Abstract
In this chapter we will overview the main points of gene expression analyses. We will illustrate using Affymetrix gene expression arrays and Illumina RNA-seq reads, but most of the underlying concepts port well to other platforms. Various preprocessing quality control metrics are discussed as well as how to evaluate the quality of the data. Next we discuss how to setup contrasts and detect differentially expressed genes.
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References
Affymetrix. Statistical algorithms description document. Tech. rep., Affymetrix, 2002.
Affymetrix. Guide to probe logarithmic intensity error (plier) estimation. Tech. rep., Affymetrix, 2005.
Anders, S., McCarthy, D. J., Chen, Y., Okoniewski, M., Smyth, G. K., Huber, W., and Robinson, M. D. Count-based differential expression analysis of rna sequencing data using r and bioconductor. Nat Protoc 8, 9 (2013), 1765–86.
Auer, P. L., and Doerge, R. W. Statistical design and analysis of rna sequencing data. Genetics 185, 2 (2010), 405–16.
Bolger, A. M., Lohse, M., and Usadel, B. Trimmomatic: a flexible trimmer for illumina sequence data. Bioinformatics 30, 15 (2014), 2114–20.
Dillies, M. A., Rau, A., Aubert, J., Hennequet-Antier, C., Jeanmougin, M., Servant, N., Keime, C., Marot, G., Castel, D., Estelle, J., Guernec, G., Jagla, B., Jouneau, L., Laloe, D., Le Gall, C., Schaeffer, B., Le Crom, S., Guedj, M., Jaffrezic, F., and French StatOmique, C. A comprehensive evaluation of normalization methods for illumina high-throughput rna sequencing data analysis. Brief Bioinform 14, 6 (2013), 671–83.
Gautier, L., Mooller, M., Friis-Hansen, L., and Knudsen, S. Alternative mapping of probes to genes for affymetrix chips. BMC Bioinformatics 5 (2004), 111.
Gentleman, R., Carey, V., Huber, W., Irizarry, R., and Dudoit, S., Eds. Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Statistics for Biology and Health. Springer, 2005.
Gondro, C., and Kinghorn, B. P. Optimization of cDNA microarray experimental designs using an evolutionary algorithm. IEEE/ACM Trans Comput Biol Bioinform 5, 4 (2008), 630–638.
Hahne, F., Huber, W., Gentleman, R., and Falcon, S. Bioconductor Case Studies. Springer, New York, 2008.
Hardiman, G. Microarray platforms - comparisons and contrasts. Pharmacogenomics 5, 5 (2004), 487–502.
Harrison, A., Johnston, C., and Orengo, C. Establishing a major cause of discrepancy in the calibration of affymetrix genechips. BMC Bioinformatics 8 (2007), 195.
Hart, S. N., Therneau, T. M., Zhang, Y., Poland, G. A., and Kocher, J. P. Calculating sample size estimates for rna sequencing data. J Comput Biol 20, 12 (2013), 970–8.
Huber, W., von Heydebreck, A., Sultmann, H., Poustka, A., and Vingron, M. Variance stabilization applied to microarray data calibration and to quantification of differential expression. Bioinformatics 18 (2002), S96–S104.
Irizarry, R., Bolstad, B., Collin, F., Cope, L., Hobbs, B., and Speed, T. Summaries of affymetrix genechip probe level data. Nucleic Acids Research 31 (2003), e15.
Irizarry, R., Hobbs, B., Collin, F., Beazer-Barclay, Y., Antonellis, K., Scherf, U., and Speed, T. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4 (2003), 249–264.
Irizarry, R., Wu, Z., and Jaffe, H. Comparison of affymetrix genechip expression measures. Bioinformatics 22 (2006), 789–794.
Langmead, B., and Salzberg, S. L. Fast gapped-read alignment with bowtie 2. Nat Methods 9, 4 (2012), 357–9.
Li, C., and Wong, W. Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection. PNAS 98 (2001), 31–36.
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., and Genome Project Data Processing, S. The sequence alignment/map format and samtools. Bioinformatics 25, 16 (2009), 2078–9.
Liu, Y., Zhou, J., and White, K. P. Rna-seq differential expression studies: more sequence or more replication? Bioinformatics 30, 3 (2014), 301–4.
Matsumura, H., Kruger, D. H., Kahl, G., and Terauchi, R. Supersage: a modern platform for genome-wide quantitative transcript profiling. Curr Pharm Biotechnol 9, 5 (2008), 368–74.
Matsumura, H., Urasaki, N., Yoshida, K., Kruger, D. H., Kahl, G., and Terauchi, R. Supersage: powerful serial analysis of gene expression. Methods Mol Biol 883 (2012), 1–17.
Morgan, M., Anders, S., Lawrence, M., Aboyoun, P., Pages, H., and Gentleman, R. Shortread: a bioconductor package for input, quality assessment and exploration of high-throughput sequence data. Bioinformatics 25, 19 (2009), 2607–8.
Oshlack, A., Robinson, M. D., and Young, M. D. From rna-seq reads to differential expression results. Genome Biol 11, 12 (2010), 220.
Rapaport, F., Khanin, R., Liang, Y. P., Pirun, M., Krek, A., Zumbo, P., Mason, C. E., Socci, N. D., and Betel, D. Comprehensive evaluation of differential gene expression analysis methods for rna-seq data. Genome Biology 14, 9 (2013).
Schena, M., Shalon, D., Davis, R., and Brown, P. Quantitative monitoring of gene expression patterns with complementary DNA microarray. Science 270 (1995), 467–470.
Schulze, A., and Downward, J. Navigating gene expression using microarrays - a technology review. Nature Cell Biology 3, 8 (2001), E190–E195.
Shendure, J. The beginning of the end for microarrays? Nature Methods 5 (2008), 585–587.
Slonim, D. K., and Yanai, I. Getting started in gene expression microarray analysis. PLoS Computational Biology 5, 10 (2009).
Smyth, G. K. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology 3, 1 (2004), 3.
Wang, Z., Gerstein, M., and Snyder, M. Rna-seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics 10, 1 (2009), 57–63.
Woo, Y., Affourtit, J., Daigle, S., Viale, A., Johnson, K., Naggert, J., and Churchill, G. A comparison of cDNA, oligonucleotide, and affymetrix genechip gene expression microarray platforms. J Biomol Tech 15, 4 (2004), 276–84.
Wu, Z., Ra, I., Gentleman, R., Murillo, F. M., and Spencer, F. A model based background adjustment for oligonucleotide expression arrays. Journal of the American Statistical Association 99 (2003), 909–917.
Xu, X., Zhang, Y., Williams, J., Antoniou, E., McCombie, W. R., Wu, S., Zhu, W., Davidson, N. O., Denoya, P., and Li, E. Parallel comparison of illumina rna-seq and affymetrix microarray platforms on transcriptomic profiles generated from 5-aza-deoxy-cytidine treated ht-29 colon cancer cells and simulated datasets. BMC Bioinformatics 14 Suppl 9 (2013), S1.
Zhang, A. Advanced analysis of gene expression microarray data. World Scientific, London, UK, 2006.
Zhao, S., Fung-Leung, W. P., Bittner, A., Ngo, K., and Liu, X. Comparison of rna-seq and microarray in transcriptome profiling of activated t cells. PLoS One 9, 1 (2014), e78644.
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Gondro, C. (2015). Gene Expression Analysis. In: Primer to Analysis of Genomic Data Using R. Use R!. Springer, Cham. https://doi.org/10.1007/978-3-319-14475-7_5
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DOI: https://doi.org/10.1007/978-3-319-14475-7_5
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