCLOO28 DLCLOO29 DLCLOO30 DLCLOO31 DLCL0032 DL.CLOO33 DLCL0034 GENE395OX GENE2531X GENE918X GENE3511lX GENE3496X GENE3484X GENE3789X GENE3692X GENE3752X GENE374OX GENE3736X GENE3682X GENE3674X GENE3673X GENE3644X GENE3472X GENE253OX GENE2287X GENE2328X GENE241 7X GENE2238X GENE1 971X GENE3086X GENE 1009X GENE1 947X GENE319OX GENE3379X GENE3184X 0.1491 0.1944 0.1996 1.1257 0.4043 0.2060 0.3583 03 18 0,1338 0,9495 0.695 1 -0,4076 -0.457 1 -0.4247 0.5 165 0.2784 0.5857 -0.2272 -1.3974 0.4945 -1.5940 -0.8553 -0.9550 1928 -1 .8963 -0.0376 -0.9w4 -0.2560 0.5847 0.2126 0.4897 0.2313 0.6442 0.0998 1.1483 -0.1185 0.6252 0.1030 0.8575 0.1963 0.8721 -0.6264 -0.1771 -0.3939 0.8419 0.4327 0.6274 0.1558 0.9324 -0.8081 1.6339 -1.2610 1.4419 -1.1640 1.4655 -1.3979 0.7670 -0.8321 0.2544 -0.1058 1.0740 0.4772 1.1318 0.0575 -0.0542 -0.2408 1.1134 0.1474 -0.5898 0.5446 0.4263 0.4075 0.3935 0.3339 -0.8612 -0.1617 0.2940 0.3214 -0.2406 1.1373 -1 .8609 -0.2054 -0.3782 0.4111 0.7753 0,8772 0,6351 0.1530 -0.2183 -0 .0079 0.4439 -0.0495 0.40 13 0. 5699 0.3654 1.1010 1.1711 1.2060 -0.1385 1.0588 0.6942 0.7921 0.0204 0.1323 1.1211 -0.1768 -0.2867 0.9263 0,7868 3.3376 0.5996 0.7446 1.1111 1.0709 0.9889 -0.6954 1.0771 0.9644 -0.2839 0. 23 11 0 .8576 1.2830 1.1697 0.9102 1. 3065 0. 9248 0.3239 0.6 146 0.4952 0.5717 1.3077 0.3 134 -0.1 063 1.0294 0.2742 9182 -1.8415 -0.5076 -0.0080 -1.7456 -0.7766 -0.5316 -1 .3847 -0.6452 -0,8297 -1 .5309 -0.7984 -0.8619 -1 .5061 -0.2429 -1.6794 0.4018 -0.1580 0.9767 -1 .0216 0.1380 1.4603 -0.9996 -0.5622 -1.2044 -0.9475 0.3460 -0.0878 -1 .1 849 -1 .0464 -0.5429 -1 .6601 -0.1777 -1.0864 -0.7183 0.2731 -1,0059 -0.6367 0.2837 -1.0198 -0.4833 0.6221 -1 .5099 -0.0998 0.88S9 -1.2379 -0.3512 -0.5817 -0.5046 -1 .0826 -0.2979 -0.9462 -1 .4385 -0.6442 -1.1868 -1.5124 -1.1270 -1.6504 -1.4392 -0.5392 -2.3862 -0.6885 0.0115 -0.4413 -1.0904 1.3071 -0.8501 1.2141 0.0682 -1.4396 -0.5538 -0.1077 3.3650 -0.2748 -0.5348 -1 .5607 0.7398 0.6773 -1.1297 0.9237 1.3402 -0.4435- -0.2833 1.0552 -1.5420 1.1312 0.4889 -0.3894 0.9113 WO 03/007177 WO 03/07177PCT/ATJO2/00934 51 GENE3122X -0.4353 0.4011 0.7739 1.1747 -0.0766 -0.5263 -0.4481 1.8590 GENE1099X 0.1466 -0.4230 0.4899 1.0282 -0.6961 1.1062 -0.7195 1.1609 GENE3032X 0.2767 -0.5326 0.4130 1.0774 -0.6860 1.1265 -a.3622 0.7111 GENE2675X 0.7253 -0.1341 0.6562 0.5162 -1.3446 0-4061 0.4862 0.5262 GENE2481X 0.3035 -0.0954 0.7115 0.8475 -1.1199 0.5030 0.8112 0.6934 GENE2878X -0.5040 -0.4101 2.1354 0.7375 -1.0986 1.7599 -0.7439 0.3932 GENE2943X -0.5424 -0.1937 2.1013 0.6388 -0.8012 1.2913 -0.7112 0.2676 GENE2977X -0.7607 -0.4059 0.8794 0.5710D -1.0743 0.8229 -1.0435 1.7843 GENE3014X -0.1470 -0.2226 1.0853 -0.0054 -1.2819 0.3395 -0.8063 0.2530 GENE2006X -0.1545 -0.3782 0,8983 -0.1281 -0.7466 0.4509 0.3587 1.7800 GENE1363X 0.3155 -0.3033 0.6249 -0.0492 -1.2316 0.4370 -0.0934 1.6967 GENEI1I84X 0.2766 -0.3712 0.5181 -0.1846 -1.1398 0.5181 -0.0967 1.3965 GENE1226X -0.5825 -1.2822 0.3867 0.4289 -0.1106 0.4289 1.0273 1.2380 GENE1228X -1.3147 -0.5781 -1.1829 0.5059 -0.8835 0.2664 1.1766 -0.4762 GENE1231X -0.6521 -1.6314 1.0327 1.2631 0.7303 0.9895 1.6232 0.9175 GENE1246X -1.5212 -0.8226 1.4583 1.0206 -0.6375 1.0459 1.2479 1.0879 GENE1172X -1.5578 0.0739 1.0690 0.3607 -1.3605 0.5400 0.9614 0.4145 GENEI164X -0.8693 -0.9191 1.7516 -0.0067 -1.3006 0.1094 0.8061 0.1758 GENE3029X -0.6353 -1.1839 0.3157 0.1145 -0.5621 0.0779 0.0231 1.7604 GENE1027X -0.8076 -1.3304 0.6797 -0.0871 0.2382 -0.5635 1.7022 0.7611 GENE1354X -0.2312 -1.3079 1.2267 0.5987 -1.1284 0.5090 0.5987 1.7650 G;ENE62X -1.3997 -0.5499 0.4852 0.8714 -1.3688 0.1299 0.1453 0.5006 GENE932X 0.8437 