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1 西 山 慧 子 * 伊 藤 貴 之 ** (*) お 茶 の 水 女 子 大 学 大 学 院 人 間 文 化 研 究 科 (**) お 茶 の 水 女 子 大 学 理 学 部 情 報 科 学 科 {nishy, itot}@itol.is.ocha.ac.jp 1. NA [1] A B C E F B,E 1 A, 1 A,B,E B E C,F 1 1 A 2 3 C,F A C F
2 {B,E},{},{C,F} 3 A 2 1 A B E C,F [2] [3] [4] (tree) Hyperbolic Tree[5] Cone Tree[6] 3
3 Tree-Maps[7] 2 3 [8] 2 [9] [10] [11] [12] 3 [13] (1) (2) (1) [13] (2) 3 GUI M N 4 m 種 類 の n 個 の 遺 伝 子 のうち マイクロアレイ 発 現 率 傾 向 の 近 いもの データ 要 素 と 階 層 n 個 の ネットワーク 遺 伝 子 マトリクス 型 データ 階 層 型 データ 4 Cluster 3.0[14] 5( ) k 1 k 9 5( ) S 1,S 2 2
4 5( ) 2 nodea, nodeb m nodea A={a 1, a 2,...a m } nodeb B={b 1, b 2,...b m } nodea node r r d d d = 1.0 (1) max A,B 2 m ( ai bi i= 1 = ) 2 (2) max d Cluster3.0[14] 8 S2 k1 k4 7 6 k2 k5 5. ( )( ) 5 k3 4 S k8 k6 k9 k1 k2 k3 k4 k5 k6 k7 k8 k9 0 1 rij k7 r
5 マルチドメインの可能性のある遺伝子同士が複雑に絡み合 ったネットワークである といえる 図 7,8 は 本手法により 注視ノードと相関性の高いノー ドを 3 次元的に引き上げた結果画像である 図 8(左)の注視ノ ードを引き上げていない画像では どのノードが注視ノード とエッジ連結されているのか 一目には理解しにくい それ に対して図 8(右)では 注視ノードを引き上げることにより 注視ノードとエッジで連結されたノードを一目瞭然に発見で きる これらの結果画像より ネットワークの注視部分を 3 次元的に引き上げることにより ノード間の連結関係が理解 しやすくなると言える 図 6 本報告を用いた 注視ノードが一つの実行例 4.2 既存ソフトウェアとの比較 図 9 平安京ビューにおける表示結果 図 7 注視ノードを1段階引き上げた表示画像 マイクロアレイデータから得られる遺伝子発現率情報の可 視化ソフトウェアの中の多くは ノード間の相互関係をエッ ジで結ぶ古典的なネットワーク 2 次元可視化手法や TreeView[15]と呼ばれるクラスタリング結果の可視化手法を 搭載しており 遺伝子分析に携わる多くの研究者がこれらを 利用している 以下 これらの手法に対する提案手法の優位 性について論じる まず前者の方法では 発現率の相関性の高いノードをエッ ジで結んで表示する事から 遺伝子間の関連性は一目瞭然で ある しかし 一画面に表示するノード数は数十 数百程度 に留まっている またクラスタリング結果を同時に表示して はいない それに対して本手法 図 9 参照 には 図 8 左 注視ノードを引き上げてない結果画像 右 注視ノードを引き上げた結果画像 整然と構造化された形で遺伝子群を表示する 数千 数万といった膨大な量の遺伝子の分布の全貌を 一画面に一括表示できる また 図 6 を詳しく調べてみると 所定の色 紫 で表示 されたノードを両端とするエッジが多く存在していること が解る このことより 図 6 に示す遺伝子ネットワークは といった点で利点があると考えられる 続いて後者の TreeView は N個の遺伝子に関する発現率を N N のマトリクスデータとして表現する この手法は全ての
6 Cluster 3.0 Michael e Hoon [1], Genetic Networks and Probilistic Models, 2001, pp , [2] Itoh T., Takakura H., Sawada A., and Koyamada K., Hierarchical Visualization of Network Intrusion etection ata in the IP Address Space, IEEE Computer Graphics and Applications, Vol. 26, No. 2, pp , [3] Mukherjea, S., J. Foley and S. Hudson, Visualizing Complex Hypermedia Networks through Multiple Hierarchical Views, Proceedings of ACM SIGCHI '95, enver, Colorado, pp , May [4] Eades, P., "A Heuristic for Graph rawing," Congressus Numerantium, Vol. 42, pp , [5] Lamping, J. and Rao, R., "The Hyperbolic Browser: A Focus + Context Technique for Visualizing Large Hierarchies," Journal of Visual Languages and Computing, Vol. 7, No. 1, pp , [6] J. Carrire and R. Kazman, "Research Report: Interacting with Huge Hierarchies: Beyond Cone Trees," Proceedings of the IEEE Conference on Information Visualization '95, IEEE CS Press, pp , [7] B. Johnson, et al., Tree-Maps: A Space-Filing Approach to the Visualization of Hierarchical Information Space, IEEE Visualization 91, pp , [8] P. Eades, et al., Multilevel Visualization of Clustered Graphs, Graph rawing 96, pp , [9]. Schaffer, et al., Navigating Hierarchically Clustered Networks through Fisheye and Full-Zoom Methods, ACM Trans. Computer-Human Interaction, Vol. 3, No. 2, pp , [10] M. Sarcar, M. H. Brown, Graphical Fisheyes Views of Graphs, Communication of the ACM, Vol. 37, pp , March [11] M. L. Huang, et al., WebOFAV Navigatingand Visualizing the Web On-Line with Animated Context Swapping, 7th WWW Conf, pp , [12] S. North, Incremental Layout in ynaag, Graph rawing 95, pp , [13],,, Vol. 38, No. 11, pp , [14] Open Source Clustering Software (Cluster 3.0), [15] TreeView,
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