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チュートリアル:適応度と発現データセットの比較解析


スライドショー Comparative_Analysis_of_Fitness_and_Expression_Datasets (約40分)
ハンドアウト Comparative_Analysis_of_Fitness_and_Expression_Datasets.pdf (6ページ)

チュートリアルのソース(訳注:リンクなし)
チュートリアルのキュレーター Anna Kuchinsky
Contents
1 背景
2 目的
3 方法の概要
4 データの説明(TAB区切りのテキストファイル)
5 Procedure
5.1 Part A: Generation and analysis of two sub networks using Cytoscape
5.2 Part B: Using cerebral plugin to spatially organize the two subnetworks
6 Independent study after tutorial
7 References and websites

背景

(脂肪酸代謝での遺伝子の上方調節を含む)代謝再編や(peroxinと呼ばれるタンパク質を含むペルオキシソームの増殖を含む)構造変化による脂肪酸の暴露に細胞は反応します。脂肪酸に対する酵母細胞の反応を計測した2つのデータセットをここでは扱います:
  1. Fitness Dataset: 脂肪酸の効率の良い代謝を行う非必須酵母遺伝子の必要性を同定するためのゲノムワイドなスクリーニング結果。
  2. Expression Dataset: 脂肪酸の有無についてマイクロアレイ発現解析により脂肪酸暴露の転写応答をする、同定された非必須酵母遺伝子。

目的

脂肪酸暴露の応答について理解するシステムレベルの解析のためのデータセットをここでは扱います。データ統合は解析のタイプの重要な見地を示します。多くの統合のアプローチにより、同じ分子により同定される同じ刺激に対する応答の表現型をデータセットにより計測することと擬陽性と擬負性との違いを見出すことが期待されている。どの様にデータを統合するかについての比較解析が必要と思われるでしょう。そのデータセット達が似ているのか補完的なのか?似ているのか違うのか?それぞれのデータセットを何により計測できるのか?どの様に統合すべきなのか?詳細は関連の記事を御覧ください。

方法の概要

ネットワーク可視化と解析ソフトウェアであるCytoscape 2.6はデータ解析とデータの潜在的な傾向を同定し、データセットの統計解析によるより詳細な研究が可能です。特に、適応度と発現データの場合はタンパク質と代謝物の相互作用を遺伝子属性の形式としてnetwork3(訳注:意味がわかりません)と他のデータセットとして統合し、互いに比較解析が可能です。Cerebral v.2 plug‐in4は、 British Columbia大で開発されている細胞領域に基づいたレンダリングとレイアウトのツールであり、データの空間的構造をより詳細に得ることができます。それぞれのネットワークは、それに含まれる遺伝子と生化学過程を細胞コンポーネントとして組織化されています。

データの説明(TAB区切りのテキストファイル)

Metabolite_gene_interaction.sif
We consider an interaction between a gene and a metabolite to exist if the gene product is known to directly use or produce the metabolite. This is a network file that has all yeast gene‐metabolite interactions (in the format gene name [TAB] pm [TAB] metabolite name). It also contains the names of all yeast genes in column 1 (even if they do not have an interaction with a metabolite).
Numerical_attributes.txt
This file contains the gene names in column one and attributes of the gene in successive columns. Attributes can be either categorical variable (e.g. GO terms) or numerical variables that can either be discrete (e.g. 0 or 1) or continuous (e.g. expression ratios). This file can be used in numerous ways to manipulate or add visual information to the network.
tp12s23cr2.mtx
This file is a multi‐experiment gene expression dataset. Column 1 contains gene names, column 2 contains gene descriptions, the next 8 columns are log10 expression ratios for 8 microarray experiments, 6 of which are a time course dataset (half, one, three, six, nine, and o_g, correspond to ½ h, 1 h, 3 h, 6 h, 9 h and 26 h after growth in oleate compared to growth in glycerol medium). The next 8 columns are lambda significance values reflecting significance of differential expression. Data are given for the ~3000 genes that had significant differential expression for at least one of the 8 experiments.
Fitness.sif
This is a list of 216 genes that are necessary for robust growth of yeast on fatty acids as a sole carbon source.
Expression.sif
This is a list of 169 genes that change in expression in response to fatty acid exposure based on analysis of two time course gene expression datasets measuring the response of yeast to fatty acid.