2.1253 -0.3542 0.5373 -0.3264 -0.5492 -1.9143 -0.9950 GENE3611IX 0.9484 -0.2675 0.7295 0.3161 1,5563 -0.7782 -0.4620 0.8025 GENE3631X 0.2949 0.6139 -0.3430 -0.4316 0.0646 -0.9455 -1.9201 -0.2898 GENE33OX 1.0254 -0.1605 0.1689 -0.2044 -0.1825 -0.0727 -1.3025 -0.9950 GENE331X -0.0729 0.8263 -0.5224 -0.1593 -0.2804 -0.1939 -0.2112 -0.1420 GENE808X -0.9638 -0,1506 0.5797 0.4460 -0,6983 1.0411 -0.0676 -0.3165 GENE487X -0.4631 -0.9314 -0.9054 0.5517 -1.6860 -0.2289 0.7598 1.7095 GENE62IX -0.3918 -0.7018 -0.7138 0.8126 -1.5843 -0,7653 1.1226 1.7069 GENE622X -0,8642 -1.0888 -0.8287 0.814' -1.6679 -0,3205 1.3342 1.7951 GENE634X 0.44C3 0.6143 -0.1562 -0.2059 0.0628 0.0302 0.0799 -0.0941 G3ENE659X 0.8965 -0.3399 0.1062 -0.0850 1.0877 0.6033 0.4376 -1.0919 GENE669X 0.9318 -0.1553 0.3606 -0.1000 1.1068 0.6738 0.3606 -1.5464 GENE674X 1.1560 0.0826 0.2787 -0.4232 0.8670 0.5057 0.1755 -1.8475 GENE675X -0.13S7 0.8634 0.1469 0.3279 1.2028 -0.1699 -0.9392 -0.3358 GENE67BX -0.2351 0.9266 -0.4892 -1 .2879 0.0674 -0.4408 -0.4408 -0.2230 GENE704X -0.6104 0.4518 -0.3127 -1.1173 1.0633 -0.1035 -0.8277 -1.1093 GENE734X -0.4929 0.2971 -0.1191 -0.6203 1.2316 -0.1956 -0.8072 -0.8242 GENE738X -0.1779 1.3589 -0.5325 -0.7453 1.2406 -0.2488 -0.1070 -0.7216 GENE456X -0.4848 -0.8628 0.4385 -0.3117 1.9082 -0.5413 -1.5517 -0.8934 GENE744X -0.3205 -0.1600 0.0966 -0.689t 1.6047 -0.6253 -2.4221 -1.1226 GENE179X -0.1265 0.6558 0.0575 0.0345 0.5788 -0.3796 -0.3106 0.2646 GENE124X 0.1302 0.8313 -1.3009 0.1874 0.6024 -0.7571 -0.0416 -0.0416 GENE122X 0.4410 0.3044 -0.9254 0.2285 0.5169 -0.864-7 -0.2878 0.2892 GENE111X 0.8689 0.3943 -0.8399 A..4349 0.7604 -1.1111 -0.2025 0.5926 GENE97X 0.2602 0.2438 -1.0996 -0.3460 -0.1002 0.0308- -0.8374 0.5550" GENE2645X 1.0515 0.8334 -0.1378 .0.1992 -0.21.71 -1.4064 -0.2171 -1.50655 GENE3408X -0.5215 -0.3381 -0.5215 0.3040 -1.6589 0.6159 0.6709.' 0-6709 WO 03/007177 WO 03/07177PCT/ATJO2/00934 52 GENE3854X GENE1406X GENE1401X GENE3462X GENE31 73X GENE3971X GENE1 756X GENE1 533X GENE1757X GENE3572X GENE3571X GENE385X GENE15614X GENE1I623X GEM El 64X GENE1 660X GENE1721X GENEl 573X GENE53X GEN El773X GEN E91 3X GENE3980X GENE3X 0.1424 -0.4876 -1.5498 0.1694 -0.0476 -0.4348 -1.2122 -1.1519 -0.5732 -0.4907 0.7118 0.6263 0.4045 -0.1935 0.0GM 0.5803 0.1343 0.5522 -0.5698 5753 0.3774 0.3734 -1.1624 -0.4263 -0.0816 0.4473 1.4712 -0.3543 1.4389 1.1855 -0.0188 1.0358 -1.1817 -0.9016 0.7613 -0.1210 -1.0563 -0.8210 -0.6706 -0.5460 0.1197 -1.1157 0.0221 0.9238 -0.2574 0.8366 -1.2193 0.9355 -1.9741 1.7153 -1.0594 0.3462 -0.51 83 -0.7596 1.5534 -0.4978 0.1586 -0.0707 -0.6546 0.0358 -1.8544 1.2085 -1.4671 -0.81 42 0.0400 -0.8563 -0.0118 0.2766 -0.91 67 0.176B -1 .2879 0.5043 0.1134 -0.5098 0.8034 0.3693 -0.3700 0.2434 -0.3387 2.2580 -0.6962 -0.7755 0.4434 -0.5046 0.9655 -1.6310 -0.1431 0.7364 -0.3081 0.3311 1.1189 0.1114 0.5324 0.540 -0.4509 0.2555 0.6311 -1 .2920 0.5029 -0.5603 0.2877 -0.3029 -0.0979 -0.3549 -0.7287 -0.7636 -0.8697 -0.8697 0.4362 0.0230 -0.6658 -0.7698 -0.0153 -0.6598 1.2008 -0.8301 0.4392 0.4625 -0.8868 0.2802 -0.0512 0.6787 -1.21 91 0.0175 1.0452 -0.7350 0.5801 1.1679 -0.4739 0.8942 -0.6922 0.9014 0.7446 -0.8943 0.8917 -0.8641 0.0636 -0.6359 -0.1506 1.9386 -0.3856 -1.8064 -0.5893 1.1114 1.7340 1.8558 0.2827 1.6247 0.3483 -1.4996 -1.8255 -0.3313 0.4876 -0.0685 0.4747 -0.8200 -0.7533 -0.8388 1.1957 0.9337 -0.8992 0.7800 0.8925 0.5109 0-0941 1.5268 1.3740 0.5025 1.1941 1. 7500 1.4164 -1 .0298 -0.1213 -0.6574 -0.9216 -0.0468 -0.3083 -0.6801 -1 .5986 -1.1571 -1.3455 0.2195 0.3314 -0.9869 RowNames DLCL0036 DLCLOO37 DLCL0039 DLCLOO40 DLCLOO41 DLCL0042 DLCLOO48 DLCLO04C

