run.plgem: Wrapper for Power Law Global Error Model (PLGEM) analysis...

In plgem: Detect differential expression in microarray and proteomics datasets with the Power Law Global Error Model (PLGEM)

Description

This function automatically performs PLGEM fitting and evaluation, determination of observed and resampled PLGEM-STN values, and selection of differentially expressed genes/proteins (DEG) using the PLGEM method.

Usage

  run.plgem(esdata, signLev=0.001, rank=100, covariate=1,
    baselineCondition=1, Iterations="automatic", trimAllZeroRows=FALSE,
    zeroMeanOrSD=c("replace", "trim"), fitting.eval=TRUE,
    plotFile=FALSE, writeFiles=FALSE, Verbose=FALSE)

Arguments

esdata	an object of class ExpressionSet; see Details for important information on how the phenoData slot of this object will be interpreted by the function.
signLev	numeric vector; significance level(s) for the DEG selection. Value(s) must be in (0,1).
rank	integer (or coercible to integer); the number of genes or proteins to be selected according to their PLGEM-STN rank. Only used if number of available replicates is too small to perform resampling (see Details).
covariate	integer, numeric or character; specifies the covariate to be used to distinguish the various experimental conditions from one another. See Details for how to specify the covariate.
baselineCondition	integer, numeric or character; specifies the condition to be treated as the baseline. See Details for how to specify the baselineCondition.
Iterations	number of iterations for the resampling step; if "automatic" it is automatically determined.
trimAllZeroRows	logical; if TRUE, rows in the data set containing only zero values are trimmed before fitting PLGEM. See help page of function plgem.fit for details.
zeroMeanOrSD	either NULL or character; what should be done if a row with non-positive mean or zero standard deviation is encountered before fitting PLGEM? Current options are one of "replace" or "trim". Partial matching is used to switch between the options and setting the value to NULL will cause the default behaviour to be enforced, i.e. to "replace". See help page of function plgem.fit for details.
fitting.eval	logical; if TRUE, the fitting is evaluated generating a diagnostic plot.
plotFile	logical; if TRUE, the generated plot is written on a file.
writeFiles	logical; if TRUE, the generated list of DEG is written on disk file(s).
Verbose	logical; if TRUE, comments are printed out while running.

Details

The phenoData slot of the ExpressionSet given as input is expected to contain the necessary information to distinguish the various experimental conditions from one another. The columns of the pData are referred to as ‘covariates’. There has to be at least one covariate defined in the input ExpressionSet. The sample attributes according to this covariate must be distinct for samples that are to be treated as distinct experimental conditions and identical for samples that are to be treated as replicates.

There is a couple different ways how to specify the covariate: If an integer or a numeric is given, it will be taken as the covariate number (in the same order in which the covariates appear in the colnames of the pData). If a character is given, it will be taken as the covariate name itself (in the same way the covariates are specified in the colnames of the pData). By default, the first covariate appearing in the colnames of the pData is used.

Similarly, there is a couple different ways how to specify which experimental condition to treat as the baseline. The available ‘condition names’ are taken from unique(as.character(pData(data)[, covariate])). If baselineCondition is given as a character, it will be taken as the condition name itself. If baselineCondition is given as an integer or a numeric value, it will be taken as the condition number (in the same order of appearance as in the ‘condition names’). By default, the first condition name is used.

The model is fitted on the most replicated condition. When more conditions exist with the maximum number of replicates, the condition providing the best fit is chosen (based on the adjusted r^2). If there is again a tie, the first one is arbitrarily taken.

If less than 3 replicates are provided for the condition used for fitting, then the selection is based on ranking according to the observed PLGEM-STN values. In this case the first rank genes or proteins are selected for each comparison.

Otherwise DEG are selected comparing the observed and resampled PLGEM-STN values at the signLev significance level(s), based on p-values obtained via a call to function plgem.pValue. See References for details.

Value

A list of four elements:

fit	the input plgemFit.
PLGEM.STN	a matrix of observed PLGEM-STN values (see plgem.obsStn for details).
p-value	a matrix of p-values (see plgem.pValue for details).
significant	a list with a number of elements equal to the number of different significance levels (delta) used as input. If ranking method is used due to insufficient number of replicates (see Details), this list will be of length 1 and named firstXXX, where XXX is the number provided by argument rank. Each element of this list is again a list, whose number of elements correspond to the number of performed comparisons (i.e. the number of conditions in the starting ExpressionSet minus the baseline). Each of these second level elements is a character vector of significant gene/protein names that passed the statistical test at the corresponding significance level.

Author(s)

Mattia Pelizzola [e-mail]

Norman Pavelka [e-mail]

References

Pavelka N, Pelizzola M, Vizzardelli C, Capozzoli M, Splendiani A, Granucci F, Ricciardi-Castagnoli P. A power law global error model for the identification of differentially expressed genes in microarray data. BMC Bioinformatics. 2004 Dec 17; 5:203; http://www.biomedcentral.com/1471-2105/5/203.

Pavelka N, Fournier ML, Swanson SK, Pelizzola M, Ricciardi-Castagnoli P, Florens L, Washburn MP. Statistical similarities between transcriptomics and quantitative shotgun proteomics data. Mol Cell Proteomics. 2008 Apr; 7(4):631-44; http://www.mcponline.org/cgi/content/abstract/7/4/631.

See Also

plgem.fit, plgem.obsStn, plgem.resampledStn, plgem.pValue, plgem.deg, plgem.write.summary

Examples

  data(LPSeset)
  set.seed(123)
  LPSdegList <- run.plgem(esdata=LPSeset, fitting.eval=FALSE)

Example output

Welcome to plgem version 1.62.0 

plgem documentation built on Nov. 8, 2020, 5:31 p.m.