An Overview of Spotfire for Gene‐Expression Studies
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- Abstract
- Table of Contents
- Figures
- Literature Cited
Abstract
Spotfire DecisionSite for Functional Genomics (referred to here as Spotfire) is a powerful data mining and visualization application with use in many disciplines. This unit provides an overview of Spotfire's utility in analyzing gene expression data obtained from DNA microarray experiments. Analysis of microarray data requires software?based solutions able to handle and manipulate the enormous amount of data generated. Spotfire provides a solution for accessing, analyzing and visualizing data generated from microarray experiments. Spotfire is designed to allow biologists with little or no programming or statistical skills to transform, process, and analyze microarray data.
Keywords: microarray; Spotfire; gene expression; overview; DNA
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PDF or HTML at Wiley Online LibraryTable of Contents
- Necessary Requirements for Using the Functional Genomics Module of Spotfire
- Overview of Spotfire Visualization Window
- DecisionSite Navigator
- Visualizations
- Query Devices
- Details‐On‐Demand
- Strengths and Weaknesses of Spotfire as a Desktop Microarray Analysis Software
- Literature Cited
- Figures
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Figure 7.7.1 Various components of Spotfire DecisionSite. View Image -
Figure 7.7.2 Different components of the DecisionSite Navigator. View Image -
Figure 7.7.3 The Properties Dialog Box. View Image -
Figure 7.7.4 Various features of the Scatter plot visualization in Spotfire. View Image -
Figure 7.7.5 The Customize Colors window for scatter plot (and other visualizations). View Image -
Figure 7.7.6 The Customize Shapes window for scatter plot. View Image -
Figure 7.7.7 The 3D Scatter Plot visualization in Spotfire. View Image -
Figure 7.7.8 The Profile Chart visualization in Spotfire. View Image -
Figure 7.7.9 The Heat Map visualization in Spotfire. View Image -
Figure 7.7.10 Heat Map Properties dialog box. View Image -
Figure 7.7.11 The Edit Color Range dialog box allows users to choose the colors for their heat map visualization. View Image -
Figure 7.7.12 The Table visualization allows users to view data in a sortable spreadsheet format. Like other visualizations, Table is also dynamically linked to the query devices and to other visualizations. View Image -
Figure 7.7.13 Annotations can be appended to most visualizations (example shown here with the scatter plot) through the Properties dialog box. View Image -
Figure 7.7.14 The number and type of columns in a scatter plot can be controlled via the Columns tab in the Properties dialog. View Image -
Figure 7.7.15 The auto‐tile feature allows all the visualizations present in a particular Spotfire session to be viewed at once. View Image -
Figure 7.7.16 Various types of query devices are assigned to different data columns. View Image -
Figure 7.7.17 (A ) Records can be marked by left‐clicking the mouse and dragging the cursor around the desired region. (B ) Marking records in an irregular shape (by lasso) can be achieved by pressing Shift while left‐clicking the mouse and dragging the cursor around the desired region. View Image -
Figure 7.7.18 Details‐on‐Demand window shows a snapshot of the marked data. Data shown in this window can be exported to Excel or as text/HTML data. View Image -
Figure 7.7.19 Details‐on‐Demand window can also be used to exhibit data for a single highlighted record. View Image -
Figure 7.7.20 (A ) Details‐on‐Demand (HTML) format. (B ) Selecting the external Web browser option from the View tab allows export of the HTML data to an external browser window (C ). View Image
Videos
Literature Cited
| Literature Cited | |
| Cheok, M.H., Yang, W., Pui, C.H., Downing, J.R., Cheng, C., Naeve, C.W., Relling, M.V., and Evans, W.E. 2003. Treatment‐specific changes in gene expression discriminate in vivo drug response in human leukemia cells. Nat. Genet. 34:85‐90. | |
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| Kerr, M.K. and Churchill, G.A. 2001. Experimental design for gene expression microarrays. Biostatistics 2:183‐201. | |
| Kozal, M.J., Shah, N., Shen, N., Yang, R., Fucini, R., Merigan, T.C., Richman, D.D., Morris, D., Hubbell, E., Chee, M., and Gingeras, T.R. 1996. Extensive polymorphisms observed in HIV‐1 clade B protease gene using high‐density oligonucleotide arrays. Nat. Med. 2:753‐759. | |
| Lee, T.I., Rinaldi, N.J., Robert, F., Odom, D.T., Bar‐Joseph, Z., Gerber, G.K., Hannett, N.M., Harbison, C.T., Thompson, C.M., Simon, I., Zeitlinger, J., Jennings, E.G., Murray, H.L., Gordon, D.B., Ren, B., Wyrick, J.J., Tagne, J.B., Volkert, T.L., Fraenkel, E., Gifford, D.K., and Young, R.A. 2002. Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298:799‐804. | |
| Leung, Y.F. and Cavalieri, D. 2003. Fundamentals of cDNA microarray data analysis. Trends Genet. 19:649‐659. | |
| Schena, M., Shalon, D, Davis, R.W., and Brown, P.O. 1995. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270:467‐470. | |
| Schena, M., Heller, R.A., Theriault, T.P., Konrad, K., Lachenmeier, E., and Davis, R.W. 1998. Microarrays: Biotechnology's discovery platform for functional genomics. Trends Biotechnol. 16:301‐306. | |
| Smyth, G.K., Yang, Y.H., and Speed, T. 2003. Statistical issues in cDNA microarray data analysis. Methods Mol. Biol. 224:111‐136. | |
| Yeoh, E.J., Ross, M.E., Shurtleff, S.A., Williams, W.K., Patel, D., Mahfouz, R., Behm, F.G., Raimondi, S.C., Relling, M.V., Patel, A., Cheng, C., Campana, D., Wilkins, D., Zhou, X., Li, J., Liu, H., Pui, C.H., Evans, W.E., Naeve, C., Wong, L., and Downing, J.R. 2002. Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. Cancer Cell 1:133‐143. |
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