Method and apparatus for displaying gene expression patterns

Abstract: The present invention aims at finding out and effectively displaying that different genes give the same expression for having the same function at one time point but give different expressions at another time point for having different roles. Furthermore, the present invention aims at automatically extracting brief groups of clusters from the clustering results of gene expression pattern data so that a user can select a desirable level of grouping.

Description: FIELD OF THE INVENTION The present invention relates to a method and an apparatus for displaying gene expression patterns resulting from hybridization with a specific gene at certain time points in a display format (or an output format) that displays the patterns in a visually apprehensible manner so that the functions and roles of the genes can easily be studied. BACKGROUND OF THE INVENTION With the increase in the number of species that have been determined of their genome sequences, so called genome comparison has extensively been performed. Genome comparison aims at finding something based on gene difference among species, for example, finding genes involved in evolution, finding a collection of genes which are considered to be common to all species, or conversely studying the nature unique to specific species. The recent development of infrastructures such as DNA chips and DNA microarrays has changed the interest in the art of molecular biology from information of interspecies to information of intraspecies, namely coexpression analysis, and broadened the study covering from extraction of information to correlation of information, including the conventional comparison between species. For example, if an unknown gene has an expression pattern identical to that of a known gene, the unknown gene can be assumed to have a similar function to that of the known gene. Such functional meanings of genes and proteins are studied as function units or function groups. The interactions between the function units or function groups are also analyzed by correlating with known enzymatic reaction data or metabolism data, or more directly, by knocking out or overreacting a specific gene to eliminate or accelerate expression of the gene to study the direct and indirect influences on the gene expression patterns of the whole collection of genes. One successful case in this art field is the expression analysis of yeast by the group of P. Brown from the Stanford University (Michel B. Eisen et al., Clustering analysis and display of genome-wide expression patterns, Proc. Natl. Acad. Sci . (1998), Dec 8; 95(25): 14863-8). They hybridized genes with a gene extracted from a cell in a time series using a DNA microarray and numerated the expression levels thereof (i.e., numerated the brightness of the hybridized fluorescent signals). By converting the values into colors, the expression pattern of each gene can be displayed in a visually apprehensible manner. At this point, genes that have a similar expression pattern during their gene cycles (genes having closer expression levels at the same point) are clustered together. Figure 24 is a diagram showing an example of displaying expression status 2400 of genes according to the above-described method, where the horizontal and vertical axes indicate time and genes, respectively. In this display, genes belonging to a common cluster may be considered to have common functional characteristics. In Figure 24 , each of the blocks 2401 represents expression status of a gene at one time point. In the figure, the expression status is schematically represented in a gray scale format. Figure 25 is a diagram showing an example of displaying expression status 2500 of genes according to the above-described method, where the horizontal and vertical axes indicate experiment cases and genes, respectively. A dendrogram shown on the left is made by stepwisely joining every two most similar clusters together. The length of each branch corresponds to the distance between the two joined clusters. In Figure 25 , each of the blocks 2501 represents an expression status of a gene at one time point. In the figure, the expression status is schematically represented in a gray scale format. The above-described displaying method allows a supposition that genes belonging to the same cluster may possibly share common functional characteristics. With the gene expression patterns, however, it is not so simple as to elucidate the relationship among all of the genes in a cell by finding some gene groups having similar expression patterns for the entire cell cycle. For example, different genes may exhibit similar expression for having similar function at a certain time point. However, they may have different roles at other time point, at which point, of course, the expressions are different. According to the conventional method in which similar expression patterns are clustered together over the entire cell cycle, these genes are classified into different clusters. Therefore, it is difficult to find the above-mentioned characteristics. In an actual analysis of gene expression patterns, enormous amount of data will be subjected to clustering as shown in Figure 25 . The number of genes is several thousands to ten-thousands, or more than hundred-thousands at maximum. The experiment cases (data) employed may be of any number, for