My Microarray Software Comparison - Statistics
Software and Technical Programming Languages
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Discription
of statistics software and technical programming languages
Suggested readings
Statistics
software and technical programming languages (in alphabetical order)
[ Excel | MATLAB
| Octave | SAS | SPSS | S-PLUS | Statistica | R ]
Discription
of statistics software and technical programming languages
A solid data analysis must be based on valid statistical fundamentals
which should be handled by statistics software or technical programming
languages that can perform statistical analysis. A common feature of
these software packages is their flexibility that every step of the
analysis can be fine-tuned by appropriate programming.
Suggested readings
I personally recommend learning R, a free version of S-plus. There are
growing number of microarray analysis tools written in R packages.
Please refer to My Microarray
Software Comparison - R packages for microarray analysis page for a
complete listing.
There are a few great books on R and some of them have helped me a
lot in learning R.
- Dalgaard P. Introductory
Statistics with R. Springer Verlag 2002
- Krause A, Olson M. The
Basics of S and S-Plus (Statistics and Computing). Springer
Verlag 2000.
- Venables WN, Ripley BD. Modern
Applied Statistics With S-Plus (Statistics and Computing).
Springer Verlag 1999.
- Selvin S. Modern
Applied Biostatistical Methods: Using S-Plus. Oxford University
Press. 1998 (I highly recommend this book because it contains: (I) a
comprehensive coverage of various statistical analysis topics carried
out by S-plus/R, which is a great revision on the basics (II)
step by step command-line of every analysis, you can learn the
operation of the program while revising the statistical background.)
This book is a handy quick reference and helps to clear up many
confusing statistical ideas, but is rather focused on medical problems.
- Campbell MJ, Machin D. Medical
Statistics: A Commonsense Approach, 3rd Edition. John Wiley
& Sons. 1999
Statistics
software and technical programming languages (in alphabetical order)
- Excel - can
performs a lot of matrix operations
- MATLAB - one
of the common used software in this category, MATLAB is an extremely
powerful language and environment for data mining
- Octave - GNU Octave is a
high-level language, primarily intended for numerical computations. The
language is mostly compatible with MATLAB and is freely distribute
under GPL
- SAS - SAS is a
powerful environment for statistical data mining.
- SPSS - SPSS can perform
clustering and factor analysis and the user inferface is very well
written and easy to use!
- S-PLUS
- based on the award-winning S language, is the premier solution for
exploratory data analysis and statistical data mining. It has launched
a ArrayAnalyzer
Solution that combines the statistical methods from Bioconductor
project and in-home proprietary software.
- Statistica -
another powerful statistical data mining software.
- The R Project for Statistical
Computing - A language and environment for statistical
computing and graphics. R is similar to the award-winning S system and
provides a wide variety of statistical and graphical techniques,
there are many useful packages for
microarray analysis written in R!
last updated: 22 May 2003
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