Limits...
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.

Show MeSH
Schematic of the major components that are central to any limma analysis. For each gene g, we have a vector of gene expression values (yg) and a design matrix X that relates these values to some coefficients of interest (βg). The limma package includes statistical methods that (i) facilitate information borrowing using empirical Bayes methods to obtain posterior variance estimators (), (ii) incorporate observation weights (wgj where j refers to sample) to allow for variations in data quality, (iii) allow variance modelling to accommodate technical or biological heterogeneity that may be present and (iv) pre-processing methods such as variance stabilization to reduce noise. These methods all help improve inference at both the gene and gene set level in small experiments.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4402510&req=5

Figure 1: Schematic of the major components that are central to any limma analysis. For each gene g, we have a vector of gene expression values (yg) and a design matrix X that relates these values to some coefficients of interest (βg). The limma package includes statistical methods that (i) facilitate information borrowing using empirical Bayes methods to obtain posterior variance estimators (), (ii) incorporate observation weights (wgj where j refers to sample) to allow for variations in data quality, (iii) allow variance modelling to accommodate technical or biological heterogeneity that may be present and (iv) pre-processing methods such as variance stabilization to reduce noise. These methods all help improve inference at both the gene and gene set level in small experiments.

Mentions: limma integrates a number of statistical principles in a way that is effective for large-scale expression studies. It operates on a matrix of expression values, where each row represents a gene, or some other genomic feature relevant to the current study, and each column corresponds to an RNA sample. On the one hand, it fits a linear model to each row of data and takes advantage of the flexibility of such models in various ways, for example to handle complex experimental designs and to test very flexible hypotheses. On the other hand, it leverages the highly parallel nature of genomic data to borrow strength between the gene-wise models, allowing for different levels of variability between genes and between samples, and making statistical conclusions more reliable when the number of samples is small. All the features of the statistical models can be accessed not just for gene-wise expression analyses but also for higher level analyses of gene expression signatures. Figure 1 depicts the linear model and highlights the statistical principles employed in a typical limma analysis.


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)

Schematic of the major components that are central to any limma analysis. For each gene g, we have a vector of gene expression values (yg) and a design matrix X that relates these values to some coefficients of interest (βg). The limma package includes statistical methods that (i) facilitate information borrowing using empirical Bayes methods to obtain posterior variance estimators (), (ii) incorporate observation weights (wgj where j refers to sample) to allow for variations in data quality, (iii) allow variance modelling to accommodate technical or biological heterogeneity that may be present and (iv) pre-processing methods such as variance stabilization to reduce noise. These methods all help improve inference at both the gene and gene set level in small experiments.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

Figure 1: Schematic of the major components that are central to any limma analysis. For each gene g, we have a vector of gene expression values (yg) and a design matrix X that relates these values to some coefficients of interest (βg). The limma package includes statistical methods that (i) facilitate information borrowing using empirical Bayes methods to obtain posterior variance estimators (), (ii) incorporate observation weights (wgj where j refers to sample) to allow for variations in data quality, (iii) allow variance modelling to accommodate technical or biological heterogeneity that may be present and (iv) pre-processing methods such as variance stabilization to reduce noise. These methods all help improve inference at both the gene and gene set level in small experiments.
Mentions: limma integrates a number of statistical principles in a way that is effective for large-scale expression studies. It operates on a matrix of expression values, where each row represents a gene, or some other genomic feature relevant to the current study, and each column corresponds to an RNA sample. On the one hand, it fits a linear model to each row of data and takes advantage of the flexibility of such models in various ways, for example to handle complex experimental designs and to test very flexible hypotheses. On the other hand, it leverages the highly parallel nature of genomic data to borrow strength between the gene-wise models, allowing for different levels of variability between genes and between samples, and making statistical conclusions more reliable when the number of samples is small. All the features of the statistical models can be accessed not just for gene-wise expression analyses but also for higher level analyses of gene expression signatures. Figure 1 depicts the linear model and highlights the statistical principles employed in a typical limma analysis.

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