OCT

GENE395OX GENE2531X GENE9I 8X GENE3511iX GENE3496X GENE3484X GEN\E3789X GENE3692X GENE3752X GENE374DX GENE3736X GENE3682X GENE3674X GENE3673X GENE3644X GENE3472X GENE2530X GENE2287X GENE2328X GENE2417X GENE2238X GENE1971X GENE3086X 0.829B -1 .2395 0.7572 -0.3684 0.8528 -0.7349 -0.61 62 -0.9555 0.7357 -1.0116 0.9153 -0.7176 -0.2625 -0.9261 -0.9170 1.8895 0.7160 -0.8733 0.6389 -0.21 22 0.4841 -0.9267 1.8895 -0.2600 1.8781 -0.3781 1.1324 -0.1133 -0.71 65 -0.0615 0.6506 -1.1383 1.3815 -0.6623 0.7921 -0.0986 0.3376 -0.6325 -0.9848 -1.1357 -1.7986 0.8794 0.9917 -0.4030 -1.0624 0.5129 1.4560 1.6061 1.5061 0.7864 0.6553 0.9644 0.9 149 0.7 159 0.6576 0.7769 1,2752 1.8824 1.4757 1.2579 1.9615 0.8908 1.1825 0.60 13 1.0652 0.7059 0.7120 0.0494 -1.2414 0.5575 -1 .0489 0.6557 -0.7559 0.5807 -0.7077 2.4038 0.6846 0.6654. -1.33-29 0.7797 -1.3107 0.3583 0.4439 -1 .0573 -0.5725 0.7632 -0.1810 0.1788 -0.3273 0.6423 -0.4125 0.7158 0.4889 0.4379 0.5695 1.0676 1.3401 1.4028 0.6707 0.4465 -1.2704 0.7304 0.6038 1.2053 0.4707 1.2704 -0.0915 -0.4263 -0.6527 -0.9803 -1 .3336 -1.0438 -0.4972 -1.4562 0.5606 2.1821 -0.7403 0.6392 2.2081 -0.7651 0.5635 2.0686 -1 .2793 0.4355 -0.5144 0.6054 1.1031 1.5088 -0.9111 0.0328 1.3533 -1.0288 -0.3482 0.01'58 0.3155 1.5785 0.0398 -0.3174 0.0143 1.2667 -0.3383 0.6688 1.7546 -0.0512 0.0408 0.51 05 -0.0829 1.0774 0.5681 -0.9981 0.6689 0.9380 -0.9985 0.7011 1.2016 -0.3166 0.9075 2.0000 -0.3890 0.8633 2.8718 -0.0457 0.2064 0.1516 -1.8199 1.7794 1.8113 -1.5402 1.5909 1.3823 -0.4833 1.7741 0.5247 -0.6376 0.0417 0.2285 -0.7571 -0.4038 2.8577 -0.1203. 0.7844 -0.4299 -0.4299 -0.7998 WO 03/007177 WO 03/07177PCT/ATJO2/00934 53 GENE1009X -1.0944 2.2476 -1.1099 -0.3949 -1.7161 -0.5037 0.5688 -0.3638 GENE1947X -0.5274 1.2249 -0.5821 -1.6499 0.9511 0.7047 -1.5404 -1-1297 GENE319OX -0.9242 -1 .3087 -0.4008 -0.7105 -0.5396 -1.1592 -0.7212 -0.1765 GENE3379X -0.1447 0.5085 -0.9800 -1.3597 0.2047 0.2654 -0.6762 -0.0991 GENE3184X -1.7678 1.6228 -0.5561 -1.2565 -0.6450 -0.2782 -0.5005 -1.2342 GENE3122X -0.0668 0.8228 -1.2203 -0.0472 -3.2243 -2.2663 -1.1519 -0.0179 GENE1099X -1.7104 2.0269 -1.4997 0.8566 1.6368 -2.0069 0.5211 -1.2734 GENE3032X -2.0916 2.0060 -1.9638 0.0807 1.6226 -1.4015 0.5152 -0.8393 GENE2675X -1.1345 0.0960 -0.3442 -1.4247 1.5366 -1.2946 0.2861 0.4361 GENE248IX -0.9386 0.2400 -0.4127 -1.6367 1.4731 -1 .4735 -0.6666 -0.267.7 GENE2878X -1.1091 2.5319 -0.9735 -0.6796 -1.2447 -0.1180 -0.9526 -0.8065 GENE2943X -1.2849 0.9763 -1.2849 -0.2049 -0.9362 -1.1049 -0.9812 -1.4199 GENE2977X -1.3468 1.6250 -0.8944 0.4116 -1.0486 -1.5525 -0.6424 -0.7144 GENE3014X 0.7286 1.8852 -0.0172 -0.5361 -0.1253 -1.5306 -1.0874 -0.5793 GENE2006X -0.1150 2.9775 -0.4177 -0.8519 -0.7335 -1.1941 -1.6941 -1.6941 GENEI368X -1.5189 1.0448 -0.6127 -0.3807 -2.9443 0.4702 -1-1211 -1.2095 GENE1184X -1.5680 1.7698 -0.6018 -0.2724 -3.3027 0.3754 -1.2276 -1.1727 GENE1226X -1.2569 0.9430 -1,1726 -0.8692 