RNA.deg: Differential Expression Statistics
In sRAP: Simplified RNA-Seq Analysis Pipeline

Description Usage Arguments Value Author(s) References See Also Examples

Provides a table of differenitally expressed genes (in .xlsx format) as well as differential expression statistics for all genes (in .xlsx format as well as returned data frame). Function automatically creates a heatmap for differentially expressed genes and user can optionally also create box-plots for each individual differentially expressed gene. The efficacy of this protocol is described in [1].

Output files will be created in the "DEG" and "Raw_Data" subfolders.

RNA.deg(sample.file, expression.table, project.name, project.folder, log2.fc.cutoff = 0.58, pvalue.cutoff = 0.05, fdr.cutoff = 0.05, box.plot = TRUE, ref.group = FALSE, ref = "none", method = "lm", color.palette = c("green", "orange", "purple", "cyan", "pink", "maroon", "yellow", "grey", "black", colors()), legend.status = FALSE)

`sample.file`	Tab-delimited text file providing group attributions for all samples considered for analysis.
`expression.table`	Data frame with genes in columns and samples in rows. Data should be log2 transformed. The RNA.norm function automatically creates this file.
`project.name`	Name for sRAP project. This determines the names for output files.
`project.folder`	Folder for sRAP output files
`log2.fc.cutoff`	If the primary variable contains two groups with a specified reference, this is the cut-off to define differentially expressed genes (default = 1.5, on a linear scale). Otherwise, this variable is ignored
`pvalue.cutoff`	Minimum p-value to define differentially expressed genes
`fdr.cutoff`	Minimum false discovery rate (FDR) to define differentially expressed genes.
`box.plot`	A logical value: Should box-plots be created for all differenitally expressed genes? If TRUE, then box-plots will be created in a separate subfolder.
`ref.group`	A logical value: Is the primary variable 2 groups, with a reference group?
`ref`	If the primary variable contains two groups (indicated by ref.group = FALSE), this is the reference used to calculate fold-change values (so, the mean expression for the reference group is substracted from the treatment group). Otherwise, this variable is ignored
`method`	Method for calculating p-values: "lm" (Default) = linear regression "aov" = ANOVA
`color.palette`	Colors for primary variable (specified in the second column of the sample file). If the primary variable is a continuous variable, this parameter is ignored.
`legend.status`	Logical value. Should legend be added to heatmap?

Data frame containing differential expression statistics.

First column contains gene name.

If the primary variable contains two groups (with a specified reference), then fold-change values are provided in the second column.

P-values and FDR values are provided for each variable in subsequent columns, starting with the primary variable.

Charles Warden <cwarden45@gmail.com>

[1] Warden CD, Yuan Y-C, and Wu X. (2013). Optimal Calculation of RNA-Seq Fold-Change Values. Int J Comput Bioinfo In Silico Model, 2(6): 285-292

sRAP goes through an entire analysis for an example dataset provided with the sRAP package.

Please post questions on the sRAP discussion group: http://sourceforge.net/p/bdfunc/discussion/srap/

	
library("sRAP")

dir <- system.file("extdata", package="sRAP")
expression.table <- file.path(dir,"MiSeq_cufflinks_genes_truncate.txt")
sample.table <- file.path(dir,"MiSeq_Sample_Description.txt")
project.folder <- getwd()
project.name <- "MiSeq_DEG"

expression.mat <- RNA.norm(expression.table, project.name, project.folder)

stat.table <- RNA.deg(sample.table, expression.mat, project.name, project.folder, box.plot=FALSE, ref.group=TRUE, ref="scramble",method="aov", color.palette=c("green","orange"), legend.status=TRUE)

#stat.table <- RNA.deg(sample.table, expression.mat, project.name, project.folder, box.plot=FALSE, #ref.group=TRUE, ref="scramble",method="aov", color.palette=c("green","orange"))