example, in an order of about ten to tens or hundreds. Thus the dendrogram shown in Figure 25 will be very complicated, containing vast numbers of small branches. Figure 26 shows such a complicated case. The left part of Figure 26 shows the entire results of clustering, targeting mass data of gene expression patterns. The right part of Figure 26 surrounded by a dotted line 2601 shows the results in a particular region enclosed in a window determined by a user to actually see a narrowed part of the entire results in more detail. The thus-obtained dendrogram 2602 represents the precise course of joining the most similar clusters. However, it is difficult for the user to find out how many clusters have briefly been classified by looking at this display to judge and guess the groupings of the genes. The present invention has an objective of solving such conventional art problems by providing a method and an apparatus for effectively displaying gene expression patterns by finding different genes exhibiting similar expression for having the same function at one time point but having different roles at a different time point. SUMMARY OF THE INVENTION In order to achieve the above-described objectives, the present invention provides a method for displaying gene expression pattern for visually displaying time sequential expression patterns of multiple genes whose expressions change according to experiment cases where a first axis represents the genes and a second axis represents the experiment cases,, the method comprising the steps of: designating a segment along the second axis in the expression pattern data of the multiple genes; and clustering the expression pattern data within the designated segment along the second axis based on a predetermined reference value, repeating clustering within the same cluster in a forward or reverse direction along the second axis while changing the reference value, and displaying the results according to a predetermined display format. The reference value refers to value for determining whether expression patterns of distinct genes are the same or different. Furthermore, the present invention displays two or more different genes according to the predetermined display format where they have the same expression pattern at the beginning but become to have different expression patterns within the segment along the second axis. The present invention also displays two or more different genes according to the predetermined display format where they have different expression patterns at the beginning but become to have the same expression pattern within the segment along the second axis. The experiment cases may be time sequential experiments, states of individual's tissue, species of individuals, individual's sites, or presence and absence of an artificial condition. Alternatively, the experiment cases may be combinations of some of the group consisting of time sequential experiments, states of individual's tissue, species of individuals, individual's sites, and presence and absence of an artificial condition. The present invention is also an apparatus for analyzing gene expression patterns, which acquires, from a database, expression pattern data of multiple genes whose expressions change according to experiment cases, and which visually displays the expression patterns on a screen of a display device where a first axis represents the genes and a second axis represents the experiment cases, the apparatus comprising: an inputting means for designating a segment along the second axis in the expression pattern data of the multiple genes obtained from the database; and an arithmetic unit for clustering the expression pattern data within the designated segment along the second axis based on a predetermined reference value, repeating clustering within the same cluster in a forward or reverse direction along the second axis while changing the reference value, and displaying the results according to a predetermined display format. The invention is carried out by performing the method steps of claim 1, as annexed. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 is a schematic view showing one embodiment of an analyzing apparatus according to the present invention. Figure 2 is a schematic view showing one example of gene expression pattern where a narrowed region has been subjected to clustering in a small range. (in forward time direction) Figure 3 is a schematic view showing another example of gene expression pattern where a narrowed region has been subjected to clustering in a small range. (in reverse time direction) Figure 4 is a flowchart showing a scheme of the clustering process. Figure 5 is a diagram for illustrating the relationship between the variables used for clustering and the actual data. Figure 6 is a flowchart showing an algorithm with respect to the process of setting the initial parameters. Figure 7 is a flowchart showing an algorithm of the process of determining the display area. Figure 8 is a flowchart showing an algorithm for clustering. Figure 9 is a flowchart showing the process following the process shown in Figure 8 . Figure 10 is a flowchart showing a general algorithm for displaying. Figure 11 is a diagram for illustrating an exemplary display of gene expression patterns resulting from clustering carried out by shifting a slit from the beginning of the clustering-applied region in a forward direction along