cliffordlai/datamicroarray

A collection of small-sample, high-dimensional microarray data sets to assess machine-learning algorithms and models.

datamicroarray

The R package datamicroarray provides a collection of scripts to download, process, and load small-sample, high-dimensional microarray data sets to assess machine learning algorithms and models. For each data set, we include a small set of scripts that automatically download, clean, and save the data set.

Data Sets

Each data set is listed below by the first author on the original paper. The data sets are organized them by category; note that most of the data sets are cancer-related. Click a data set to see its description, a link to the original paper, and additional information.

• Breast Cancer
  • Chin (2006)
  • Chowdary (2006)
  • Gravier (2010)
  • Sorlie (2001)
  • West (2001)
• Central Nervous System Disorders
  • Pomeroy (2002)
• Crohn's Disease
  • Burczynski (2006)
• Colon Cancer
  • Alon (1999)
• Glioma
  • Sun (2006)
• Huntington's Disease
  • Borovecki (2005)
• Leukemia
  • Chiaretti (2004)
  • Golub (1999)
  • Yeoh (2002)
• Lung Cancer
  • Gordon (2002)
• Lymphoma
  • Shipp (2002)
• Myeloma
  • Tian (2003)
• Prostate Cancer
  • Singh (2002)
• Sarcoma
  • Nakayama (2007)
• Small Round Blue Cell Tumors
  • Khan (2001)
• Miscellaneous
  • Christensen (2009)
  • Su (2002)
  • Subramanian (2005)

Installation

You can install the latest package version by typing the following at the R console:

library(devtools)
install_github('ramhiser/datamicroarray')

Note that you need to install the devtools package beforehand.

Usage

Once you have installed and loaded the datamicroarray package, you can load a data set with the data command. For example, to load the well-known Alon et al. (1999) Colon Cancer data set, type the following at the R console:

library(datamicroarray)
data('alon', package = 'datamicroarray')

After loading the data set, the resulting object is a named list with two elements:

1. x - the data matrix. The rows are the n observations, and the columns are the p features.
2. y - a factor vector of length n with the corresponding class labels.

Here is a summary for the Alon et al. (1999) Colon Cancer data set.

> dim(alon$x)
[1]   62 2000
> table(alon$y)
 n  t 
22 40 

You can see all of the data sets available along with a brief summary of each with the describe_data helper function. Here it is in action:

> describe_data()
        author year   n     p K              Disease
1         alon 1999  62  2000 2         Colon Cancer
2    borovecki 2005  31 22283 2 Huntington's Disease
3   burczynski 2006 127 22283 3      Crohn's Disease
4    chiaretti 2004 111 12625 2             Leukemia
5         chin 2006 118 22215 2        Breast Cancer
6     chowdary 2006 104 22283 2        Breast Cancer
7  christensen 2009 217  1413 3                  N/A
8        golub 1999  72  7129 3             Leukemia
9       gordon 2002 181 12533 2          Lung Cancer
10     gravier 2010 168  2905 2        Breast Cancer
11        khan 2001  63  2308 4                SRBCT
12     pomeroy 2002  60  7128 2            CNS Tumor
13       shipp 2002  58  6817 2             Lymphoma
14       singh 2002 102 12600 2      Prostate Cancer
15      sorlie 2001  85   456 5        Breast Cancer
16          su 2002 102  5565 4                  N/A
17 subramanian 2005  50 10100 2                  N/A
18        tian 2003 173 12625 2              Myeloma
19        west 2001  49  7129 2        Breast Cancer
20        yeoh 2002 248 12625 6             Leukemia