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An Overview of Spotfire for Gene‐Expression Studies
Publication Name:
Current Protocols in Bioinformatics
Unit Number:
Unit 7.7
DOI:
10.1002/0471250953.bi0707s6
Online Posting Date:
September, 2004 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
Table of Contents
- Unit Introduction
- 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
Figures
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Figure 7.7.1Various components of Spotfire DecisionSite. -
Figure 7.7.2Different components of the DecisionSite Navigator. -
Figure 7.7.3The Properties Dialog Box. -
Figure 7.7.4Various features of the Scatter plot visualization in Spotfire. -
Figure 7.7.5The Customize Colors window for scatter plot (and other visualizations). -
Figure 7.7.6The Customize Shapes window for scatter plot. -
Figure 7.7.7The 3D Scatter Plot visualization in Spotfire. -
Figure 7.7.8The Profile Chart visualization in Spotfire. -
Figure 7.7.9The Heat Map visualization in Spotfire. -
Figure 7.7.10Heat Map Properties dialog box. -
Figure 7.7.11The Edit Color Range dialog box allows users to choose the colors for their heat map visualization. -
Figure 7.7.12The 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. -
Figure 7.7.13Annotations can be appended to most visualizations (example shown here with the scatter plot) through the Properties dialog box. -
Figure 7.7.14The number and type of columns in a scatter plot can be controlled via the Columns tab in the Properties dialog. -
Figure 7.7.15The auto‐tile feature allows all the visualizations present in a particular Spotfire session to be viewed at once. -
Figure 7.7.16Various types of query devices are assigned to different data columns. -
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. -
Figure 7.7.18Details‐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. -
Figure 7.7.19Details‐on‐Demand window can also be used to exhibit data for a single highlighted record. -
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).
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. | |
| Iyer, V.R., Eisen, M.B., Ross, D.T., Schuler, G., Moore, T., Lee, J.C., Trent, J.M., Staudt, L.M., Hudson, J. Jr., Boguski, M.S., Lashkari, D., Shalon, D., Botstein, D., and Brown, P.O. 1999. The transcriptional program in the response of human fibroblasts to serum. Science 283:83‐87. | |
| 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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