1.1 Cutoff Plot - Input: a data frame.

cutoff.result <- plot_cutoff(data = df,
                       gen.plot = TRUE,
                       gen.3d.plot = TRUE)

The result object cutoff.result takes a data frame as an input data and contains 3 objects:  

1. A table that summarizes the number of DE genes under threshold combination  

head(cutoff.result[[1]])

2. A 3D plotly object, where the x-axis is Fold change threshold, y-axis is FDR cutoff, and z-axis is the number of DE genes under the x,y combination:  

cutoff.result[[2]]

3. A plot to visualize it:

cutoff.result[[3]]

Saving figures  

Figures can be saved using two approaches:  

1. Using imbedded fucntion with predetermined dpi  

plot_cutoff(data = df,
            plot.save.to = "cut_off_selection_plot.png")

2. Using ggsave from the ggplot2 library with option to customize the width, height and dpi.  

library(ggplot2)
ggsave("cut_off_selection_plot.png", cutoff.result[[3]], width = 5, height = 5, dpi = 300)

1.2 Cutoff Plot - Input: a list.

cutoff.result.list <- plot_cutoff(data = d1, 
                                  comp.names = c('a', 'b'))

The result object cutoff.result.list takes a list as an input data and contains 2 objects:  

1. A table that summarizes the number of DE genes under threshold combination for each of the data frames in the list.  

head(cutoff.result.list[[1]])

2. A plot to visualize it. A 3D plotly object is not created for a list input data.  

cutoff.result.list

Saving figures  

Figures can be saved using two approaches:  

1. Using imbedded fucntion with predetermined dpi  

plot_cutoff(data = d1,
            comp.names = c("A", "B"),
            plot.save.to = "cut_off_list_plot.png")

2. Using ggsave from the ggplot2 library with option to customize the width, height and dpi.  

library(ggplot2)
ggsave("cut_off_list_plot.png", cutoff.result.list, width = 5, height = 5, dpi = 300)

2.1 QQ Plot - Input: a data frame.

qq.result <- plot_qq(df)
qq.result

Saving figures  

Figures can be saved using two approaches:  

1. Using imbedded fucntion with predetermined dpi  

plot_qq(data = df,
        plot.save.to = "qq_plot.png")

2. Using ggsave from the ggplot2 library with option to customize the width, height and dpi.  

library(ggplot2)
ggsave("qq_plot.png", qq.result, width = 5, height = 5, dpi = 300)

2.2 QQ Plot - Input: a list.

plot_qq function can also take a list as an input data, but requires comp.names to be specified. The result object is a set of qq plots for each of the data frames in the list.  

qq.list.result <- plot_qq(data = d1, 
        comp.names = c('A', 'B'))
qq.list.result

Saving figures  

Figures can be saved using two approaches:  

1. Using imbedded fucntion with predetermined dpi  

plot_qq(data = d1,
        comp.names = c("A", "B"),
        plot.save.to = "qq_list_plot.png")

2. Using ggsave from the ggplot2 library with option to customize the width, height and dpi.  

library(ggplot2)
ggsave("qq_list_plot.png", qq.list.result, width = 5, height = 5, dpi = 300)

3.1 Volcano Plot - Input: a data frame.

plot_volcano(data = df)

3.2 Volcano Plot - Input: a list.

Volcano Plot can also take a list as an input data with specified comp.name for each data frame.  

plot_volcano(data = d1, 
             comp.names = c('a', 'b'))

3.3 Highlight genes of interest in the volcano plot

You can highlight gene sets (like disease related genes from a Disease vs Healthy comparison).  

The gene set to be highlighted in the volcano plot can be spesified in two ways:  

1. A summary statistics table with the highlighted genes as row names (the gene name format needs to be consistent with the main summary statistics table). For example, this summary statistics table could be the statistical analysis output from a Disease vs Healthy comparison (only containing the subsetted significant genes).  

2. One or two vectors consisting of gene names. The gene name format needs to be consistent with the main summary statistics table. It can be set by the parameters highlight.1 and highlight.2. For example, you can assign the up-regulated gene list from the Disease vs Healthy comparison to highlight.1 and down-regulated gene list from the comparison to highlight.2.  

