An Overview of Spotfire for Gene‐Expression Studies

Deepak Kaushal1, Clayton W. Naeve1

1 St. Jude Children's Research Hospital, Memphis, Tennessee
Publication Name:  Current Protocols in Bioinformatics
Unit Number:  Unit 7.7
DOI:  10.1002/0471250953.bi0707s6
Online Posting Date:  September, 2004
GO TO THE FULL TEXT: PDF or HTML at Wiley Online Library


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‚Äźbased 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

PDF or HTML at Wiley Online Library

Table 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
PDF or HTML at Wiley Online Library


PDF or HTML at Wiley Online Library



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.
PDF or HTML at Wiley Online Library