Michael O’Connell and Richard Park gave a nice web seminar on the S+ArrayAnalyzer, a software program for analysis of microarray data that is marketed by Insightful Corporation. This company makes a lot of very nice software
- S+, an object oriented language for statistical analysis,
- S+SeqTrial, a system for designing and analyzing group sequential trials,
- Insightful Miner, data mining software,
- Infact, text mining software.
The S+ArrayAnalyzer software is built on the open source Bioconductor project. It remains faithful to the Bioconductor implementaiton of expression sets and code written for Bioconductor will work in S+ArrayAnalyzer. S+ArrayAnalyzer adds additional slots, consistent accessor methods, and a graphical user interface. It also offers Affymetrix API support, and an SPXML library for graphics.
You can run S+ArrayAnalyzer algorithms within the Spotfire DecisionSite application. Details are available at Spotfire S-PLUS Server Solution [pdf].
The speakers described two experiments. The first experiment looked at granulocyte differentiation in a series of mice, with measurements at day 0, 1, 2, ..., 6 with four mice evaluated at each day. The goal was to identify genes that are differentially expressed while minimizing the number of false positives.
The second experiment looked at young versus old animals in the time 0, 0.5, 1, 2, 4 hours after surgically induced injury. There were 3 animals of each age at each time point. The goal was to see the effect of age on recovery.
S+ArrayAnalyzer can read the CEL and CHP formats as well as AADM links used by Affymetrix chips. It can also read a variety of formats for the two color spotted arrays.
Initial exploratory methods include MvA plots (Bland-Altman plots), box plots, image plots of spatial expression, and RNA degradation plots. I had not heard about the RNA degradation plot before. This plot aligns all the Affymetrix probes from the 5' end of the gene to the 3' end. Since RNA degradation starts at the 5' end, any degradation would appear as a trend in the plot with lower expression values on the 5' end. A brief description of this plot appears on page 17 of the pdf handout, Introduction to Affymetrix GeneChip Data Analysis, by Han-Ming Wu and the AffyRNAdeg function in Bioconductor will produce this graph.
Affymetrix chips have a set of Mismatch probes that attempt to adjust for background and cross hybridization. There are several ways to incorporate the mismatch probes. The approach used by Affymetrix is called MAS 5 and is described at
Alternative approaches for handling the mismatch probes appear in the following references:
- Li C, Wong W (2001). Model-based analysis of oligonucleotide arrays: Expression index computation and
outlier detection. Proceedings of the National Academy of Science U S A 98:31-36.
- Affymetrix MAS 5 method
- Irizarry, RA, Hobbs, B, Collin, F, Beazer-Barclay, YD, Antonellis, KJ, Scherf, U, Speed, TP (2003)
Exploration, Normalization, and Summaries of High Density Oligonucleotide Array Probe Level Data.
- Zhang PDNN
Differential expression is tricky because of the large number of genes tested. To minimize the number of false positives, you need to use an approach with control of Family Wise Error Rate. The best known approach is the Bonferroni correction, but this is very conservative. Alternative to Bonferroni include
- Holm step-down procedure. Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics. 6: 65-70. (1979)
- Hochberg step-down procedure. Hochberg, Y. (1988). A sharper Bonferroni procedure for multiple tests of significance. Biometrika 75: 800-802.
- Westfall and Young resampling methods. Westfall, P. H. and Young, S. S. Resampling-based multiple testing: Examples and methods for p-value adjustment. John Wiley & Sons, 1993.
Alternately you can consider an approach with control of False Discovery Rate. Some references for this approach are:
- Benjamini, Y., Yekutieli, D. (2001). The control of the false discovery rate in multiple hypothesis testing under dependency. Annals of Statistics 29,4: 1165-1188.
- Reiner A, Yekutieli D, Benjamini Y. Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics. 2003 Feb 12;19(3):368-75. [Medline]
- Benjamini Y, Hochberg Y (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B, Methodological 57:289-300.
