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- Which are the best statistical programming languages to study for a bioinformatician?

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Started by Jitendra Narayan 1446 days ago Replies (8)

In Bio-informatics based genome sequencing and predicting metabolic pathways research jobs I used Matlab, SAS, SPSS, R and several Bioconductor packages. Matlab had a lot of powerful tools and was easy to use, whereas SPSS is for non-programmers and R need programming skills. I am wondering what other people think is best? or there might not be one specific language but a few that lend themselves best to Bio-informatics work that is math heavy and deals with a large amount of data.

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- Neelam Jha 1401 days ago
Phylogenetics in R

R in Ecology and Evolution – http://r-eco-evo.blogspot.com.au/ R bloggers: Recology http://www.r-bloggers.com/author/recology-r/ Talk introducing phylogenetics in R: http://www.r-bloggers.com/my-talk-on-doing-phylogenetics-in-r-2/ Finding meaningful clusters in trees phytools blog, “Phylogenetic Tools for Comparative Biology”

- Jit 1378 days ago
R is by far the best known open source statistical programming language for bioinformatician. However, you can not ignore MATLAB Bioinformatics Toolbox.

I am a big fan of Perl, and love to do all sort of analysis using Perl, therefore I prefer PDL ("Perl Data Language"), which gives standard Perl the ability to compactly store and speedily manipulate the large N-dimensional data arrays which are the bread and butter of scientific computing.

- Rahul Nayak 1378 days ago
Who cares which language is the more popular, programming languages are tools, if it does what I need it to do, it's fine by me.

- Abhimanyu Singh 1214 days ago
Lisp-Stat is an extensible environment for statistical computing and dynamic graphics based on the Lisp language. XLISP-STAT is a version of Lisp-Stat based on a dialect of Lisp called XLISP.

http://homepage.stat.uiowa.edu/~luke/xls/xlsinfo/xlsinfo.html

- John Parker 1046 days ago
I like the R language. The following table comparing the statistical capabilities of software packages: http://stanfordphd.com/Statistical_Software.html In stastistical language war, a/c to this metric, R wins

**TYPE OF STATISTICAL ANALYSIS****R****MATLAB****SAS****STATA****SPSS****Nonparametric Tests**Yes

Yes

Yes

Yes

Yes

**T-test**Yes

Yes

Yes

Yes

Yes

**ANOVA & MANOVA**Yes

Yes

Yes

Yes

Yes

**ANCOVA & MANCOVA**Yes

Yes

Yes

Yes

Yes

**Linear Regression**Yes

Yes

Yes

Yes

Yes

**Generalized Least Squares**Yes

Yes

Yes

Yes

Yes

**Ridge Regression**Yes

Yes

Yes

**Lasso**Yes

Yes

Yes

**Generalized Linear Models**Yes

Yes

Yes

Yes

Yes

**Mixed Effects Models**Yes

Yes

Yes

Yes

Yes

**Logistic Regression**Yes

Yes

Yes

Yes

Yes

**Nonlinear Regression**Yes

Yes

Yes

**Discriminant Analysis**Yes

Yes

Yes

Yes

Yes

**Nearest Neighbor**Yes

Yes

Yes

Yes

**Factor & Principal Components Analysis**Yes

Yes

Yes

Yes

Yes

**Copula Models**Yes

Yes

Experimental

**Cross-Validation**Yes

Yes

Yes

**Bayesian Statistics**Yes

Yes

Limited

**Monte Carlo, Classic Methods**Yes

Yes

Yes

Yes

Limited

**Markov Chain Monte Carlo**Yes

Yes

Yes

**Bootstrap & Jackknife**Yes

Yes

Yes

Yes

**EM Algorithm**Yes

Yes

Yes

**Missing Data Imputation**Yes

Yes

Yes

Yes

Yes

**Outlier Diagnostics**Yes

Yes

Yes

Yes

Yes

**Robust Estimation**Yes

Yes

Yes

Yes

**Longitudinal (Panel) Data**Yes

Yes

Yes

Yes

Limited

**Survival Analysis**Yes

Yes

Yes

Yes

Yes

**Path Analysis**Yes

Yes

Yes

**Propensity Score Matching**Yes

Yes

Limited

Limited

**Stratified Samples (Survey Data)**Yes

Yes

Yes

Yes

Yes

**Experimental Design**Yes

Yes

**Quality Control**Yes

Yes

Yes

Yes

**Reliability Theory**Yes

Yes

Yes

Yes

Yes

**Univariate Time Series**Yes

Yes

Yes

Yes

Limited

**Multivariate Time Series**Yes

Yes

Yes

Yes

**Markov Chains**Yes

Yes

**Hidden Markov Models**Yes

Yes

**Stochastic Volatility Models**Yes

Yes

Limited

Limited

Limited

**Diffusions**Yes

Yes

**Counting Processes**Yes

Yes

Yes

**Filtering**Yes

Yes

Limited

Limited

**Instrumental Variables**Yes

Yes

Yes

Yes

**Simultaneous Equations**Yes

Yes

Yes

Yes

**Splines**Yes

Yes

Yes

Yes

**Nonparametric Smoothing Methods**Yes

Yes

Yes

Yes

**Extreme Value Theory**Yes

Yes

**Variance Stabilization**Yes

Yes

**Cluster Analysis**Yes

Yes

Yes

Yes

Yes

**Neural Networks**Yes

Yes

Yes

Limited

**Classification & Regression Trees**Yes

Yes

Yes

Limited

**Boosting Classification & Regression Trees**Yes

Yes

**Random Forests**Yes

Yes

**Support Vector Machines**Yes

Yes

Yes

**Signal Processing**Yes

Yes

**Wavelet Analysis**Yes

Yes

Yes

**ROC Curves**Yes

Yes

Yes

Yes

Yes

**Optimization**Yes

Yes

Yes

Limited

- Neelam Jha 1040 days ago
R Passes SPSS in Scholarly Use, Stata Growing Rapidly http://www.r-bloggers.com/r-passes-spss-in-scholarly-use-stata-growing-rapidly/

- Jitendra Narayan 902 days ago
The recent article on Nature explain it better ... R becoming the most popular language amongst biological researchers http://www.nature.com/news/programming-tools-adventures-with-r-1.16609?

- Rahul Nayak 759 days ago
A nice comparision between R, SAS and Python http://www.datasciencecentral.com/forum/topics/which-one-is-best-r-sas-or-python-for-data-science