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Significance analysis of microarrays

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Significance analysis of microarrays (SAM) is a statistical technique, established in 2001 by Virginia Tusher, Robert Tibshirani and Gilbert Chu, for determining whether changes in gene expression are statistically significant. With the advent of DNA microarrays it is now possible to measure the expression of thousands of genes in a single hybridization experiment. The data generated is considerable and a method for sorting out what is significant and what isn’t is essential. SAM is distributed by Stanford University in an R-package.

SAM identifies statistically significant genes by carrying out gene specific t-tests and computes a statistic dj for each gene j, which measures the strength of the relationship between gene expression and a response variable.[1][2][3] This analysis uses non-parametric statistics, since the data may not follow a normal distribution. The response variable describes and groups the data based on experimental conditions. In this method, repeated permutations of the data are used to determine if the expression of any gene is significant related to the response. The use of permutation-based analysis accounts for correlations in genes and avoids parametric assumptions about the distribution of individual genes. This is an advantage over other techniques (for example ANOVA and Bonferroni), which assume equal variance and/or independence of genes.[4]

Basic protocol

  • Perform microarray experiments — DNA microarray with oligo and cDNA primers, SNP arrays, protein arrays, etc.
  • Input Expression Analysis in Microsoft Excel — see below
  • Run SAM as a Microsoft Excel Add-Ins
  • Adjust the Delta tuning parameter to get a significant # of genes along with an acceptable false discovery rate (FDR)) and Assess Sample Size by calculating the mean difference in expression in the SAM Plot Controller
  • List Differentially Expressed Genes (Positively and Negatively Expressed Genes)

Running SAM

  • SAM is available for download online at http://www-stat.stanford.edu/~tibs/SAM/ for academic and non-academic users after completion of a registration step.
  • SAM is run as an Excel Add-In, and the SAM Plot Controller allows Customization of the False Discovery Rate and Delta, while the SAM Plot and SAM Output functionality generate a List of Significant Genes, Delta Table, and Assessment of Sample Sizes
  • Block Permutations
    • Blocks are batches of microarrays; for example for eight samples split into two groups (control and affected) there are 4!=24 permutations for each block and the total number of permutations is (24)(24)= 576. A minimum of 1000 permutations are recommended;[1][5][6]

the number of permutations is set by the user when imputing correct values for the data set to run SAM

Response formats[1]

Types

    • Quantitative — real-valued (such as heart rate)
    • One class — tests whether the mean gene expression differs from zero
    • Two class — two sets of measurements
      • Unpaired — measurement units are different in the two groups; e.g. control and treatment groups with samples from different patients
      • Paired — same experimental units are measured in the two groups; e.g. samples before and after treatment from the same patients
    • Multiclass — more than two groups with each containing different experimental units; generalization of two class unpaired type
    • Survival — data of a time until an event (for example death or relapse)
    • Time course — each experimental units is measured at more than one time point; experimental units fall into a one or two class design
    • Pattern discovery — no explicit response parameter is specified; the user specifies eigengene (principal component) of the expression data and treats it as a quantitative response

Algorithm

SAM calculates a test statistic for relative difference in gene expression based on permutation analysis of expression data and calculates a false discovery rate. The principal calculations of the program are illustrated below.[1][2]Cite error: The <ref> tag has too many names (see the help page). [5] [6] [7] [4] [2] [3] }}

  • Kooperberg, C., S. Sipione, et al. (2002). "Evaluating test statistics to select interesting genes in microarray experiments." Hum. Mol. Genet. 11(19): 2223–2232.
  1. ^ a b c d Cite error: The named reference R1 was invoked but never defined (see the help page).
  2. ^ a b c Zang, S., R. Guo, et al. (2007). "Integration of statistical inference methods and a novel control measure to improve sensitivity and specificity of data analysis in expression profiling studies." Journal of Biomedical Informatics 40(5): 552–560
  3. ^ a b Zhang, S. (2007). "A comprehensive evaluation of SAM, the SAM R-package and a simple modification to improve its performance." BMC Bioinformatics 8: 230.
  4. ^ a b Tusher, V. G., R. Tibshirani, et al. (2001). "Significance analysis of microarrays applied to the ionizing radiation response." Proceedings of the National Academy of Sciences 98(9): 5116–5121. [1]
  5. ^ a b Dinu, I. P., JD; Mueller, T; Liu, Q; Adewale, AJ; Jhangri, GS; Einecke, G; Famulski, KS; Halloran, P; Yasui, Y. (2007). "Improving gene set analysis of microarray data by SAM-GS." BMC Bioinformatics 8: 242.
  6. ^ a b Jeffery, I. H., DG; Culhane, AC. (2006). "Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data." BMC Bioinformatics 7: 359.
  7. ^ Larsson, O. W., C; Timmons, JA. (2005). "Considerations when using the significance analysis of microarrays (SAM) algorithm." BMC Bioinformatics 6: 129.
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