genBart (version 1.0.1)

genModelResults: Generate formatted results file

Description

Generate formatted results file from result objects returned by limma, DESeq2, and edgeR pipelines

Usage

genModelResults(y = NULL, data.type = "rnaseq", method = "limma", object,
  lm.Fit = NULL, comp.names = NULL, var.symbols = NULL,
  gene.sets = NULL, annotations = NULL)

Arguments

y

Expression data frame the model was run on. Default is NULL. See Details for further description.

data.type

Type of data being analyzed ("rnaseq", "microarray", "flow", "metab"). Default is data.type="rnaseq".

method

string denoting modeling method used ("limma","deseq2","edgeR")

object

results object generated from eBayes (limma), results (DESeq2), glmLRT or glmQLFTest (edgeR). Objects generated from results, glmLRT, or glmlQLFTest must be wrapped in a list. See Details for further description.

lm.Fit

linear model fit object generated from lmFit (limma). This parameter can can be left as NULL when method equals "deseq2" or "edgeR".

comp.names

Optional vector of comparison (contrast) names. Default is NULL.

var.symbols

Optional vector of additional annotations for the variables. Otherwise, the rownames of the expression data are used. Default is NULL.

gene.sets

A list of gene sets. Default is NULL. See Details for further description.

annotations

A data frame of additional annotations for the gene sets. Default is NULL. See Details for further description.

Value

data.type string denoting the type of data that was analyzed

results the formatted results returned as a data frame

resids data frame of residuals. Returned only if data.type="microarray" or "rnaseq" and method="limma". Used to estimate the VIFs when running the Qusage algorithm in runQgen.

gene.sets list of gene sets provided by the user. NULL if no list provided.

annotations data frame of gene set annotations provided by the user. Null if no annotations are provided.

Details

This function takes results obtained from differential analysis pipelines found in limma, DESeq2, or edgeR and formats them for the BART app.

The expression data y and lm.Fit objects are used to obtain the residual matrix from the fitted model. These parameters are only needed when method = "limma" and data.type = "rnaseq" or "microarray" and can otherwise be left as NULL. It is important to remember that y should be the expression data used for modeling (e.g. voom transformed data). The residual matrix is stored as an element of the returned list and can be used in downstream gene set analysis using runQgen (Please visit for more details).

The object parameter takes as input model result objects returned by functions in limma, DESeq2, or edgeR. When method = "limma", the expected input is the single object returned by eBayes since it is able to store results across multiple comparisons. When method = "deseq2" or "edgeR", the result object(s) returned by results, glmLRT, or glmQLFTest must be wrapped in a list in which each element is an object containing the results for a single comparison.

The comp.names parameter is a character vector of comparison names that is particularly useful when method = "deseq2" or "edgeR" since comparison names are not extracted from the result objects generated by either of those pipelines. When using limma, the comparison names can also be defined in makeContrasts. It is important that the names are written in the correct order. For example, if object = list(AvsB, CvsD), where AvsB and CvsD are result objects for the comparisons "group A vs group B" and "group C vs group D" respectively, then comp.names = c("CvsD", "AvsB") would incorrectly assign the name "CvsD" to the comparison "group A vs group B" and vice versa. The var.symbols parameter is typically used to provide a character vector of gene symbols. The vector provided must be the same length and in the same order as the row names of the data used for modeling.

The gene.sets parameter is a list in which each element is a character vector of gene names comprising a gene set. The gene names must match the rownames of the data used for modeling. The gene sets are used to create modular maps for each comparison in the DGE section of BART. The annotations parameter is a data frame consisting of two columns. The first column consists of gene set names and the second column consists of additional descriptions for the gene sets.

Examples

# NOT RUN {
# Example data
data(tb.expr)
data(tb.design)

# Only use first 100 genes to demonstrate
dat <- tb.expr[1:100,]

# Generate lmFit and eBayes (limma) objects needed for genModelResults
tb.design$Group <- paste(tb.design$clinical_status,tb.design$timepoint,sep = "")
grp <- factor(tb.design$Group)
design2 <- model.matrix(~0+grp)
colnames(design2) <- levels(grp)

dupcor <- limma::duplicateCorrelation(dat, design2, block = tb.design$monkey_id)
fit <- limma::lmFit(dat, design2, block = tb.design$monkey_id, 
             correlation = dupcor$consensus.correlation)
contrasts <- limma::makeContrasts(A_20vsPre = Active20-Active0, A_42vsPre = Active42-Active0, 
                                  levels=design2)
fit2 <- limma::contrasts.fit(fit, contrasts)
fit2 <- limma::eBayes(fit2, trend = FALSE)

# Format results
model.results <- genModelResults(y = dat, data.type = "microarray", object = fit2,
                                 lm.Fit = fit, method = "limma")
# }