DEVis (version 1.0.1)

de_profile_plot: Visualize gene-wise expression of differentially expressed genes.

Description

This function plots log2 fold-change values for differentially expressed genes for each contrast in a result set. The set of genes displayed can be selected by means of several sorting methods. This makes it possible to view expression differences from a variety of perspectives. This function can be applied to a single or multiple result sets, making it possible to compare expression changes in specific genes across different experimental conditions.

Usage

de_profile_plot(res_list, filename = "de_profile_plot.pdf",
  sort_choice = "max", specific_genes = "", numGenes = 50,
  theme = 1, customLabels = FALSE, returnData = FALSE)

Arguments

res_list

A list of DESeq result sets. Results can be calculated individually using DESeq's results() function. Lists of results can be created by creating a list(result1, result2 ... result_N).

filename

Filename for output plot. Valid extensions are ".pdf" and ".png". File generation can be turned off using set_output_mode("screen"). Output will be written to the /DE/profile_plots/ directory.

sort_choice

Gene selection is based on sorting method in cases where not all genes are displayed. sort_choice options are: "max", "min", "variance", "max_mean", "min_mean". "max" sorts genes based on the highest expression level of any single gene in a result set. In contrast, "max_mean" first calculates mean expression across all result sets and subsequently sorts by maximum mean values. Min and min_mean function similarly. Variance sorts genes by highest gene-wise variance in expression, displaying genes that showed the highest variability across all samples.

specific_genes

A character vector of gene names can be passed to this parameter to plot the genes specified. This overrides sorting and numGene parameters.

numGenes

The number of genes to include in this plot.

theme

Theme for the layout and color scheme for the plot. Valid selections are integers between 1-6.

customLabels

If customLabels is set to TRUE, the user will be prompted to provide a custom label for each label.

returnData

Boolean. Determines if this visualization should return data used to generate the visualization. Default=FALSE.

Value

If returnData is true, this function will return a data frame for sort-selected differentially expressed genes containing gene names and log2 fold-change values relative to the experimental control condition.

Examples

# NOT RUN {
 #This example assumes an experimenal design of ~Condition_Time.

#Prepare a result list.
res.day1 <- results(dds, contrast=c("Condition_Time", "day1_disease", "day1_control"))
res.day2 <- results(dds, contrast=c("Condition_Time", "day2_disease", "day2_control"))
res.day3 <- results(dds, contrast=c("Condition_Time", "day3_disease", "day3_control"))
myResList <- list(res.day1, res.day2, res.day3)

/*
 * Sort data by the highest expression level for any individual gene in any sample.
 * Select the top 50 genes from this sort and visualize them in the plot.
 */
de_profile_plot(res_list=myResList, filename="DE_profile_upReg50.pdf",
                  sort_choice="max",
                  numGenes=50, theme=1, returnData=FALSE)

/*
 * Calculate the mean expression for each gene across all three time points.
 * Sort the data by minimum mean expression, select the top 25 genes,
 * and visualize them in the plot.
 */
de_profile_plot(res_list=myResList, filename="DE_profile_meanDownReg25.pdf",
                  sort_choice="min_mean",
                  numGenes=25, theme=1, returnData=FALSE)


/*
 * Calculate the variance for each gene across all three time points.
 * Sort the data by the highest gene-wise variance, select 30 genes
 * with the highest variance, and visualize them in the plot.
 * Save the data used to generate the plot as highVar_df.
 */
highVar_df <- de_profile_plot(res_list=myResList, filename="DE_profile_highVar30.pdf",
                                sort_choice="variance", numGenes=30, theme=1, returnData=TRUE)

# }