0.1254 -1.2737 -1.2063 -0.0179 GENE1228X -0.8416 1.3563 -0.6679 -0.6559 -1.1410 -1.0452 0.0687 -0.7577 GENE1231X -1.0410 0.3559 -0.3209 -1.1130 -1.1130 0.9895 0.1543 -0.4361 GENE1246X -0.2587 0.6334 0.2968 -1 .0751 -0.5533 0.71 76 -0.2250 -0.6033 GENE1172X -0.1503 1.5262 0.2442 -0.8854 -0.2399 0.1904 -0.2847 -0.1323 GENE1164X -0.0233 1.5028 0.4743 -0.8693 0.4246 -0.3717 -0.5873 0.4578 GENE3029X 0.3705 0.51 69 -1 .0010 -1 .51 31 0.8277 -1.3851 -1 .4034 -0.2330 GENE1027X -1.2259 1.2375 -0.9237 -0.0407 -0.7959 -1.1561 -0.0174 -0.1104 GENE1354X 0.1276 0.8230 -0.5228 -0.7247 -2.1602 -3.9322 -1.0836 0.5090 GENE62X -0.9980 1.4585 0.0681 -1 .3070 -0.8898 -0.5035 0.2534 0.3925 GENE932X 1.6795 0.6209 0.4259 0.2587 0.2308 2.0138 0.5652 1.4845 GENE3611X 0.5350 0.3891 0.4620 0.7538 0.6809 2.9181 0.2432 -0.0730 GENE3631X 0.4544 -0.2721 0.6316 0.1000 2.2973 0.9683 -0.3607 1.5530 GENE33OX -0.424D -0.1605 -00946 -0.0068 1.3987 2.6055 0.7179 2.1893 GENE331X 0.930) -0.8184 0.9127 0.6015 -0.0037 0.8781 0.2557 0.1865 GENE808X -0.7979 0.0984 -1.4286 -1.5779 -0.9804 0.0486 -0.3331 -0.4825 GENE487X -1.0615 1.0720 0.0833 -0.7883 -0.2939 -2.1543 0.7078 0.5517 GENE621X -1.2981 0.8245 0.0733 -1.0954 -0.2487 -1.8705 0.8603 0.3833 GENE622X -1.2305 0.2468 0.5659 -1.0297 -0.1432 -1.8452 0.6368 0.6014 GENE634X 0.4900 -0.8149 0.6267 0.2663 -2.0576 3.1122 1.4966 0.0178 GENE659X 0.6415 -0.7478 0.3102 -0.4801 -1.9459 1.5975 0.8582 -0.6840 GENE669X 0.3422 -0.6528 0.5817 -0.1829 -2.0991 1.4016 0.6278 -0.4961 GENE674X 0.2993 -0.7431 0.4645 -0.2684 -2.1262 1.3005 0.9599 -0.4438 GENE675X 1.4366 -0.8638 0.4712 0.5843 -0.4489 1.5497 0.8483 0.2977 GENE676X -0.0657 -1.1185 -1.0822 1.7374 -1.3969 0.5273 1.0960 0.9266 GENE704X 1.2967 0.2506 0.9587 0.9185 3.1152 0.8219 0.8058 1.1438 GENE734X 1.2061 -0.7902 0.9597 1.0277 3.2704 1.0956 1.0532 1.3260 -GENE738X 0M496 -0.0124 2.0445 0.6260 -1.1472 1.3589 0.1768 0.6496 GENE456X 1.2499 -0.8934 1.1887 0.7753 2.0766 2.3063 0.1017 0.4844 GENE744X 1.1394 -0.8820 1.7811 0.8025 2.1982 1.7170 -0.0477 0.1929 GENE179X 1.0929 1.1389 1.6681 1.3690 0.7018 1.6221 1.7602 0.4717 GENEI24X 0.3305 -0.8000 0.9172 1.7615 1.1032 1.5755 0.4020 0.1731 WO 03/007177 WO 03/07177PCT/ATJO2/00934 54 GENE122X 0.3044 -1.0621 0.8206 1.9593 1.0787 1.2002 1.0332 0.5018 GENElIIX 0.7197 -1.1518 0.5027 1.1944 1.1808 1.8047 0.6655 -0.2296 GENE97X -0.4934 -0.6572 -0.0183 0.9482 -0.3951 3.1435 1.8820 0.4404 GENE2645X 0.5361 0.4370 -1.1685 1.944 -1.2676 0.2190 1.2893 0.9325 GENE3408X 1.6983 -0.2464 -0.5215 -0.8884 -1.42C5 -0.1730 -0.8517 -1.1269 GENE3854X 0.9695 -0.4263 -0.9433 -1 .4775 -0.5814 0.5387 -0.6331 -0.5125 GENE1405X 0.4028 2.2058 -1.0663 -0.7102 0.6031 -1.3334 -1.2666 -1.1108 GENE1401X -0.8891 0.0075 -1.3925 -0.3071 0.2120 -0.0240 -0.0554 -0.7318 GENE3462X 0.9408 -1 .0161 -0.3011 0.5833 0.6279 2.6908 0.01 88 -0.3763 GENE3173X 1.8484 -0.4708 0.3418 2.1023 0.8158 1.0189 -0.2507 -1.1140 GENE3971X -2.2436 1.6365 