the time axis. Figure 12 is a diagram for illustrating an exemplary display of gene expression patterns resulting from clustering carried out by shifting a slit from the end of the clustering-applied region in a reverse direction along the time axis. Figure 13 is a schematic view showing an exemplary display of the results of a clustering analysis of gene expression patterns according to the present invention. Figure 14 is a schematic diagram showing an exemplary structure of gene expression pattern data. Figure 15 is a schematic diagram showing an exemplary structure of a cluster. Figure 16 is a schematic diagram showing an example of generating a tree structure of clusters. Figure 17 is a diagram showing an example of display data. Figure 18 is a flowchart showing a general process of displaying gene expression patterns according to the invention. Figure 19 is a flowchart for illustrating clustering analysis with respect to the process of generating a cluster tree. Figure 20 is a flowchart for illustrating the clustering analysis with respect to the process of setting the cluster level. Figure 21 is a flowchart for illustrating the clustering analysis with respect to the process of generating display data. Figure 22 is a flowchart showing Process A (in Figure 21 ) of display data generation in detail. Figure 23 is a view showing an example of combining and clustering cancerous and normal cells. Figure 24 is a diagram for illustrating an exemplary display of gene expression patterns obtained by clustering similar expression patterns together over the entire cell process. Figure 25 is a diagram showing an exemplary display of the results of a standard clustering analysis of gene expression patterns. Figure 26 is a schematic diagram showing an exemplary display of the entire results of clustering analysis and a corresponding display of the targeted cluster tree. DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, the present invention will be described in more detail with reference to the accompanying drawings. In the following example, time sequential experiments are exemplified as typical experiment cases. Figure 1 is a schematic view showing a system structure of one embodiment of an apparatus for analyzing gene expression patterns, which employs a method of the invention for displaying gene expression patterns. The analyzing apparatus of this embodiment is provided with a storage medium (or database) 101 for storing data of gene expression patterns obtained by numerating the levels of gene expressions obtained during a series of cell process, a display 102 for visualizing and displaying the data of expression patterns, a keyboard 103 and a mouse 104 for inputting values into the system or for selection, and a clustering processor 105 for clustering the expression pattern data along the course of gene expressions. The clustering processor 105 is embodied with a computer and a program therefor. An alternative embodiment is configured such that the data of gene expression patterns is acquired from, instead of the storage medium 101 , database supervised by a remotely provided server computer via a network or the like. According to this embodiment, a time segment is designated among the cell cycle to perform clustering within this time segment in a small range. Specifically, genes belonging to the same cluster are gathered together and a dividing line is drawn between distinctive clusters. Clustering is further performed for the genes belonging to the same cluster. As shown in Figure 2 , when clustering is repeatedly performed in small range from the beginning of the clustering-applied region in the forward direction along the time axis, the course of gene expressions can be expressed as a tree structure. In Figure 2 , the designated time segment is shown as a clustering-applied region 201 . In other words, the expression patterns within the clustering-applied region are arranged that they have the same expression levels at the beginning but become different at some point in the time segment. When such a display is obtained, it can be assumed that different genes exhibited similar expressions at the beginning for having the same function, but became to give different expressions at some time point for having different roles. Similarly, when small-range clustering is performed from the end of the clustering-applied region in reverse time direction, the gene expression patterns may be arranged into a reverse-tree structure as shown in Figure 3 . This display indicates expression patterns that are different at the beginning of the region but become the same at some point in the time segment. It can be assumed that, in this case, different genes have different functions at the beginning but at some time point become to have similar roles. Figure 4 is a flowchart showing a scheme of algorithm employed in the clustering processor 105 for clustering and displaying gene expression pattern data. First, initial parameters are set (Step 401 ) and a display area is determined (Step 402 ). The initial parameters will be described later in more detail. Then, displaying process is performed (Step 403 ), whereby the whole processing is ended. The present algorithm is for displaying expression patterns of different genes which at the beginning are the same but becomes different at some time point (Figure 2 ). Figure 5 is an illustration showing the relationship