Example using option 1 (use summary statistics table’s row name to highlight genes):  

#disease gene set used to color volcanoplot
dgs <- RVA::Sample_disease_gene_set 

head(dgs)

You can also specify the range of the plot by xlim and ylim.  

plot_volcano(data = df,
             geneset = dgs,
             upcolor = "#FF0000",
             downcolor = "#0000FF",
             xlim = c(-3,3),
             ylim = c(0,14))

By default, the genes which have positive fold change in the provided geneset parameter will be colored yellow, and negative fold will be colored purple, this also can be changed by specifying upcolor and downcolor:  

plot_volcano(data = d1,
             comp.names = c('a', 'b'),
             geneset = dgs,
             upcolor = "#FF0000",
             downcolor = "#0000FF",
             xlim = c(-3,3),
             ylim = c(0,14))

Example with option 2 You can also specify the color of highlight.1 with upcolor parameter and highlight.2 with downcolor parameter.  

volcano.result <- plot_volcano(data = df,
                  highlight.1 = c("ENSG00000169031.19","ENSG00000197385.5","ENSG00000111291.8"),
                  highlight.2 = c("ENSG00000123610.5","ENSG00000120217.14", "ENSG00000138646.9", "ENSG00000119922.10","ENSG00000185745.10"),
                  upcolor = "darkred",
                  downcolor = "darkblue",
                  xlim = c(-3,3),
                  ylim = c(0,14))
volcano.result

Saving figures  

Figures can be saved using two approaches:  

1. Using imbedded fucntion with predetermined dpi  

plot_volcano(data = df,
             geneset = dgs,
             plot.save.to = "volcano_plot.png")

2. Using ggsave from the ggplot2 library with option to customize the width, height and dpi.  

library(ggplot2)
ggsave("volcano_plot.png", volcano.result, width = 5, height = 5, dpi = 300)

4.1 Pathway analysis result is a list that contains 5 objects:

1. Pathway analysis table with directional result (test up-regulated gene set and down-regulated gene set respectively).  

head(pathway.result[[1]])

2. Pathway analysis table with non-directional fisher’s enrichment test result for all DE genes regardless of direction.  

head(pathway.result[[2]])

3. Pathway analysis plot with directional result.  

pathway.result[[3]]

4. Pathway analysis plot with non-directional result.  

pathway.result[[4]]

NULL

5. Pathway analysis plot with combined direaction and non-directional result.  

pathway.result[[5]]

Saving figures  

Figures can be saved using ggsave from the ggplot2 library.  

library(ggplot2)
ggsave("joint_plot.png",pathway.result[[5]], width = 5, height = 5, dpi = 300)

4.2 Pathway analysis for the list of summary tables will result in a list that contains 4 objects:

Pathways with list of data as input, the list can be replaced with d1 from the top. When list inputs are given comp.names should be speicified in order to identify the comparison groups.  

list.pathway.result <- plot_pathway(data = list(df,df1),comp.names=c("A","B"),pathway.db = "Hallmark", gene.id.type = "ENSEMBL")

1. Pathway analysis table with directional result for all datasets submited.  

head(list.pathway.result[[1]])

2. Pathway analysis table with non directional result for all datasets submited.  

head(list.pathway.result[[2]])

3. Pathway analysis plot with directional result for list of summary tables.  

list.pathway.result[[3]]

4. Pathway analysis plot with non directional result for list of summary tables.  

list.pathway.result[[4]]

Saving figures  

Figures can be saved using ggsave from the ggplot2 library.  

library(ggplot2)
ggsave("non-directional.png",pathway.result[[4]], width = 5, height = 5, dpi = 300)

5.1 Heatmap

You can plot a heatmap from raw data rather than a summary statistics table. plot_heatmap.expr has the ability to calculate average expression values and change from baseline. Importantly, these calculations do not calculate statistical signifance or correct for confounding factors - they should not be used as statistical analyses but as data overviews.  