Procedure

Part A: Generation and analysis of two sub networks using Cytoscape

Open Cytoscape 2.6.2: If Cytoscape icon is on the desktop, double click to open. If not, go to start\my computer\local disk\program files\cytoscape _v2.6.2 directory and double click cytoscape.bat. Make sure you open version 2.6.2 and not an older version.
Load the metabolite‐gene network into Cytoscape: From text tool bar at top choose File/import/network (multiple file types) then select Metabolite_gene_interactions.sif from C:\cytoscape_demo files directory. Data name and size (6962 nodes, 2,092 edges) are in the control panel (left). The network is highlighted red because it is very large and therefore not displayed. To display the network, right click the network name and choose “create view”. Nodes in new network can be laid out by clicking “layout” on the text tool bar at the top /yfiles/organic is a nice layout.
If you wish to see details, zoom in using the zoom tool on the graphic tool bar at the top. (In this version of Cytoscape, large networks are not shown in detail until you zoom in. This allows for larger datasets to be loaded into the program). Then restore the view by clicking the restore 1:1 magnifying glass icon in the graphic tool bar.
Load node attribute table: Select File/import/attribute from table (text/MS excel) and then select “select file(s)” next to input file and choose “numerical_attributes.txt” from the C:\cytoscape_demo_files directory. Before importing, we want to specify file type and attribute names. To do this, first click “show text file import options” under the heading “advanced”. Delimiter should be “tab” and attribute names should be “transfer first line as attribute names”. Now click “import” in lower right corner. 3595 entries
To view the attributes, go to the data panel at the bottom of the screen. Click on the first “select attributes” icon (see image below). Then, select attributes to view by clicking the box next to the attribute. After this is done, genes that are selected/highlighted in the network will appear in this data panel with attributes.
Visualize expression data: You can visualize different node attributes in the network using the “set visual style” icon or by clicking the VizMapper tab in the control panel at the right. For example, for visualizing gene expression data as node color, first set the default node color to white by double clicking the display nodes under defaults in the VizMapper in the control panel. Choose node_fill_color and set it to white. Expand node visual mapping (by clicking the adjacent + sign) and then expand node color (click the adjacent + sign). Choose expression_oleate as node color, choose continuous mapping as mapping type and then chose display colors for expression ratios. Here we will set minimum to red, maximum to green and 0 (no change in expression) to white.
Make a sub network of genes with fitness defect and interacting metabolites: To do this, choose the parent network (metabolite_gene_interaction) in the control panel and then chose select/nodes/from file from the text toolbar along the top of the screen. Choose “fitness.sif”. The description of the network at the left shows that 216 nodes are selected. To choose interacting metabolites, choose select/nodes/first neighbors of selected nodes from text toolbar along the top of the screen. There should now be 264 nodes selected from the network (at the left). To make a new network from this selection, choose file/new/network/from selected nodes, all edges. Rename this network to “fitness” by right clicking the network name in the control panel at the left and then choosing edit network title.
Customize the visual display of the new network: Nodes in new network can be laid out by clicking “layout” on the text tool bar at the top /yfiles/organic is a nice layout. To differentiate between metabolites and genes, Change the node shape of the metabolites using the node attribute file: choose VizMapper in the control panel at the left and under unused properties find node shape (unused properties may need to beexpand first by clicking the adjacent + sign). Double click beside node shape see available properties (each property is a column of the node attribute or expression Choose metab (a column that has 0 for genes and 1 for metabolites), then choose discrete mapping for mapping type. Set 0 to Ellipse and 1 to diamond. You can zoom in to see gene names.
Make a sub network of genes of the expression dataset and interacting metabolites: To do this, choose the parent network (metabolite_gene_interaction) in the control panel and deselect all selected nodes by left clicking a blank space in the network. Next, choose select/nodes/from file from the text toolbar along the top of the screen and choose "expression.sif". The description of the network at the left shows that 169 nodes are selected. To choose interacting metabolites, choose select/nodes/first neighbors of selected nodes from text toolbar along the top of the screen. There should now be 284 nodes selected from the network (at the left). To make a new network from this selection, choose file/new/network/from selected nodes, all edges. Rename this network to "expression" by right clicking the network name in the control panel at the left then choosing edit network title. Nodes in new network can be laid out by clicking Layout on the text tool bar at the top and then /yFiles/organic.
Visually compare the fitness and expression networks: To do this, first hide the parent network by right clicking the parent network in the control panel at the left and choosing destroy view. Then, arrange the fitness and expression networks in tile conformation by choosing View in the text toolbar at the top of the screen and choosing /arrange network windows/tiled. What can you say about the overlap between the two datasets? (two noticeable differences)
Part B: Using cerebral plugin to spatially organize the two subnetworks