0.9072 -0.6099 -2.0394 1.3740 -0.4057 -0.9308 GENE1756X -0.0119 1.3443 -0.4016 -0.2301 1.1105 -1.3525 0.5849 -0.6510 GENE1533X -0.2496 0.2166 -0.8661 -0.5202 0.8181 -0.6105 0.9685 -0.7759 GENE1757X -1.1302 0.3099 -0.7498 -1.1030 -2.3529 -1.3204 0.2555 -0.1520 GENE3572X -0.3785 0.2305 -1.2920 -1.4843 -1.1477 -0.7631 0.4869 -0.7952 GENE3571X 2.8319 -0.4543 0.8329 -0.3635 -0.5906 1.4841 -0.5603 0.4240 GENE385X 2.7289 -2.2939 0.7665 1.1403 -0.5184 0.5329 1.1403 0.6497 GENE1614X 2.4646 0.4045 0.6382 1.0842 -0.4450 -0.2963 0.6594 0.2559 GENE1623X 1.0462 -2.8304 -0.5871 1.3021 -0.4100 0.8495 0.3968 -0.3509 GENE1646X 3.8354 0.2676 0.9906 2.5623 0.0947 -0.8484 -0.7698 -0.2825 GENE166OX -0.3365 -0.4352 0.2136 0.4110 -1.7469 -2.5790 -0.8160 0.2277 GENE1721X -0.0845 1.8847 0.3166 0.8272 -1.3366 -3.0870 -0.8625 0.2194 GENE1573X 0.0753 1.1166 1.0485 2.8976 1.5838 0.1337 -0.5378 1.4573 GENE1553X 0.4029 -0.3496 0,4212 1.4306 -1.0836 1.6692 0.8984 0.3662 GENE1773X 0.6814 0.8436 0.8639 1.7963 -1.1834 1.4720 0.1139 0.3977 GENE9I3X -1.8551 1.0880 0.5927 -0.8788 -1.7761 -1.8048 -0.2687 -1.3528 GENE398OX -1.8189 1.1788 0.4574 -0.9854 -2.0990 -1.8189 -0.1729 -1.39B5 GENE3X 0.6278 0.7329 1.2594 1.5928 -0.6008 0.9786 -0.6008 0.8205 RowNames DLCLOO51 DLCLOO52 GENE395OX -1.7024 -2.8006 GENE2531X -2.0292 -2.2322 GENE918X -2.0232 -2.1684 GENE3511X -1.2043 -1.4193 GENE3496X -1.6643 -1.7446 GENE34B4X -1.6699 -1.6163 GENE3789X -1.6753 -1.8037 GENE3692X -0.1133 2.3233 GENE3752X -0.9678 -0.4957 GENE374OX -0.81 03 0.8574 GENE3736X 0.6951 -2.2716 GENE3682X -1.1782 -1.3402 GENE3674X -1.2693 -1.4610 GENE3673X -1 .3244 -1.6575 GENE3644X -0.21 56 -1 .7376 GENE3472X -0.9702 -1.1023 GENE253OX -2.4891 -1 .2592 GENE2287X -2.6513 -0.6220 GENE2328X 0.7294 -0.8751 WO 03/007177 WO 03/07177PCT/ATJO2/00934 56- GENE456X -1.2762 -0.2657 GENE744X -1.3312 -0.5290 GENE179X -0.4027 -0.9779 GENE124X 0.4593 -2.2024 GENE122X 0.5929 -2.2160 GENE1iIX 0.8553 -2.0875 GENE97X 1.0829 -0.3624 GENE2645X -1.7830 -0.7919 GENE3408X 1.3313 0.8544 GENE3854X 1.2453 0.8317 GENE1406X -0.1537 2.0054 GENE14OIX -0.5903 2.4299 GENE3462X -0.0941 0.6774 GENE3173X -0.9786 -1 .4865 GENE3971X 0.0903 1.4907 GENE1756X 0.5493 1.3911 GENE1533X 1.4046 2.0814 GENE1757X -0.8721 3.4890 GENE3572X 3.0670 -0.3625 GENP-3571X -0.9238 -1.4084 GENE385X -0.0979 2.1215 GENE1614X -0.3388 1.6363 GENE1623X -1.4332 0.4165 GENE1646X 0.2676 -1 .0055 GENE166OX 0.8482 0.6200 GENE1721X 0.8515 0.7664 GENE1573X -1.8127 -2.6010 GENE1553X -1.9462 -0.3312 GENE1773X -2.2779 -0.41 31 GENE913X 0.0974 0.5999 GEN4E3980X 0.1422 0.8567 GENE3X -1.2151 -1.4783 In the claims which follow and in the preceding description of the invention, except where 'he context requires otherwise due to express language or necessary imnlication, the word "comprising" is used in the sense of "including", i.e. the features specified may be associated with further features in various embodiments of the invention.

