LIMMA Convenience Wrapper

A convenience wrapper for differential analysis with LIMMA. Performs moderated t-tests, F-tests, or linear regression.

Usage

limma_full(
  object,
  model.str,
  coef.str,
  contrasts,
  trend = TRUE,
  robust = TRUE,
  var.group = character(0),
  block = NULL,
  plot = FALSE,
  adjust.method = "BH",
  adjust.globally = TRUE,
  ...
)

Arguments

object: Object of class ExpressionSet. The exprs slot can be either a matrix of log\(_2\) relative abundances or counts. If the latter, the limma--edgeR pipeline (https://doi.org/10.12688/f1000research.9005.3) will automatically be used.
model.str: character; formulation of the model (e.g. "~ a + b"). If contrasts are provided, this should be a no-intercept model (it should include 0 or -1 as terms). See lm for more details.
coef.str: character; coefficient of interest. One of colnames(pData(object)).
contrasts: character; optional contrasts of the form "a-b" to test.
trend: logical; should an intensity-dependent trend be allowed for the prior variance? If FALSE, then the prior variance is constant. Alternatively, trend can be a row-wise numeric vector, which will be used as the covariate for the prior variance. See eBayes for more details.
robust: logical; should the estimation of df.prior and var.prior be robustified against outlier sample variances? See eBayes for more details.
var.group: character; the column in pData(object) indicating groups that will have different relative quality weights. The default (character(0)) will weight each sample equally (all samples given a weight of 1). Weights affect the logFC and P.Value columns of the results.
block: NULL or character; name of a column in pData(object) specifying a blocking variable. Passed to duplicateCorrelation. Section 9.7 "Multi-level Experiments" of the LIMMA User's Guide (see limmaUsersGuide) explains when to use duplicateCorrelation. Currently ignored if exprs(object) is a matrix of counts.
plot: logical; whether to generate diagnostic plots. If TRUE, generates a barplot of weights applied to each sample and a plot of the mean-variance trend using plotSA.
adjust.method: method for p-value adjustment. Default is "BH" (Benjamini-Hochberg), which will control the false discovery rate (FDR). See p.adjust for details.
adjust.globally: logical; should p-values from all contrasts be adjusted together using adjust.method? Set to FALSE if the contrasts being tested are not closely related. See Section 13.3 "Multiple Testing Across Contrasts" of the LIMMA User's Guide (limmaUsersGuide) for more information.
...: additional arguments passed to plotSA.

Value

data.frame. Output of topTable with additional columns feature, contrast, and column(s) for the standard error (SE) of the logFC. All columns from fData are also included.

Details

An MDS plot (plotMDS) is used to determine the appropriate value of var.group. If samples within phenotype groups tend to cluster well and have similar dispersions, the default var.group = character(0) is recommended. If samples within phenotype groups tend to cluster well, but different groups are more or less dispersed, it may be beneficial to set var.group to the name of the phenotype column. If one or more samples tend to cluster poorly with samples of the same phenotype, it may be beneficial to weight by sample. That is, set var.group to the name of the column in pData(object) that uniquely identifies each sample. If variation between samples or groups of samples is biological rather than technical, weighting is not recommended.

The plot of the mean-variance trend helps determine whether to fit a trend line to the prior variance (default is trend = TRUE). It also helps determine if the prior variance should be robustified against outlier sample variances (robust = TRUE). See eBayes for more details.

References

Smyth G. K. (2004). Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Statistical applications in genetics and molecular biology, 3, Article3. https://doi.org/10.2202/1544-6115.1027

Smyth, G. K., Michaud, J., and Scott, H. (2005). The use of within-array replicate spots for assessing differential expression in microarray experiments. Bioinformatics 21(9), 2067--2075. http://bioinformatics.oxfordjournals.org/content/21/9/2067 Preprint with corrections: http://www.statsci.org/smyth/pubs/dupcor.pdf

Liu, R., Holik, A. Z., Su, S., Jansz, N., Chen, K., Leong, H. S., Blewitt, M. E., Asselin-Labat, M. L., Smyth, G. K., & Ritchie, M. E. (2015). Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses. Nucleic acids research, 43(15), e97. https://doi.org/10.1093/nar/gkv412

Law, C. W., Alhamdoosh, M., Su, S., Dong, X., Tian, L., Smyth, G. K., & Ritchie, M. E. (2016). RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR. F1000Research, 5, ISCB Comm J-1408. https://doi.org/10.12688/f1000research.9005.3

Phipson, B., Lee, S., Majewski, I. J., Alexander, W. S., & Smyth, G. K. (2016). ROBUST HYPERPARAMETER ESTIMATION PROTECTS AGAINST HYPERVARIABLE GENES AND IMPROVES POWER TO DETECT DIFFERENTIAL EXPRESSION. The annals of applied statistics, 10(2), 946--963. https://doi.org/10.1214/16-AOAS920

Author

Tyler Sagendorf, Vlad Petyuk