My Microarray Software Comparison - Data Mining Software


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Definition of data mining software
Suggested readings


 

Definition of data mining software


Turnkey system

A Turnkey system is defined as a computer system that is customized for a particular application. A microarray turnkey Data Mining system includes everything like operating system, server software, database, client software, statistics software and even hardware customized for the whole Data Mining process.

Comprehensive software

A comprehensive software incorporates many different analyses at different stages of microarray analysis like data preprocessing, dimensionality reduction, normalization, clustering and visualization in a single package.This type of software does not have any accompanied database although they are usually equipped with an interface for Open DataBase Connectivity (ODBC), a standard for accessing different database systems.

Specific analysis software

Specific analysis software is defined as a software which performs only one analysis or a few specific analyses. The distinction between comprehensive and specific analysis software is not clear-cut, but in general a specific analysis software is more specialized in a particularly confined analytical problem, while a comprehensive software aims at providing an all-in-one package for the general user.

Extensions of existing data mining software

This kind software is usually existed as a plugin of comprehensive package to extend its functionality, but it can also be available as a standalone tool.

Suggested readings

    Experimental Design
  1. Glonek GF, Solomon PJ. Factorial and time course designs for cDNA microarray experiments. Biostatistics. 2004 Jan;5(1):89-111. [PubMed]
  2. Simon RM, Dobbin K. Experimental design of DNA microarray experiments. Biotechniques. 2003 Mar;Suppl:16-21 [PubMed]
  3. Kerr MK. Experimental design to make the most of microarray studies. Methods Mol Biol. 2003;224:137-47. [PubMed]
  4. Yang YH, Speed T. Design issues for cDNA microarray experiments. Nat Rev Genet. 2002 Aug;3(8):579-588. [PubMed][full text][pdf][web supplement]
  5. Lee ML, Whitmore GA. Power and sample size for DNA microarray studies. Stat Med. 2002 Dec 15;21(23):3543-70. [PubMed]
  6. Hwang D, Schmitt WA, Stephanopoulos G, Stephanopoulos G. Determination of minimum sample size and discriminatory expression patterns in microarray data. Bioinformatics. 2002 Sep;18(9):1184-93. [PubMed]
  7. Simon R, Radmacher MD, Dobbin K. Design of studies using DNA microarrays. Genet Epidemiol. 2002 Jun;23(1):21-36. [PubMed]
  8. Kerr MK, Churchill GA. Experimental design for gene expression microarrays. Biostatistics. 2001 Jun;2(2):183-201. [PubMed]
    Data mining
  1. Leung YF, Cavalieri D. Fundamentals of cDNA microarray data analysis. Trends Genet. 2003 Nov;19(11):649-59. [PubMed]
  2. Smyth GK, Yang YH, Speed T. Statistical issues in cDNA microarray data analysis. Methods Mol Biol. 2003;224:111-36. [PubMed]
  3. Nadon R, Shoemaker J. Statistical issues with microarrays: processing and analysis. Trends Genet. 2002 May;18(5):265-71. [PubMed]
  4. Sherlock G. Analysis of large-scale gene expression data. Brief Bioinform. 2001 Dec;2(4):350-62. [PubMed]
  5. Wu TD. Analysing gene expression data from DNA microarrays to identify candidate genes. J Pathol. 2001 Sep;195(1):53-65. [PubMed]
  6. Quackenbush J. Computational genetics computational analysis of microarray data. Nat Rev Genet. 2001 Jun;2(6):418-27. [PubMed][pdf]
  7. Brazma A, Vilo J. Gene expression data analysis. FEBS Lett. 2000 Aug 25;480(1):17-24. [PubMed]
last updated: 23 Nov 2003
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