between the variables used in the present algorithm and the actual data. Figure 6 is a flowchart showing details of the algorithm with respect to the process of setting the initial parameters (Step 401 in Figure 4 ). First, data of gene expression patterns are read out from the storage medium 101 . As shown in Figure 5 , the data of gene expression patterns contains expression pattern data of m+1 number of sample genes g 0 , g 1 , ...g m , obtained by experiments at time T 0 , T 1 , . . .T n . The observed expression value of gene g i at time T j is indicated as g[j][i] (Step 601 ). Then, the keyboard 103 and the mouse 104 are used to input a clustering-applied region (starting time T start and ending time T end ), positive values (K start , K start+1 ,...K end ) as reference values for distinguishing clusters, an integer (S) indicating a range of clustering, and the methods of clustering (Step 602 ). The solid lines 201 in Figures 2 and 3 indicate the clustering-applied regions, namely a time segment within the cell cycle designated for further detailed clustering. For example, if a cell shows a particular expression pattern at some time point during the cell cycle, the clustering-applied region may be designated to include that time point to monitor in further detail the expression status of every gene. The present invention is different from the conventional clustering fundamentally in that it does not cluster genes with similar expression status over the entire cell cycle as shown in Figure 24 , but arranges the gene patterns such that different genes have the same expression patterns at the beginning of the region but become to have different expression patterns within the region as shown in Figure 2 . The reference value for distinction between different clusters is a minimum value, namely threshold K , of dissimilarity between the clusters. Since thresholds can variably be set as K start , K start+1 ,..., K end , the level of clustering may be regulated to be brief or detailed along the time axis. The present system does not target the entire expression data obtained at time T 0 , T 1 , ...T n for calculating dissimilarity for clustering, but selects a certain time segment to target data within that time segment for calculating dissimilarity. As shown in Figure 5 , this time segment is called a slit 501 and the length S (a width along the time axis) of this slit 501 is called a clustering range. According to the present algorithm, the beginning of the slit 501 is set to T start for clustering data within a range T start to T start+g . Then, the slit 501 is shifted in a forward direction along the time axis to cluster each of the grouped clusters within a range T start+1 to T start+S+1 . This procedure is repeated until the end of the slit reaches T end . The smaller the clustering range is (i.e., the shorter the width of the time segment is), the more detailed difference between the gene expressions will be obtained. In the clustering process, the similarity or dissimilarity which indicates correlation between the genes upon clustering (Pearson's correlation coefficient, squared Euclidean distance, standardized squared Euclidean distance, Mahalanobis' distance, Minkowsky distance, etc.) and algorithm for joining the clusters (nearest neighbor method, furthest neighbor method, group average method, centroid method, median method, Ward method, flexible method, etc.) are designated. The present algorithm utilizes dissimilarity. When similarity is selected for clustering, a minus sign may be applied in front of the calculated similarity to give an inverse number as for conversion to dissimilarity. Once these values are set, each item is checked whether it is proper or not. The items are (i) whether the clustering-applied region T start to T end is included in the region T 0 to T n (Step 603), (ii) whether the clustering range S is within the width of the clustering-applied region ( S ≦ end-start) (Step 604 ), and (iii) whether the similarity or dissimilarity complies with the algorithm of the selected clustering method (for example, when centroid method, median method or Ward method is employed as the algorithm, squared Euclidean distance should be selected) (Step 606 ). If any of these values is not proper, an error signal is output to the display device 102 to demand re-entering (Step 607 ). When all of the set items are confirmed proper, an average level G i = (g[0][i]+g[1][i]+...g[n][i])/n) of expressions of a gene g i (where i = 1, 2,...m) is calculated (Step 608 ). In order to store display information of each gene, an array l [I] (I = 0, 1,...m) 502 (Figure 5) and an integer variable lmax are prepared. Each l [I] is a structure data that consists of a member indicating an index of a gene ( index ) and a member indicating the location of the dividing line between distinctive clusters ( linepos ) as shown in Figure 5 . The members of the structure can be set or referred to as 1 [ I ].index or 1 [I].linepos. For all "I", the value of 1 [I].linepos is initiated as T end (Step 609), and the value of 1max is set to "0" (Step 610 ). Then, the value of "start" is set to variable t (Step 611 ). The present algorithm employs an abstract data type called cluster" which indicates set of integers. A cluster has an interface for registration and deletion of an integer and for reference of the registered data. Finally, cluster B is generated