For this, you need a count table and annotation table. The count table should have the geneid as row names and the samples as column names. These column names must match the sample.id column in your annotation file:  

count <- RVA::count_table[,1:50]

count[1:6,1:5]

annot <- RVA::sample_annotation[1:50,]

head(annot)

Plot a simple summary of expression values:  

Use help(plot_heatmap.expr) for more information on the parameters.  

hm.expr <- plot_heatmap.expr(data = count, 
                             annot = annot,
                             sample.id = "sample_id",
                             annot.flags = c("day", "Treatment"),
                             ct.table.id.type = "ENSEMBL",
                             gene.id.type = "SYMBOL",
                             gene.names = NULL,
                             gene.count = 10,
                             title = "RVA Heatmap",
                             fill = "CPM",
                             baseline.flag = "day",
                             baseline.val = "0",
                             plot.save.to = NULL,
                             input.type = "count")

The result of plot_heatmap.expr with fill = CPM contains 2 objects:  

1. Heat map  

2. A data frame of CPM values (fill = CPM in this example) for each geneid split by treatment group and time point.  

head(hm.expr[[2]])

Customize the plot & Save the figure  

Here is an example of how you can customize your output dimensions and save your new output using the png() function. Always make sure that the ComplexHeatmap library is loaded for the draw function.  

library(ComplexHeatmap)
png("heatmap_plots2cp.png", width = 500, height = 500)
draw(hm.expr$gp)
dev.off()

To calculate CFB from your input data, you must specify the baseline. The heatmap shown below compares each treatment on days 14 and 28 to the respective treatment on day 0.  

Use help(plot_heatmap.expr) for more information on the parameters.  

hm.expr.cfb <- plot_heatmap.expr(data = count, 
                                 annot = annot,
                                 sample.id = "sample_id",
                                 annot.flags = c("day", "Treatment"),
                                 ct.table.id.type = "ENSEMBL",
                                 gene.id.type = "SYMBOL",
                                 gene.names = NULL,
                                 gene.count = 10,
                                 title = "RVA Heatmap",
                                 fill = "CFB",
                                 baseline.flag = "day",
                                 baseline.val = "0",
                                 plot.save.to = NULL,
                                 input.type = "count")

The result of plot_heatmap.expr with fill = CFB contains 2 objects:

1. Heat map  

2. A data frame of change from baselines values (fill = CFB in this example) for each geneid split by treatment group and time point.  

head(hm.expr.cfb[[2]])

Customize the plot & Save the figure  

Here is an example of how you can customize your output dimensions.  

library(ComplexHeatmap)
png("heatmap_plots1cf.png", width = 500, height = 500)
draw(hm.expr.cfb$gp)
dev.off()

6.1 Gene expression

Let’s load in the sample data provided in this package. Note that the count table containing data must have the geneid set as the rownames and must have column names which match the sample.id column of the annotation file.  

anno <- RVA::sample_annotation

head(anno)

ct <- RVA::sample_count_cpm

ct[1:6,1:5]

Below is a simple plot using the defaults. Further parameter changes can allow you to change the log scaling, the input type to either cpm or count, and the genes selected for plotting. The sample table we are using already has data points as CPM, so we will use CPM as our input.type.  

Use help(plot_gene) for more information on the parameters.  

gene.result <- plot_gene(ct, 
               anno,
               gene.names = c("AAAS", "A2ML1", "AADACL3", "AARS"),
               ct.table.id.type = "ENSEMBL",
               gene.id.type = "SYMBOL",
               treatment = "Treatment",
               sample.id = "sample_id",
               time = "day",
               log.option = TRUE,
               plot.save.to = NULL,
               input.type = "cpm")

The result of plot_gene contains 2 objects:  

1. A gene expression plot that distinguishes log cpm gene expression for each geneid across the treatment groups and time points.  

2. A table that shows gene expression values by gene id, treatment group and time point with both sample ids and gene symbols.  

head(gene.result[[2]])

Customize the plot & Save the figure  

Here is an example of how you can customize your output dimensions and save your new plot using the ggsave function. Always make sure that the ggplot2 library is loaded.  

library(ggplot2)
ggsave(gene.result, "gene_plots1_4.png", device = "png", width = 100, height = 100, dpi = 200, limitsize = FALSE)