DGEobj.utils: A toolkit facilitating a limma/voom workflow Differential Gene Expression analysis

This package implements a set of utility functions to enable a limma/voom workflow capturing the results in the DGEobj data structure. Aside from implementing a well developed and popular workflow in DGEobj format, the run* functions in the package illustrate how to wrap the individual processing steps in a workflow in functions that capture important metadata, processing parameters, and intermediate data items in the DGEobj data structure. This function- based approach to utilizing the DGEobj data structure insures consistency among a collection of projects processed by these methods and thus facilitates downstream automated meta-analysis.

Functionality includes:

Analysis

• runContrasts: Build contrast matrix and calculate contrast fits
• runEdgeRNorm: Run edgeR normalization on DGEobj
• runIHW: Apply Independent Hypothesis Weighting (IHW) to a list of topTable dataframes
• runPower: Run a power analysis on counts and design matrix
• runQvalue: Calculate and add q-value and lFDR to dataframe
• runSVA: Test for surrogate variables
• runVoom: Run functions in a typical voom/lmFit workflow

Utilities

• convertCounts: Convert count matrix to CPM, FPKM, FPK, or TPM
• extractCol: Extract a named column from a series of df or matrices
• lowIntFilter: Apply low intensity filters to a DGEobj
• rsqCalc: Calculate R-squared for each gene fit
• summarizeSigCounts: Summarize a contrast list
• topTable.merge: Merge specified topTable df cols
• tpm.direct: Convert countsMatrix and geneLength to TPM units
• tpm.on.subset: Calculate TPM for a subsetted DGEobj