- Hochberg Y, Benjamini Y. More powerful procedures for multiple significance testing. Stat Med. 1990 Jul;9(7):811-8. [Medline]
- Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci U S A. 2003 Aug 5;100(16):9440-5. Epub 2003 Jul 25. [Medline] [Abstract] [Full text] [PDF]
Cluster analysis will filter the genes into groups of genes that behave similarly
- Model Based
A heat map will allow you to see how well the clustered genes behave.
The final step is annotation, which tries to place the genes in context and link to freely available web resources like
- Locus Link http://www.ncbi.nlm.nih.gov/LocusLink/ LocusLink provides a single query interface to curated sequence and descriptive information about genetic loci. It presents information on official nomenclature, aliases, sequence accessions, phenotypes, EC numbers, MIM numbers, UniGene clusters, homology, map locations, and related web sites.
- Unigene http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=unigene UniGene is an experimental system for automatically partitioning GenBank sequences into a non-redundant set of gene-oriented clusters. Each UniGene cluster contains sequences that represent a unique gene, as well as related information such as the tissue types in which the gene has been expressed and map location.
- Pubmed http://www.ncbi.nlm.nih.gov/PubMed/ PubMed, a service of the National Library of Medicine, includes over 14 million citations for biomedical articles back to the 1950’s. These citations are from MEDLINE and additional life science journals. PubMed includes links to many sites providing full text articles and other related resources.
- GO http://www.geneontology.org/ The goal of the Gene OntologyTM (GO) Consortium is to produce a controlled vocabulary that can be applied to all organisms even as knowledge of gene and protein roles in cells is accumulating and changing. GO provides three structured networks of defined terms to describe gene product attributes. GO is one of the controlled vocabularies of the Open Biological Ontologies.
- KEGG http://www.genome.ad.jp/kegg/ A grand challenge in the post-genomic era is a complete computer representation of the cell and the organism, which will enable computational prediction of higher-level complexity of cellular processes and organism behaviors from genomic information. Towards this end we have been developing a bioinformatics resource named KEGG, Kyoto Encyclopedia of Genes and Genomes, as part of the research projects in the Kanehisa Laboratory of Kyoto University Bioinformatics Center.
- Affymetrix GO Browser
- Onto-Express http://vortex.cs.wayne.edu/projects.htm The typical result of a microarray experiment is a list of tens or hundreds of genes found to be differentially regulated in the condition under study. Independently of the methods used to select these genes, the common task faced by any researcher is to translate these lists of genes into a better understanding of the biological phenomena involved. Currently, this is done through a tedious combination of searches through the literature and a number of public databases. We developed Onto-Express (OE) as a novel tool able to automatically translate such lists of differentially regulated genes into functional profiles characterizing the impact of the condition studied. OE constructs functional profiles (using Gene Ontology terms) for the following categories: biochemical function, biological process, cellular role, cellular component, molecular function and chromosome location. Statistical significance values are calculated for each category. We demonstrated the validity and the utility of this comprehensive global analysis of gene function by analyzing two breast cancer data sets from two separate laboratories. OE was able to identify correctly all biological processes postulated by the original authors, as well as discover novel relevant mechanisms (Draghici et.al, Genomics, 81(2), 2003). Other results obtained with Onto-Express can be found in Ostermeier et.al, Lancet, 360(9335), 2002.
- DAVID/EASE http://david.niaid.nih.gov/david/ease.htm EASE is a customizable, standalone software application that facilitates the biological interpretation of gene lists derived from the results of microarray, proteomic, and SAGE experiments. EASE provides statistical methods for discovering enriched biological themes within gene lists, generates gene annotation tables, and enables automated linking to online analysis tools.
- Swiss-Prot http://us.expasy.org/sprot/ Swiss-Prot; a curated protein sequence database which strives to provide a high level of annotation (such as the description of the function of a protein, its domains structure, post-translational modifications, variants, etc.), a minimal level of redundancy and high level of integration with other databases
For further details look at the handout for this web seminar [pdf].
You can find an earlier version of this page on my original website.