It is to be understood that a reference herein to a prior art document does not constitute an admission that WO 03/007177 WO 03/07177PCT/ATJO2/00934 GENE2417X -1.1206 -1.5131 GENE2238X -0.2736 0.9537 GENE1971X -0.8365 -1.4208 GENE3086X 0.7993 -0.3583 GENE1009X 1.5015 1.2683 GENE1947X 0.7868 1.0058 GENE319OX -0.9562 1.8209 GENE3379X 0.2502 1.9969 GENE3184X 0.3777 2.1342 GENE3122X 0.2167 1.4484 GENE1099X 1.0126 1.4027 GENE3032X 1.0604 1.9037 GENE2675X -0.2241 1.5166 GENE248IX 0.0043 1.7542 GENE2878X -0.5562 0.7062 GENE2943X -1.0712 0.2113 GENE2977X -1.1463 1.6095 GENE3014X -1.1955 0.6637 GENE2006X -0.0097 0.5167 GENE1368X -0.6901 1.8846 GENE1184X -0.8433 1.7039 GENE1226X 0.3867 0.6733 GENE1228X 2.4403 -0.5182 GENE1231X 1.0471 1.6664 GENE1246X 0.9617 1.3825 GENEl172X 0.2532 1.4007 GENE1164X -0.3717 -0.7366 GENE302OX -0.4341 1.1935 GENE1027X 2.2832 -0.1104 GENE1354X -0.2088 0.7781 GENE62X -0.1946 1.0105 GENE932X -1.8029 -0.4099 GENE3611X -0.3161 -0.268 GENE3631X 1.3227 -1.3708 GENE33OX 0.0591 -0.1386 GENE331X 1.8637 -1.7847 GENE808X 3.9324 0.9117 GENE487X -0.6842 0.1484 GENE621X' -0.6422 0.4310 GENE622X -0.1078 0.9678 GENE634X -0.4048 -1 .6227 GENE659X 0.6925 -1.4998 GENE669X 0.0290 -2.2004 GENE674X -0.2684 -2.3635 GENE675X 0.0262 -2.7342 GENE676X -1.5179 -1.2516 GENE704X -1.3668 -1 .6323 GENE734X -1.7332 -1.0536 GENE738X -0.8399 -0.7216 WO 03/007177 PCT/AU02/00934 57 the document forms part of the common general knowledge in the art in Australia or in any other country.