Set up sub clustering of the peroxisome compartment for the expression network: With the expression dataset selected, choose plugin and then "create cerebral view" from the text toolbar at the top of the screen. Select the localization data that will be used to organize the network by choosing Cellular component general from the pulldown menu of node attributes we have loaded. Compartments are shown in the table below. Change the order of compartments in the display by selecting the row and moving it with the up and down arrows at the right. An order that reflects the shape of the cell can work well. I will use the order: other, cell wall, plasma membrane, ER/golgi, nucleus, mitochondrion, cytoplasm, peroxisomes. Different variations of this order are fine, but peroxisome should appear last because the last compartment can be spatially arranged by a second attribute (see below), and peroxisomes are most closely linked to fat metabolism.
Set up sub clustering of the peroxisome compartment for the expression network: In the downstream nodes pull down menu of available attributes choose pxml. This attribute annotates genes with peroxisomal products with 1 and all other genes with 0. In the next window, choose genes annotated with 1 to include in the sub clustering analysis. In the next window, choose the label for this cluster to be peroxisome. In the bottom window, choose the annotation that will be used for sub clustering from the pull-down menu (biological_process) and then select Layout. Edge curviness, label density and group label size can be edited under the cerebral tab in the control panel at the left under the cerebral tab.
Setup cerebral display of fitness network: This can be done by following steps 11 and 12 with the fitness sub network selected.
Does the cerebral display provide further biological insight?
Independent study after tutorial

Cerebral has a multi-experiment comparison tool. With this functionality, users can now upload, compare, contrast and cluster quantitative data from multiple experiments. A time course gene expression data file, tp12s23cr2.mtx, that can be used with the multi-experiment comparison tool is in the Cytoscape_data_files directory and described in the "Description of data sets" section above.
References and websites

Companion article: Jennifer J Smith, Yaroslav Sydorskyy, Marcello Marelli, Daehee Hwang, Hamid Bolouri, Richard A Rachubinski and John D Aitchison (2006) Expression and functional profiling reveal distinct gene classes involved in fatty acid metabolism. Molecular Systems Biology 2 doi:10.1038/msb4100051
http://www.nature.com/msb/journal/v2/n1/full/msb41...
Cytoscape v 2.6.0
Download from http://www.cytoscape.org/ Paul Shannon, Andrew Markiel, Owen Ozier, Nitin S. Baliga, Jonathan T. Wang, Daniel Ramage, Nada Amin, Benno Schwikowski and Trey Ideker (2003) Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Res. 13:2498-2504.
Metabolite-protein interactions are from Prinz et al. (Prinz S, Avila-Campillo I, Aldridge C, Srinivasan A, Dimitrov K, SiegelAF, Galitski T (2004) Control of yeast filamentous-form growth by modules in an integrated molecular network. Genome Res 14:380-390, ), which is a modified version of interactions compiled previously (Forster J, Famili I, Fu P, Palsson BO, Nielsen J (2003) Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res 13: 244-253).
Cerebral v.2 Cytoscape plug-in
http://www.pathogenomics.ca/cerebral/ Barsky A, Gardy JL, Hancock REW, and Munzner T. (2007) Cerebral: a Cytoscape plugin for layout of and interaction with biological networks using subcellular localization annotation. Bioinformatics 23(8):1040-2.
A comprehensive set of physical interactions amongst yeast proteins and genetic interactions among yeast genes can be downloaded from Saccharomyces Genome Database (SGD)
http://db.yeastgenome.org/cgi‐bin/batchDownload
Teacher contact info: jsmith@systemsbiology.org; skillcoyne@systemsbiology.org

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