diffexp_advise_lipidomics: diffexp_advise_lipidomics

In ShinyFabio/ADViSELipidomics: Shinyapp for the acquisition and the analysis of lipidomics data

diffexp_advise_lipidomics

Description

diffexp_advise_lipidomics is the function for creation of the experimental design.

diffexp_advise_lipidomics is the function for eventually removing the batch effects and applying the differential analysis per lipids among replicates/samples, taking into account the comparisons of interest.

Usage

diffexp_advise_lipidomics(
  out,
  rep_mean = FALSE,
  rep_effect = TRUE,
  bs_norm = "none",
  batch_type = "remove",
  batch_method = "limma",
  batch_vars = "",
  eb_robust = FALSE,
  eb_trend = FALSE,
  thresh = 0.05,
  decide_met = "separate"
)

Arguments

out	List. It is the result from the expdesign_advise_lipidomics function.
rep_mean	Logical value. It is for the selection of the dataset with the replicates (FALSE) or the dataset with the samples (TRUE). Default = FALSE.
rep_effect	Logical value. If rep_mean == FALSE, it is possible to consider the influence of the replicates presence for fitting the linear model, taking into account a value of correlation among the replicates. Default = TRUE.
bs_norm	Character. It is possible to normalize data between replicates or samples. Possible options are "none", "scale" and "quantile" (only if it isn't a concentration matrix). Default = "none".
batch_type	Character string. Different methodologies to cope with the eventual presence of batch effects: "remove", "fit". Default = "remove".
batch_method	Algorithm used for the batch effect. Choices are: "limma", "combat_nonparam" and "combat_param".
batch_vars	Character vector, with the names of the variables from the target file, considered as batch effects for the entire experiment. Default = "".
eb_robust	Logical value for inner use of empirical Bayes moderation of the standard errors towards a global value. More information in "limma' package (TRUE for RNAseq data). Default = FALSE.
eb_trend	Logical value for inner use of empirical Bayes moderation of the standard errors towards a global value. More information in "limma' package (TRUE for RNAseq data). Default = FALSE.
thresh	Numerical value. Threshold on adjust P-value in order to consider a lipid differentially expressed or not. Default = 0.05.
decide_met	Character string specifying how lipids and contrasts are to be combined in the multiple testing scheme. Choices are "separate", "global", "hierarchical" or "nestedF". More information in "limma' package. Default = "separate".

Details

This function has many tasks. At first, it log-transforms data for subsequent analysis and provides a simple method for normalizing data (scaling) per replicates or samples. Then, it is possible to remove batch effects by "limma" method (at most two batch variables) or by ComBat method (from "sva" package), in parametric or non-parametric modes. In particular the option "combat_param_full" will run ComBat with parametric adjustment and the null model with design matrix, "combat_nonparam_full" will run ComBat with nonparametric adjustment and the null model with design matrix (non-parametric approach can be very time consuming). After this correction, the linear model is fitted and the result are reported in form of table (raw tables and filtered tables) and in form of plots (MA-plots, Volcano plots, number of differential expressed lipids plots), separated for all the comparisons of interest.

Value

res: a list with results from the experimental design, updated with the results from fitting the linear model and appling decisional test on the results from the model. Moreover, all the tables and plots for differential expressed lipids are available.

res: a list with results from the experimental design, updated with the results from fitting the linear model and appling decisional test on the results from the model. Moreover, all the tables and plots for differential expressed lipids are available.

Note

last change 17/12/2021

ShinyFabio/ADViSELipidomics documentation built on March 21, 2023, 7:30 a.m.