Claims

1. A method for identifying components of a system from data generated from the system, which exhibit a response pattern associated with a test condition applied to the system, comprising the steps of: specifying design factors to specify a response pattern for the test condition; identifying a linear combination of components from the input data which correlate with the response pattern.

2. The method of claim 1 wherein the design factors are specified as a matrix of design factors.

3. A method according to claim 1 or 2 wherein the linear combination of components is in the form of: Y=aiXI a 2 X 2 a 3 X 3 anXn wherein Y is the linear combination, al-an are component weights generated from the method and XI-Xn are data values for components of the system.

4. A method of claim 3 further comprising the step of: establishing the weights of the components by maximising the value X of a test for significance of a linear regression of the linear combination of the components on the design factors. A method of claim 4, wherein the test for significance of the linear regression is performed by calculating S= atBa atWa where W is a within groups matrix, and B is a between groups matrix AMEDED SHEET IPEPVAU PCT/AU02/00934 59 Received 10 April 2003 wherein B XPX T and W X(I-P)XT, wherein X is a data matrix having n rows of components and k columns of test conditions, P T(TTT)-TT wherein T is a matrix of k rows of design factors and r columns, and a is a weight matrix for the linear combination yT=a X.

6. A method of claim 5, wherein the maximum value of k is obtained by solving the equation (B-W)a 0, (1) to determine a and k.

7. A method of claim 6, further comprising the steps of: substituting X(I-P)X T +U2I for the within groups matrix W; and solving Equation 1 to identify the linear combination.

8. A method of claim 6 or 7 further comprising the step of solving Equation 1 without requiring calculation of B or W by using the generalised singular value decomposition.

9. A method of any one of claims 6 to 8, further comprising the step of generating at least one intermediate matrix in solving Equation 1, wherein the size of each intermediate matrix is no greater than the size of the data matrix X. A method according to any one of claims 6 to 9, further comprising the steps of: a) establishing a model covariance matrix V substituting V for the within groups matrix W in Equation 1; and AMENDED SHEET IPEA/AU PCT/AU02/00934 Received 10 April 2003 solving Equation 1 to identify the linear combination using the matrix V substituted for the within groups matrix W.

11. A method according to claim 10, further comprising the steps of: establishing a model of the data generated from the system; and estimating the covariance matrix in the model given the available data.

12. A method according to claim 10 or 11, wherein the covariance matix V is of the form VAA 2= wherein Ais an n by s matrix of factor loadings,$D is a diagonal s by s matrix and 02 is a variance parameter;

13. A method according to claim 11 or 12, further comprising the steps of: establish a model for the residuals of the regression of the input data on the design factors; and estimating parameters for the model.

14. A method for identifying components of a system from data generated from the system, which exhibit response patterns to a test condition applied to the system, comprising the steps of: specifying design factors to specify a response pattern for a test condition; establishing a model for the residuals of a regression of the input data on the design factors; estimating parameters for the model; and computing a linear combination of components using the model and the estimated parameters. A method of claim 14, wherein the linear combination of components is in the form of: AAMENDED SHEET IPSEVAU PCT/AU02/00934 61 Received 10 April 2003 Y=a l XI a 2 X 2 a 3 X 3 anXn wherein Y is the linear combination, a,-an are component weights generated from the method and X 1 -Xn are data values for components of the system; and wherein the method further comprising the step of: establishing the weights of the components by maximising the value k of a test for significance of a linear regression of the linear combination of the components on the design factors, wherein the maximum value of k is obtained by solving the equation (B-XW)a 0, (1) to determine a and k wherein B XPXT and W X(I-P)XT, wherein X is a data matrix having n rows of components and k columns of test conditions, P T(TTT)-TT wherein T is a matrix of k rows of design factors and r columns, and a is a weight matrix for the linear combination y=a T X.

16. A method of claim 13 or 15, further comprising the steps of: modelling the data using a multivariate normal distribution which is specified by mean model and variance model to establish the data model using the data model to model for the residuals estimating the parameters in the mean model and the variance model; and establishing the covariance matrix from the data model in the form of: vA(+A2 =I wherein Ais an n by s matrix of factor loadings, is a diagonal s by s matrix and a2 is a variance parameter; AMENDED SHEET IPEAAU PCT/AU02/00934 62 Received 10 April 2003

17. The method of any one of claims 12 or 16,wherein the estimate of A may be computed from the left singular vectors of R, wherein R=X-B TT, and .=X T T(T T T) 1

18. The method of claim 17 wherein the estimate of a 2 is computed from the equation: s {tr{RR T I I=1 wherein the 8ii are the squares of the singular values of R.

19. The method of claim 18 wherein the estimate of d>is computed from the equation: 2 /k A method of claim 19, wherein the linear combination is identified from the equation: a= Xpu (2) wherein a is the vector of weights for the linear combination yT=aX,P T(TT)-TT,u is an eigenvector of P(XV-XT)P or equivalently a right singular vector of V-1/2XP; and X is an nxk data matrix of data generated from a method applied to a system, wherein the data is from n components and k test conditions.