to which {0,1,2,...m} is registered (Step 612 ), whereby the whole process is ended. After the above-described initialization, the clustering-applied region 201 is processed. Specifically, the display area is determined by using t and B set above as arguments (Process A in Step 402 of Figure 4 ). Figure 7 is a flowchart showing in detail the process of determining the display area (Process A ) in Figure 4 . The display information is registered in the array l during this Process A . First, let the given cluster and time be B and t , respectively, as arguments (Step 701 ). Then, B is subjected to clustering (Process B ) where t and B are given as arguments. As a result of Process B , the total number of the clusters and the clustering results are set to cmax and A[J](J=1,2,...,cmax), respectively (Step 702 ). Details of Process B will be described later. Then, whether "t + S" equals "end" is judged (Step 703 ). When they are equal, it means that the end of the slit 501 has reached the end of the clustering-applied region 201 , whereby the clustering process is ended. The following process is executed for each cluster until J exceeds cmax (starting from J = 1) (Steps 704 and 705 ). Where the elements of cluster A[J] are {i 1 ,...,i k }, these elements are aligned and displayed under a certain rule. Here, the average of the expression levels G il ,...G ik corresponding to the elements are aligned in a descending order to be rearranged as G j1 ,...G jk (Step 706 ). Then the value for array l is entered. Specifically, " l [].index " which indicates the positional information of the expression pattern data is set as l [1max].index = j 1 , 1 [1max+1].index = j 2 ,..., 1 [1max+k-1].index = j k such that they are arranged in a descending order according to their average brightness (Step 707 ). The value "t" is entered into " l [lmax+k-1].linepos" which indicates the line for dividing distinctive clusters (a solid line 202 extending in horizontal direction is representatively shown in Figure 2) from time "t" to " t + S (= T end )" (Step 708 ). Next, k is added to lmax for indicating the maximum number of the already entered data of array l (Step 709 ). Then, J is incremented to perform the next clustering (Step 710 ). On the other hand, when "t + S" does not equal "end" (i.e., when the end of the slit 501 does not reach the end of the clustering-applied region 201 ) at Step 703 , t is incremented and J is set to "1" (Step 711 ). The following procedure is performed for each cluster until J exceeds cmax (Step 712 ). Specifically, A[J] is substituted for B (Step 713 ), and the display area is determined using time t and cluster B as arguments (Process A ) (Step 714 ). Then, the value "t" is entered into " l [1max-1].1inepos" which indicates a line for dividing distinctive clusters from time "t" to "T end " (Step 715 ). Then, J is incremented to perform the next clustering (Step 716 ). If the procedure is finished for all clusters A[J] (J = 1,...,cmax), the process is ended. Figures 8 and 9 are flowcharts showing algorithm of the clustering process (Process B ). First, the entered cluster and time are used as arguments B and t , respectively (Step 801 ). Then, where the elements of cluster B are i 1 ,...i k , similarity or dissimilarity d ij (i < j and i, j ∈ {i 1 ,i 2 ,...,i k }) between genes corresponding to i 1 ,...,i k from time t to time t + S is calculated (Step 802 ). The similarity (dissimilarity) of gene expression data {g [0] [i],g[1][i],...,g[n][i]} of genes g i ,g j is a value obtained, for example, by the following calculation ( Step 802 ). (1) When Pearson's correlation coefficient is designated as similarity, where Since the present algorithm targets dissimilarity, the obtained similarity has to be converted to dissimilarity by providing with a minus sign to give an inverse number. (2) When squared Euclidean distance is designated as dissimilarity, (3) When standardized squared Euclidean distance is designated, where s 2 k is a variance of variable g[k] [0],...,g[k] [n] (4) When Mahalanobis' distance is designated, d i,j = t ( g [ i ]- g [ j ] S -1 ( g [ i ]- g [ j ]) where g[l] = t (g[t][l],...,g[t+S][l]), and S is covariance matrix of g[i],g[j] (5) When Minkowsky distance is designated, Then, clusters C[l],...,C[k] are generated which are registered as C[l]←{i 1 },...,C[k]←{i 2 } (Step 803 ). Variable ccnt indicating the number of the generated clusters is substituted with k (Step 804 ). Then, a null set of cluster D is generated (Step 805 ). Then the minimum value d p,q of the calculated dissimilarity d i,j (i,j ∈ {1,2,...,ccnt}-D) is obtained to judge whether it is less than the preset threshold K t (Steps 806 and 807 ) . When d p,q is less than K t , the following procedure takes place. Cluster C[ccnt+1] is newly generated. A sum set of elements contained in clusters C[p] and C[q] is registered into cluster C[ccnt+1] (Step 808 ) and the elements are deleted (Step 809 ). Since C[p] and C[q] are no longer necessary, p,q are registered into D (Step 810 ). Then, dissimilarity d h,ccnt+1 between cluster C[h] (h ∈ {1,2,...,ccnt}-D) and cluster C[ccnt+1] from time t to time t+S is obtained (Step 811 ). d h,ccnt+1 can be obtained by the following calculation, where when n(k) is the number of elements in cluster C[k] and: (1) when the clustering method is nearest neighbor method, α d h,ccnt+1 = αd h,P + βd h,q + γd p,q + δ | d h,p - d h,q | = 0.5, β = 0.5, γ = 0 and δ = -0.5; (2) when the clustering method is furthest neighbor method, α = 0.5, β = 0.5, γ= 0 and δ = 0.5; (3) when the clustering method is group average method, α = n(p)/n(ccnt+1), β = n(q)/n(ccnt+1), γ = 0 and δ = 0; (4) when the clustering method is centroid point, a = n(p)/n(ccnt+1), β = n(q)/n(ccnt+1), γ = -n(p)n(q)/n(ccnt+1) 2 , and δ = 0; (5) when the clustering method is median method, α = 0.5, β = 0.5, γ= -0.25 and δ = 0; or (6) when the clustering method is Ward method, α = {n(h)+n(p)}/{n(h)+n(ccnt+1)}, β = {n(h)+n(q)}/{n(h)+n(ccnt+1)}, γ = - n(h)/{n(h)+n(ccnt+1)} and δ = 0. Then, "1" is added to the variable ccnt indicating the number of the generated clusters (Step 812 ). The above-described procedure is repeated until the minimum value of updated d i,j (i,j ∈ {1,2,...ccnt}-D) exceeds K t . When the minimum value d p,q of d i,j exceeds K t at Step 807, clustering is finished and the results are output. First, clusters which do not contain a null set are judged and determined among clusters C[1] to C[ccnt] and the total number thereof is entered as cmax (Step 813 ). Then, cmax number of clusters A[1],...,A[cmax] are generated (Step 814 ). Average expression levels of genes contained in the clusters without a null set are calculated, that is, to obtain G' p = (G il +...+G ik )/k for clusters C[p] = {i 1 ,...,i k }. Where the obtained values sequentially aligned in a descending order are G' p1 ,...G' pcmax , A[1],...A[cmax] are registered as C[p 1 ],... C[p cmax ] (Step 815 ). Finally, the total cluster number cmax and clusters A[1],...,A[cmax] are output (Step 816), thereby ending the whole process. Figure 10 is a flowchart showing detailed algorithm of the displaying process shown in Figure 4 . This algorithm reads out array l [I] and displays expression data of a corresponding gene. First, let value i be "0" (Step 1000 ) and repeat the following procedure for each gene expression data until value i equals lmax (Step 1001 ). Then, the values of expression data g[k][x](k = 0,1,...,n) corresponding to a single row of a gene indicated by x = l [i].index is converted to correlating display colors to be displayed as an i th row (Step 1002 ). The dividing line between the clusters is drawn immediately below the just displayed i th row from time l [i].linepos to T end (Step 1003 ). Where l [i].linepos is initial value T and , there is no need of drawing a dividing line between the clusters. i is incremented for 1 (Step 1004 ) and the entire procedure is ended when i becomes lmax at Step 1001 . By the above-described process, expression patterns of different genes can effectively be displayed such that the patterns are arranged to be the same at the beginning of the clustering-applied region and become different at some time point in the region, as shown in Figure 2 . On the other hand, gene expression patterns of different genes can effectively be displayed such that the patterns are arranged to be different at the beginning of a clustering-applied region and become the same at some time point in the region, as shown in Figure 3 . Such display can be realized by setting 1 [i].linepos to T start at Step 609 (Figure 6 ), setting t to end at Step 611 , setting the judgment condition t+S=end to t-S=start at Step 703 (Figure 7 ), substituting t←t-1 for t←t+1 at Step 711 , and drawing a dividing line between clusters within a range T start to l [i].linepos. This means that the end of a slit is set to T end at the beginning and then clustering step is performed while shifting the slit in a reverse direction along the time axis. Hereinafter, an exemplary application of such clustering method will be described, where clustering is performed by shifting the slit from the beginning of the clustering-applied region in a forward direction along the time axis to give a display shown in Figure 11 . When expression patterns resembling each other (enclosed with dotted lines 1101 and 1102 in Figure 11 ) are obtained, these genes are marked ( 1103 ) and clustering is performed for them from the end of the clustering-applied region 201 in the reverse direction along the time axis. If the marked genes ( 1103 ) are at close locations as shown in Figure 12 (e.g., (1) and (4), (3) and (6)), these genes should have different expression patterns at the beginning and should become to have the same expression patterns at some point. Such bi-directional clustering allows easy guessing of expression status of each gene. Furthermore, where T start , T end and slit width S are set to T 0 , T n and n , respectively, the same display as that of the result of P. Brown et al. mentioned in the Background of the Invention can be obtained. The present example is not limited to the above description, and details can be modified upon practice. For example, the boundary where the expression pattern changes can be displayed with a combination of known display formats, such as a flicker display, a bright display and a color reversion display. The processing by the clustering processor 105 can be stored as a program in a storage medium (e.g., CD-ROM) to be provided to a user of a computer. The data of genes is not limited to time sequential expression data, and the horizontal axis (time axis) shown in Figures 2 and 3 may represent other basis. For example, expression patterns of cancerous and normal cells from the same human tissue can be combined and simultaneously