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limma powers differential expression analyses for RNA-sequencing and microarray studies.

Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK - Nucleic Acids Res. (2015)

Bottom Line: Recently, the capabilities of limma have been significantly expanded in two important directions.Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures.This provides enhanced possibilities for biological interpretation of gene expression differences.

View Article: PubMed Central - PubMed

Affiliation: Molecular Medicine Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3052, Australia Department of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria 3010, Australia.

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Example diagnostic plots produced by limma. (A) Plot of variability versus count size for RNA-seq data, generated by voom with plot=TRUE. This plot shows that technical variability decreases with count size. Total variability asymptotes to biological variability as count sizes increases. (B) Mean-difference plot produced by the plotMA function for a two-colour microarray. The plot highlights negative (NC), constant (DR) and differentially expressed (D03, D10, U03, U10) spike-in controls. Regular probes are non-highlighted. (C) Multidimensional scaling (MDS) plot of a set of 30 microarrays, generated by plotMDS. All arrays are biologically identical and the plot reveals strong batch effects. Distances represent leading log2-fold changes between samples.
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Figure 3: Example diagnostic plots produced by limma. (A) Plot of variability versus count size for RNA-seq data, generated by voom with plot=TRUE. This plot shows that technical variability decreases with count size. Total variability asymptotes to biological variability as count sizes increases. (B) Mean-difference plot produced by the plotMA function for a two-colour microarray. The plot highlights negative (NC), constant (DR) and differentially expressed (D03, D10, U03, U10) spike-in controls. Regular probes are non-highlighted. (C) Multidimensional scaling (MDS) plot of a set of 30 microarrays, generated by plotMDS. All arrays are biologically identical and the plot reveals strong batch effects. Distances represent leading log2-fold changes between samples.

Mentions: Figure 3 shows example diagnostic plots. Panel (A) shows RNA-seq data from Pickrell et al. (9) that has been analysed as described by Law et al. (10). Panels (B) and (C) display the two-colour microarray quality control data set presented by Ritchie et al. (11). Panel (B) displays background corrected but non-normalized intensities from one typical array. Panel (C) was generated from a subset of 30 of the control arrays after print-tip loess normalization (12).


limma powers differential expression analyses for RNA-sequencing and microarray studies.

Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK - Nucleic Acids Res. (2015)

Example diagnostic plots produced by limma. (A) Plot of variability versus count size for RNA-seq data, generated by voom with plot=TRUE. This plot shows that technical variability decreases with count size. Total variability asymptotes to biological variability as count sizes increases. (B) Mean-difference plot produced by the plotMA function for a two-colour microarray. The plot highlights negative (NC), constant (DR) and differentially expressed (D03, D10, U03, U10) spike-in controls. Regular probes are non-highlighted. (C) Multidimensional scaling (MDS) plot of a set of 30 microarrays, generated by plotMDS. All arrays are biologically identical and the plot reveals strong batch effects. Distances represent leading log2-fold changes between samples.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4402510&req=5

Figure 3: Example diagnostic plots produced by limma. (A) Plot of variability versus count size for RNA-seq data, generated by voom with plot=TRUE. This plot shows that technical variability decreases with count size. Total variability asymptotes to biological variability as count sizes increases. (B) Mean-difference plot produced by the plotMA function for a two-colour microarray. The plot highlights negative (NC), constant (DR) and differentially expressed (D03, D10, U03, U10) spike-in controls. Regular probes are non-highlighted. (C) Multidimensional scaling (MDS) plot of a set of 30 microarrays, generated by plotMDS. All arrays are biologically identical and the plot reveals strong batch effects. Distances represent leading log2-fold changes between samples.
Mentions: Figure 3 shows example diagnostic plots. Panel (A) shows RNA-seq data from Pickrell et al. (9) that has been analysed as described by Law et al. (10). Panels (B) and (C) display the two-colour microarray quality control data set presented by Ritchie et al. (11). Panel (B) displays background corrected but non-normalized intensities from one typical array. Panel (C) was generated from a subset of 30 of the control arrays after print-tip loess normalization (12).

Bottom Line: Recently, the capabilities of limma have been significantly expanded in two important directions.Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures.This provides enhanced possibilities for biological interpretation of gene expression differences.

View Article: PubMed Central - PubMed

Affiliation: Molecular Medicine Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3052, Australia Department of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria 3010, Australia.

Show MeSH