Microarray data preprocessing
Affy
package
(version
3.0,
http://www.bioconductor.org/packages/release/bioc/html/affy.html) [1] is written in
statistical scripting language R and implements algorithms for processing raw
microarray data into expression measures. Firstly, the CEL file data were read into Cel
and AffyBatch objects using functions read.cefile and read.affybatch, including the
MIAME experiment description data, information about experimental design and
supplemental covariate information. A few of steps are involved in turning probe
intensity data into gene expression measures, including background correction to
eliminate background noise, normalization to detect and correct systematic
differences between chips, perfect match correction, and computation of expression
values to obtain expression matrix.
Screening of differentially expressed genes
Limma
package
(version
http://www.bioconductor.org/packages/release/bioc/html/limma.html)
3.0,
[2]
in
R
language provides differential expression analysis for microarray data. Limma
package is able to fit gene-wise linear models to gene expression data so as to assess
differentially expressed genes (DEGs) using empirical Bayes method. Usually, the
genes with P-value < 0.05 and |log2 fold change (FC)| > 0.5 are taken as differentially
expressed. In the empirical Bayes moderated method, the corresponding P-values can
be adjusted to control the false discovery rate using Benjamini and Hochberg method.
Protein-protein interaction (PPI) analysis
Proteins encoded by genes with similar functions are closely interacted with each
other in complex biological metabolisms. As interactions between proteins possess
such a crucial part in modern biology [3], STRING (Search Tool for the Retrieval of
Interacting Genes, http://string-db.org/) emerges as an online database to provide
uniquely comprehensive information for assembling, evaluating and disseminating
PPIs in a user-friendly way [4]. Commonly, the value of combined score > 0.4 is
regarded as criterion to screen out PPIs. Cytoscape software (version 3.2.1,
http://cytoscape.org/) [5], a popular bioinformatics package for biological network
visualization and data integration, can be used for global charting of PPI network. The
PPI network with topological features provides a global picture used to understand
molecular mechanisms and biological processes of disease [6, 7]. In the PPI network,
nodes represent proteins, while edges represent interactions between proteins. The
hub nodes are known to be the most important nodes in PPI network, which may
corresponds to the important proteins in metabolic networks.
Functional and pathway enrichment analysis
Researches may be confused about biological interpretation of large gene lists
derived from microarray analysis. DAVID (the Database for Annotation, Visualization
and Integration Discovery) is developed as a publicly available high-throughput
functional annotation tool for high-throughput data [8]. Using DAVID tools, KEGG
(Kyoto
Encyclopedia
of
Genes
and
Genomes,
http://www.genome.jp/kegg/pathway.html) pathway enrichment analysis [9] can be
performed to identify involved biological pathways. Also, GO (Gene Ontology,
http://geneontology.org/) term analysis [10] can be implemented to identify biological
processes, molecular mechanisms and cellular components enriched by interested
genes. Usually, P-value < 0.05 and number of enriched genes > 2 are used as criteria
to identify significant GO terms and KEGG pathways.
References
1. Gautier L, Cope L, Bolstad BM, Irizarry RA. affy--analysis of Affymetrix GeneChip data at the
probe level. Bioinformatics (Oxford, England). 2004;20:307-15.
2. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential
expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015.
3. Jia P, Kao C-F, Kuo P-H, Zhao Z. A comprehensive network and pathway analysis of candidate
genes in major depressive disorder. BMC systems biology. 2011;5:S12.
4. Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, Lin J, Minguez P, Bork P,
von Mering C. STRING v9. 1: protein-protein interaction networks, with increased coverage and
integration. Nucleic acids research. 2013;41:D808-D15.
5. Cline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C, Christmas R, Avila-Campilo I,
Creech M, Gross B, Hanspers K, Isserlin R, Kelley R, Killcoyne S, Lotia S, Maere S, Morris J, Ono K,
Pavlovic V, Pico AR, Vailaya A, Wang PL, Adler A, Conklin BR, Hood L, Kuiper M, Sander C,
Schmulevich I, Schwikowski B, Warner GJ, Ideker T, Bader GD. Integration of biological networks
and gene expression data using Cytoscape. Nat Protoc. 2007;2:2366-82.
6. Kar G, Gursoy A, Keskin O. Human cancer protein-protein interaction network: a structural
perspective. PLoS computational biology. 2009;5:e1000601.
7. Song J, Singh M. How and when should interactome-derived clusters be used to predict functional
modules and protein function? Bioinformatics. 2009;25:3143-50.
8. Da Wei Huang BTS, Lempicki RA. Systematic and integrative analysis of large gene lists using
DAVID bioinformatics resources. Nature protocols. 2008;4:44-57.
9. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic acids research.
2000;28:27-30.
10. Gene Ontology C. The Gene Ontology (GO) database and informatics resource. Nucleic acids
research. 2004;32:D258-D61.