clustered so as to find genes that specifically function to cancer, genes that specifically function to normal cells and genes that specifically function to both types of cells. Figure 23 is a view showing an example of combining and clustering cancerous and normal cells according to the present embodiment. Referring to a gene group 1 ( 2301 ) in Figure 23 , genes as a whole are strongly expressed in the cancerous cells while some of the genes are expressed but some are not in the normal cells. Referring to other gene group 2 ( 2302 ), while weakly expressed genes are gathered in the cancerous cells, some of the genes are expressed but some are not in the normal cells. By simultaneously clustering two types of cells as described above, the behaviors of genes can generally be understood in detail. Other than comparing difference between states at different time points or between states of tissues, comparison can be made for difference between species such as human and yeast, difference between sites of individual such as stomach, large intestine and heart, difference of expression patterns obtained upon electric shock, shock by high temperature or shock by low temperature, difference between presence and absence of an artificial condition, for example, expression patterns before and after drug administration, or difference of a combination thereof. According to the present invention, expression of a part of cell cycle can be designated to perform clustering in that region in a small range. Based on the displayed results, the user can observe the course of expression status of genes in more detail to efficiently study the biological functions of the genes from its expression status.

Claims: A method for displaying gene expression patterns of multiple genes whose expressions change according to experiment cases, where a first axis represents the genes and a second axis represents the experiment cases, the method comprising the steps of: designating a segment along the second axis in the expression pattern data of the multiple genes; and clustering the expression pattern data within the designated segment along the second axis based on a predetermined reference value, repeating clustering within the same cluster in a forward or reverse direction along the second axis while changing the reference value, and displaying the results according to a predetermined display format. A method for displaying gene expression pattern according to claim 1, wherein the reference value determines whether two expression patterns of different genes are identical or not. A method for displaying gene expression pattern according to claim 1 or 2, wherein two or more different genes are displayed according to the predetermined display format where they have the same expression pattern at the beginning but become to have different expression patterns within the segment along the second axis. A method for displaying gene expression pattern according to claim 1 or 2, wherein two or more different genes are displayed according to the predetermined display format where they have different expression patterns at the beginning but become to have the same expression pattern within the segment along the second axis. A method for displaying gene expression pattern according to claim 1, wherein the experiment cases are conducted in a time sequence. A method for displaying gene expression pattern according to one of claims 1 to 5, wherein the experiment cases are states of individual's tissue. A method for displaying gene expression pattern according to one of claims 1 to 5, wherein the experiment cases are species of individuals. A method for displaying gene expression pattern according to one of claims 1 to 7, wherein the experiment cases are individual's sites. A method for displaying gene expression pattern according to one of claims 1 to 8, wherein the experiment cases are presence and absence of an artificial condition. A method for displaying gene expression pattern according to one of claims 1 to 5, wherein the experiment cases are a combination of time sequential experiments, states of individual's tissue, species of individuals, individual's sites, and presence and absence of an artificial condition. An apparatus for analyzing gene expression patterns, which acquires, from a database, expression pattern data of multiple genes whose expressions change according to experiment cases, and which visually displays the expression patterns on a screen of a display device, where a first axis represents the genes and a second axis represents the experiment cases, the apparatus comprising: an inputting means for designating a segment along the second axis in the expression pattern data of the multiple genes obtained from the database; and an arithmetic unit for clustering the expression pattern data within the designated segment along the second axis based on a predetermined reference value, repeating clustering within the same cluster in a forward or reverse direction along the second axis while changing the reference value, and displaying the results according to a predetermined display format.

Inventors: NAKASHIGE RYO, NOZAKI YASUYUKI, TAMURA TAKURO, WATANABE TSUNEHIKO, NAKASHIGE, RYO, NOZAKI, YASUYUKI, TAMURA, TAKURO, WATANABE, TSUNEHIKO
Applicants: HITACHI SOFTWARE ENG, HITACHI SOFTWARE ENGINEERING CO., LTD.
Application year: 2000
Main classification: G06F19
IPC codes: G06F19