Research articles on automatic pathway construction

Research articles on automatic pathway construction:

1. Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information (2011, citation:8)
2. Biological Pathway Extension Using Microarray Gene Expression Data (2008, citation:1)
3. Microarray analysis of gene expression: considerations in data mining and statistical treatment (2006, citation: 66)
4. Genomic analysis of metabolic pathway gene expression in mice (2005, citation: 67)
5. PathMAPA: a tool for displaying gene expression and performing statistical tests on metabolic pathways at multiple levels for Arabidopsis (2003, citation: 54)
6. Bayesian Consensus Pathway Construction and Expansion Using Microarray Gene Expression Data (From NCBI)
7. Biological Networks (Software from UCSD)
8. Reconstructing dynamic gene regulatory networks from sample-based transcriptional data (2012, citation:3)
9. Reconstructing regulatory networks from the dynamic plasticity of gene expression by mutual information (2013, citation:0) 
10. A Gaussian graphical model for identifying significantly responsive regulatory networks from time series gene expression data (2012, citation:1) 
11. An integrative genomics approach to the reconstruction of gene networks in segregating populations (2004, citation:135) 
12. Integrating genetic and gene expression data: application to cardiovascular and metabolic traits in mice () 
13. Uncovering regulatory pathways that affect hematopoietic stem cell function using 'genetical genomics' (Nature Genetics, 2005, citation:333) 
14. Inferring gene transcriptional modulatory relations: a genetical genomics approach (2005, citation:77) 
15. Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function (Nature Genetics, 2005, citation:515) 
16. Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease (Nature Genetics, 2005, citation:410) 
17. An integrative genomics approach to infer causal associations between gene expression and disease (Nature Genetics, 2005, citation:544)

Mapping from Illumina to Affymetrix

http://www.switchtoi.com/probemapping.ilmn

Preprocessing of microarray data

Normalization:

When microarray data is obtained from multiple arrays, it is necessary to normalize the dataset to avoid variation due to different environments. There are several normalization techniques available in the literature. For example, Lowess normalization, Quantile normalization etc. Among these, quantile normalization is the current favorite method applied on microarray analysis.

Transformation:

Besides normalization, it is also beneficial to transform the data to correctly treat both up- and down-regulated data. The most widely used transformation technique is the logarithmic base 2. Notably, logarithms treat numbers and their reciprocals symmetrically. For example: log₂(1) = 0, log₂(2) = 1, log₂(1/2) = -1, log₂(4) = 2, log₂(1/4) = -2.

Filtering:

If the intensity of hybridization in microarray is low (close to the background), then usually relative error becomes high. The common practice is to filter out (discard) the array elements which are statistically significantly different from the background.

References:

1. Slonim DK, Yanai I (2009) Getting Started in Gene Expression Microarray Analysis. PLoS Comput Biol 5(10): e1000543. doi:10.1371/journal.pcbi.1000543
2. Quackenbush, J. (2002) Microarray data normalization and transformation. Nature Genetics. Vol.32 supplement pp496-501.