limma: Linear Models for Microarray Data
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Citations
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References
Controlling the false discovery rate: a practical and powerful approach to multiple testing
Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments
Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation
Variance stabilization applied to microarray data calibration and to the quantification of differential expression.
Normalization of cDNA microarray data.
Related Papers (5)
Controlling the false discovery rate: a practical and powerful approach to multiple testing
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Frequently Asked Questions (10)
Q2. How many coefficients do you need to model the systematic part of your data?
With Affymetrix or single-channel data, or with two-color with a common reference, you will need as many coefficients as you have distinct RNA sources, no more and no less.
Q3. How can asymmetric differential expression be tolerated?
Oshlack et al [23] show that loess normalization can tolerate up to about 30% asymmetric differential expression while still giving good results.
Q4. What is the way to highlight the heterogeneity of an array?
Spatial heterogeneity on individual arrays can be highlighted by examining imageplots of the background intensities, for example> imageplot(log2(RG$Gb[,1]),RG$printer)plots the green background for the first array.
Q5. What are the normalization methods in limma?
Marray provides some normalization methods which are not in limma including 2-D loess normalization and print-tip-scale normalization.
Q6. What is the way to estimate the dye effects?
If there are at least two arrays with each dye-orientation, then it is possible to estimate and adjust for any probe-specific dye effects.
Q7. What is the way to normalize a print-tip array?
In these cases one should either use global "loess" normalization or else use robust spline normalization> MA <- normalizeWithinArrays(RG, method="robustspline")which is an empirical Bayes compromise between print-tip and global loess normalization, with 5- parameter regression splines used in place of the loess curves.
Q8. What functions are used to process Illumina BeadChip data?
If you use the read.ilmn, nec or neqc functions to process Illumina BeadChip data, please cite:Shi, W, Oshlack, A, and Smyth, GK (2010).
Q9. What is the way to compare two-color microarray experiments with a common reference?
If the same channel has been used for the common reference throughout the experiment, then the expression log-ratios may be analysed exactly as if they were log-expression values from a single channel experiment.
Q10. What program is used to obtain the red and green foreground and background intensities for each?
The TIFF images have then been processed using an image analysis program such a ArrayVision, ImaGene, GenePix, QuantArray or SPOT to acquire the red and green foreground and background intensities for each spot.