21. A method of any one of claims 12,16 to 20 wherein the number of factors s in the variance model V is computed using the Bayesian method whereby the number of factors is chosen to maximise AMENDED SHEET IPEAJAU PCT/AU02/00934 63 Received 10 April 2003 log P(R s) log P(u) 0.5nX log(Aj) j=1 .5n(k log(v) +0.5(m s) log(2z) log det(A) 0.5s log(n) where m=ks-s(s+l)/2, lo gP(u)= -s log(2)+ {log(F((k- i 1) 2)) i=1 log()} k j=s+l and s k logdet(A) C log(( A)n) i=1 j=i+1 where Afor j<k v,otherwise. and the Xj are the squared singular values of the matrix R.

22. A method for estimating missing values from the results of the method applied to the system of any one of claims 1 to 21 when using the data model of claim 16, the method comprising the steps of: estimating initial values of B, A,AD and a by replacing missing values with simple estimates and calculating maximum likelihood estimates assuming the data was complete; computing E{Xlol,... Ok) and E{RRTIol,...,Ok) the expected values of the data array and the residual matrix under the model given the observed data and current parameter estimates; substitute quantities from into likelihood equations assuming the data is complete to obtain estimates of B, A, Oand C 2 repeat steps and until convergence. /4AMENDED SHEET io=A- NO IR PCT/AU02/00934 64 Received 10 April 2003

23. A method of any one of claims 1 to 22 comprising the further step of: determining the significance of each weight of the linear combination; and setting non-significant weights to zero.

24. A method of claim 23 wherein the significance of the weights of the linear combination is determined by a permutation test comprising the steps of: a) randomising the data for the components of a linear combination; b) computing the weights and eigenvalues from the randomised data; c) repeating steps a) and b) a plurality of times; d) determining a distribution for the weights and eigenvalues computed from the randomised data; e) determining the position of weights and eigenvalues computed from non-randomised data relative to the distribution of the weights and eigenvalues computed from randomised data; and f) determining the significance of each weight computed from the non-randomised data. A method of any one of claims 1 to 24 wherein the significance of the overall linear combination is determined by a permutation test comprising the steps of: randomising the data for the components of a linear combination; computing the weights and eigenvalues from the randomised data, and from these computing the squared multiple correlation coefficient of the linear combination with the columns of the design basis; repeating steps a) and b) a plurality of times; determining a distribution for squared multiple correlation coefficient computed from the randomised data; AENDED SHEET PCT/AU02/00934 Reeived 10 April 2003 determining the position of the squared multiple correlation coefficient from non-randomised data relative to the distribution of the squared multiple correlation coefficient computed from randomised data; and estimating the significance of the squared multiple correlation coefficient computed from the non-randomised data.

26. The method of any one of claims 1 to 25 wherein the response pattern as specified by the design factors is derived from known data.

27. A method of any one of claims 1 to 25 wherein the response pattern as specified by the design factors is derived from the input array data.

28. A method of any one of claims 1 to 25 wherein the response pattern as specified by the design factors is selected to identify an arbitrary response pattern.

29. A method of any one of claims 1 to 28 wherein the data is generated from the system using a method selected from the group consisting of DNA array analysis, DNA microarray analysis, RNA array analysis, RNA microarray analysis, DNA microchip analysis, RNA microchip analysis, protein microchip analysis, carbohydrate analysis, DNA electrophoresis, RNA electrophoresis, one dimensional or two dimensional protein electrophoresis, proteomics, antibody array analysis. A computer program which includes instructions arranged to control a computing device to identify linear combinations of components from input data which correlate with a response pattern in a defined matrix of design factors specifying types of response patterns for a set of test conditions in a system.

31. A computer readable medium providing the computer medium of claim AMENDED SHEET IP4 UA PCT/AU02/00934 66 Received 10 April 2003

32. A computer program which includes instructions arranged to control a computing device, in a method of identifying components from a system which exhibit a response pattern to a test condition applied to the system, and wherein a matrix of design factors specifying the response patterns for the test conditions is defined, to formulate a module for the residuals of a regression of the input data on the design factors, to estimate parameters for the model and compute a linear combination of components using the estimated parameters.

33. A computer readable medium providing the computer program of claim 32.

34. An apparatus for identifying components from a system which exhibit a response pattern associated with test conditions applied to the system, and wherein a matrix of design factors to specify the type of response patterns for the set of tests and conditions is defined, the apparatus including a calculation device for identifying linear combinations of components from the input data which correlate with the response pattern. An apparatus for identifying components from a system which exhibit a preselected response pattern to a set of test conditions applied to the biotechnology array, wherein a matrix of design factors to specify the response pattern(s) for the test conditions is defined, the apparatus including a means for formulating a model for the residuals on a regression of the input array data on the design factors, means for estimating perimeters for the model and means for computing a linear combination of components using the estimated perimeters.

36. A computer program which includes instructions arranged to control a computing device to implement the method of any one of claims 1 to 29. AMENDED SHEET IPEA/AU